User’s Guide for T.E.S.T. (version 4.2) (Toxicity Estimation Software Tool) A Program to Estimate Toxicity from Molecular Structure

EPA Science Inventory

The user's guide describes the methods used by TEST to predict toxicity and physical properties (including the new mode of action based method used to predict acute aquatic toxicity). It describes all of the experimental data sets included in the tool. It gives the prediction res...

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.

The Structured Assessment of Violence Risk in Adults with Intellectual Disability: A Systematic Review.

PubMed

Hounsome, J; Whittington, R; Brown, A; Greenhill, B; McGuire, J

2018-01-01

While structured professional judgement approaches to assessing and managing the risk of violence have been extensively examined in mental health/forensic settings, the application of the findings to people with an intellectual disability is less extensively researched and reviewed. This review aimed to assess whether risk assessment tools have adequate predictive validity for violence in adults with an intellectual disability. Standard systematic review methodology was used to identify and synthesize appropriate studies. A total of 14 studies were identified as meeting the inclusion criteria. These studies assessed the predictive validity of 18 different risk assessment tools, mainly in forensic settings. All studies concluded that the tools assessed were successful in predicting violence. Studies were generally of a high quality. There is good quality evidence that risk assessment tools are valid for people with intellectual disability who offend but further research is required to validate tools for use with people with intellectual disability who offend. © 2016 John Wiley & Sons Ltd.

Freiburg RNA tools: a central online resource for RNA-focused research and teaching.

PubMed

Raden, Martin; Ali, Syed M; Alkhnbashi, Omer S; Busch, Anke; Costa, Fabrizio; Davis, Jason A; Eggenhofer, Florian; Gelhausen, Rick; Georg, Jens; Heyne, Steffen; Hiller, Michael; Kundu, Kousik; Kleinkauf, Robert; Lott, Steffen C; Mohamed, Mostafa M; Mattheis, Alexander; Miladi, Milad; Richter, Andreas S; Will, Sebastian; Wolff, Joachim; Wright, Patrick R; Backofen, Rolf

2018-05-21

The Freiburg RNA tools webserver is a well established online resource for RNA-focused research. It provides a unified user interface and comprehensive result visualization for efficient command line tools. The webserver includes RNA-RNA interaction prediction (IntaRNA, CopraRNA, metaMIR), sRNA homology search (GLASSgo), sequence-structure alignments (LocARNA, MARNA, CARNA, ExpaRNA), CRISPR repeat classification (CRISPRmap), sequence design (antaRNA, INFO-RNA, SECISDesign), structure aberration evaluation of point mutations (RaSE), and RNA/protein-family models visualization (CMV), and other methods. Open education resources offer interactive visualizations of RNA structure and RNA-RNA interaction prediction as well as basic and advanced sequence alignment algorithms. The services are freely available at http://rna.informatik.uni-freiburg.de.

Development and Validation of a Multidisciplinary Tool for Accurate and Efficient Rotorcraft Noise Prediction (MUTE)

NASA Technical Reports Server (NTRS)

Liu, Yi; Anusonti-Inthra, Phuriwat; Diskin, Boris

2011-01-01

A physics-based, systematically coupled, multidisciplinary prediction tool (MUTE) for rotorcraft noise was developed and validated with a wide range of flight configurations and conditions. MUTE is an aggregation of multidisciplinary computational tools that accurately and efficiently model the physics of the source of rotorcraft noise, and predict the noise at far-field observer locations. It uses systematic coupling approaches among multiple disciplines including Computational Fluid Dynamics (CFD), Computational Structural Dynamics (CSD), and high fidelity acoustics. Within MUTE, advanced high-order CFD tools are used around the rotor blade to predict the transonic flow (shock wave) effects, which generate the high-speed impulsive noise. Predictions of the blade-vortex interaction noise in low speed flight are also improved by using the Particle Vortex Transport Method (PVTM), which preserves the wake flow details required for blade/wake and fuselage/wake interactions. The accuracy of the source noise prediction is further improved by utilizing a coupling approach between CFD and CSD, so that the effects of key structural dynamics, elastic blade deformations, and trim solutions are correctly represented in the analysis. The blade loading information and/or the flow field parameters around the rotor blade predicted by the CFD/CSD coupling approach are used to predict the acoustic signatures at far-field observer locations with a high-fidelity noise propagation code (WOPWOP3). The predicted results from the MUTE tool for rotor blade aerodynamic loading and far-field acoustic signatures are compared and validated with a variation of experimental data sets, such as UH60-A data, DNW test data and HART II test data.

BepiPred-2.0: improving sequence-based B-cell epitope prediction using conformational epitopes

PubMed Central

Jespersen, Martin Closter; Peters, Bjoern

2017-01-01

Abstract Antibodies have become an indispensable tool for many biotechnological and clinical applications. They bind their molecular target (antigen) by recognizing a portion of its structure (epitope) in a highly specific manner. The ability to predict epitopes from antigen sequences alone is a complex task. Despite substantial effort, limited advancement has been achieved over the last decade in the accuracy of epitope prediction methods, especially for those that rely on the sequence of the antigen only. Here, we present BepiPred-2.0 (http://www.cbs.dtu.dk/services/BepiPred/), a web server for predicting B-cell epitopes from antigen sequences. BepiPred-2.0 is based on a random forest algorithm trained on epitopes annotated from antibody-antigen protein structures. This new method was found to outperform other available tools for sequence-based epitope prediction both on epitope data derived from solved 3D structures, and on a large collection of linear epitopes downloaded from the IEDB database. The method displays results in a user-friendly and informative way, both for computer-savvy and non-expert users. We believe that BepiPred-2.0 will be a valuable tool for the bioinformatics and immunology community. PMID:28472356

JNSViewer—A JavaScript-based Nucleotide Sequence Viewer for DNA/RNA secondary structures

PubMed Central

Dong, Min; Graham, Mitchell; Yadav, Nehul

2017-01-01

Many tools are available for visualizing RNA or DNA secondary structures, but there is scarce implementation in JavaScript that provides seamless integration with the increasingly popular web computational platforms. We have developed JNSViewer, a highly interactive web service, which is bundled with several popular tools for DNA/RNA secondary structure prediction and can provide precise and interactive correspondence among nucleotides, dot-bracket data, secondary structure graphs, and genic annotations. In JNSViewer, users can perform RNA secondary structure predictions with different programs and settings, add customized genic annotations in GFF format to structure graphs, search for specific linear motifs, and extract relevant structure graphs of sub-sequences. JNSViewer also allows users to choose a transcript or specific segment of Arabidopsis thaliana genome sequences and predict the corresponding secondary structure. Popular genome browsers (i.e., JBrowse and BrowserGenome) were integrated into JNSViewer to provide powerful visualizations of chromosomal locations, genic annotations, and secondary structures. In addition, we used StructureFold with default settings to predict some RNA structures for Arabidopsis by incorporating in vivo high-throughput RNA structure profiling data and stored the results in our web server, which might be a useful resource for RNA secondary structure studies in plants. JNSViewer is available at http://bioinfolab.miamioh.edu/jnsviewer/index.html. PMID:28582416

Structural behavior of composites with progressive fracture

NASA Technical Reports Server (NTRS)

Minnetyan, L.; Murthy, P. L. N.; Chamis, C. C.

1989-01-01

The objective of the study is to unify several computational tools developed for the prediction of progressive damage and fracture with efforts for the prediction of the overall response of damaged composite structures. In particular, a computational finite element model for the damaged structure is developed using a computer program as a byproduct of the analysis of progressive damage and fracture. Thus, a single computational investigation can predict progressive fracture and the resulting variation in structural properties of angleplied composites.

Artificial neural network prediction of aircraft aeroelastic behavior

NASA Astrophysics Data System (ADS)

Pesonen, Urpo Juhani

An Artificial Neural Network that predicts aeroelastic behavior of aircraft is presented. The neural net was designed to predict the shape of a flexible wing in static flight conditions using results from a structural analysis and an aerodynamic analysis performed with traditional computational tools. To generate reliable training and testing data for the network, an aeroelastic analysis code using these tools as components was designed and validated. To demonstrate the advantages and reliability of Artificial Neural Networks, a network was also designed and trained to predict airfoil maximum lift at low Reynolds numbers where wind tunnel data was used for the training. Finally, a neural net was designed and trained to predict the static aeroelastic behavior of a wing without the need to iterate between the structural and aerodynamic solvers.

From molecule to solid: The prediction of organic crystal structures

NASA Astrophysics Data System (ADS)

Dzyabchenko, A. V.

2008-10-01

A method for predicting the structure of a molecular crystal based on the systematic search for a global potential energy minimum is considered. The method takes into account unequal occurrences of the structural classes of organic crystals and symmetry of the multidimensional configuration space. The programs of global minimization PMC, comparison of crystal structures CRYCOM, and approximation to the distributions of the electrostatic potentials of molecules FitMEP are presented as tools for numerically solving the problem. Examples of predicted structures substantiated experimentally and the experience of author’s participation in international tests of crystal structure prediction organized by the Cambridge Crystallographic Data Center (Cambridge, UK) are considered.

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

PubMed Central

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

2015-01-01

A 3D model of RNA structure can provide information about its function and regulation that is not possible with just the sequence or secondary structure. Current models suffer from low accuracy and long running times and either neglect or presume knowledge of the long-range interactions which stabilize the tertiary structure. Our coarse-grained, helix-based, tertiary structure model operates with only a few degrees of freedom compared with all-atom models while preserving the ability to sample tertiary structures given a secondary structure. It strikes a balance between the precision of an all-atom tertiary structure model and the simplicity and effectiveness of a secondary structure representation. It provides a simplified tool for exploring global arrangements of helices and loops within RNA structures. We provide an example of a novel energy function relying only on the positions of stems and loops. We show that coupling our model to this energy function produces predictions as good as or better than the current state of the art tools. We propose that given the wide range of conformational space that needs to be explored, a coarse-grain approach can explore more conformations in less iterations than an all-atom model coupled to a fine-grain energy function. Finally, we emphasize the overarching theme of providing an ensemble of predicted structures, something which our tool excels at, rather than providing a handful of the lowest energy structures. PMID:25904133

Automatic Tool for Local Assembly Structures

DOE Office of Scientific and Technical Information (OSTI.GOV)

Whole community shotgun sequencing of total DNA (i.e. metagenomics) and total RNA (i.e. metatranscriptomics) has provided a wealth of information in the microbial community structure, predicted functions, metabolic networks, and is even able to reconstruct complete genomes directly. Here we present ATLAS (Automatic Tool for Local Assembly Structures) a comprehensive pipeline for assembly, annotation, genomic binning of metagenomic and metatranscriptomic data with an integrated framework for Multi-Omics. This will provide an open source tool for the Multi-Omic community at large.

The Phyre2 web portal for protein modelling, prediction and analysis

PubMed Central

Kelley, Lawrence A; Mezulis, Stefans; Yates, Christopher M; Wass, Mark N; Sternberg, Michael JE

2017-01-01

Summary Phyre2 is a suite of tools available on the web to predict and analyse protein structure, function and mutations. The focus of Phyre2 is to provide biologists with a simple and intuitive interface to state-of-the-art protein bioinformatics tools. Phyre2 replaces Phyre, the original version of the server for which we previously published a protocol. In this updated protocol, we describe Phyre2, which uses advanced remote homology detection methods to build 3D models, predict ligand binding sites, and analyse the effect of amino-acid variants (e.g. nsSNPs) for a user’s protein sequence. Users are guided through results by a simple interface at a level of detail determined by them. This protocol will guide a user from submitting a protein sequence to interpreting the secondary and tertiary structure of their models, their domain composition and model quality. A range of additional available tools is described to find a protein structure in a genome, to submit large number of sequences at once and to automatically run weekly searches for proteins difficult to model. The server is available at http://www.sbg.bio.ic.ac.uk/phyre2. A typical structure prediction will be returned between 30mins and 2 hours after submission. PMID:25950237

Mid-frequency Band Dynamics of Large Space Structures

NASA Technical Reports Server (NTRS)

Coppolino, Robert N.; Adams, Douglas S.

2004-01-01

High and low intensity dynamic environments experienced by a spacecraft during launch and on-orbit operations, respectively, induce structural loads and motions, which are difficult to reliably predict. Structural dynamics in low- and mid-frequency bands are sensitive to component interface uncertainty and non-linearity as evidenced in laboratory testing and flight operations. Analytical tools for prediction of linear system response are not necessarily adequate for reliable prediction of mid-frequency band dynamics and analysis of measured laboratory and flight data. A new MATLAB toolbox, designed to address the key challenges of mid-frequency band dynamics, is introduced in this paper. Finite-element models of major subassemblies are defined following rational frequency-wavelength guidelines. For computational efficiency, these subassemblies are described as linear, component mode models. The complete structural system model is composed of component mode subassemblies and linear or non-linear joint descriptions. Computation and display of structural dynamic responses are accomplished employing well-established, stable numerical methods, modern signal processing procedures and descriptive graphical tools. Parametric sensitivity and Monte-Carlo based system identification tools are used to reconcile models with experimental data and investigate the effects of uncertainties. Models and dynamic responses are exported for employment in applications, such as detailed structural integrity and mechanical-optical-control performance analyses.

Protein asparagine deamidation prediction based on structures with machine learning methods.

PubMed

Jia, Lei; Sun, Yaxiong

2017-01-01

Chemical stability is a major concern in the development of protein therapeutics due to its impact on both efficacy and safety. Protein "hotspots" are amino acid residues that are subject to various chemical modifications, including deamidation, isomerization, glycosylation, oxidation etc. A more accurate prediction method for potential hotspot residues would allow their elimination or reduction as early as possible in the drug discovery process. In this work, we focus on prediction models for asparagine (Asn) deamidation. Sequence-based prediction method simply identifies the NG motif (amino acid asparagine followed by a glycine) to be liable to deamidation. It still dominates deamidation evaluation process in most pharmaceutical setup due to its convenience. However, the simple sequence-based method is less accurate and often causes over-engineering a protein. We introduce structure-based prediction models by mining available experimental and structural data of deamidated proteins. Our training set contains 194 Asn residues from 25 proteins that all have available high-resolution crystal structures. Experimentally measured deamidation half-life of Asn in penta-peptides as well as 3D structure-based properties, such as solvent exposure, crystallographic B-factors, local secondary structure and dihedral angles etc., were used to train prediction models with several machine learning algorithms. The prediction tools were cross-validated as well as tested with an external test data set. The random forest model had high enrichment in ranking deamidated residues higher than non-deamidated residues while effectively eliminated false positive predictions. It is possible that such quantitative protein structure-function relationship tools can also be applied to other protein hotspot predictions. In addition, we extensively discussed metrics being used to evaluate the performance of predicting unbalanced data sets such as the deamidation case.

BepiPred-2.0: improving sequence-based B-cell epitope prediction using conformational epitopes.

PubMed

Jespersen, Martin Closter; Peters, Bjoern; Nielsen, Morten; Marcatili, Paolo

2017-07-03

Antibodies have become an indispensable tool for many biotechnological and clinical applications. They bind their molecular target (antigen) by recognizing a portion of its structure (epitope) in a highly specific manner. The ability to predict epitopes from antigen sequences alone is a complex task. Despite substantial effort, limited advancement has been achieved over the last decade in the accuracy of epitope prediction methods, especially for those that rely on the sequence of the antigen only. Here, we present BepiPred-2.0 (http://www.cbs.dtu.dk/services/BepiPred/), a web server for predicting B-cell epitopes from antigen sequences. BepiPred-2.0 is based on a random forest algorithm trained on epitopes annotated from antibody-antigen protein structures. This new method was found to outperform other available tools for sequence-based epitope prediction both on epitope data derived from solved 3D structures, and on a large collection of linear epitopes downloaded from the IEDB database. The method displays results in a user-friendly and informative way, both for computer-savvy and non-expert users. We believe that BepiPred-2.0 will be a valuable tool for the bioinformatics and immunology community. © The Author(s) 2017. Published by Oxford University Press on behalf of Nucleic Acids Research.

PREDICTION OF THE SOLUBILITY, ACTIVITY COEFFICIENT AND LIQUID/LIQUID PARTITION COEFFICIENT OF ORGANIC COMPOUNDS

EPA Science Inventory

Solvation models, based on fundamental chemical structure theory, were developed in the SPARC mechanistic tool box to predict a large array of physical properties of organic compounds in water and in non-aqueous solvents strictly from molecular structure. The SPARC self-interact...

The ViennaRNA web services.

PubMed

Gruber, Andreas R; Bernhart, Stephan H; Lorenz, Ronny

2015-01-01

The ViennaRNA package is a widely used collection of programs for thermodynamic RNA secondary structure prediction. Over the years, many additional tools have been developed building on the core programs of the package to also address issues related to noncoding RNA detection, RNA folding kinetics, or efficient sequence design considering RNA-RNA hybridizations. The ViennaRNA web services provide easy and user-friendly web access to these tools. This chapter describes how to use this online platform to perform tasks such as prediction of minimum free energy structures, prediction of RNA-RNA hybrids, or noncoding RNA detection. The ViennaRNA web services can be used free of charge and can be accessed via http://rna.tbi.univie.ac.at.

modPDZpep: a web resource for structure based analysis of human PDZ-mediated interaction networks.

PubMed

Sain, Neetu; Mohanty, Debasisa

2016-09-21

PDZ domains recognize short sequence stretches usually present in C-terminal of their interaction partners. Because of the involvement of PDZ domains in many important biological processes, several attempts have been made for developing bioinformatics tools for genome-wide identification of PDZ interaction networks. Currently available tools for prediction of interaction partners of PDZ domains utilize machine learning approach. Since, they have been trained using experimental substrate specificity data for specific PDZ families, their applicability is limited to PDZ families closely related to the training set. These tools also do not allow analysis of PDZ-peptide interaction interfaces. We have used a structure based approach to develop modPDZpep, a program to predict the interaction partners of human PDZ domains and analyze structural details of PDZ interaction interfaces. modPDZpep predicts interaction partners by using structural models of PDZ-peptide complexes and evaluating binding energy scores using residue based statistical pair potentials. Since, it does not require training using experimental data on peptide binding affinity, it can predict substrates for diverse PDZ families. Because of the use of simple scoring function for binding energy, it is also fast enough for genome scale structure based analysis of PDZ interaction networks. Benchmarking using artificial as well as real negative datasets indicates good predictive power with ROC-AUC values in the range of 0.7 to 0.9 for a large number of human PDZ domains. Another novel feature of modPDZpep is its ability to map novel PDZ mediated interactions in human protein-protein interaction networks, either by utilizing available experimental phage display data or by structure based predictions. In summary, we have developed modPDZpep, a web-server for structure based analysis of human PDZ domains. It is freely available at http://www.nii.ac.in/modPDZpep.html or http://202.54.226.235/modPDZpep.html . This article was reviewed by Michael Gromiha and Zoltán Gáspári.

Comparative analysis of machine learning methods in ligand-based virtual screening of large compound libraries.

PubMed

Ma, Xiao H; Jia, Jia; Zhu, Feng; Xue, Ying; Li, Ze R; Chen, Yu Z

2009-05-01

Machine learning methods have been explored as ligand-based virtual screening tools for facilitating drug lead discovery. These methods predict compounds of specific pharmacodynamic, pharmacokinetic or toxicological properties based on their structure-derived structural and physicochemical properties. Increasing attention has been directed at these methods because of their capability in predicting compounds of diverse structures and complex structure-activity relationships without requiring the knowledge of target 3D structure. This article reviews current progresses in using machine learning methods for virtual screening of pharmacodynamically active compounds from large compound libraries, and analyzes and compares the reported performances of machine learning tools with those of structure-based and other ligand-based (such as pharmacophore and clustering) virtual screening methods. The feasibility to improve the performance of machine learning methods in screening large libraries is discussed.

Numerical tools to predict the environmental loads for offshore structures under extreme weather conditions

NASA Astrophysics Data System (ADS)

Wu, Yanling

2018-05-01

In this paper, the extreme waves were generated using the open source computational fluid dynamic (CFD) tools — OpenFOAM and Waves2FOAM — using linear and nonlinear NewWave input. They were used to conduct the numerical simulation of the wave impact process. Numerical tools based on first-order (with and without stretching) and second-order NewWave are investigated. The simulation to predict force loading for the offshore platform under the extreme weather condition is implemented and compared.

Comparison of in silico models for prediction of mutagenicity.

PubMed

Bakhtyari, Nazanin G; Raitano, Giuseppa; Benfenati, Emilio; Martin, Todd; Young, Douglas

2013-01-01

Using a dataset with more than 6000 compounds, the performance of eight quantitative structure activity relationships (QSAR) models was evaluated: ACD/Tox Suite, Absorption, Distribution, Metabolism, Elimination, and Toxicity of chemical substances (ADMET) predictor, Derek, Toxicity Estimation Software Tool (T.E.S.T.), TOxicity Prediction by Komputer Assisted Technology (TOPKAT), Toxtree, CEASAR, and SARpy (SAR in python). In general, the results showed a high level of performance. To have a realistic estimate of the predictive ability, the results for chemicals inside and outside the training set for each model were considered. The effect of applicability domain tools (when available) on the prediction accuracy was also evaluated. The predictive tools included QSAR models, knowledge-based systems, and a combination of both methods. Models based on statistical QSAR methods gave better results.

ProBiS tools (algorithm, database, and web servers) for predicting and modeling of biologically interesting proteins.

PubMed

Konc, Janez; Janežič, Dušanka

2017-09-01

ProBiS (Protein Binding Sites) Tools consist of algorithm, database, and web servers for prediction of binding sites and protein ligands based on the detection of structurally similar binding sites in the Protein Data Bank. In this article, we review the operations that ProBiS Tools perform, provide comments on the evolution of the tools, and give some implementation details. We review some of its applications to biologically interesting proteins. ProBiS Tools are freely available at http://probis.cmm.ki.si and http://probis.nih.gov. Copyright © 2017 Elsevier Ltd. All rights reserved.

Development of Multi-Layered Floating Floor for Cabin Noise Reduction

NASA Astrophysics Data System (ADS)

Song, Jee-Hun; Hong, Suk-Yoon; Kwon, Hyun-Wung

2017-12-01

Recently, regulations pertaining to the noise and vibration environment of ship cabins have been strengthened. In this paper, a numerical model is developed for multi-layered floating floor to predict the structure-borne noise in ship cabins. The theoretical model consists of multi-panel structures lined with high-density mineral wool. The predicted results for structure-borne noise when multi-layered floating floor is used are compared to the measure-ments made of a mock-up. A comparison of the predicted results and the experimental one shows that the developed model could be an effective tool for predicting structure-borne noise in ship cabins.

A computer program for cyclic plasticity and structural fatigue analysis

NASA Technical Reports Server (NTRS)

Kalev, I.

1980-01-01

A computerized tool for the analysis of time independent cyclic plasticity structural response, life to crack initiation prediction, and crack growth rate prediction for metallic materials is described. Three analytical items are combined: the finite element method with its associated numerical techniques for idealization of the structural component, cyclic plasticity models for idealization of the material behavior, and damage accumulation criteria for the fatigue failure.

Improved Aerodynamic Analysis for Hybrid Wing Body Conceptual Design Optimization

NASA Technical Reports Server (NTRS)

Gern, Frank H.

2012-01-01

This paper provides an overview of ongoing efforts to develop, evaluate, and validate different tools for improved aerodynamic modeling and systems analysis of Hybrid Wing Body (HWB) aircraft configurations. Results are being presented for the evaluation of different aerodynamic tools including panel methods, enhanced panel methods with viscous drag prediction, and computational fluid dynamics. Emphasis is placed on proper prediction of aerodynamic loads for structural sizing as well as viscous drag prediction to develop drag polars for HWB conceptual design optimization. Data from transonic wind tunnel tests at the Arnold Engineering Development Center s 16-Foot Transonic Tunnel was used as a reference data set in order to evaluate the accuracy of the aerodynamic tools. Triangularized surface data and Vehicle Sketch Pad (VSP) models of an X-48B 2% scale wind tunnel model were used to generate input and model files for the different analysis tools. In support of ongoing HWB scaling studies within the NASA Environmentally Responsible Aviation (ERA) program, an improved finite element based structural analysis and weight estimation tool for HWB center bodies is currently under development. Aerodynamic results from these analyses are used to provide additional aerodynamic validation data.

Overcoming redundancies in bedside nursing assessments by validating a parsimonious meta-tool: findings from a methodological exercise study.

PubMed

Palese, Alvisa; Marini, Eva; Guarnier, Annamaria; Barelli, Paolo; Zambiasi, Paola; Allegrini, Elisabetta; Bazoli, Letizia; Casson, Paola; Marin, Meri; Padovan, Marisa; Picogna, Michele; Taddia, Patrizia; Chiari, Paolo; Salmaso, Daniele; Marognolli, Oliva; Canzan, Federica; Ambrosi, Elisa; Saiani, Luisa; Grassetti, Luca

2016-10-01

There is growing interest in validating tools aimed at supporting the clinical decision-making process and research. However, an increased bureaucratization of clinical practice and redundancies in the measures collected have been reported by clinicians. Redundancies in clinical assessments affect negatively both patients and nurses. To validate a meta-tool measuring the risks/problems currently estimated by multiple tools used in daily practice. A secondary analysis of a database was performed, using a cross-validation and a longitudinal study designs. In total, 1464 patients admitted to 12 medical units in 2012 were assessed at admission with the Brass, Barthel, Conley and Braden tools. Pertinent outcomes such as the occurrence of post-discharge need for resources and functional decline at discharge, as well as falls and pressure sores, were measured. Explorative factor analysis of each tool, inter-tool correlations and a conceptual evaluation of the redundant/similar items across tools were performed. Therefore, the validation of the meta-tool was performed through explorative factor analysis, confirmatory factor analysis and the structural equation model to establish the ability of the meta-tool to predict the outcomes estimated by the original tools. High correlations between the tools have emerged (from r 0.428 to 0.867) with a common variance from 18.3% to 75.1%. Through a conceptual evaluation and explorative factor analysis, the items were reduced from 42 to 20, and the three factors that emerged were confirmed by confirmatory factor analysis. According to the structural equation model results, two out of three emerged factors predicted the outcomes. From the initial 42 items, the meta-tool is composed of 20 items capable of predicting the outcomes as with the original tools. © 2016 John Wiley & Sons, Ltd.

SAbPred: a structure-based antibody prediction server

PubMed Central

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

2016-01-01

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

Implementation of structure-mapping inference by event-file binding and action planning: a model of tool-improvisation analogies.

PubMed

Fields, Chris

2011-03-01

Structure-mapping inferences are generally regarded as dependent upon relational concepts that are understood and expressible in language by subjects capable of analogical reasoning. However, tool-improvisation inferences are executed by members of a variety of non-human primate and other species. Tool improvisation requires correctly inferring the motion and force-transfer affordances of an object; hence tool improvisation requires structure mapping driven by relational properties. Observational and experimental evidence can be interpreted to indicate that structure-mapping analogies in tool improvisation are implemented by multi-step manipulation of event files by binding and action-planning mechanisms that act in a language-independent manner. A functional model of language-independent event-file manipulations that implement structure mapping in the tool-improvisation domain is developed. This model provides a mechanism by which motion and force representations commonly employed in tool-improvisation structure mappings may be sufficiently reinforced to be available to inwardly directed attention and hence conceptualization. Predictions and potential experimental tests of this model are outlined.

StructRNAfinder: an automated pipeline and web server for RNA families prediction.

PubMed

Arias-Carrasco, Raúl; Vásquez-Morán, Yessenia; Nakaya, Helder I; Maracaja-Coutinho, Vinicius

2018-02-17

The function of many noncoding RNAs (ncRNAs) depend upon their secondary structures. Over the last decades, several methodologies have been developed to predict such structures or to use them to functionally annotate RNAs into RNA families. However, to fully perform this analysis, researchers should utilize multiple tools, which require the constant parsing and processing of several intermediate files. This makes the large-scale prediction and annotation of RNAs a daunting task even to researchers with good computational or bioinformatics skills. We present an automated pipeline named StructRNAfinder that predicts and annotates RNA families in transcript or genome sequences. This single tool not only displays the sequence/structural consensus alignments for each RNA family, according to Rfam database but also provides a taxonomic overview for each assigned functional RNA. Moreover, we implemented a user-friendly web service that allows researchers to upload their own nucleotide sequences in order to perform the whole analysis. Finally, we provided a stand-alone version of StructRNAfinder to be used in large-scale projects. The tool was developed under GNU General Public License (GPLv3) and is freely available at http://structrnafinder.integrativebioinformatics.me . The main advantage of StructRNAfinder relies on the large-scale processing and integrating the data obtained by each tool and database employed along the workflow, of which several files are generated and displayed in user-friendly reports, useful for downstream analyses and data exploration.

Toxicity Estimation Software Tool (TEST)

EPA Science Inventory

The Toxicity Estimation Software Tool (TEST) was developed to allow users to easily estimate the toxicity of chemicals using Quantitative Structure Activity Relationships (QSARs) methodologies. QSARs are mathematical models used to predict measures of toxicity from the physical c...

FAST Simulation Tool Containing Methods for Predicting the Dynamic Response of Wind Turbines

DOE Office of Scientific and Technical Information (OSTI.GOV)

Jonkman, Jason

2015-08-12

FAST is a simulation tool (computer software) for modeling tlie dynamic response of horizontal-axis wind turbines. FAST employs a combined modal and multibody structural-dynamics formulation in the time domain.

Family-Based Benchmarking of Copy Number Variation Detection Software.

PubMed

Nutsua, Marcel Elie; Fischer, Annegret; Nebel, Almut; Hofmann, Sylvia; Schreiber, Stefan; Krawczak, Michael; Nothnagel, Michael

2015-01-01

The analysis of structural variants, in particular of copy-number variations (CNVs), has proven valuable in unraveling the genetic basis of human diseases. Hence, a large number of algorithms have been developed for the detection of CNVs in SNP array signal intensity data. Using the European and African HapMap trio data, we undertook a comparative evaluation of six commonly used CNV detection software tools, namely Affymetrix Power Tools (APT), QuantiSNP, PennCNV, GLAD, R-gada and VEGA, and assessed their level of pair-wise prediction concordance. The tool-specific CNV prediction accuracy was assessed in silico by way of intra-familial validation. Software tools differed greatly in terms of the number and length of the CNVs predicted as well as the number of markers included in a CNV. All software tools predicted substantially more deletions than duplications. Intra-familial validation revealed consistently low levels of prediction accuracy as measured by the proportion of validated CNVs (34-60%). Moreover, up to 20% of apparent family-based validations were found to be due to chance alone. Software using Hidden Markov models (HMM) showed a trend to predict fewer CNVs than segmentation-based algorithms albeit with greater validity. PennCNV yielded the highest prediction accuracy (60.9%). Finally, the pairwise concordance of CNV prediction was found to vary widely with the software tools involved. We recommend HMM-based software, in particular PennCNV, rather than segmentation-based algorithms when validity is the primary concern of CNV detection. QuantiSNP may be used as an additional tool to detect sets of CNVs not detectable by the other tools. Our study also reemphasizes the need for laboratory-based validation, such as qPCR, of CNVs predicted in silico.

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

PubMed Central

2014-01-01

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

ProTSAV: A protein tertiary structure analysis and validation server.

PubMed

Singh, Ankita; Kaushik, Rahul; Mishra, Avinash; Shanker, Asheesh; Jayaram, B

2016-01-01

Quality assessment of predicted model structures of proteins is as important as the protein tertiary structure prediction. A highly efficient quality assessment of predicted model structures directs further research on function. Here we present a new server ProTSAV, capable of evaluating predicted model structures based on some popular online servers and standalone tools. ProTSAV furnishes the user with a single quality score in case of individual protein structure along with a graphical representation and ranking in case of multiple protein structure assessment. The server is validated on ~64,446 protein structures including experimental structures from RCSB and predicted model structures for CASP targets and from public decoy sets. ProTSAV succeeds in predicting quality of protein structures with a specificity of 100% and a sensitivity of 98% on experimentally solved structures and achieves a specificity of 88%and a sensitivity of 91% on predicted protein structures of CASP11 targets under 2Å.The server overcomes the limitations of any single server/method and is seen to be robust in helping in quality assessment. ProTSAV is freely available at http://www.scfbio-iitd.res.in/software/proteomics/protsav.jsp. Copyright © 2015 Elsevier B.V. All rights reserved.

RAG-3D: A search tool for RNA 3D substructures

DOE PAGES

Zahran, Mai; Sevim Bayrak, Cigdem; Elmetwaly, Shereef; ...

2015-08-24

In this study, to address many challenges in RNA structure/function prediction, the characterization of RNA's modular architectural units is required. Using the RNA-As-Graphs (RAG) database, we have previously explored the existence of secondary structure (2D) submotifs within larger RNA structures. Here we present RAG-3D—a dataset of RNA tertiary (3D) structures and substructures plus a web-based search tool—designed to exploit graph representations of RNAs for the goal of searching for similar 3D structural fragments. The objects in RAG-3D consist of 3D structures translated into 3D graphs, cataloged based on the connectivity between their secondary structure elements. Each graph is additionally describedmore » in terms of its subgraph building blocks. The RAG-3D search tool then compares a query RNA 3D structure to those in the database to obtain structurally similar structures and substructures. This comparison reveals conserved 3D RNA features and thus may suggest functional connections. Though RNA search programs based on similarity in sequence, 2D, and/or 3D structural elements are available, our graph-based search tool may be advantageous for illuminating similarities that are not obvious; using motifs rather than sequence space also reduces search times considerably. Ultimately, such substructuring could be useful for RNA 3D structure prediction, structure/function inference and inverse folding.« less

RAG-3D: a search tool for RNA 3D substructures

PubMed Central

Zahran, Mai; Sevim Bayrak, Cigdem; Elmetwaly, Shereef; Schlick, Tamar

2015-01-01

To address many challenges in RNA structure/function prediction, the characterization of RNA's modular architectural units is required. Using the RNA-As-Graphs (RAG) database, we have previously explored the existence of secondary structure (2D) submotifs within larger RNA structures. Here we present RAG-3D—a dataset of RNA tertiary (3D) structures and substructures plus a web-based search tool—designed to exploit graph representations of RNAs for the goal of searching for similar 3D structural fragments. The objects in RAG-3D consist of 3D structures translated into 3D graphs, cataloged based on the connectivity between their secondary structure elements. Each graph is additionally described in terms of its subgraph building blocks. The RAG-3D search tool then compares a query RNA 3D structure to those in the database to obtain structurally similar structures and substructures. This comparison reveals conserved 3D RNA features and thus may suggest functional connections. Though RNA search programs based on similarity in sequence, 2D, and/or 3D structural elements are available, our graph-based search tool may be advantageous for illuminating similarities that are not obvious; using motifs rather than sequence space also reduces search times considerably. Ultimately, such substructuring could be useful for RNA 3D structure prediction, structure/function inference and inverse folding. PMID:26304547

RAG-3D: A search tool for RNA 3D substructures

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zahran, Mai; Sevim Bayrak, Cigdem; Elmetwaly, Shereef

In this study, to address many challenges in RNA structure/function prediction, the characterization of RNA's modular architectural units is required. Using the RNA-As-Graphs (RAG) database, we have previously explored the existence of secondary structure (2D) submotifs within larger RNA structures. Here we present RAG-3D—a dataset of RNA tertiary (3D) structures and substructures plus a web-based search tool—designed to exploit graph representations of RNAs for the goal of searching for similar 3D structural fragments. The objects in RAG-3D consist of 3D structures translated into 3D graphs, cataloged based on the connectivity between their secondary structure elements. Each graph is additionally describedmore » in terms of its subgraph building blocks. The RAG-3D search tool then compares a query RNA 3D structure to those in the database to obtain structurally similar structures and substructures. This comparison reveals conserved 3D RNA features and thus may suggest functional connections. Though RNA search programs based on similarity in sequence, 2D, and/or 3D structural elements are available, our graph-based search tool may be advantageous for illuminating similarities that are not obvious; using motifs rather than sequence space also reduces search times considerably. Ultimately, such substructuring could be useful for RNA 3D structure prediction, structure/function inference and inverse folding.« less

Challenges Facing Design and Analysis Tools

NASA Technical Reports Server (NTRS)

Knight, Norman F., Jr.; Broduer, Steve (Technical Monitor)

2001-01-01

The design and analysis of future aerospace systems will strongly rely on advanced engineering analysis tools used in combination with risk mitigation procedures. The implications of such a trend place increased demands on these tools to assess off-nominal conditions, residual strength, damage propagation, and extreme loading conditions in order to understand and quantify these effects as they affect mission success. Advances in computer hardware such as CPU processing speed, memory, secondary storage, and visualization provide significant resources for the engineer to exploit in engineering design. The challenges facing design and analysis tools fall into three primary areas. The first area involves mechanics needs such as constitutive modeling, contact and penetration simulation, crack growth prediction, damage initiation and progression prediction, transient dynamics and deployment simulations, and solution algorithms. The second area involves computational needs such as fast, robust solvers, adaptivity for model and solution strategies, control processes for concurrent, distributed computing for uncertainty assessments, and immersive technology. Traditional finite element codes still require fast direct solvers which when coupled to current CPU power enables new insight as a result of high-fidelity modeling. The third area involves decision making by the analyst. This area involves the integration and interrogation of vast amounts of information - some global in character while local details are critical and often drive the design. The proposed presentation will describe and illustrate these areas using composite structures, energy-absorbing structures, and inflatable space structures. While certain engineering approximations within the finite element model may be adequate for global response prediction, they generally are inadequate in a design setting or when local response prediction is critical. Pitfalls to be avoided and trends for emerging analysis tools will be described.

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

PubMed Central

Kmiecik, Sebastian; Jamroz, Michal; Kolinski, Michal

2014-01-01

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

DEVELOPMENT OF STRUCTURE ACTIVITY RELATIONSHIPS FOR ASSESSING ECOLOGICAL RISKS

EPA Science Inventory

In the field of environmental toxicology, structure activity relationships (SARs) have developed as scientifically-credible tools for predicting the effects of chemicals when little or no empirical data are available.

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/.

CONFOLD2: improved contact-driven ab initio protein structure modeling.

PubMed

Adhikari, Badri; Cheng, Jianlin

2018-01-25

Contact-guided protein structure prediction methods are becoming more and more successful because of the latest advances in residue-residue contact prediction. To support contact-driven structure prediction, effective tools that can quickly build tertiary structural models of good quality from predicted contacts need to be developed. We develop an improved contact-driven protein modelling method, CONFOLD2, and study how it may be effectively used for ab initio protein structure prediction with predicted contacts as input. It builds models using various subsets of input contacts to explore the fold space under the guidance of a soft square energy function, and then clusters the models to obtain the top five models. CONFOLD2 obtains an average reconstruction accuracy of 0.57 TM-score for the 150 proteins in the PSICOV contact prediction dataset. When benchmarked on the CASP11 contacts predicted using CONSIP2 and CASP12 contacts predicted using Raptor-X, CONFOLD2 achieves a mean TM-score of 0.41 on both datasets. CONFOLD2 allows to quickly generate top five structural models for a protein sequence when its secondary structures and contacts predictions at hand. The source code of CONFOLD2 is publicly available at https://github.com/multicom-toolbox/CONFOLD2/ .

MicroRNAfold: pre-microRNA secondary structure prediction based on modified NCM model with thermodynamics-based scoring strategy.

PubMed

Han, Dianwei; Zhang, Jun; Tang, Guiliang

2012-01-01

An accurate prediction of the pre-microRNA secondary structure is important in miRNA informatics. Based on a recently proposed model, nucleotide cyclic motifs (NCM), to predict RNA secondary structure, we propose and implement a Modified NCM (MNCM) model with a physics-based scoring strategy to tackle the problem of pre-microRNA folding. Our microRNAfold is implemented using a global optimal algorithm based on the bottom-up local optimal solutions. Our experimental results show that microRNAfold outperforms the current leading prediction tools in terms of True Negative rate, False Negative rate, Specificity, and Matthews coefficient ratio.

Predictive and Experimental Approaches for Elucidating Protein–Protein Interactions and Quaternary Structures

PubMed Central

Nealon, John Oliver; Philomina, Limcy Seby

2017-01-01

The elucidation of protein–protein interactions is vital for determining the function and action of quaternary protein structures. Here, we discuss the difficulty and importance of establishing protein quaternary structure and review in vitro and in silico methods for doing so. Determining the interacting partner proteins of predicted protein structures is very time-consuming when using in vitro methods, this can be somewhat alleviated by use of predictive methods. However, developing reliably accurate predictive tools has proved to be difficult. We review the current state of the art in predictive protein interaction software and discuss the problem of scoring and therefore ranking predictions. Current community-based predictive exercises are discussed in relation to the growth of protein interaction prediction as an area within these exercises. We suggest a fusion of experimental and predictive methods that make use of sparse experimental data to determine higher resolution predicted protein interactions as being necessary to drive forward development. PMID:29206185

Mutagenicity in a Molecule: Identification of Core Structural Features of Mutagenicity Using a Scaffold Analysis

PubMed Central

Hsu, Kuo-Hsiang; Su, Bo-Han; Tu, Yi-Shu; Lin, Olivia A.; Tseng, Yufeng J.

2016-01-01

With advances in the development and application of Ames mutagenicity in silico prediction tools, the International Conference on Harmonisation (ICH) has amended its M7 guideline to reflect the use of such prediction models for the detection of mutagenic activity in early drug safety evaluation processes. Since current Ames mutagenicity prediction tools only focus on functional group alerts or side chain modifications of an analog series, these tools are unable to identify mutagenicity derived from core structures or specific scaffolds of a compound. In this study, a large collection of 6512 compounds are used to perform scaffold tree analysis. By relating different scaffolds on constructed scaffold trees with Ames mutagenicity, four major and one minor novel mutagenic groups of scaffold are identified. The recognized mutagenic groups of scaffold can serve as a guide for medicinal chemists to prevent the development of potentially mutagenic therapeutic agents in early drug design or development phases, by modifying the core structures of mutagenic compounds to form non-mutagenic compounds. In addition, five series of substructures are provided as recommendations, for direct modification of potentially mutagenic scaffolds to decrease associated mutagenic activities. PMID:26863515

Interactome INSIDER: a structural interactome browser for genomic studies.

PubMed

Meyer, Michael J; Beltrán, Juan Felipe; Liang, Siqi; Fragoza, Robert; Rumack, Aaron; Liang, Jin; Wei, Xiaomu; Yu, Haiyuan

2018-01-01

We present Interactome INSIDER, a tool to link genomic variant information with structural protein-protein interactomes. Underlying this tool is the application of machine learning to predict protein interaction interfaces for 185,957 protein interactions with previously unresolved interfaces in human and seven model organisms, including the entire experimentally determined human binary interactome. Predicted interfaces exhibit functional properties similar to those of known interfaces, including enrichment for disease mutations and recurrent cancer mutations. Through 2,164 de novo mutagenesis experiments, we show that mutations of predicted and known interface residues disrupt interactions at a similar rate and much more frequently than mutations outside of predicted interfaces. To spur functional genomic studies, Interactome INSIDER (http://interactomeinsider.yulab.org) enables users to identify whether variants or disease mutations are enriched in known and predicted interaction interfaces at various resolutions. Users may explore known population variants, disease mutations, and somatic cancer mutations, or they may upload their own set of mutations for this purpose.

New Tool Released for Engine-Airframe Blade-Out Structural Simulations

NASA Technical Reports Server (NTRS)

Lawrence, Charles

2004-01-01

Researchers at the NASA Glenn Research Center have enhanced a general-purpose finite element code, NASTRAN, for engine-airframe structural simulations during steady-state and transient operating conditions. For steady-state simulations, the code can predict critical operating speeds, natural modes of vibration, and forced response (e.g., cabin noise and component fatigue). The code can be used to perform static analysis to predict engine-airframe response and component stresses due to maneuver loads. For transient response, the simulation code can be used to predict response due to bladeoff events and subsequent engine shutdown and windmilling conditions. In addition, the code can be used as a pretest analysis tool to predict the results of the bladeout test required for FAA certification of new and derivative aircraft engines. Before the present analysis code was developed, all the major aircraft engine and airframe manufacturers in the United States and overseas were performing similar types of analyses to ensure the structural integrity of engine-airframe systems. Although there were many similarities among the analysis procedures, each manufacturer was developing and maintaining its own structural analysis capabilities independently. This situation led to high software development and maintenance costs, complications with manufacturers exchanging models and results, and limitations in predicting the structural response to the desired degree of accuracy. An industry-NASA team was formed to overcome these problems by developing a common analysis tool that would satisfy all the structural analysis needs of the industry and that would be available and supported by a commercial software vendor so that the team members would be relieved of maintenance and development responsibilities. Input from all the team members was used to ensure that everyone's requirements were satisfied and that the best technology was incorporated into the code. Furthermore, because the code would be distributed by a commercial software vendor, it would be more readily available to engine and airframe manufacturers, as well as to nonaircraft companies that did not previously have access to this capability.

Computational predictive models for P-glycoprotein inhibition of in-house chalcone derivatives and drug-bank compounds.

PubMed

Ngo, Trieu-Du; Tran, Thanh-Dao; Le, Minh-Tri; Thai, Khac-Minh

2016-11-01

The human P-glycoprotein (P-gp) efflux pump is of great interest for medicinal chemists because of its important role in multidrug resistance (MDR). Because of the high polyspecificity as well as the unavailability of high-resolution X-ray crystal structures of this transmembrane protein, ligand-based, and structure-based approaches which were machine learning, homology modeling, and molecular docking were combined for this study. In ligand-based approach, individual two-dimensional quantitative structure-activity relationship models were developed using different machine learning algorithms and subsequently combined into the Ensemble model which showed good performance on both the diverse training set and the validation sets. The applicability domain and the prediction quality of the developed models were also judged using the state-of-the-art methods and tools. In our structure-based approach, the P-gp structure and its binding region were predicted for a docking study to determine possible interactions between the ligands and the receptor. Based on these in silico tools, hit compounds for reversing MDR were discovered from the in-house and DrugBank databases through virtual screening using prediction models and molecular docking in an attempt to restore cancer cell sensitivity to cytotoxic drugs.

DEMO: Sequence Alignment to Predict Across Species Susceptibility

EPA Science Inventory

The US Environmental Protection Agency Sequence Alignment to Predict Across Species Susceptibility tool (SeqAPASS; https://seqapass.epa.gov/seqapass/) was developed to comparatively evaluate protein sequence and structural similarity across species as a means to extrapolate toxic...

Overview of T.E.S.T. (Toxicity Estimation Software Tool)

EPA Science Inventory

This talk provides an overview of T.E.S.T. (Toxicity Estimation Software Tool). T.E.S.T. predicts toxicity values and physical properties using a variety of different QSAR (quantitative structure activity relationship) approaches including hierarchical clustering, group contribut...

An ensemble model of QSAR tools for regulatory risk assessment.

PubMed

Pradeep, Prachi; Povinelli, Richard J; White, Shannon; Merrill, Stephen J

2016-01-01

Quantitative structure activity relationships (QSARs) are theoretical models that relate a quantitative measure of chemical structure to a physical property or a biological effect. QSAR predictions can be used for chemical risk assessment for protection of human and environmental health, which makes them interesting to regulators, especially in the absence of experimental data. For compatibility with regulatory use, QSAR models should be transparent, reproducible and optimized to minimize the number of false negatives. In silico QSAR tools are gaining wide acceptance as a faster alternative to otherwise time-consuming clinical and animal testing methods. However, different QSAR tools often make conflicting predictions for a given chemical and may also vary in their predictive performance across different chemical datasets. In a regulatory context, conflicting predictions raise interpretation, validation and adequacy concerns. To address these concerns, ensemble learning techniques in the machine learning paradigm can be used to integrate predictions from multiple tools. By leveraging various underlying QSAR algorithms and training datasets, the resulting consensus prediction should yield better overall predictive ability. We present a novel ensemble QSAR model using Bayesian classification. The model allows for varying a cut-off parameter that allows for a selection in the desirable trade-off between model sensitivity and specificity. The predictive performance of the ensemble model is compared with four in silico tools (Toxtree, Lazar, OECD Toolbox, and Danish QSAR) to predict carcinogenicity for a dataset of air toxins (332 chemicals) and a subset of the gold carcinogenic potency database (480 chemicals). Leave-one-out cross validation results show that the ensemble model achieves the best trade-off between sensitivity and specificity (accuracy: 83.8 % and 80.4 %, and balanced accuracy: 80.6 % and 80.8 %) and highest inter-rater agreement [kappa ( κ ): 0.63 and 0.62] for both the datasets. The ROC curves demonstrate the utility of the cut-off feature in the predictive ability of the ensemble model. This feature provides an additional control to the regulators in grading a chemical based on the severity of the toxic endpoint under study.

An ensemble model of QSAR tools for regulatory risk assessment

DOE PAGES

Pradeep, Prachi; Povinelli, Richard J.; White, Shannon; ...

2016-09-22

Quantitative structure activity relationships (QSARs) are theoretical models that relate a quantitative measure of chemical structure to a physical property or a biological effect. QSAR predictions can be used for chemical risk assessment for protection of human and environmental health, which makes them interesting to regulators, especially in the absence of experimental data. For compatibility with regulatory use, QSAR models should be transparent, reproducible and optimized to minimize the number of false negatives. In silico QSAR tools are gaining wide acceptance as a faster alternative to otherwise time-consuming clinical and animal testing methods. However, different QSAR tools often make conflictingmore » predictions for a given chemical and may also vary in their predictive performance across different chemical datasets. In a regulatory context, conflicting predictions raise interpretation, validation and adequacy concerns. To address these concerns, ensemble learning techniques in the machine learning paradigm can be used to integrate predictions from multiple tools. By leveraging various underlying QSAR algorithms and training datasets, the resulting consensus prediction should yield better overall predictive ability. We present a novel ensemble QSAR model using Bayesian classification. The model allows for varying a cut-off parameter that allows for a selection in the desirable trade-off between model sensitivity and specificity. The predictive performance of the ensemble model is compared with four in silico tools (Toxtree, Lazar, OECD Toolbox, and Danish QSAR) to predict carcinogenicity for a dataset of air toxins (332 chemicals) and a subset of the gold carcinogenic potency database (480 chemicals). Leave-one-out cross validation results show that the ensemble model achieves the best trade-off between sensitivity and specificity (accuracy: 83.8 % and 80.4 %, and balanced accuracy: 80.6 % and 80.8 %) and highest inter-rater agreement [kappa (κ): 0.63 and 0.62] for both the datasets. The ROC curves demonstrate the utility of the cut-off feature in the predictive ability of the ensemble model. In conclusion, this feature provides an additional control to the regulators in grading a chemical based on the severity of the toxic endpoint under study.« less

From Structure to Function: A Comprehensive Compendium of Tools to Unveil Protein Domains and Understand Their Role in Cytokinesis.

PubMed

Rincon, Sergio A; Paoletti, Anne

2016-01-01

Unveiling the function of a novel protein is a challenging task that requires careful experimental design. Yeast cytokinesis is a conserved process that involves modular structural and regulatory proteins. For such proteins, an important step is to identify their domains and structural organization. Here we briefly discuss a collection of methods commonly used for sequence alignment and prediction of protein structure that represent powerful tools for the identification homologous domains and design of structure-function approaches to test experimentally the function of multi-domain proteins such as those implicated in yeast cytokinesis.

Local Debonding and Fiber Breakage in Composite Materials Modeled Accurately

NASA Technical Reports Server (NTRS)

Bednarcyk, Brett A.; Arnold, Steven M.

2001-01-01

A prerequisite for full utilization of composite materials in aerospace components is accurate design and life prediction tools that enable the assessment of component performance and reliability. Such tools assist both structural analysts, who design and optimize structures composed of composite materials, and materials scientists who design and optimize the composite materials themselves. NASA Glenn Research Center's Micromechanics Analysis Code with Generalized Method of Cells (MAC/GMC) software package (http://www.grc.nasa.gov/WWW/LPB/mac) addresses this need for composite design and life prediction tools by providing a widely applicable and accurate approach to modeling composite materials. Furthermore, MAC/GMC serves as a platform for incorporating new local models and capabilities that are under development at NASA, thus enabling these new capabilities to progress rapidly to a stage in which they can be employed by the code's end users.

Prelude and Fugue, predicting local protein structure, early folding regions and structural weaknesses.

PubMed

Kwasigroch, Jean Marc; Rooman, Marianne

2006-07-15

Prelude&Fugue are bioinformatics tools aiming at predicting the local 3D structure of a protein from its amino acid sequence in terms of seven backbone torsion angle domains, using database-derived potentials. Prelude(&Fugue) computes all lowest free energy conformations of a protein or protein region, ranked by increasing energy, and possibly satisfying some interresidue distance constraints specified by the user. (Prelude&)Fugue detects sequence regions whose predicted structure is significantly preferred relative to other conformations in the absence of tertiary interactions. These programs can be used for predicting secondary structure, tertiary structure of short peptides, flickering early folding sequences and peptides that adopt a preferred conformation in solution. They can also be used for detecting structural weaknesses, i.e. sequence regions that are not optimal with respect to the tertiary fold. http://babylone.ulb.ac.be/Prelude_and_Fugue.

Comparison of Hydrodynamic Load Predictions Between Engineering Models and Computational Fluid Dynamics for the OC4-DeepCwind Semi-Submersible: Preprint

DOE Office of Scientific and Technical Information (OSTI.GOV)

Benitz, M. A.; Schmidt, D. P.; Lackner, M. A.

Hydrodynamic loads on the platforms of floating offshore wind turbines are often predicted with computer-aided engineering tools that employ Morison's equation and/or potential-flow theory. This work compares results from one such tool, FAST, NREL's wind turbine computer-aided engineering tool, and the computational fluid dynamics package, OpenFOAM, for the OC4-DeepCwind semi-submersible analyzed in the International Energy Agency Wind Task 30 project. Load predictions from HydroDyn, the offshore hydrodynamics module of FAST, are compared with high-fidelity results from OpenFOAM. HydroDyn uses a combination of Morison's equations and potential flow to predict the hydrodynamic forces on the structure. The implications of the assumptionsmore » in HydroDyn are evaluated based on this code-to-code comparison.« less

Prediction of Hydrolysis Products of Organic Chemicals under Environmental pH Conditions

EPA Science Inventory

Cheminformatics-based software tools can predict the molecular structure of transformation products using a library of transformation reaction schemes. This paper presents the development of such a library for abiotic hydrolysis of organic chemicals under environmentally relevant...

G-LoSA for Prediction of Protein-Ligand Binding Sites and Structures.

PubMed

Lee, Hui Sun; Im, Wonpil

2017-01-01

Recent advances in high-throughput structure determination and computational protein structure prediction have significantly enriched the universe of protein structure. However, there is still a large gap between the number of available protein structures and that of proteins with annotated function in high accuracy. Computational structure-based protein function prediction has emerged to reduce this knowledge gap. The identification of a ligand binding site and its structure is critical to the determination of a protein's molecular function. We present a computational methodology for predicting small molecule ligand binding site and ligand structure using G-LoSA, our protein local structure alignment and similarity measurement tool. All the computational procedures described here can be easily implemented using G-LoSA Toolkit, a package of standalone software programs and preprocessed PDB structure libraries. G-LoSA and G-LoSA Toolkit are freely available to academic users at http://compbio.lehigh.edu/GLoSA . We also illustrate a case study to show the potential of our template-based approach harnessing G-LoSA for protein function prediction.

Validation of structural analysis methods using the in-house liner cyclic rigs

NASA Technical Reports Server (NTRS)

Thompson, R. L.

1982-01-01

Test conditions and variables to be considered in each of the test rigs and test configurations, and also used in the validation of the structural predictive theories and tools, include: thermal and mechanical load histories (simulating an engine mission cycle; different boundary conditions; specimens and components of different dimensions and geometries; different materials; various cooling schemes and cooling hole configurations; several advanced burner liner structural design concepts; and the simulation of hot streaks. Based on these test conditions and test variables, the test matrices for each rig and configurations can be established to verify the predictive tools over as wide a range of test conditions as possible using the simplest possible tests. A flow chart for the thermal/structural analysis of a burner liner and how the analysis relates to the tests is shown schematically. The chart shows that several nonlinear constitutive theories are to be evaluated.

Modelling of Tool Wear and Residual Stress during Machining of AISI H13 Tool Steel

NASA Astrophysics Data System (ADS)

Outeiro, José C.; Umbrello, Domenico; Pina, José C.; Rizzuti, Stefania

2007-05-01

Residual stresses can enhance or impair the ability of a component to withstand loading conditions in service (fatigue, creep, stress corrosion cracking, etc.), depending on their nature: compressive or tensile, respectively. This poses enormous problems in structural assembly as this affects the structural integrity of the whole part. In addition, tool wear issues are of critical importance in manufacturing since these affect component quality, tool life and machining cost. Therefore, prediction and control of both tool wear and the residual stresses in machining are absolutely necessary. In this work, a two-dimensional Finite Element model using an implicit Lagrangian formulation with an automatic remeshing was applied to simulate the orthogonal cutting process of AISI H13 tool steel. To validate such model the predicted and experimentally measured chip geometry, cutting forces, temperatures, tool wear and residual stresses on the machined affected layers were compared. The proposed FE model allowed us to investigate the influence of tool geometry, cutting regime parameters and tool wear on residual stress distribution in the machined surface and subsurface of AISI H13 tool steel. The obtained results permit to conclude that in order to reduce the magnitude of surface residual stresses, the cutting speed should be increased, the uncut chip thickness (or feed) should be reduced and machining with honed tools having large cutting edge radii produce better results than chamfered tools. Moreover, increasing tool wear increases the magnitude of surface residual stresses.

Coupled rotor/airframe vibration analysis

NASA Technical Reports Server (NTRS)

Sopher, R.; Studwell, R. E.; Cassarino, S.; Kottapalli, S. B. R.

1982-01-01

A coupled rotor/airframe vibration analysis developed as a design tool for predicting helicopter vibrations and a research tool to quantify the effects of structural properties, aerodynamic interactions, and vibration reduction devices on vehicle vibration levels is described. The analysis consists of a base program utilizing an impedance matching technique to represent the coupled rotor/airframe dynamics of the system supported by inputs from several external programs supplying sophisticated rotor and airframe aerodynamic and structural dynamic representation. The theoretical background, computer program capabilities and limited correlation results are presented in this report. Correlation results using scale model wind tunnel results show that the analysis can adequately predict trends of vibration variations with airspeed and higher harmonic control effects. Predictions of absolute values of vibration levels were found to be very sensitive to modal characteristics and results were not representative of measured values.

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

PubMed

Kmiecik, Sebastian; Jamroz, Michal; Kolinski, Michal

2014-06-03

G-protein-coupled receptors (GPCRs) play key roles in living organisms. Therefore, it is important to determine their functional structures. The second extracellular loop (ECL2) is a functionally important region of GPCRs, which poses significant challenge for computational structure prediction methods. In this work, we evaluated CABS, a well-established protein modeling tool for predicting ECL2 structure in 13 GPCRs. The ECL2s (with between 13 and 34 residues) are predicted in an environment of other extracellular loops being fully flexible and the transmembrane domain fixed in its x-ray conformation. The modeling procedure used theoretical predictions of ECL2 secondary structure and experimental constraints on disulfide bridges. Our approach yielded ensembles of low-energy conformers and the most populated conformers that contained models close to the available x-ray structures. The level of similarity between the predicted models and x-ray structures is comparable to that of other state-of-the-art computational methods. Our results extend other studies by including newly crystallized GPCRs. Copyright © 2014 The Authors. Published by Elsevier Inc. All rights reserved.

Screen Twice, Cut Once: Assessing the Predictive Validity of Teacher Selection Tools. Working Paper 120

ERIC Educational Resources Information Center

Goldhaber, Dan; Grout, Cyrus; Huntington-Klein, Nick

2014-01-01

Evidence suggests that teacher hiring in public schools is ad hoc and often fails to result in good selection among applicants. Some districts use structured selection instruments in the hiring process, but we know little about the efficacy of such tools. In this paper, we evaluate the ability of applicant selection tools used by the Spokane…

A method for probing the mutational landscape of amyloid structure.

PubMed

O'Donnell, Charles W; Waldispühl, Jérôme; Lis, Mieszko; Halfmann, Randal; Devadas, Srinivas; Lindquist, Susan; Berger, Bonnie

2011-07-01

Proteins of all kinds can self-assemble into highly ordered β-sheet aggregates known as amyloid fibrils, important both biologically and clinically. However, the specific molecular structure of a fibril can vary dramatically depending on sequence and environmental conditions, and mutations can drastically alter amyloid function and pathogenicity. Experimental structure determination has proven extremely difficult with only a handful of NMR-based models proposed, suggesting a need for computational methods. We present AmyloidMutants, a statistical mechanics approach for de novo prediction and analysis of wild-type and mutant amyloid structures. Based on the premise of protein mutational landscapes, AmyloidMutants energetically quantifies the effects of sequence mutation on fibril conformation and stability. Tested on non-mutant, full-length amyloid structures with known chemical shift data, AmyloidMutants offers roughly 2-fold improvement in prediction accuracy over existing tools. Moreover, AmyloidMutants is the only method to predict complete super-secondary structures, enabling accurate discrimination of topologically dissimilar amyloid conformations that correspond to the same sequence locations. Applied to mutant prediction, AmyloidMutants identifies a global conformational switch between Aβ and its highly-toxic 'Iowa' mutant in agreement with a recent experimental model based on partial chemical shift data. Predictions on mutant, yeast-toxic strains of HET-s suggest similar alternate folds. When applied to HET-s and a HET-s mutant with core asparagines replaced by glutamines (both highly amyloidogenic chemically similar residues abundant in many amyloids), AmyloidMutants surprisingly predicts a greatly reduced capacity of the glutamine mutant to form amyloid. We confirm this finding by conducting mutagenesis experiments. Our tool is publically available on the web at http://amyloid.csail.mit.edu/. lindquist_admin@wi.mit.edu; bab@csail.mit.edu.

Ab initio RNA folding by discrete molecular dynamics: From structure prediction to folding mechanisms

PubMed Central

Ding, Feng; Sharma, Shantanu; Chalasani, Poornima; Demidov, Vadim V.; Broude, Natalia E.; Dokholyan, Nikolay V.

2008-01-01

RNA molecules with novel functions have revived interest in the accurate prediction of RNA three-dimensional (3D) structure and folding dynamics. However, existing methods are inefficient in automated 3D structure prediction. Here, we report a robust computational approach for rapid folding of RNA molecules. We develop a simplified RNA model for discrete molecular dynamics (DMD) simulations, incorporating base-pairing and base-stacking interactions. We demonstrate correct folding of 150 structurally diverse RNA sequences. The majority of DMD-predicted 3D structures have <4 Å deviations from experimental structures. The secondary structures corresponding to the predicted 3D structures consist of 94% native base-pair interactions. Folding thermodynamics and kinetics of tRNAPhe, pseudoknots, and mRNA fragments in DMD simulations are in agreement with previous experimental findings. Folding of RNA molecules features transient, non-native conformations, suggesting non-hierarchical RNA folding. Our method allows rapid conformational sampling of RNA folding, with computational time increasing linearly with RNA length. We envision this approach as a promising tool for RNA structural and functional analyses. PMID:18456842

G23D: Online tool for mapping and visualization of genomic variants on 3D protein structures.

PubMed

Solomon, Oz; Kunik, Vered; Simon, Amos; Kol, Nitzan; Barel, Ortal; Lev, Atar; Amariglio, Ninette; Somech, Raz; Rechavi, Gidi; Eyal, Eran

2016-08-26

Evaluation of the possible implications of genomic variants is an increasingly important task in the current high throughput sequencing era. Structural information however is still not routinely exploited during this evaluation process. The main reasons can be attributed to the partial structural coverage of the human proteome and the lack of tools which conveniently convert genomic positions, which are the frequent output of genomic pipelines, to proteins and structure coordinates. We present G23D, a tool for conversion of human genomic coordinates to protein coordinates and protein structures. G23D allows mapping of genomic positions/variants on evolutionary related (and not only identical) protein three dimensional (3D) structures as well as on theoretical models. By doing so it significantly extends the space of variants for which structural insight is feasible. To facilitate interpretation of the variant consequence, pathogenic variants, functional sites and polymorphism sites are displayed on protein sequence and structure diagrams alongside the input variants. G23D also provides modeling of the mutant structure, analysis of intra-protein contacts and instant access to functional predictions and predictions of thermo-stability changes. G23D is available at http://www.sheba-cancer.org.il/G23D . G23D extends the fraction of variants for which structural analysis is applicable and provides better and faster accessibility for structural data to biologists and geneticists who routinely work with genomic information.

Bi-objective integer programming for RNA secondary structure prediction with pseudoknots.

PubMed

Legendre, Audrey; Angel, Eric; Tahi, Fariza

2018-01-15

RNA structure prediction is an important field in bioinformatics, and numerous methods and tools have been proposed. Pseudoknots are specific motifs of RNA secondary structures that are difficult to predict. Almost all existing methods are based on a single model and return one solution, often missing the real structure. An alternative approach would be to combine different models and return a (small) set of solutions, maximizing its quality and diversity in order to increase the probability that it contains the real structure. We propose here an original method for predicting RNA secondary structures with pseudoknots, based on integer programming. We developed a generic bi-objective integer programming algorithm allowing to return optimal and sub-optimal solutions optimizing simultaneously two models. This algorithm was then applied to the combination of two known models of RNA secondary structure prediction, namely MEA and MFE. The resulting tool, called BiokoP, is compared with the other methods in the literature. The results show that the best solution (structure with the highest F 1 -score) is, in most cases, given by BiokoP. Moreover, the results of BiokoP are homogeneous, regardless of the pseudoknot type or the presence or not of pseudoknots. Indeed, the F 1 -scores are always higher than 70% for any number of solutions returned. The results obtained by BiokoP show that combining the MEA and the MFE models, as well as returning several optimal and several sub-optimal solutions, allow to improve the prediction of secondary structures. One perspective of our work is to combine better mono-criterion models, in particular to combine a model based on the comparative approach with the MEA and the MFE models. This leads to develop in the future a new multi-objective algorithm to combine more than two models. BiokoP is available on the EvryRNA platform: https://EvryRNA.ibisc.univ-evry.fr .

Construct measurement quality improves predictive accuracy in violence risk assessment: an illustration using the personality assessment inventory.

PubMed

Hendry, Melissa C; Douglas, Kevin S; Winter, Elizabeth A; Edens, John F

2013-01-01

Much of the risk assessment literature has focused on the predictive validity of risk assessment tools. However, these tools often comprise a list of risk factors that are themselves complex constructs, and focusing on the quality of measurement of individual risk factors may improve the predictive validity of the tools. The present study illustrates this concern using the Antisocial Features and Aggression scales of the Personality Assessment Inventory (Morey, 1991). In a sample of 1,545 prison inmates and offenders undergoing treatment for substance abuse (85% male), we evaluated (a) the factorial validity of the ANT and AGG scales, (b) the utility of original ANT and AGG scales and newly derived ANT and AGG scales for predicting antisocial outcomes (recidivism and institutional infractions), and (c) whether items with a stronger relationship to the underlying constructs (higher factor loadings) were in turn more strongly related to antisocial outcomes. Confirmatory factor analyses (CFAs) indicated that ANT and AGG items were not structured optimally in these data in terms of correspondence to the subscale structure identified in the PAI manual. Exploratory factor analyses were conducted on a random split-half of the sample to derive optimized alternative factor structures, and cross-validated in the second split-half using CFA. Four-factor models emerged for both the ANT and AGG scales, and, as predicted, the size of item factor loadings was associated with the strength with which items were associated with institutional infractions and community recidivism. This suggests that the quality by which a construct is measured is associated with its predictive strength. Implications for risk assessment are discussed. Copyright © 2013 John Wiley & Sons, Ltd.

Distributed Structure Searchable Toxicity

EPA Pesticide Factsheets

The Distributed Structure Searchable Toxicity (DSSTox) online resource provides high quality chemical structures and annotations in association with toxicity data. It helps to build a data foundation for improved structure-activity relationships and predictive toxicology. DSSTox publishes summarized chemical activity representations for structure-activity modeling and provides a structure browser. This tool also houses the chemical inventories for the ToxCast and Tox21 projects.

Construction of crystal structure prototype database: methods and applications.

PubMed

Su, Chuanxun; Lv, Jian; Li, Quan; Wang, Hui; Zhang, Lijun; Wang, Yanchao; Ma, Yanming

2017-04-26

Crystal structure prototype data have become a useful source of information for materials discovery in the fields of crystallography, chemistry, physics, and materials science. This work reports the development of a robust and efficient method for assessing the similarity of structures on the basis of their interatomic distances. Using this method, we proposed a simple and unambiguous definition of crystal structure prototype based on hierarchical clustering theory, and constructed the crystal structure prototype database (CSPD) by filtering the known crystallographic structures in a database. With similar method, a program structure prototype analysis package (SPAP) was developed to remove similar structures in CALYPSO prediction results and extract predicted low energy structures for a separate theoretical structure database. A series of statistics describing the distribution of crystal structure prototypes in the CSPD was compiled to provide an important insight for structure prediction and high-throughput calculations. Illustrative examples of the application of the proposed database are given, including the generation of initial structures for structure prediction and determination of the prototype structure in databases. These examples demonstrate the CSPD to be a generally applicable and useful tool for materials discovery.

Construction of crystal structure prototype database: methods and applications

NASA Astrophysics Data System (ADS)

Su, Chuanxun; Lv, Jian; Li, Quan; Wang, Hui; Zhang, Lijun; Wang, Yanchao; Ma, Yanming

2017-04-01

Crystal structure prototype data have become a useful source of information for materials discovery in the fields of crystallography, chemistry, physics, and materials science. This work reports the development of a robust and efficient method for assessing the similarity of structures on the basis of their interatomic distances. Using this method, we proposed a simple and unambiguous definition of crystal structure prototype based on hierarchical clustering theory, and constructed the crystal structure prototype database (CSPD) by filtering the known crystallographic structures in a database. With similar method, a program structure prototype analysis package (SPAP) was developed to remove similar structures in CALYPSO prediction results and extract predicted low energy structures for a separate theoretical structure database. A series of statistics describing the distribution of crystal structure prototypes in the CSPD was compiled to provide an important insight for structure prediction and high-throughput calculations. Illustrative examples of the application of the proposed database are given, including the generation of initial structures for structure prediction and determination of the prototype structure in databases. These examples demonstrate the CSPD to be a generally applicable and useful tool for materials discovery.

Test-Analysis Correlation for Space Shuttle External Tank Foam Impacting RCC Wing Leading Edge Component Panels

NASA Technical Reports Server (NTRS)

Lyle, Karen H.

2008-01-01

The Space Shuttle Columbia Accident Investigation Board recommended that NASA develop, validate, and maintain a modeling tool capable of predicting the damage threshold for debris impacts on the Space Shuttle Reinforced Carbon-Carbon (RCC) wing leading edge and nosecap assembly. The results presented in this paper are one part of a multi-level approach that supported the development of the predictive tool used to recertify the shuttle for flight following the Columbia Accident. The assessment of predictive capability was largely based on test analysis comparisons for simpler component structures. This paper provides comparisons of finite element simulations with test data for external tank foam debris impacts onto 6-in. square RCC flat panels. Both quantitative displacement and qualitative damage assessment correlations are provided. The comparisons show good agreement and provided the Space Shuttle Program with confidence in the predictive tool.

Injection-Molded Long-Fiber Thermoplastic Composites: From Process Modeling to Prediction of Mechanical Properties

DOE Office of Scientific and Technical Information (OSTI.GOV)

Nguyen, Ba Nghiep; Kunc, Vlastimil; Jin, Xiaoshi

2013-12-18

This article illustrates the predictive capabilities for long-fiber thermoplastic (LFT) composites that first simulate the injection molding of LFT structures by Autodesk® Simulation Moldflow® Insight (ASMI) to accurately predict fiber orientation and length distributions in these structures. After validating fiber orientation and length predictions against the experimental data, the predicted results are used by ASMI to compute distributions of elastic properties in the molded structures. In addition, local stress-strain responses and damage accumulation under tensile loading are predicted by an elastic-plastic damage model of EMTA-NLA, a nonlinear analysis tool implemented in ABAQUS® via user-subroutines using an incremental Eshelby-Mori-Tanaka approach. Predictedmore » stress-strain responses up to failure and damage accumulations are compared to the experimental results to validate the model.« less

RCrane: semi-automated RNA model building.

PubMed

Keating, Kevin S; Pyle, Anna Marie

2012-08-01

RNA crystals typically diffract to much lower resolutions than protein crystals. This low-resolution diffraction results in unclear density maps, which cause considerable difficulties during the model-building process. These difficulties are exacerbated by the lack of computational tools for RNA modeling. Here, RCrane, a tool for the partially automated building of RNA into electron-density maps of low or intermediate resolution, is presented. This tool works within Coot, a common program for macromolecular model building. RCrane helps crystallographers to place phosphates and bases into electron density and then automatically predicts and builds the detailed all-atom structure of the traced nucleotides. RCrane then allows the crystallographer to review the newly built structure and select alternative backbone conformations where desired. This tool can also be used to automatically correct the backbone structure of previously built nucleotides. These automated corrections can fix incorrect sugar puckers, steric clashes and other structural problems.

Engineering Property Prediction Tools for Tailored Polymer Composite Structures (49465)

DOE Office of Scientific and Technical Information (OSTI.GOV)

Nguyen, Ba Nghiep; Kunc, Vlastimil

2009-12-29

Process and constitutive models as well as characterization tools and testing methods were developed to determine stress-strain responses, damage development, strengths and creep of long-fiber thermoplastics (LFTs). The developed models were implemented in Moldflow and ABAQUS and have been validated against LFT data obtained experimentally.

NeEMO: a method using residue interaction networks to improve prediction of protein stability upon mutation.

PubMed

Giollo, Manuel; Martin, Alberto J M; Walsh, Ian; Ferrari, Carlo; Tosatto, Silvio C E

2014-01-01

The rapid growth of un-annotated missense variants poses challenges requiring novel strategies for their interpretation. From the thermodynamic point of view, amino acid changes can lead to a change in the internal energy of a protein and induce structural rearrangements. This is of great relevance for the study of diseases and protein design, justifying the development of prediction methods for variant-induced stability changes. Here we propose NeEMO, a tool for the evaluation of stability changes using an effective representation of proteins based on residue interaction networks (RINs). RINs are used to extract useful features describing interactions of the mutant amino acid with its structural environment. Benchmarking shows NeEMO to be very effective, allowing reliable predictions in different parts of the protein such as β-strands and buried residues. Validation on a previously published independent dataset shows that NeEMO has a Pearson correlation coefficient of 0.77 and a standard error of 1 Kcal/mol, outperforming nine recent methods. The NeEMO web server can be freely accessed from URL: http://protein.bio.unipd.it/neemo/. NeEMO offers an innovative and reliable tool for the annotation of amino acid changes. A key contribution are RINs, which can be used for modeling proteins and their interactions effectively. Interestingly, the approach is very general, and can motivate the development of a new family of RIN-based protein structure analyzers. NeEMO may suggest innovative strategies for bioinformatics tools beyond protein stability prediction.

Computational prediction of hinge axes in proteins

PubMed Central

2014-01-01

Background A protein's function is determined by the wide range of motions exhibited by its 3D structure. However, current experimental techniques are not able to reliably provide the level of detail required for elucidating the exact mechanisms of protein motion essential for effective drug screening and design. Computational tools are instrumental in the study of the underlying structure-function relationship. We focus on a special type of proteins called "hinge proteins" which exhibit a motion that can be interpreted as a rotation of one domain relative to another. Results This work proposes a computational approach that uses the geometric structure of a single conformation to predict the feasible motions of the protein and is founded in recent work from rigidity theory, an area of mathematics that studies flexibility properties of general structures. Given a single conformational state, our analysis predicts a relative axis of motion between two specified domains. We analyze a dataset of 19 structures known to exhibit this hinge-like behavior. For 15, the predicted axis is consistent with a motion to a second, known conformation. We present a detailed case study for three proteins whose dynamics have been well-studied in the literature: calmodulin, the LAO binding protein and the Bence-Jones protein. Conclusions Our results show that incorporating rigidity-theoretic analyses can lead to effective computational methods for understanding hinge motions in macromolecules. This initial investigation is the first step towards a new tool for probing the structure-dynamics relationship in proteins. PMID:25080829

A unified approach for composite cost reporting and prediction in the ACT program

NASA Technical Reports Server (NTRS)

Freeman, W. Tom; Vosteen, Louis F.; Siddiqi, Shahid

1991-01-01

The Structures Technology Program Office (STPO) at NASA Langley Research Center has held two workshops with representatives from the commercial airframe companies to establish a plan for development of a standard cost reporting format and a cost prediction tool for conceptual and preliminary designers. This paper reviews the findings of the workshop representatives with a plan for implementation of their recommendations. The recommendations of the cost tracking and reporting committee will be implemented by reinstituting the collection of composite part fabrication data in a format similar to the DoD/NASA Structural Composites Fabrication Guide. The process of data collection will be automated by taking advantage of current technology with user friendly computer interfaces and electronic data transmission. Development of a conceptual and preliminary designers' cost prediction model will be initiated. The model will provide a technically sound method for evaluating the relative cost of different composite structural designs, fabrication processes, and assembly methods that can be compared to equivalent metallic parts or assemblies. The feasibility of developing cost prediction software in a modular form for interfacing with state of the art preliminary design tools and computer aided design (CAD) programs is assessed.

CABS-fold: Server for the de novo and consensus-based prediction of protein structure.

PubMed

Blaszczyk, Maciej; Jamroz, Michal; Kmiecik, Sebastian; Kolinski, Andrzej

2013-07-01

The CABS-fold web server provides tools for protein structure prediction from sequence only (de novo modeling) and also using alternative templates (consensus modeling). The web server is based on the CABS modeling procedures ranked in previous Critical Assessment of techniques for protein Structure Prediction competitions as one of the leading approaches for de novo and template-based modeling. Except for template data, fragmentary distance restraints can also be incorporated into the modeling process. The web server output is a coarse-grained trajectory of generated conformations, its Jmol representation and predicted models in all-atom resolution (together with accompanying analysis). CABS-fold can be freely accessed at http://biocomp.chem.uw.edu.pl/CABSfold.

CABS-fold: server for the de novo and consensus-based prediction of protein structure

PubMed Central

Blaszczyk, Maciej; Jamroz, Michal; Kmiecik, Sebastian; Kolinski, Andrzej

2013-01-01

The CABS-fold web server provides tools for protein structure prediction from sequence only (de novo modeling) and also using alternative templates (consensus modeling). The web server is based on the CABS modeling procedures ranked in previous Critical Assessment of techniques for protein Structure Prediction competitions as one of the leading approaches for de novo and template-based modeling. Except for template data, fragmentary distance restraints can also be incorporated into the modeling process. The web server output is a coarse-grained trajectory of generated conformations, its Jmol representation and predicted models in all-atom resolution (together with accompanying analysis). CABS-fold can be freely accessed at http://biocomp.chem.uw.edu.pl/CABSfold. PMID:23748950

Designing and benchmarking the MULTICOM protein structure prediction system

PubMed Central

2013-01-01

Background Predicting protein structure from sequence is one of the most significant and challenging problems in bioinformatics. Numerous bioinformatics techniques and tools have been developed to tackle almost every aspect of protein structure prediction ranging from structural feature prediction, template identification and query-template alignment to structure sampling, model quality assessment, and model refinement. How to synergistically select, integrate and improve the strengths of the complementary techniques at each prediction stage and build a high-performance system is becoming a critical issue for constructing a successful, competitive protein structure predictor. Results Over the past several years, we have constructed a standalone protein structure prediction system MULTICOM that combines multiple sources of information and complementary methods at all five stages of the protein structure prediction process including template identification, template combination, model generation, model assessment, and model refinement. The system was blindly tested during the ninth Critical Assessment of Techniques for Protein Structure Prediction (CASP9) in 2010 and yielded very good performance. In addition to studying the overall performance on the CASP9 benchmark, we thoroughly investigated the performance and contributions of each component at each stage of prediction. Conclusions Our comprehensive and comparative study not only provides useful and practical insights about how to select, improve, and integrate complementary methods to build a cutting-edge protein structure prediction system but also identifies a few new sources of information that may help improve the design of a protein structure prediction system. Several components used in the MULTICOM system are available at: http://sysbio.rnet.missouri.edu/multicom_toolbox/. PMID:23442819

Genetic Epidemiology of Glucose-6-Dehydrogenase Deficiency in the Arab World.

PubMed

Doss, C George Priya; Alasmar, Dima R; Bux, Reem I; Sneha, P; Bakhsh, Fadheela Dad; Al-Azwani, Iman; Bekay, Rajaa El; Zayed, Hatem

2016-11-17

A systematic search was implemented using four literature databases (PubMed, Embase, Science Direct and Web of Science) to capture all the causative mutations of Glucose-6-phosphate dehydrogenase (G6PD) deficiency (G6PDD) in the 22 Arab countries. Our search yielded 43 studies that captured 33 mutations (23 missense, one silent, two deletions, and seven intronic mutations), in 3,430 Arab patients with G6PDD. The 23 missense mutations were then subjected to phenotypic classification using in silico prediction tools, which were compared to the WHO pathogenicity scale as a reference. These in silico tools were tested for their predicting efficiency using rigorous statistical analyses. Of the 23 missense mutations, p.S188F, p.I48T, p.N126D, and p.V68M, were identified as the most common mutations among Arab populations, but were not unique to the Arab world, interestingly, our search strategy found four other mutations (p.N135T, p.S179N, p.R246L, and p.Q307P) that are unique to Arabs. These mutations were exposed to structural analysis and molecular dynamics simulation analysis (MDSA), which predicting these mutant forms as potentially affect the enzyme function. The combination of the MDSA, structural analysis, and in silico predictions and statistical tools we used will provide a platform for future prediction accuracy for the pathogenicity of genetic mutations.

Antibody specific epitope prediction-emergence of a new paradigm.

PubMed

Sela-Culang, Inbal; Ofran, Yanay; Peters, Bjoern

2015-04-01

The development of accurate tools for predicting B-cell epitopes is important but difficult. Traditional methods have examined which regions in an antigen are likely binding sites of an antibody. However, it is becoming increasingly clear that most antigen surface residues will be able to bind one or more of the myriad of possible antibodies. In recent years, new approaches have emerged for predicting an epitope for a specific antibody, utilizing information encoded in antibody sequence or structure. Applying such antibody-specific predictions to groups of antibodies in combination with easily obtainable experimental data improves the performance of epitope predictions. We expect that further advances of such tools will be possible with the integration of immunoglobulin repertoire sequencing data. Copyright © 2015 Elsevier B.V. All rights reserved.

In silico study of breast cancer associated gene 3 using LION Target Engine and other tools.

PubMed

León, Darryl A; Cànaves, Jaume M

2003-12-01

Sequence analysis of individual targets is an important step in annotation and validation. As a test case, we investigated human breast cancer associated gene 3 (BCA3) with LION Target Engine and with other bioinformatics tools. LION Target Engine confirmed that the BCA3 gene is located on 11p15.4 and that the two most likely splice variants (lacking exon 3 and exons 3 and 5, respectively) exist. Based on our manual curation of sequence data, it is proposed that an additional variant (missing only exon 5) published in a public sequence repository, is a prediction artifact. A significant number of new orthologs were also identified, and these were the basis for a high-quality protein secondary structure prediction. Moreover, our research confirmed several distinct functional domains as described in earlier reports. Sequence conservation from multiple sequence alignments, splice variant identification, secondary structure predictions, and predicted phosphorylation sites suggest that the removal of interaction sites through alternative splicing might play a modulatory role in BCA3. This in silico approach shows the depth and relevance of an analysis that can be accomplished by including a variety of publicly available tools with an integrated and customizable life science informatics platform.

Modeling of fiber orientation in viscous fluid flow with application to self-compacting concrete

NASA Astrophysics Data System (ADS)

Kolařík, Filip; Patzák, Bořek

2013-10-01

In recent years, unconventional concrete reinforcement is of growing popularity. Especially fiber reinforcement has very wide usage in high performance concretes like "Self Compacting Concrete" (SCC). The design of advanced tailor-made structures made of SCC can take advantage of anisotropic orientation of fibers. Tools for fiber orientation predictions can contribute to design of tailor made structure and allow to develop casting procedures that enable to achieve the desired fiber distribution and orientation. This paper deals with development and implementation of suitable tool for prediction of fiber orientation in a fluid based on the knowledge of the velocity field. Statistical approach to the topic is employed. Fiber orientation is described by a probability distribution of the fiber angle.

Using kaizen to improve employee well-being: Results from two organizational intervention studies.

PubMed

von Thiele Schwarz, Ulrica; Nielsen, Karina M; Stenfors-Hayes, Terese; Hasson, Henna

2017-08-01

Participatory intervention approaches that are embedded in existing organizational structures may improve the efficiency and effectiveness of organizational interventions, but concrete tools are lacking. In the present article, we use a realist evaluation approach to explore the role of kaizen, a lean tool for participatory continuous improvement, in improving employee well-being in two cluster-randomized, controlled participatory intervention studies. Case 1 is from the Danish Postal Service, where kaizen boards were used to implement action plans. The results of multi-group structural equation modeling showed that kaizen served as a mechanism that increased the level of awareness of and capacity to manage psychosocial issues, which, in turn, predicted increased job satisfaction and mental health. Case 2 is from a regional hospital in Sweden that integrated occupational health processes with a pre-existing kaizen system. Multi-group structural equation modeling revealed that, in the intervention group, kaizen work predicted better integration of organizational and employee objectives after 12 months, which, in turn, predicted increased job satisfaction and decreased discomfort at 24 months. The findings suggest that participatory and structured problem-solving approaches that are familiar and visual to employees can facilitate organizational interventions.

Using kaizen to improve employee well-being: Results from two organizational intervention studies

PubMed Central

von Thiele Schwarz, Ulrica; Nielsen, Karina M; Stenfors-Hayes, Terese; Hasson, Henna

2016-01-01

Participatory intervention approaches that are embedded in existing organizational structures may improve the efficiency and effectiveness of organizational interventions, but concrete tools are lacking. In the present article, we use a realist evaluation approach to explore the role of kaizen, a lean tool for participatory continuous improvement, in improving employee well-being in two cluster-randomized, controlled participatory intervention studies. Case 1 is from the Danish Postal Service, where kaizen boards were used to implement action plans. The results of multi-group structural equation modeling showed that kaizen served as a mechanism that increased the level of awareness of and capacity to manage psychosocial issues, which, in turn, predicted increased job satisfaction and mental health. Case 2 is from a regional hospital in Sweden that integrated occupational health processes with a pre-existing kaizen system. Multi-group structural equation modeling revealed that, in the intervention group, kaizen work predicted better integration of organizational and employee objectives after 12 months, which, in turn, predicted increased job satisfaction and decreased discomfort at 24 months. The findings suggest that participatory and structured problem-solving approaches that are familiar and visual to employees can facilitate organizational interventions. PMID:28736455

Contingency Table Browser - prediction of early stage protein structure.

PubMed

Kalinowska, Barbara; Krzykalski, Artur; Roterman, Irena

2015-01-01

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

Complete fold annotation of the human proteome using a novel structural feature space.

PubMed

Middleton, Sarah A; Illuminati, Joseph; Kim, Junhyong

2017-04-13

Recognition of protein structural fold is the starting point for many structure prediction tools and protein function inference. Fold prediction is computationally demanding and recognizing novel folds is difficult such that the majority of proteins have not been annotated for fold classification. Here we describe a new machine learning approach using a novel feature space that can be used for accurate recognition of all 1,221 currently known folds and inference of unknown novel folds. We show that our method achieves better than 94% accuracy even when many folds have only one training example. We demonstrate the utility of this method by predicting the folds of 34,330 human protein domains and showing that these predictions can yield useful insights into potential biological function, such as prediction of RNA-binding ability. Our method can be applied to de novo fold prediction of entire proteomes and identify candidate novel fold families.

Complete fold annotation of the human proteome using a novel structural feature space

PubMed Central

Middleton, Sarah A.; Illuminati, Joseph; Kim, Junhyong

2017-01-01

Recognition of protein structural fold is the starting point for many structure prediction tools and protein function inference. Fold prediction is computationally demanding and recognizing novel folds is difficult such that the majority of proteins have not been annotated for fold classification. Here we describe a new machine learning approach using a novel feature space that can be used for accurate recognition of all 1,221 currently known folds and inference of unknown novel folds. We show that our method achieves better than 94% accuracy even when many folds have only one training example. We demonstrate the utility of this method by predicting the folds of 34,330 human protein domains and showing that these predictions can yield useful insights into potential biological function, such as prediction of RNA-binding ability. Our method can be applied to de novo fold prediction of entire proteomes and identify candidate novel fold families. PMID:28406174

Vfold: a web server for RNA structure and folding thermodynamics prediction.

PubMed

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

2014-01-01

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

A Comparative Study of Adolescent Risk Assessment Instruments: Predictive and Incremental Validity

ERIC Educational Resources Information Center

Welsh, Jennifer L.; Schmidt, Fred; McKinnon, Lauren; Chattha, H. K.; Meyers, Joanna R.

2008-01-01

Promising new adolescent risk assessment tools are being incorporated into clinical practice but currently possess limited evidence of predictive validity regarding their individual and/or combined use in risk assessments. The current study compares three structured adolescent risk instruments, Youth Level of Service/Case Management Inventory…

It's Selective, but Is It Effective? Exploring the Predictive Validity of Teacher Selection Tools. CEDR Brief. Policy Brief No. 2014-­9

ERIC Educational Resources Information Center

Goldhaber, Dan; Grout, Cyrus; Huntington-Klein, Nick

2014-01-01

Evidence suggests teacher hiring in public schools is ad-hoc and often does not result in good selection amongst applicants. Some districts use structured selection instruments in the hiring process, but we know little about the efficacy of such tools. In this paper we evaluate the ability of applicant selection tools used by the Spokane Public…

An elastic-plastic contact model for line contact structures

NASA Astrophysics Data System (ADS)

Zhu, Haibin; Zhao, Yingtao; He, Zhifeng; Zhang, Ruinan; Ma, Shaopeng

2018-06-01

Although numerical simulation tools are now very powerful, the development of analytical models is very important for the prediction of the mechanical behaviour of line contact structures for deeply understanding contact problems and engineering applications. For the line contact structures widely used in the engineering field, few analytical models are available for predicting the mechanical behaviour when the structures deform plastically, as the classic Hertz's theory would be invalid. Thus, the present study proposed an elastic-plastic model for line contact structures based on the understanding of the yield mechanism. A mathematical expression describing the global relationship between load history and contact width evolution of line contact structures was obtained. The proposed model was verified through an actual line contact test and a corresponding numerical simulation. The results confirmed that this model can be used to accurately predict the elastic-plastic mechanical behaviour of a line contact structure.

Evaluation of in silico tools to predict the skin sensitization potential of chemicals.

PubMed

Verheyen, G R; Braeken, E; Van Deun, K; Van Miert, S

2017-01-01

Public domain and commercial in silico tools were compared for their performance in predicting the skin sensitization potential of chemicals. The packages were either statistical based (Vega, CASE Ultra) or rule based (OECD Toolbox, Toxtree, Derek Nexus). In practice, several of these in silico tools are used in gap filling and read-across, but here their use was limited to make predictions based on presence/absence of structural features associated to sensitization. The top 400 ranking substances of the ATSDR 2011 Priority List of Hazardous Substances were selected as a starting point. Experimental information was identified for 160 chemically diverse substances (82 positive and 78 negative). The prediction for skin sensitization potential was compared with the experimental data. Rule-based tools perform slightly better, with accuracies ranging from 0.6 (OECD Toolbox) to 0.78 (Derek Nexus), compared with statistical tools that had accuracies ranging from 0.48 (Vega) to 0.73 (CASE Ultra - LLNA weak model). Combining models increased the performance, with positive and negative predictive values up to 80% and 84%, respectively. However, the number of substances that were predicted positive or negative for skin sensitization in both models was low. Adding more substances to the dataset will increase the confidence in the conclusions reached. The insights obtained in this evaluation are incorporated in a web database www.asopus.weebly.com that provides a potential end user context for the scope and performance of different in silico tools with respect to a common dataset of curated skin sensitization data.

Bitter or not? BitterPredict, a tool for predicting taste from chemical structure.

PubMed

Dagan-Wiener, Ayana; Nissim, Ido; Ben Abu, Natalie; Borgonovo, Gigliola; Bassoli, Angela; Niv, Masha Y

2017-09-21

Bitter taste is an innately aversive taste modality that is considered to protect animals from consuming toxic compounds. Yet, bitterness is not always noxious and some bitter compounds have beneficial effects on health. Hundreds of bitter compounds were reported (and are accessible via the BitterDB http://bitterdb.agri.huji.ac.il/dbbitter.php ), but numerous additional bitter molecules are still unknown. The dramatic chemical diversity of bitterants makes bitterness prediction a difficult task. Here we present a machine learning classifier, BitterPredict, which predicts whether a compound is bitter or not, based on its chemical structure. BitterDB was used as the positive set, and non-bitter molecules were gathered from literature to create the negative set. Adaptive Boosting (AdaBoost), based on decision trees machine-learning algorithm was applied to molecules that were represented using physicochemical and ADME/Tox descriptors. BitterPredict correctly classifies over 80% of the compounds in the hold-out test set, and 70-90% of the compounds in three independent external sets and in sensory test validation, providing a quick and reliable tool for classifying large sets of compounds into bitter and non-bitter groups. BitterPredict suggests that about 40% of random molecules, and a large portion (66%) of clinical and experimental drugs, and of natural products (77%) are bitter.

SU-D-BRB-01: A Predictive Planning Tool for Stereotactic Radiosurgery

DOE Office of Scientific and Technical Information (OSTI.GOV)

Palefsky, S; Roper, J; Elder, E

Purpose: To demonstrate the feasibility of a predictive planning tool which provides SRS planning guidance based on simple patient anatomical properties: PTV size, PTV shape and distance from critical structures. Methods: Ten framed SRS cases treated at Winship Cancer Institute of Emory University were analyzed to extract data on PTV size, sphericity (shape), and distance from critical structures such as the brainstem and optic chiasm. The cases consisted of five pairs. Each pair consisted of two cases with a similar diagnosis (such as pituitary adenoma or arteriovenous malformation) that were treated with different techniques: DCA, or IMRS. A Naive Bayesmore » Classifier was trained on this data to establish the conditions under which each treatment modality was used. This model was validated by classifying ten other randomly-selected cases into DCA or IMRS classes, calculating the probability of each technique, and comparing results to the treated technique. Results: Of the ten cases used to validate the model, nine had their technique predicted correctly. The three cases treated with IMRS were all identified as such. Their probabilities of being treated with IMRS ranged between 59% and 100%. Six of the seven cases treated with DCA were correctly classified. These probabilities ranged between 51% and 95%. One case treated with DCA was incorrectly predicted to be an IMRS plan. The model’s confidence in this case was 91%. Conclusion: These findings indicate that a predictive planning tool based on simple patient anatomical properties can predict the SRS technique used for treatment. The algorithm operated with 90% accuracy. With further validation on larger patient populations, this tool may be used clinically to guide planners in choosing an appropriate treatment technique. The prediction algorithm could also be adapted to guide selection of treatment parameters such as treatment modality and number of fields for radiotherapy across anatomical sites.« less

A ligand predication tool based on modeling and reasoning with imprecise probabilistic knowledge.

PubMed

Liu, Weiru; Yue, Anbu; Timson, David J

2010-04-01

Ligand prediction has been driven by a fundamental desire to understand more about how biomolecules recognize their ligands and by the commercial imperative to develop new drugs. Most of the current available software systems are very complex and time-consuming to use. Therefore, developing simple and efficient tools to perform initial screening of interesting compounds is an appealing idea. In this paper, we introduce our tool for very rapid screening for likely ligands (either substrates or inhibitors) based on reasoning with imprecise probabilistic knowledge elicited from past experiments. Probabilistic knowledge is input to the system via a user-friendly interface showing a base compound structure. A prediction of whether a particular compound is a substrate is queried against the acquired probabilistic knowledge base and a probability is returned as an indication of the prediction. This tool will be particularly useful in situations where a number of similar compounds have been screened experimentally, but information is not available for all possible members of that group of compounds. We use two case studies to demonstrate how to use the tool. 2009 Elsevier Ireland Ltd. All rights reserved.

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

PubMed Central

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

2014-01-01

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

Prediction Of Tensile And Shear Strength Of Friction Surfaced Tool Steel Deposit By Using Artificial Neural Networks

NASA Astrophysics Data System (ADS)

Manzoor Hussain, M.; Pitchi Raju, V.; Kandasamy, J.; Govardhan, D.

2018-04-01

Friction surface treatment is well-established solid technology and is used for deposition, abrasion and corrosion protection coatings on rigid materials. This novel process has wide range of industrial applications, particularly in the field of reclamation and repair of damaged and worn engineering components. In this paper, we present the prediction of tensile and shear strength of friction surface treated tool steel using ANN for simulated results of friction surface treatment. This experiment was carried out to obtain tool steel coatings of low carbon steel parts by changing contribution process parameters essentially friction pressure, rotational speed and welding speed. The simulation is performed by a 33-factor design that takes into account the maximum and least limits of the experimental work performed with the 23-factor design. Neural network structures, such as the Feed Forward Neural Network (FFNN), were used to predict tensile and shear strength of tool steel sediments caused by friction.

Assessment of Lightning Transients on a De-Iced Rotor Blade with Predictive Tools and Coaxial Return Measurements

NASA Astrophysics Data System (ADS)

Guillet, S.; Gosmain, A.; Ducoux, W.; Ponçon, M.; Fontaine, G.; Desseix, P.; Perraud, P.

2012-05-01

The increasing use of composite materials in aircrafts primary structures has led to different problematics in the field of safety of flight in lightning conditions. The consequences of this technological mutation, which occurs in a parallel context of extension of electrified critical functions, are addressed by aircraft manufacturers through the enhancement of their available assessment means of lightning transient. On the one hand, simulation tools, provided an accurate description of aircraft design, are today valuable assessment tools, in both predictive and operative terms. On the other hand, in-house test means allow confirmation and consolidation of design office hardening solutions. The combined use of predictive simulation tools and in- house test means offers an efficient and reliable support for all aircraft developments in their various life-time stages. The present paper provides PREFACE research project results that illustrate the above introduced strategy on the de-icing system of the NH90 composite main rotor blade.

Structure prediction, expression, and antigenicity of c-terminal of GRP78.

PubMed

Aghamollaei, Hossein; Mousavi Gargari, Seyed Latif; Ghanei, Mostafa; Rasaee, Mohamad Javad; Amani, Jafar; Bakherad, Hamid; Farnoosh, Gholamreza

2017-01-01

Glucose-regulated protein 78 (GRP78) is a typical endoplasmic reticulum luminal chaperone having a main role in the activation of the unfolded protein response. Because of hypoxia and nutrient deprivation in the tumor microenvironment, expression of GRP78 in these cells becomes higher than the native cells, which makes it a suitable candidate for cancer targeting. Suppression of survival signals by antibody production against C-terminal domain of GR78 (CGRP) can induce apoptosis of cancer cells. The aim of this study was in silico analysis, recombinant production, and characterization of CGRP in Escherichia coli. Structural prediction of CGRP by bioinformatics tools was done and the construct containing optimized sequence was transferred to E. coli T7 shuffle. Expression was induced by isopropyl-β-d-thiogalactoside, and recombinant protein was purified by Ni-NTA agarose resin. The content of secondary structures was obtained by circular dichroism (CD) spectrum. CGRP immunogenicity was evaluated from the immunized mouse sera. SDS-PAGE analysis showed CGRP expression in E. coli. CD spectrum also confirmed prediction of structures by bioinformatics tools. The enzyme-linked immunosorbent assay using sera from immunized mice revealed CGRP as a good immunogen. The results obtained in this study showed that the structure of truncated CGRP is very similar to its structure in the whole protein context. This protein can be used in cancer researches. © 2015 International Union of Biochemistry and Molecular Biology, Inc.

Prediction of enzyme activity with neural network models based on electronic and geometrical features of substrates.

PubMed

Szaleniec, Maciej

2012-01-01

Artificial Neural Networks (ANNs) are introduced as robust and versatile tools in quantitative structure-activity relationship (QSAR) modeling. Their application to the modeling of enzyme reactivity is discussed, along with methodological issues. Methods of input variable selection, optimization of network internal structure, data set division and model validation are discussed. The application of ANNs in the modeling of enzyme activity over the last 20 years is briefly recounted. The discussed methodology is exemplified by the case of ethylbenzene dehydrogenase (EBDH). Intelligent Problem Solver and genetic algorithms are applied for input vector selection, whereas k-means clustering is used to partition the data into training and test cases. The obtained models exhibit high correlation between the predicted and experimental values (R(2) > 0.9). Sensitivity analyses and study of the response curves are used as tools for the physicochemical interpretation of the models in terms of the EBDH reaction mechanism. Neural networks are shown to be a versatile tool for the construction of robust QSAR models that can be applied to a range of aspects important in drug design and the prediction of biological activity.

RepeatsDB-lite: a web server for unit annotation of tandem repeat proteins.

PubMed

Hirsh, Layla; Paladin, Lisanna; Piovesan, Damiano; Tosatto, Silvio C E

2018-05-09

RepeatsDB-lite (http://protein.bio.unipd.it/repeatsdb-lite) is a web server for the prediction of repetitive structural elements and units in tandem repeat (TR) proteins. TRs are a widespread but poorly annotated class of non-globular proteins carrying heterogeneous functions. RepeatsDB-lite extends the prediction to all TR types and strongly improves the performance both in terms of computational time and accuracy over previous methods, with precision above 95% for solenoid structures. The algorithm exploits an improved TR unit library derived from the RepeatsDB database to perform an iterative structural search and assignment. The web interface provides tools for analyzing the evolutionary relationships between units and manually refine the prediction by changing unit positions and protein classification. An all-against-all structure-based sequence similarity matrix is calculated and visualized in real-time for every user edit. Reviewed predictions can be submitted to RepeatsDB for review and inclusion.

Patterns of covariance between forest stand and canopy structure in the Pacific Northwest.

Treesearch

Michael A. Lefsky; Andrew T. Hudak; Warren B. Cohen; S.A. Acker

2005-01-01

In the past decade, LIDAR (light detection and ranging) has emerged as a powerful tool for remotely sensing forest canopy and stand structure, including the estimation of aboveground biomass and carbon storage. Numerous papers have documented the use of LIDAR measurements to predict important aspects of forest stand structure, including aboveground biomass. Other...

Structure identification by Mass Spectrometry Non-Targeted Analysis using the US EPA’s CompTox Chemistry Dashboard

EPA Science Inventory

Identification of unknowns in mass spectrometry based non-targeted analyses (NTA) requires the integration of complementary pieces of data to arrive at a confident, consensus structure. Researchers use chemical reference databases, spectral matching, fragment prediction tools, r...

Recent developments in structural proteomics for protein structure determination.

PubMed

Liu, Hsuan-Liang; Hsu, Jyh-Ping

2005-05-01

The major challenges in structural proteomics include identifying all the proteins on the genome-wide scale, determining their structure-function relationships, and outlining the precise three-dimensional structures of the proteins. Protein structures are typically determined by experimental approaches such as X-ray crystallography or nuclear magnetic resonance (NMR) spectroscopy. However, the knowledge of three-dimensional space by these techniques is still limited. Thus, computational methods such as comparative and de novo approaches and molecular dynamic simulations are intensively used as alternative tools to predict the three-dimensional structures and dynamic behavior of proteins. This review summarizes recent developments in structural proteomics for protein structure determination; including instrumental methods such as X-ray crystallography and NMR spectroscopy, and computational methods such as comparative and de novo structure prediction and molecular dynamics simulations.

Progressive Damage and Failure Analysis of Composite Laminates

NASA Astrophysics Data System (ADS)

Joseph, Ashith P. K.

Composite materials are widely used in various industries for making structural parts due to higher strength to weight ratio, better fatigue life, corrosion resistance and material property tailorability. To fully exploit the capability of composites, it is required to know the load carrying capacity of the parts made of them. Unlike metals, composites are orthotropic in nature and fails in a complex manner under various loading conditions which makes it a hard problem to analyze. Lack of reliable and efficient failure analysis tools for composites have led industries to rely more on coupon and component level testing to estimate the design space. Due to the complex failure mechanisms, composite materials require a very large number of coupon level tests to fully characterize the behavior. This makes the entire testing process very time consuming and costly. The alternative is to use virtual testing tools which can predict the complex failure mechanisms accurately. This reduces the cost only to it's associated computational expenses making significant savings. Some of the most desired features in a virtual testing tool are - (1) Accurate representation of failure mechanism: Failure progression predicted by the virtual tool must be same as those observed in experiments. A tool has to be assessed based on the mechanisms it can capture. (2) Computational efficiency: The greatest advantages of a virtual tools are the savings in time and money and hence computational efficiency is one of the most needed features. (3) Applicability to a wide range of problems: Structural parts are subjected to a variety of loading conditions including static, dynamic and fatigue conditions. A good virtual testing tool should be able to make good predictions for all these different loading conditions. The aim of this PhD thesis is to develop a computational tool which can model the progressive failure of composite laminates under different quasi-static loading conditions. The analysis tool is validated by comparing the simulations against experiments for a selected number of quasi-static loading cases.

Image Texture Predicts Avian Density and Species Richness

PubMed Central

Wood, Eric M.; Pidgeon, Anna M.; Radeloff, Volker C.; Keuler, Nicholas S.

2013-01-01

For decades, ecologists have measured habitat attributes in the field to understand and predict patterns of animal distribution and abundance. However, the scale of inference possible from field measured data is typically limited because large-scale data collection is rarely feasible. This is problematic given that conservation and management typical require data that are fine grained yet broad in extent. Recent advances in remote sensing methodology offer alternative tools for efficiently characterizing wildlife habitat across broad areas. We explored the use of remotely sensed image texture, which is a surrogate for vegetation structure, calculated from both an air photo and from a Landsat TM satellite image, compared with field-measured vegetation structure, characterized by foliage-height diversity and horizontal vegetation structure, to predict avian density and species richness within grassland, savanna, and woodland habitats at Fort McCoy Military Installation, Wisconsin, USA. Image texture calculated from the air photo best predicted density of a grassland associated species, grasshopper sparrow (Ammodramus savannarum), within grassland habitat (R2 = 0.52, p-value <0.001), and avian species richness among habitats (R2 = 0.54, p-value <0.001). Density of field sparrow (Spizella pusilla), a savanna associated species, was not particularly well captured by either field-measured or remotely sensed vegetation structure variables, but was best predicted by air photo image texture (R2 = 0.13, p-value = 0.002). Density of ovenbird (Seiurus aurocapillus), a woodland associated species, was best predicted by pixel-level satellite data (mean NDVI, R2 = 0.54, p-value <0.001). Surprisingly and interestingly, remotely sensed vegetation structure measures (i.e., image texture) were often better predictors of avian density and species richness than field-measured vegetation structure, and thus show promise as a valuable tool for mapping habitat quality and characterizing biodiversity across broad areas. PMID:23675463

Integrated Modeling Activities for the James Webb Space Telescope: Structural-Thermal-Optical Analysis

NASA Technical Reports Server (NTRS)

Johnston, John D.; Howard, Joseph M.; Mosier, Gary E.; Parrish, Keith A.; McGinnis, Mark A.; Bluth, Marcel; Kim, Kevin; Ha, Kong Q.

2004-01-01

The James Web Space Telescope (JWST) is a large, infrared-optimized space telescope scheduled for launch in 2011. This is a continuation of a series of papers on modeling activities for JWST. The structural-thermal-optical, often referred to as STOP, analysis process is used to predict the effect of thermal distortion on optical performance. The benchmark STOP analysis for JWST assesses the effect of an observatory slew on wavefront error. Temperatures predicted using geometric and thermal math models are mapped to a structural finite element model in order to predict thermally induced deformations. Motions and deformations at optical surfaces are then input to optical models, and optical performance is predicted using either an optical ray trace or a linear optical analysis tool. In addition to baseline performance predictions, a process for performing sensitivity studies to assess modeling uncertainties is described.

DockBench as docking selector tool: the lesson learned from D3R Grand Challenge 2015

NASA Astrophysics Data System (ADS)

Salmaso, Veronica; Sturlese, Mattia; Cuzzolin, Alberto; Moro, Stefano

2016-09-01

Structure-based drug design (SBDD) has matured within the last two decades as a valuable tool for the optimization of low molecular weight lead compounds to highly potent drugs. The key step in SBDD requires knowledge of the three-dimensional structure of the target-ligand complex, which is usually determined by X-ray crystallography. In the absence of structural information for the complex, SBDD relies on the generation of plausible molecular docking models. However, molecular docking protocols suffer from inaccuracies in the description of the interaction energies between the ligand and the target molecule, and often fail in the prediction of the correct binding mode. In this context, the appropriate selection of the most accurate docking protocol is absolutely relevant for the final molecular docking result, even if addressing this point is absolutely not a trivial task. D3R Grand Challenge 2015 has represented a precious opportunity to test the performance of DockBench, an integrate informatics platform to automatically compare RMDS-based molecular docking performances of different docking/scoring methods. The overall performance resulted in the blind prediction are encouraging in particular for the pose prediction task, in which several complex were predicted with a sufficient accuracy for medicinal chemistry purposes.

DockBench as docking selector tool: the lesson learned from D3R Grand Challenge 2015.

PubMed

Salmaso, Veronica; Sturlese, Mattia; Cuzzolin, Alberto; Moro, Stefano

2016-09-01

Structure-based drug design (SBDD) has matured within the last two decades as a valuable tool for the optimization of low molecular weight lead compounds to highly potent drugs. The key step in SBDD requires knowledge of the three-dimensional structure of the target-ligand complex, which is usually determined by X-ray crystallography. In the absence of structural information for the complex, SBDD relies on the generation of plausible molecular docking models. However, molecular docking protocols suffer from inaccuracies in the description of the interaction energies between the ligand and the target molecule, and often fail in the prediction of the correct binding mode. In this context, the appropriate selection of the most accurate docking protocol is absolutely relevant for the final molecular docking result, even if addressing this point is absolutely not a trivial task. D3R Grand Challenge 2015 has represented a precious opportunity to test the performance of DockBench, an integrate informatics platform to automatically compare RMDS-based molecular docking performances of different docking/scoring methods. The overall performance resulted in the blind prediction are encouraging in particular for the pose prediction task, in which several complex were predicted with a sufficient accuracy for medicinal chemistry purposes.

Crysalis: an integrated server for computational analysis and design of protein crystallization.

PubMed

Wang, Huilin; Feng, Liubin; Zhang, Ziding; Webb, Geoffrey I; Lin, Donghai; Song, Jiangning

2016-02-24

The failure of multi-step experimental procedures to yield diffraction-quality crystals is a major bottleneck in protein structure determination. Accordingly, several bioinformatics methods have been successfully developed and employed to select crystallizable proteins. Unfortunately, the majority of existing in silico methods only allow the prediction of crystallization propensity, seldom enabling computational design of protein mutants that can be targeted for enhancing protein crystallizability. Here, we present Crysalis, an integrated crystallization analysis tool that builds on support-vector regression (SVR) models to facilitate computational protein crystallization prediction, analysis, and design. More specifically, the functionality of this new tool includes: (1) rapid selection of target crystallizable proteins at the proteome level, (2) identification of site non-optimality for protein crystallization and systematic analysis of all potential single-point mutations that might enhance protein crystallization propensity, and (3) annotation of target protein based on predicted structural properties. We applied the design mode of Crysalis to identify site non-optimality for protein crystallization on a proteome-scale, focusing on proteins currently classified as non-crystallizable. Our results revealed that site non-optimality is based on biases related to residues, predicted structures, physicochemical properties, and sequence loci, which provides in-depth understanding of the features influencing protein crystallization. Crysalis is freely available at http://nmrcen.xmu.edu.cn/crysalis/.

Crysalis: an integrated server for computational analysis and design of protein crystallization

PubMed Central

Wang, Huilin; Feng, Liubin; Zhang, Ziding; Webb, Geoffrey I.; Lin, Donghai; Song, Jiangning

2016-01-01

The failure of multi-step experimental procedures to yield diffraction-quality crystals is a major bottleneck in protein structure determination. Accordingly, several bioinformatics methods have been successfully developed and employed to select crystallizable proteins. Unfortunately, the majority of existing in silico methods only allow the prediction of crystallization propensity, seldom enabling computational design of protein mutants that can be targeted for enhancing protein crystallizability. Here, we present Crysalis, an integrated crystallization analysis tool that builds on support-vector regression (SVR) models to facilitate computational protein crystallization prediction, analysis, and design. More specifically, the functionality of this new tool includes: (1) rapid selection of target crystallizable proteins at the proteome level, (2) identification of site non-optimality for protein crystallization and systematic analysis of all potential single-point mutations that might enhance protein crystallization propensity, and (3) annotation of target protein based on predicted structural properties. We applied the design mode of Crysalis to identify site non-optimality for protein crystallization on a proteome-scale, focusing on proteins currently classified as non-crystallizable. Our results revealed that site non-optimality is based on biases related to residues, predicted structures, physicochemical properties, and sequence loci, which provides in-depth understanding of the features influencing protein crystallization. Crysalis is freely available at http://nmrcen.xmu.edu.cn/crysalis/. PMID:26906024

Orbiter Boundary Layer Transition Prediction Tool Enhancements

NASA Technical Reports Server (NTRS)

Berry, Scott A.; King, Rudolph A.; Kegerise, Michael A.; Wood, William A.; McGinley, Catherine B.; Berger, Karen T.; Anderson, Brian P.

2010-01-01

Updates to an analytic tool developed for Shuttle support to predict the onset of boundary layer transition resulting from thermal protection system damage or repair are presented. The boundary layer transition tool is part of a suite of tools that analyze the local aerothermodynamic environment to enable informed disposition of damage for making recommendations to fly as is or to repair. Using mission specific trajectory information and details of each d agmea site or repair, the expected time (and thus Mach number) of transition onset is predicted to help define proper environments for use in subsequent thermal and stress analysis of the thermal protection system and structure. The boundary layer transition criteria utilized within the tool were updated based on new local boundary layer properties obtained from high fidelity computational solutions. Also, new ground-based measurements were obtained to allow for a wider parametric variation with both protuberances and cavities and then the resulting correlations were calibrated against updated flight data. The end result is to provide correlations that allow increased confidence with the resulting transition predictions. Recently, a new approach was adopted to remove conservatism in terms of sustained turbulence along the wing leading edge. Finally, some of the newer flight data are also discussed in terms of how these results reflect back on the updated correlations.

20180318 - Structure identification by Mass Spectrometry Non-Targeted Analysis using the US EPA’s CompTox Chemistry Dashboard (ACS Spring)

EPA Science Inventory

Identification of unknowns in mass spectrometry based non-targeted analyses (NTA) requires the integration of complementary pieces of data to arrive at a confident, consensus structure. Researchers use chemical reference databases, spectral matching, fragment prediction tools, r...

Fatigue-Crack-Growth Structural Analysis

NASA Technical Reports Server (NTRS)

Newman, J. C., Jr.

1986-01-01

Elastic and plastic deformations calculated under variety of loading conditions. Prediction of fatigue-crack-growth lives made with FatigueCrack-Growth Structural Analysis (FASTRAN) computer program. As cyclic loads are applied to initial crack configuration, FASTRAN predicts crack length and other parameters until complete break occurs. Loads are tensile or compressive and of variable or constant amplitude. FASTRAN incorporates linear-elastic fracture mechanics with modifications of load-interaction effects caused by crack closure. FASTRAN considered research tool, because of lengthy calculation times. FASTRAN written in FORTRAN IV for batch execution.

SGP-1: Prediction and Validation of Homologous Genes Based on Sequence Alignments

PubMed Central

Wiehe, Thomas; Gebauer-Jung, Steffi; Mitchell-Olds, Thomas; Guigó, Roderic

2001-01-01

Conventional methods of gene prediction rely on the recognition of DNA-sequence signals, the coding potential or the comparison of a genomic sequence with a cDNA, EST, or protein database. Reasons for limited accuracy in many circumstances are species-specific training and the incompleteness of reference databases. Lately, comparative genome analysis has attracted increasing attention. Several analysis tools that are based on human/mouse comparisons are already available. Here, we present a program for the prediction of protein-coding genes, termed SGP-1 (Syntenic Gene Prediction), which is based on the similarity of homologous genomic sequences. In contrast to most existing tools, the accuracy of SGP-1 depends little on species-specific properties such as codon usage or the nucleotide distribution. SGP-1 may therefore be applied to nonstandard model organisms in vertebrates as well as in plants, without the need for extensive parameter training. In addition to predicting genes in large-scale genomic sequences, the program may be useful to validate gene structure annotations from databases. To this end, SGP-1 output also contains comparisons between predicted and annotated gene structures in HTML format. The program can be accessed via a Web server at http://soft.ice.mpg.de/sgp-1. The source code, written in ANSI C, is available on request from the authors. PMID:11544202

eTOXlab, an open source modeling framework for implementing predictive models in production environments.

PubMed

Carrió, Pau; López, Oriol; Sanz, Ferran; Pastor, Manuel

2015-01-01

Computational models based in Quantitative-Structure Activity Relationship (QSAR) methodologies are widely used tools for predicting the biological properties of new compounds. In many instances, such models are used as a routine in the industry (e.g. food, cosmetic or pharmaceutical industry) for the early assessment of the biological properties of new compounds. However, most of the tools currently available for developing QSAR models are not well suited for supporting the whole QSAR model life cycle in production environments. We have developed eTOXlab; an open source modeling framework designed to be used at the core of a self-contained virtual machine that can be easily deployed in production environments, providing predictions as web services. eTOXlab consists on a collection of object-oriented Python modules with methods mapping common tasks of standard modeling workflows. This framework allows building and validating QSAR models as well as predicting the properties of new compounds using either a command line interface or a graphic user interface (GUI). Simple models can be easily generated by setting a few parameters, while more complex models can be implemented by overriding pieces of the original source code. eTOXlab benefits from the object-oriented capabilities of Python for providing high flexibility: any model implemented using eTOXlab inherits the features implemented in the parent model, like common tools and services or the automatic exposure of the models as prediction web services. The particular eTOXlab architecture as a self-contained, portable prediction engine allows building models with confidential information within corporate facilities, which can be safely exported and used for prediction without disclosing the structures of the training series. The software presented here provides full support to the specific needs of users that want to develop, use and maintain predictive models in corporate environments. The technologies used by eTOXlab (web services, VM, object-oriented programming) provide an elegant solution to common practical issues; the system can be installed easily in heterogeneous environments and integrates well with other software. Moreover, the system provides a simple and safe solution for building models with confidential structures that can be shared without disclosing sensitive information.

Validity of the growth model of the 'computerized visual perception assessment tool for Chinese characters structures'.

PubMed

Wu, Huey-Min; Li, Cheng-Hsaun; Kuo, Bor-Chen; Yang, Yu-Mao; Lin, Chin-Kai; Wan, Wei-Hsiang

2017-08-01

Morphological awareness is the foundation for the important developmental skills involved with vocabulary, as well as understanding the meaning of words, orthographic knowledge, reading, and writing. Visual perception of space and radicals in two-dimensional positions of Chinese characters' morphology is very important in identifying Chinese characters. The important predictive variables of special and visual perception in Chinese characters identification were investigated in the growth model in this research. The assessment tool is the "Computerized Visual Perception Assessment Tool for Chinese Characters Structures" developed by this study. There are two constructs, basic stroke and character structure. In the basic stroke, there are three subtests of one, two, and more than three strokes. In the character structure, there are three subtests of single-component character, horizontal-compound character, and vertical-compound character. This study used purposive sampling. In the first year, 551 children 4-6 years old participated in the study and were monitored for one year. In the second year, 388 children remained in the study and the successful follow-up rate was 70.4%. This study used a two-wave cross-lagged panel design to validate the growth model of the basic stroke and the character structure. There was significant correlation of the basic stroke and the character structure at different time points. The abilities in the basic stroke and in the character structure steadily developed over time for preschool children. Children's knowledge of the basic stroke effectively predicted their knowledge of the basic stroke and the character structure. Copyright © 2017 Elsevier Ltd. All rights reserved.

Virtual Interactomics of Proteins from Biochemical Standpoint

PubMed Central

Kubrycht, Jaroslav; Sigler, Karel; Souček, Pavel

2012-01-01

Virtual interactomics represents a rapidly developing scientific area on the boundary line of bioinformatics and interactomics. Protein-related virtual interactomics then comprises instrumental tools for prediction, simulation, and networking of the majority of interactions important for structural and individual reproduction, differentiation, recognition, signaling, regulation, and metabolic pathways of cells and organisms. Here, we describe the main areas of virtual protein interactomics, that is, structurally based comparative analysis and prediction of functionally important interacting sites, mimotope-assisted and combined epitope prediction, molecular (protein) docking studies, and investigation of protein interaction networks. Detailed information about some interesting methodological approaches and online accessible programs or databases is displayed in our tables. Considerable part of the text deals with the searches for common conserved or functionally convergent protein regions and subgraphs of conserved interaction networks, new outstanding trends and clinically interesting results. In agreement with the presented data and relationships, virtual interactomic tools improve our scientific knowledge, help us to formulate working hypotheses, and they frequently also mediate variously important in silico simulations. PMID:22928109

Building blocks for automated elucidation of metabolites: machine learning methods for NMR prediction.

PubMed

Kuhn, Stefan; Egert, Björn; Neumann, Steffen; Steinbeck, Christoph

2008-09-25

Current efforts in Metabolomics, such as the Human Metabolome Project, collect structures of biological metabolites as well as data for their characterisation, such as spectra for identification of substances and measurements of their concentration. Still, only a fraction of existing metabolites and their spectral fingerprints are known. Computer-Assisted Structure Elucidation (CASE) of biological metabolites will be an important tool to leverage this lack of knowledge. Indispensable for CASE are modules to predict spectra for hypothetical structures. This paper evaluates different statistical and machine learning methods to perform predictions of proton NMR spectra based on data from our open database NMRShiftDB. A mean absolute error of 0.18 ppm was achieved for the prediction of proton NMR shifts ranging from 0 to 11 ppm. Random forest, J48 decision tree and support vector machines achieved similar overall errors. HOSE codes being a notably simple method achieved a comparatively good result of 0.17 ppm mean absolute error. NMR prediction methods applied in the course of this work delivered precise predictions which can serve as a building block for Computer-Assisted Structure Elucidation for biological metabolites.

A sampling-based method for ranking protein structural models by integrating multiple scores and features.

PubMed

Shi, Xiaohu; Zhang, Jingfen; He, Zhiquan; Shang, Yi; Xu, Dong

2011-09-01

One of the major challenges in protein tertiary structure prediction is structure quality assessment. In many cases, protein structure prediction tools generate good structural models, but fail to select the best models from a huge number of candidates as the final output. In this study, we developed a sampling-based machine-learning method to rank protein structural models by integrating multiple scores and features. First, features such as predicted secondary structure, solvent accessibility and residue-residue contact information are integrated by two Radial Basis Function (RBF) models trained from different datasets. Then, the two RBF scores and five selected scoring functions developed by others, i.e., Opus-CA, Opus-PSP, DFIRE, RAPDF, and Cheng Score are synthesized by a sampling method. At last, another integrated RBF model ranks the structural models according to the features of sampling distribution. We tested the proposed method by using two different datasets, including the CASP server prediction models of all CASP8 targets and a set of models generated by our in-house software MUFOLD. The test result shows that our method outperforms any individual scoring function on both best model selection, and overall correlation between the predicted ranking and the actual ranking of structural quality.

Damage Instability and Transition From Quasi-Static to Dynamic Fracture

NASA Technical Reports Server (NTRS)

Davila, Carlos G.

2015-01-01

In a typical mechanical test, the loading phase is intended to be a quasi-static process, while the failure and collapse is usually a dynamic event. The structural strength and modes of damage can seldom be predicted without accounting for these two aspects of the response. For a proper prediction, it is therefore essential to use tools and methodologies that are capable of addressing both aspects of responses. In some cases, implicit quasi-static models have been shown to be able to predict the entire response of a structure, including the unstable path that leads to fracture. However, is it acceptable to ignore the effect of inertial forces in the formation of damage? In this presentation we examine aspects of the damage processes that must be simulated for an accurate prediction of structural strength and modes of failure.

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

PubMed

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

2013-09-01

The research aims to develop global modeling tools capable of categorizing structurally diverse chemicals in various toxicity classes according to the EEC and European Community directives, and to predict their acute toxicity in fathead minnow using set of selected molecular descriptors. Accordingly, artificial intelligence approach based classification and regression models, such as probabilistic neural networks (PNN), generalized regression neural networks (GRNN), multilayer perceptron neural network (MLPN), radial basis function neural network (RBFN), support vector machines (SVM), gene expression programming (GEP), and decision tree (DT) were constructed using the experimental toxicity data. Diversity and non-linearity in the chemicals' data were tested using the Tanimoto similarity index and Brock-Dechert-Scheinkman statistics. Predictive and generalization abilities of various models constructed here were compared using several statistical parameters. PNN and GRNN models performed relatively better than MLPN, RBFN, SVM, GEP, and DT. Both in two and four category classifications, PNN yielded a considerably high accuracy of classification in training (95.85 percent and 90.07 percent) and validation data (91.30 percent and 86.96 percent), respectively. GRNN rendered a high correlation between the measured and model predicted -log LC50 values both for the training (0.929) and validation (0.910) data and low prediction errors (RMSE) of 0.52 and 0.49 for two sets. Efficiency of the selected PNN and GRNN models in predicting acute toxicity of new chemicals was adequately validated using external datasets of different fish species (fathead minnow, bluegill, trout, and guppy). The PNN and GRNN models showed good predictive and generalization abilities and can be used as tools for predicting toxicities of structurally diverse chemical compounds. Copyright © 2013 Elsevier Inc. All rights reserved.

Can time-dependent density functional theory predict intersystem crossing in organic chromophores? A case study on benzo(bis)-X-diazole based donor-acceptor-donor type molecules.

PubMed

Tam, Teck Lip Dexter; Lin, Ting Ting; Chua, Ming Hui

2017-06-21

Here we utilized new diagnostic tools in time-dependent density functional theory to explain the trend of intersystem crossing in benzo(bis)-X-diazole based donor-acceptor-donor type molecules. These molecules display a wide range of fluorescence quantum yields and triplet yields, making them excellent candidates for testing the validity of these diagnostic tools. We believe that these tools are cost-effective and can be applied to structurally similar organic chromophores to predict/explain the trends of intersystem crossing, and thus fluorescence quantum yields and triplet yields without the use of complex and expensive multireference configuration interaction or multireference pertubation theory methods.

RNA 3D Modules in Genome-Wide Predictions of RNA 2D Structure

PubMed Central

Theis, Corinna; Zirbel, Craig L.; zu Siederdissen, Christian Höner; Anthon, Christian; Hofacker, Ivo L.; Nielsen, Henrik; Gorodkin, Jan

2015-01-01

Recent experimental and computational progress has revealed a large potential for RNA structure in the genome. This has been driven by computational strategies that exploit multiple genomes of related organisms to identify common sequences and secondary structures. However, these computational approaches have two main challenges: they are computationally expensive and they have a relatively high false discovery rate (FDR). Simultaneously, RNA 3D structure analysis has revealed modules composed of non-canonical base pairs which occur in non-homologous positions, apparently by independent evolution. These modules can, for example, occur inside structural elements which in RNA 2D predictions appear as internal loops. Hence one question is if the use of such RNA 3D information can improve the prediction accuracy of RNA secondary structure at a genome-wide level. Here, we use RNAz in combination with 3D module prediction tools and apply them on a 13-way vertebrate sequence-based alignment. We find that RNA 3D modules predicted by metaRNAmodules and JAR3D are significantly enriched in the screened windows compared to their shuffled counterparts. The initially estimated FDR of 47.0% is lowered to below 25% when certain 3D module predictions are present in the window of the 2D prediction. We discuss the implications and prospects for further development of computational strategies for detection of RNA 2D structure in genomic sequence. PMID:26509713

Polymer physics predicts the effects of structural variants on chromatin architecture.

PubMed

Bianco, Simona; Lupiáñez, Darío G; Chiariello, Andrea M; Annunziatella, Carlo; Kraft, Katerina; Schöpflin, Robert; Wittler, Lars; Andrey, Guillaume; Vingron, Martin; Pombo, Ana; Mundlos, Stefan; Nicodemi, Mario

2018-05-01

Structural variants (SVs) can result in changes in gene expression due to abnormal chromatin folding and cause disease. However, the prediction of such effects remains a challenge. Here we present a polymer-physics-based approach (PRISMR) to model 3D chromatin folding and to predict enhancer-promoter contacts. PRISMR predicts higher-order chromatin structure from genome-wide chromosome conformation capture (Hi-C) data. Using the EPHA4 locus as a model, the effects of pathogenic SVs are predicted in silico and compared to Hi-C data generated from mouse limb buds and patient-derived fibroblasts. PRISMR deconvolves the folding complexity of the EPHA4 locus and identifies SV-induced ectopic contacts and alterations of 3D genome organization in homozygous or heterozygous states. We show that SVs can reconfigure topologically associating domains, thereby producing extensive rewiring of regulatory interactions and causing disease by gene misexpression. PRISMR can be used to predict interactions in silico, thereby providing a tool for analyzing the disease-causing potential of SVs.

Complete fold annotation of the human proteome using a novel structural feature space

DOE PAGES

Middleton, Sarah A.; Illuminati, Joseph; Kim, Junhyong

2017-04-13

Recognition of protein structural fold is the starting point for many structure prediction tools and protein function inference. Fold prediction is computationally demanding and recognizing novel folds is difficult such that the majority of proteins have not been annotated for fold classification. Here we describe a new machine learning approach using a novel feature space that can be used for accurate recognition of all 1,221 currently known folds and inference of unknown novel folds. We show that our method achieves better than 94% accuracy even when many folds have only one training example. We demonstrate the utility of this methodmore » by predicting the folds of 34,330 human protein domains and showing that these predictions can yield useful insights into potential biological function, such as prediction of RNA-binding ability. Finally, our method can be applied to de novo fold prediction of entire proteomes and identify candidate novel fold families.« less

Computational Multi-Scale Modeling of the Microstructure and Segregation of Cast Mg Alloys at Low Superheat

NASA Astrophysics Data System (ADS)

Nastac, Laurentiu; El-Kaddah, Nagy

It is well known that casting at low superheat has a strong influence on the solidification structures of the cast alloy. Recent studies on casting magnesium AZ alloys at low superheat using the Magnetic Suspension Melting (MSM) process have shown that the cast alloy exhibit a fine globular grain structure, and the grain size depend on the cooling rate. This paper describes a stochastic mesoscopic model for predicting the grain structure and segregation in cast alloys at low superheat. This model was applied to predict the globular solidification morphology and solute redistribution of Al in cast Mg AZ31B alloy at different cooling rates. The predictions were found to be in good agreement with the observed grain structure and Al segregation. This makes the model a very useful tool for optimizing the solidification structure of cast magnesium alloys.

Network Simulation Models

DTIC Science & Technology

2008-12-01

1979; Wasserman and Faust, 1994). SNA thus relies heavily on graph theory to make predictions about network structure and thus social behavior...becomes a tool for increasing the specificity of theory , thinking through the theoretical implications, and generating testable predictions. In...to summarize Construct and its roots in constructural sociological theory . We discover that the (LPM) provides a mathematical bridge between

Aeroelastic Ground Wind Loads Analysis Tool for Launch Vehicles

NASA Technical Reports Server (NTRS)

Ivanco, Thomas G.

2016-01-01

Launch vehicles are exposed to ground winds during rollout and on the launch pad that can induce static and dynamic loads. Of particular concern are the dynamic loads caused by vortex shedding from nearly-cylindrical structures. When the frequency of vortex shedding nears that of a lowly-damped structural mode, the dynamic loads can be more than an order of magnitude greater than mean drag loads. Accurately predicting vehicle response to vortex shedding during the design and analysis cycles is difficult and typically exceeds the practical capabilities of modern computational fluid dynamics codes. Therefore, mitigating the ground wind loads risk typically requires wind-tunnel tests of dynamically-scaled models that are time consuming and expensive to conduct. In recent years, NASA has developed a ground wind loads analysis tool for launch vehicles to fill this analytical capability gap in order to provide predictions for prelaunch static and dynamic loads. This paper includes a background of the ground wind loads problem and the current state-of-the-art. It then discusses the history and significance of the analysis tool and the methodology used to develop it. Finally, results of the analysis tool are compared to wind-tunnel and full-scale data of various geometries and Reynolds numbers.

Chemical Sensor Array Response Modeling Using Quantitative Structure-Activity Relationships Technique

NASA Astrophysics Data System (ADS)

Shevade, Abhijit V.; Ryan, Margaret A.; Homer, Margie L.; Zhou, Hanying; Manfreda, Allison M.; Lara, Liana M.; Yen, Shiao-Pin S.; Jewell, April D.; Manatt, Kenneth S.; Kisor, Adam K.

We have developed a Quantitative Structure-Activity Relationships (QSAR) based approach to correlate the response of chemical sensors in an array with molecular descriptors. A novel molecular descriptor set has been developed; this set combines descriptors of sensing film-analyte interactions, representing sensor response, with a basic analyte descriptor set commonly used in QSAR studies. The descriptors are obtained using a combination of molecular modeling tools and empirical and semi-empirical Quantitative Structure-Property Relationships (QSPR) methods. The sensors under investigation are polymer-carbon sensing films which have been exposed to analyte vapors at parts-per-million (ppm) concentrations; response is measured as change in film resistance. Statistically validated QSAR models have been developed using Genetic Function Approximations (GFA) for a sensor array for a given training data set. The applicability of the sensor response models has been tested by using it to predict the sensor activities for test analytes not considered in the training set for the model development. The validated QSAR sensor response models show good predictive ability. The QSAR approach is a promising computational tool for sensing materials evaluation and selection. It can also be used to predict response of an existing sensing film to new target analytes.

Ligand Binding Site Detection by Local Structure Alignment and Its Performance Complementarity

PubMed Central

Lee, Hui Sun; Im, Wonpil

2013-01-01

Accurate determination of potential ligand binding sites (BS) is a key step for protein function characterization and structure-based drug design. Despite promising results of template-based BS prediction methods using global structure alignment (GSA), there is a room to improve the performance by properly incorporating local structure alignment (LSA) because BS are local structures and often similar for proteins with dissimilar global folds. We present a template-based ligand BS prediction method using G-LoSA, our LSA tool. A large benchmark set validation shows that G-LoSA predicts drug-like ligands’ positions in single-chain protein targets more precisely than TM-align, a GSA-based method, while the overall success rate of TM-align is better. G-LoSA is particularly efficient for accurate detection of local structures conserved across proteins with diverse global topologies. Recognizing the performance complementarity of G-LoSA to TM-align and a non-template geometry-based method, fpocket, a robust consensus scoring method, CMCS-BSP (Complementary Methods and Consensus Scoring for ligand Binding Site Prediction), is developed and shows improvement on prediction accuracy. The G-LoSA source code is freely available at http://im.bioinformatics.ku.edu/GLoSA. PMID:23957286

Prediction of Acoustic Loads Generated by Propulsion Systems

NASA Technical Reports Server (NTRS)

Perez, Linamaria; Allgood, Daniel C.

2011-01-01

NASA Stennis Space Center is one of the nation's premier facilities for conducting large-scale rocket engine testing. As liquid rocket engines vary in size, so do the acoustic loads that they produce. When these acoustic loads reach very high levels they may cause damages both to humans and to actual structures surrounding the testing area. To prevent these damages, prediction tools are used to estimate the spectral content and levels of the acoustics being generated by the rocket engine plumes and model their propagation through the surrounding atmosphere. Prior to the current work, two different acoustic prediction tools were being implemented at Stennis Space Center, each having their own advantages and disadvantages depending on the application. Therefore, a new prediction tool was created, using NASA SP-8072 handbook as a guide, which would replicate the same prediction methods as the previous codes, but eliminate any of the drawbacks the individual codes had. Aside from replicating the previous modeling capability in a single framework, additional modeling functions were added thereby expanding the current modeling capability. To verify that the new code could reproduce the same predictions as the previous codes, two verification test cases were defined. These verification test cases also served as validation cases as the predicted results were compared to actual test data.

The prediction of drug metabolism, tissue distribution, and bioavailability of 50 structurally diverse compounds in rat using mechanism-based absorption, distribution, and metabolism prediction tools.

PubMed

De Buck, Stefan S; Sinha, Vikash K; Fenu, Luca A; Gilissen, Ron A; Mackie, Claire E; Nijsen, Marjoleen J

2007-04-01

The aim of this study was to assess a physiologically based modeling approach for predicting drug metabolism, tissue distribution, and bioavailability in rat for a structurally diverse set of neutral and moderate-to-strong basic compounds (n = 50). Hepatic blood clearance (CL(h)) was projected using microsomal data and shown to be well predicted, irrespective of the type of hepatic extraction model (80% within 2-fold). Best predictions of CL(h) were obtained disregarding both plasma and microsomal protein binding, whereas strong bias was seen using either blood binding only or both plasma and microsomal protein binding. Two mechanistic tissue composition-based equations were evaluated for predicting volume of distribution (V(dss)) and tissue-to-plasma partitioning (P(tp)). A first approach, which accounted for ionic interactions with acidic phospholipids, resulted in accurate predictions of V(dss) (80% within 2-fold). In contrast, a second approach, which disregarded ionic interactions, was a poor predictor of V(dss) (60% within 2-fold). The first approach also yielded accurate predictions of P(tp) in muscle, heart, and kidney (80% within 3-fold), whereas in lung, liver, and brain, predictions ranged from 47% to 62% within 3-fold. Using the second approach, P(tp) prediction accuracy in muscle, heart, and kidney was on average 70% within 3-fold, and ranged from 24% to 54% in all other tissues. Combining all methods for predicting V(dss) and CL(h) resulted in accurate predictions of the in vivo half-life (70% within 2-fold). Oral bioavailability was well predicted using CL(h) data and Gastroplus Software (80% within 2-fold). These results illustrate that physiologically based prediction tools can provide accurate predictions of rat pharmacokinetics.

Structure and Stability of Molecular Crystals with Many-Body Dispersion-Inclusive Density Functional Tight Binding.

PubMed

Mortazavi, Majid; Brandenburg, Jan Gerit; Maurer, Reinhard J; Tkatchenko, Alexandre

2018-01-18

Accurate prediction of structure and stability of molecular crystals is crucial in materials science and requires reliable modeling of long-range dispersion interactions. Semiempirical electronic structure methods are computationally more efficient than their ab initio counterparts, allowing structure sampling with significant speedups. We combine the Tkatchenko-Scheffler van der Waals method (TS) and the many-body dispersion method (MBD) with third-order density functional tight-binding (DFTB3) via a charge population-based method. We find an overall good performance for the X23 benchmark database of molecular crystals, despite an underestimation of crystal volume that can be traced to the DFTB parametrization. We achieve accurate lattice energy predictions with DFT+MBD energetics on top of vdW-inclusive DFTB3 structures, resulting in a speedup of up to 3000 times compared with a full DFT treatment. This suggests that vdW-inclusive DFTB3 can serve as a viable structural prescreening tool in crystal structure prediction.

Molecular Docking of Enzyme Inhibitors: A Computational Tool for Structure-Based Drug Design

ERIC Educational Resources Information Center

Rudnitskaya, Aleksandra; Torok, Bela; Torok, Marianna

2010-01-01

Molecular docking is a frequently used method in structure-based rational drug design. It is used for evaluating the complex formation of small ligands with large biomolecules, predicting the strength of the bonding forces and finding the best geometrical arrangements. The major goal of this advanced undergraduate biochemistry laboratory exercise…

Structure identification for Non-Targeted Analytical Chemistry using the US EPA’s CompTox Chemistry Dashboard (ACS 2017 Fall meeting 3 of 3)

EPA Science Inventory

Identification of unknowns in non-targeted analyses (NTA) requires the integration of complementary data types to generate a confident consensus structure. Researchers use a variety of data and tools (e.g., chemical reference databases, spectral matching, fragment prediction too...

miRCat2: accurate prediction of plant and animal microRNAs from next-generation sequencing datasets

PubMed Central

Paicu, Claudia; Mohorianu, Irina; Stocks, Matthew; Xu, Ping; Coince, Aurore; Billmeier, Martina; Dalmay, Tamas; Moulton, Vincent; Moxon, Simon

2017-01-01

Abstract Motivation MicroRNAs are a class of ∼21–22 nt small RNAs which are excised from a stable hairpin-like secondary structure. They have important gene regulatory functions and are involved in many pathways including developmental timing, organogenesis and development in eukaryotes. There are several computational tools for miRNA detection from next-generation sequencing datasets. However, many of these tools suffer from high false positive and false negative rates. Here we present a novel miRNA prediction algorithm, miRCat2. miRCat2 incorporates a new entropy-based approach to detect miRNA loci, which is designed to cope with the high sequencing depth of current next-generation sequencing datasets. It has a user-friendly interface and produces graphical representations of the hairpin structure and plots depicting the alignment of sequences on the secondary structure. Results We test miRCat2 on a number of animal and plant datasets and present a comparative analysis with miRCat, miRDeep2, miRPlant and miReap. We also use mutants in the miRNA biogenesis pathway to evaluate the predictions of these tools. Results indicate that miRCat2 has an improved accuracy compared with other methods tested. Moreover, miRCat2 predicts several new miRNAs that are differentially expressed in wild-type versus mutants in the miRNA biogenesis pathway. Availability and Implementation miRCat2 is part of the UEA small RNA Workbench and is freely available from http://srna-workbench.cmp.uea.ac.uk/. Contact v.moulton@uea.ac.uk or s.moxon@uea.ac.uk Supplementary information Supplementary data are available at Bioinformatics online. PMID:28407097

Measurement and Prediction of the Thermomechanical Response of Shape Memory Alloy Hybrid Composite Beams

NASA Technical Reports Server (NTRS)

Davis, Brian; Turner, Travis L.; Seelecke, Stefan

2008-01-01

An experimental and numerical investigation into the static and dynamic responses of shape memory alloy hybrid composite (SMAHC) beams is performed to provide quantitative validation of a recently commercialized numerical analysis/design tool for SMAHC structures. The SMAHC beam specimens consist of a composite matrix with embedded pre-strained SMA actuators, which act against the mechanical boundaries of the structure when thermally activated to adaptively stiffen the structure. Numerical results are produced from the numerical model as implemented into the commercial finite element code ABAQUS. A rigorous experimental investigation is undertaken to acquire high fidelity measurements including infrared thermography and projection moire interferometry for full-field temperature and displacement measurements, respectively. High fidelity numerical results are also obtained from the numerical model and include measured parameters, such as geometric imperfection and thermal load. Excellent agreement is achieved between the predicted and measured results of the static and dynamic thermomechanical response, thereby providing quantitative validation of the numerical tool.

D2N: Distance to the native.

PubMed

Mishra, Avinash; Rana, Prashant Singh; Mittal, Aditya; Jayaram, B

2014-10-01

Root-mean-square-deviation (RMSD), of computationally-derived protein structures from experimentally determined structures, is a critical index to assessing protein-structure-prediction-algorithms (PSPAs). The development of PSPAs to obtain 0Å RMSD from native structures is considered central to computational biology. However, till date it has been quite challenging to measure how far a predicted protein structure is from its native - in the absence of a known experimental/native structure. In this work, we report the development of a metric "D2N" (distance to the native) - that predicts the "RMSD" of any structure without actually knowing the native structure. By combining physico-chemical properties and known universalities in spatial organization of soluble proteins to develop D2N, we demonstrate the ability to predict the distance of a proposed structure to within ±1.5Ǻ error with a remarkable average accuracy of 93.6% for structures below 5Ǻ from the native. We believe that this work opens up a completely new avenue towards assigning reliable structures to whole proteomes even in the absence of experimentally determined native structures. The D2N tool is freely available at http://www.scfbio-iitd.res.in/software/d2n.jsp. Copyright © 2014 Elsevier B.V. All rights reserved.

PSSP-RFE: accurate prediction of protein structural class by recursive feature extraction from PSI-BLAST profile, physical-chemical property and functional annotations.

PubMed

Li, Liqi; Cui, Xiang; Yu, Sanjiu; Zhang, Yuan; Luo, Zhong; Yang, Hua; Zhou, Yue; Zheng, Xiaoqi

2014-01-01

Protein structure prediction is critical to functional annotation of the massively accumulated biological sequences, which prompts an imperative need for the development of high-throughput technologies. As a first and key step in protein structure prediction, protein structural class prediction becomes an increasingly challenging task. Amongst most homological-based approaches, the accuracies of protein structural class prediction are sufficiently high for high similarity datasets, but still far from being satisfactory for low similarity datasets, i.e., below 40% in pairwise sequence similarity. Therefore, we present a novel method for accurate and reliable protein structural class prediction for both high and low similarity datasets. This method is based on Support Vector Machine (SVM) in conjunction with integrated features from position-specific score matrix (PSSM), PROFEAT and Gene Ontology (GO). A feature selection approach, SVM-RFE, is also used to rank the integrated feature vectors through recursively removing the feature with the lowest ranking score. The definitive top features selected by SVM-RFE are input into the SVM engines to predict the structural class of a query protein. To validate our method, jackknife tests were applied to seven widely used benchmark datasets, reaching overall accuracies between 84.61% and 99.79%, which are significantly higher than those achieved by state-of-the-art tools. These results suggest that our method could serve as an accurate and cost-effective alternative to existing methods in protein structural classification, especially for low similarity datasets.

Modeling the reactivities of hydroxyl radical and ozone towards atmospheric organic chemicals using quantitative structure-reactivity relationship approaches.

PubMed

Gupta, Shikha; Basant, Nikita; Mohan, Dinesh; Singh, Kunwar P

2016-07-01

The persistence and the removal of organic chemicals from the atmosphere are largely determined by their reactions with the OH radical and O3. Experimental determinations of the kinetic rate constants of OH and O3 with a large number of chemicals are tedious and resource intensive and development of computational approaches has widely been advocated. Recently, ensemble machine learning (EML) methods have emerged as unbiased tools to establish relationship between independent and dependent variables having a nonlinear dependence. In this study, EML-based, temperature-dependent quantitative structure-reactivity relationship (QSRR) models have been developed for predicting the kinetic rate constants for OH (kOH) and O3 (kO3) reactions with diverse chemicals. Structural diversity of chemicals was evaluated using a Tanimoto similarity index. The generalization and prediction abilities of the constructed models were established through rigorous internal and external validation performed employing statistical checks. In test data, the EML QSRR models yielded correlation (R (2)) of ≥0.91 between the measured and the predicted reactivities. The applicability domains of the constructed models were determined using methods based on descriptors range, Euclidean distance, leverage, and standardization approaches. The prediction accuracies for the higher reactivity compounds were relatively better than those of the low reactivity compounds. Proposed EML QSRR models performed well and outperformed the previous reports. The proposed QSRR models can make predictions of rate constants at different temperatures. The proposed models can be useful tools in predicting the reactivities of chemicals towards OH radical and O3 in the atmosphere.

Correlation of chemical shifts predicted by molecular dynamics simulations for partially disordered proteins.

PubMed

Karp, Jerome M; Eryilmaz, Ertan; Erylimaz, Ertan; Cowburn, David

2015-01-01

There has been a longstanding interest in being able to accurately predict NMR chemical shifts from structural data. Recent studies have focused on using molecular dynamics (MD) simulation data as input for improved prediction. Here we examine the accuracy of chemical shift prediction for intein systems, which have regions of intrinsic disorder. We find that using MD simulation data as input for chemical shift prediction does not consistently improve prediction accuracy over use of a static X-ray crystal structure. This appears to result from the complex conformational ensemble of the disordered protein segments. We show that using accelerated molecular dynamics (aMD) simulations improves chemical shift prediction, suggesting that methods which better sample the conformational ensemble like aMD are more appropriate tools for use in chemical shift prediction for proteins with disordered regions. Moreover, our study suggests that data accurately reflecting protein dynamics must be used as input for chemical shift prediction in order to correctly predict chemical shifts in systems with disorder.

A Business Analytics Software Tool for Monitoring and Predicting Radiology Throughput Performance.

PubMed

Jones, Stephen; Cournane, Seán; Sheehy, Niall; Hederman, Lucy

2016-12-01

Business analytics (BA) is increasingly being utilised by radiology departments to analyse and present data. It encompasses statistical analysis, forecasting and predictive modelling and is used as an umbrella term for decision support and business intelligence systems. The primary aim of this study was to determine whether utilising BA technologies could contribute towards improved decision support and resource management within radiology departments. A set of information technology requirements were identified with key stakeholders, and a prototype BA software tool was designed, developed and implemented. A qualitative evaluation of the tool was carried out through a series of semi-structured interviews with key stakeholders. Feedback was collated, and emergent themes were identified. The results indicated that BA software applications can provide visibility of radiology performance data across all time horizons. The study demonstrated that the tool could potentially assist with improving operational efficiencies and management of radiology resources.

Three-dimensional (3D) structure prediction of the American and African oil-palms β-ketoacyl-[ACP] synthase-II protein by comparative modelling

PubMed Central

Wang, Edina; Chinni, Suresh; Bhore, Subhash Janardhan

2014-01-01

Background: The fatty-acid profile of the vegetable oils determines its properties and nutritional value. Palm-oil obtained from the African oil-palm [Elaeis guineensis Jacq. (Tenera)] contains 44% palmitic acid (C16:0), but, palm-oil obtained from the American oilpalm [Elaeis oleifera] contains only 25% C16:0. In part, the b-ketoacyl-[ACP] synthase II (KASII) [EC: 2.3.1.179] protein is responsible for the high level of C16:0 in palm-oil derived from the African oil-palm. To understand more about E. guineensis KASII (EgKASII) and E. oleifera KASII (EoKASII) proteins, it is essential to know its structures. Hence, this study was undertaken. Objective: The objective of this study was to predict three-dimensional (3D) structure of EgKASII and EoKASII proteins using molecular modelling tools. Materials and Methods: The amino-acid sequences for KASII proteins were retrieved from the protein database of National Center for Biotechnology Information (NCBI), USA. The 3D structures were predicted for both proteins using homology modelling and ab-initio technique approach of protein structure prediction. The molecular dynamics (MD) simulation was performed to refine the predicted structures. The predicted structure models were evaluated and root mean square deviation (RMSD) and root mean square fluctuation (RMSF) values were calculated. Results: The homology modelling showed that EgKASII and EoKASII proteins are 78% and 74% similar with Streptococcus pneumonia KASII and Brucella melitensis KASII, respectively. The EgKASII and EoKASII structures predicted by using ab-initio technique approach shows 6% and 9% deviation to its structures predicted by homology modelling, respectively. The structure refinement and validation confirmed that the predicted structures are accurate. Conclusion: The 3D structures for EgKASII and EoKASII proteins were predicted. However, further research is essential to understand the interaction of EgKASII and EoKASII proteins with its substrates. PMID:24748752

Three-dimensional (3D) structure prediction of the American and African oil-palms β-ketoacyl-[ACP] synthase-II protein by comparative modelling.

PubMed

Wang, Edina; Chinni, Suresh; Bhore, Subhash Janardhan

2014-01-01

The fatty-acid profile of the vegetable oils determines its properties and nutritional value. Palm-oil obtained from the African oil-palm [Elaeis guineensis Jacq. (Tenera)] contains 44% palmitic acid (C16:0), but, palm-oil obtained from the American oilpalm [Elaeis oleifera] contains only 25% C16:0. In part, the b-ketoacyl-[ACP] synthase II (KASII) [EC: 2.3.1.179] protein is responsible for the high level of C16:0 in palm-oil derived from the African oil-palm. To understand more about E. guineensis KASII (EgKASII) and E. oleifera KASII (EoKASII) proteins, it is essential to know its structures. Hence, this study was undertaken. The objective of this study was to predict three-dimensional (3D) structure of EgKASII and EoKASII proteins using molecular modelling tools. The amino-acid sequences for KASII proteins were retrieved from the protein database of National Center for Biotechnology Information (NCBI), USA. The 3D structures were predicted for both proteins using homology modelling and ab-initio technique approach of protein structure prediction. The molecular dynamics (MD) simulation was performed to refine the predicted structures. The predicted structure models were evaluated and root mean square deviation (RMSD) and root mean square fluctuation (RMSF) values were calculated. The homology modelling showed that EgKASII and EoKASII proteins are 78% and 74% similar with Streptococcus pneumonia KASII and Brucella melitensis KASII, respectively. The EgKASII and EoKASII structures predicted by using ab-initio technique approach shows 6% and 9% deviation to its structures predicted by homology modelling, respectively. The structure refinement and validation confirmed that the predicted structures are accurate. The 3D structures for EgKASII and EoKASII proteins were predicted. However, further research is essential to understand the interaction of EgKASII and EoKASII proteins with its substrates.

xGDBvm: A Web GUI-Driven Workflow for Annotating Eukaryotic Genomes in the Cloud[OPEN

PubMed Central

Merchant, Nirav

2016-01-01

Genome-wide annotation of gene structure requires the integration of numerous computational steps. Currently, annotation is arguably best accomplished through collaboration of bioinformatics and domain experts, with broad community involvement. However, such a collaborative approach is not scalable at today’s pace of sequence generation. To address this problem, we developed the xGDBvm software, which uses an intuitive graphical user interface to access a number of common genome analysis and gene structure tools, preconfigured in a self-contained virtual machine image. Once their virtual machine instance is deployed through iPlant’s Atmosphere cloud services, users access the xGDBvm workflow via a unified Web interface to manage inputs, set program parameters, configure links to high-performance computing (HPC) resources, view and manage output, apply analysis and editing tools, or access contextual help. The xGDBvm workflow will mask the genome, compute spliced alignments from transcript and/or protein inputs (locally or on a remote HPC cluster), predict gene structures and gene structure quality, and display output in a public or private genome browser complete with accessory tools. Problematic gene predictions are flagged and can be reannotated using the integrated yrGATE annotation tool. xGDBvm can also be configured to append or replace existing data or load precomputed data. Multiple genomes can be annotated and displayed, and outputs can be archived for sharing or backup. xGDBvm can be adapted to a variety of use cases including de novo genome annotation, reannotation, comparison of different annotations, and training or teaching. PMID:27020957

xGDBvm: A Web GUI-Driven Workflow for Annotating Eukaryotic Genomes in the Cloud.

PubMed

Duvick, Jon; Standage, Daniel S; Merchant, Nirav; Brendel, Volker P

2016-04-01

Genome-wide annotation of gene structure requires the integration of numerous computational steps. Currently, annotation is arguably best accomplished through collaboration of bioinformatics and domain experts, with broad community involvement. However, such a collaborative approach is not scalable at today's pace of sequence generation. To address this problem, we developed the xGDBvm software, which uses an intuitive graphical user interface to access a number of common genome analysis and gene structure tools, preconfigured in a self-contained virtual machine image. Once their virtual machine instance is deployed through iPlant's Atmosphere cloud services, users access the xGDBvm workflow via a unified Web interface to manage inputs, set program parameters, configure links to high-performance computing (HPC) resources, view and manage output, apply analysis and editing tools, or access contextual help. The xGDBvm workflow will mask the genome, compute spliced alignments from transcript and/or protein inputs (locally or on a remote HPC cluster), predict gene structures and gene structure quality, and display output in a public or private genome browser complete with accessory tools. Problematic gene predictions are flagged and can be reannotated using the integrated yrGATE annotation tool. xGDBvm can also be configured to append or replace existing data or load precomputed data. Multiple genomes can be annotated and displayed, and outputs can be archived for sharing or backup. xGDBvm can be adapted to a variety of use cases including de novo genome annotation, reannotation, comparison of different annotations, and training or teaching. © 2016 American Society of Plant Biologists. All rights reserved.

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.

NIAS-Server: Neighbors Influence of Amino acids and Secondary Structures in Proteins.

PubMed

Borguesan, Bruno; Inostroza-Ponta, Mario; Dorn, Márcio

2017-03-01

The exponential growth in the number of experimentally determined three-dimensional protein structures provide a new and relevant knowledge about the conformation of amino acids in proteins. Only a few of probability densities of amino acids are publicly available for use in structure validation and prediction methods. NIAS (Neighbors Influence of Amino acids and Secondary structures) is a web-based tool used to extract information about conformational preferences of amino acid residues and secondary structures in experimental-determined protein templates. This information is useful, for example, to characterize folds and local motifs in proteins, molecular folding, and can help the solution of complex problems such as protein structure prediction, protein design, among others. The NIAS-Server and supplementary data are available at http://sbcb.inf.ufrgs.br/nias .

Validation of mechanical models for reinforced concrete structures: Presentation of the French project ``Benchmark des Poutres de la Rance''

NASA Astrophysics Data System (ADS)

L'Hostis, V.; Brunet, C.; Poupard, O.; Petre-Lazar, I.

2006-11-01

Several ageing models are available for the prediction of the mechanical consequences of rebar corrosion. They are used for service life prediction of reinforced concrete structures. Concerning corrosion diagnosis of reinforced concrete, some Non Destructive Testing (NDT) tools have been developed, and have been in use for some years. However, these developments require validation on existing concrete structures. The French project “Benchmark des Poutres de la Rance” contributes to this aspect. It has two main objectives: (i) validation of mechanical models to estimate the influence of rebar corrosion on the load bearing capacity of a structure, (ii) qualification of the use of the NDT results to collect information on steel corrosion within reinforced-concrete structures. Ten French and European institutions from both academic research laboratories and industrial companies contributed during the years 2004 and 2005. This paper presents the project that was divided into several work packages: (i) the reinforced concrete beams were characterized from non-destructive testing tools, (ii) the mechanical behaviour of the beams was experimentally tested, (iii) complementary laboratory analysis were performed and (iv) finally numerical simulations results were compared to the experimental results obtained with the mechanical tests.

Classifying Membrane Proteins in the Proteome by Using Artificial Neural Networks Based on the Preferential Parameters of Amino Acids

NASA Astrophysics Data System (ADS)

Bose, Subrata K.; Browne, Antony; Kazemian, Hassan; White, Kenneth

Membrane proteins (MPs) are large set of biological macromolecules that play a fundamental role in physiology and pathophysiology for survival. From a pharma-economical perspective, though it is the fact that MPs constitute ˜75% of possible targets for novel drugs but MPs are one of the most understudied groups of proteins in biochemical research. This is mainly because of the technical difficulties of obtaining structural information about trans-membrane regions (these are small sequences that crossways the bilayer lipid membrane). It is quite useful to predict the location of transmembrane segments down the sequence, since these are the elementary structural building blocks defining their topology. There have been several attempts over the last 20 years to develop tools for predicting membrane-spanning regions but current tools are far away from achieving a considerable reliability in prediction. This study aims to exploit the knowledge and current understanding in the field of artificial neural networks (ANNs) in particular data representation through the development of a system to identify and predict membrane-spanning regions by analysing primary amino acids sequence. In this paper we present a novel neural network (NNs) architecture and algorithms for predicting membrane spanning regions from primary amino acids sequences by using their preference parameters.

Predicting protein thermal stability changes upon point mutations using statistical potentials: Introducing HoTMuSiC.

PubMed

Pucci, Fabrizio; Bourgeas, Raphaël; Rooman, Marianne

2016-03-18

The accurate prediction of the impact of an amino acid substitution on the thermal stability of a protein is a central issue in protein science, and is of key relevance for the rational optimization of various bioprocesses that use enzymes in unusual conditions. Here we present one of the first computational tools to predict the change in melting temperature ΔTm upon point mutations, given the protein structure and, when available, the melting temperature Tm of the wild-type protein. The key ingredients of our model structure are standard and temperature-dependent statistical potentials, which are combined with the help of an artificial neural network. The model structure was chosen on the basis of a detailed thermodynamic analysis of the system. The parameters of the model were identified on a set of more than 1,600 mutations with experimentally measured ΔTm. The performance of our method was tested using a strict 5-fold cross-validation procedure, and was found to be significantly superior to that of competing methods. We obtained a root mean square deviation between predicted and experimental ΔTm values of 4.2 °C that reduces to 2.9 °C when ten percent outliers are removed. A webserver-based tool is freely available for non-commercial use at soft.dezyme.com.

Know Your Enemy: Successful Bioinformatic Approaches to Predict Functional RNA Structures in Viral RNAs.

PubMed

Lim, Chun Shen; Brown, Chris M

2017-01-01

Structured RNA elements may control virus replication, transcription and translation, and their distinct features are being exploited by novel antiviral strategies. Viral RNA elements continue to be discovered using combinations of experimental and computational analyses. However, the wealth of sequence data, notably from deep viral RNA sequencing, viromes, and metagenomes, necessitates computational approaches being used as an essential discovery tool. In this review, we describe practical approaches being used to discover functional RNA elements in viral genomes. In addition to success stories in new and emerging viruses, these approaches have revealed some surprising new features of well-studied viruses e.g., human immunodeficiency virus, hepatitis C virus, influenza, and dengue viruses. Some notable discoveries were facilitated by new comparative analyses of diverse viral genome alignments. Importantly, comparative approaches for finding RNA elements embedded in coding and non-coding regions differ. With the exponential growth of computer power we have progressed from stem-loop prediction on single sequences to cutting edge 3D prediction, and from command line to user friendly web interfaces. Despite these advances, many powerful, user friendly prediction tools and resources are underutilized by the virology community.

Know Your Enemy: Successful Bioinformatic Approaches to Predict Functional RNA Structures in Viral RNAs

PubMed Central

Lim, Chun Shen; Brown, Chris M.

2018-01-01

Structured RNA elements may control virus replication, transcription and translation, and their distinct features are being exploited by novel antiviral strategies. Viral RNA elements continue to be discovered using combinations of experimental and computational analyses. However, the wealth of sequence data, notably from deep viral RNA sequencing, viromes, and metagenomes, necessitates computational approaches being used as an essential discovery tool. In this review, we describe practical approaches being used to discover functional RNA elements in viral genomes. In addition to success stories in new and emerging viruses, these approaches have revealed some surprising new features of well-studied viruses e.g., human immunodeficiency virus, hepatitis C virus, influenza, and dengue viruses. Some notable discoveries were facilitated by new comparative analyses of diverse viral genome alignments. Importantly, comparative approaches for finding RNA elements embedded in coding and non-coding regions differ. With the exponential growth of computer power we have progressed from stem-loop prediction on single sequences to cutting edge 3D prediction, and from command line to user friendly web interfaces. Despite these advances, many powerful, user friendly prediction tools and resources are underutilized by the virology community. PMID:29354101

Fatigue damage prognosis of internal delamination in composite plates under cyclic compression loadings using affine arithmetic as uncertainty propagation tool

NASA Astrophysics Data System (ADS)

Gbaguidi, Audrey J.-M.

Structural health monitoring (SHM) has become indispensable for reducing maintenance costs and increasing the in-service capacity of a structure. The increased use of lightweight composite materials in aircraft structures drastically increased the effects of fatigue induced damage on their critical structural components and thus the necessity to predict the remaining life of those components. Damage prognosis, one of the least investigated fields in SHM, uses the current damage state of the system to forecast its future performance by estimating the expected loading environments. A successful damage prediction model requires the integration of technologies in areas like measurements, materials science, mechanics of materials, and probability theories, but most importantly the quantification of uncertainty in all these areas. In this study, Affine Arithmetic is used as a method for incorporating the uncertainties due to the material properties into the fatigue life prognosis of composite plates subjected to cyclic compressive loadings. When loadings are compressive in nature, the composite plates undergo repeated buckling-unloading of the delaminated layer which induces mixed modes I and II states of stress at the tip of the delamination in the plates. The Kardomateas model-based prediction law is used to predict the growth of the delamination, while the integration of the effects of the uncertainties for modes I and II coefficients in the fatigue life prediction model is handled using Affine arithmetic. The Mode I and Mode II interlaminar fracture toughness and fatigue characterization of the composite plates are first experimentally studied to obtain the material coefficients and fracture toughness, respectively. Next, these obtained coefficients are used in the Kardomateas law to predict the delamination lengths in the composite plates while using Affine Arithmetic to handle their uncertainties. At last, the fatigue characterization of the composite plates during compressive-buckling loadings is experimentally studied, and the delamination lengths obtained are compared with the predicted values to check the performance of Affine Arithmetic as an uncertainty propagation tool.

Final Report, DOE Early Career Award: Predictive modeling of complex physical systems: new tools for statistical inference, uncertainty quantification, and experimental design

DOE Office of Scientific and Technical Information (OSTI.GOV)

Marzouk, Youssef

Predictive simulation of complex physical systems increasingly rests on the interplay of experimental observations with computational models. Key inputs, parameters, or structural aspects of models may be incomplete or unknown, and must be developed from indirect and limited observations. At the same time, quantified uncertainties are needed to qualify computational predictions in the support of design and decision-making. In this context, Bayesian statistics provides a foundation for inference from noisy and limited data, but at prohibitive computional expense. This project intends to make rigorous predictive modeling *feasible* in complex physical systems, via accelerated and scalable tools for uncertainty quantification, Bayesianmore » inference, and experimental design. Specific objectives are as follows: 1. Develop adaptive posterior approximations and dimensionality reduction approaches for Bayesian inference in high-dimensional nonlinear systems. 2. Extend accelerated Bayesian methodologies to large-scale {\\em sequential} data assimilation, fully treating nonlinear models and non-Gaussian state and parameter distributions. 3. Devise efficient surrogate-based methods for Bayesian model selection and the learning of model structure. 4. Develop scalable simulation/optimization approaches to nonlinear Bayesian experimental design, for both parameter inference and model selection. 5. Demonstrate these inferential tools on chemical kinetic models in reacting flow, constructing and refining thermochemical and electrochemical models from limited data. Demonstrate Bayesian filtering on canonical stochastic PDEs and in the dynamic estimation of inhomogeneous subsurface properties and flow fields.« less

A Probabilistic Approach to Model Update

NASA Technical Reports Server (NTRS)

Horta, Lucas G.; Reaves, Mercedes C.; Voracek, David F.

2001-01-01

Finite element models are often developed for load validation, structural certification, response predictions, and to study alternate design concepts. In rare occasions, models developed with a nominal set of parameters agree with experimental data without the need to update parameter values. Today, model updating is generally heuristic and often performed by a skilled analyst with in-depth understanding of the model assumptions. Parameter uncertainties play a key role in understanding the model update problem and therefore probabilistic analysis tools, developed for reliability and risk analysis, may be used to incorporate uncertainty in the analysis. In this work, probability analysis (PA) tools are used to aid the parameter update task using experimental data and some basic knowledge of potential error sources. Discussed here is the first application of PA tools to update parameters of a finite element model for a composite wing structure. Static deflection data at six locations are used to update five parameters. It is shown that while prediction of individual response values may not be matched identically, the system response is significantly improved with moderate changes in parameter values.

bpRNA: large-scale automated annotation and analysis of RNA secondary structure.

PubMed

Danaee, Padideh; Rouches, Mason; Wiley, Michelle; Deng, Dezhong; Huang, Liang; Hendrix, David

2018-05-09

While RNA secondary structure prediction from sequence data has made remarkable progress, there is a need for improved strategies for annotating the features of RNA secondary structures. Here, we present bpRNA, a novel annotation tool capable of parsing RNA structures, including complex pseudoknot-containing RNAs, to yield an objective, precise, compact, unambiguous, easily-interpretable description of all loops, stems, and pseudoknots, along with the positions, sequence, and flanking base pairs of each such structural feature. We also introduce several new informative representations of RNA structure types to improve structure visualization and interpretation. We have further used bpRNA to generate a web-accessible meta-database, 'bpRNA-1m', of over 100 000 single-molecule, known secondary structures; this is both more fully and accurately annotated and over 20-times larger than existing databases. We use a subset of the database with highly similar (≥90% identical) sequences filtered out to report on statistical trends in sequence, flanking base pairs, and length. Both the bpRNA method and the bpRNA-1m database will be valuable resources both for specific analysis of individual RNA molecules and large-scale analyses such as are useful for updating RNA energy parameters for computational thermodynamic predictions, improving machine learning models for structure prediction, and for benchmarking structure-prediction algorithms.

Using RNA Sequence and Structure for the Prediction of Riboswitch Aptamer: A Comprehensive Review of Available Software and Tools

PubMed Central

Antunes, Deborah; Jorge, Natasha A. N.; Caffarena, Ernesto R.; Passetti, Fabio

2018-01-01

RNA molecules are essential players in many fundamental biological processes. Prokaryotes and eukaryotes have distinct RNA classes with specific structural features and functional roles. Computational prediction of protein structures is a research field in which high confidence three-dimensional protein models can be proposed based on the sequence alignment between target and templates. However, to date, only a few approaches have been developed for the computational prediction of RNA structures. Similar to proteins, RNA structures may be altered due to the interaction with various ligands, including proteins, other RNAs, and metabolites. A riboswitch is a molecular mechanism, found in the three kingdoms of life, in which the RNA structure is modified by the binding of a metabolite. It can regulate multiple gene expression mechanisms, such as transcription, translation initiation, and mRNA splicing and processing. Due to their nature, these entities also act on the regulation of gene expression and detection of small metabolites and have the potential to helping in the discovery of new classes of antimicrobial agents. In this review, we describe software and web servers currently available for riboswitch aptamer identification and secondary and tertiary structure prediction, including applications. PMID:29403526

(PS)2: protein structure prediction server version 3.0.

PubMed

Huang, Tsun-Tsao; Hwang, Jenn-Kang; Chen, Chu-Huang; Chu, Chih-Sheng; Lee, Chi-Wen; Chen, Chih-Chieh

2015-07-01

Protein complexes are involved in many biological processes. Examining coupling between subunits of a complex would be useful to understand the molecular basis of protein function. Here, our updated (PS)(2) web server predicts the three-dimensional structures of protein complexes based on comparative modeling; furthermore, this server examines the coupling between subunits of the predicted complex by combining structural and evolutionary considerations. The predicted complex structure could be indicated and visualized by Java-based 3D graphics viewers and the structural and evolutionary profiles are shown and compared chain-by-chain. For each subunit, considerations with or without the packing contribution of other subunits cause the differences in similarities between structural and evolutionary profiles, and these differences imply which form, complex or monomeric, is preferred in the biological condition for the subunit. We believe that the (PS)(2) server would be a useful tool for biologists who are interested not only in the structures of protein complexes but also in the coupling between subunits of the complexes. The (PS)(2) is freely available at http://ps2v3.life.nctu.edu.tw/. © The Author(s) 2015. Published by Oxford University Press on behalf of Nucleic Acids Research.

Seqping: gene prediction pipeline for plant genomes using self-training gene models and transcriptomic data.

PubMed

Chan, Kuang-Lim; Rosli, Rozana; Tatarinova, Tatiana V; Hogan, Michael; Firdaus-Raih, Mohd; Low, Eng-Ti Leslie

2017-01-27

Gene prediction is one of the most important steps in the genome annotation process. A large number of software tools and pipelines developed by various computing techniques are available for gene prediction. However, these systems have yet to accurately predict all or even most of the protein-coding regions. Furthermore, none of the currently available gene-finders has a universal Hidden Markov Model (HMM) that can perform gene prediction for all organisms equally well in an automatic fashion. We present an automated gene prediction pipeline, Seqping that uses self-training HMM models and transcriptomic data. The pipeline processes the genome and transcriptome sequences of the target species using GlimmerHMM, SNAP, and AUGUSTUS pipelines, followed by MAKER2 program to combine predictions from the three tools in association with the transcriptomic evidence. Seqping generates species-specific HMMs that are able to offer unbiased gene predictions. The pipeline was evaluated using the Oryza sativa and Arabidopsis thaliana genomes. Benchmarking Universal Single-Copy Orthologs (BUSCO) analysis showed that the pipeline was able to identify at least 95% of BUSCO's plantae dataset. Our evaluation shows that Seqping was able to generate better gene predictions compared to three HMM-based programs (MAKER2, GlimmerHMM and AUGUSTUS) using their respective available HMMs. Seqping had the highest accuracy in rice (0.5648 for CDS, 0.4468 for exon, and 0.6695 nucleotide structure) and A. thaliana (0.5808 for CDS, 0.5955 for exon, and 0.8839 nucleotide structure). Seqping provides researchers a seamless pipeline to train species-specific HMMs and predict genes in newly sequenced or less-studied genomes. We conclude that the Seqping pipeline predictions are more accurate than gene predictions using the other three approaches with the default or available HMMs.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Middleton, Sarah A.; Illuminati, Joseph; Kim, Junhyong

Recognition of protein structural fold is the starting point for many structure prediction tools and protein function inference. Fold prediction is computationally demanding and recognizing novel folds is difficult such that the majority of proteins have not been annotated for fold classification. Here we describe a new machine learning approach using a novel feature space that can be used for accurate recognition of all 1,221 currently known folds and inference of unknown novel folds. We show that our method achieves better than 94% accuracy even when many folds have only one training example. We demonstrate the utility of this methodmore » by predicting the folds of 34,330 human protein domains and showing that these predictions can yield useful insights into potential biological function, such as prediction of RNA-binding ability. Finally, our method can be applied to de novo fold prediction of entire proteomes and identify candidate novel fold families.« less

Estimating Model Prediction Error: Should You Treat Predictions as Fixed or Random?

NASA Technical Reports Server (NTRS)

Wallach, Daniel; Thorburn, Peter; Asseng, Senthold; Challinor, Andrew J.; Ewert, Frank; Jones, James W.; Rotter, Reimund; Ruane, Alexander

2016-01-01

Crop models are important tools for impact assessment of climate change, as well as for exploring management options under current climate. It is essential to evaluate the uncertainty associated with predictions of these models. We compare two criteria of prediction error; MSEP fixed, which evaluates mean squared error of prediction for a model with fixed structure, parameters and inputs, and MSEP uncertain( X), which evaluates mean squared error averaged over the distributions of model structure, inputs and parameters. Comparison of model outputs with data can be used to estimate the former. The latter has a squared bias term, which can be estimated using hindcasts, and a model variance term, which can be estimated from a simulation experiment. The separate contributions to MSEP uncertain (X) can be estimated using a random effects ANOVA. It is argued that MSEP uncertain (X) is the more informative uncertainty criterion, because it is specific to each prediction situation.

New Approach to Synthesis of Powder and Composite Materials by Electron Beam. Part 1. Technological Features of the Process

NASA Astrophysics Data System (ADS)

Rudskoy, A. I.; Kondrat'ev, S. Yu.; Sokolov, Yu. A.

2016-05-01

Possibilities of electron beam synthesis of structural and tool composite materials are considered. It is shown that a novel process involving mathematical modeling of each individual operation makes it possible to create materials with programmable structure and predictable properties from granules of various specified chemical compositions and sizes.

Direct glycan structure determination of intact N-linked glycopeptides by low-energy collision-induced dissociation tandem mass spectrometry and predicted spectral library searching.

PubMed

Pai, Pei-Jing; Hu, Yingwei; Lam, Henry

2016-08-31

Intact glycopeptide MS analysis to reveal site-specific protein glycosylation is an important frontier of proteomics. However, computational tools for analyzing MS/MS spectra of intact glycopeptides are still limited and not well-integrated into existing workflows. In this work, a new computational tool which combines the spectral library building/searching tool, SpectraST (Lam et al. Nat. Methods2008, 5, 873-875), and the glycopeptide fragmentation prediction tool, MassAnalyzer (Zhang et al. Anal. Chem.2010, 82, 10194-10202) for intact glycopeptide analysis has been developed. Specifically, this tool enables the determination of the glycan structure directly from low-energy collision-induced dissociation (CID) spectra of intact glycopeptides. Given a list of possible glycopeptide sequences as input, a sample-specific spectral library of MassAnalyzer-predicted spectra is built using SpectraST. Glycan identification from CID spectra is achieved by spectral library searching against this library, in which both m/z and intensity information of the possible fragmentation ions are taken into consideration for improved accuracy. We validated our method using a standard glycoprotein, human transferrin, and evaluated its potential to be used in site-specific glycosylation profiling of glycoprotein datasets from LC-MS/MS. In addition, we further applied our method to reveal, for the first time, the site-specific N-glycosylation profile of recombinant human acetylcholinesterase expressed in HEK293 cells. For maximum usability, SpectraST is developed as part of the Trans-Proteomic Pipeline (TPP), a freely available and open-source software suite for MS data analysis. Copyright © 2016 Elsevier B.V. All rights reserved.

An Engineering Tool for the Prediction of Internal Dielectric Charging

NASA Astrophysics Data System (ADS)

Rodgers, D. J.; Ryden, K. A.; Wrenn, G. L.; Latham, P. M.; Sorensen, J.; Levy, L.

1998-11-01

A practical internal charging tool has been developed. It provides an easy-to-use means for satellite engineers to predict whether on-board dielectrics are vulnerable to electrostatic discharge in the outer radiation belt. The tool is designed to simulate irradiation of single-dielectric planar or cylindrical structures with or without shielding. Analytical equations are used to describe current deposition in the dielectric. This is fast and gives charging currents to sufficient accuracy given the uncertainties in other aspects of the problem - particularly material characteristics. Time-dependent internal electric fields are calculated, taking into account the effect on conductivity of electric field, dose rate and temperature. A worst-case model of electron fluxes in the outer belt has been created specifically for the internal charging problem and is built into the code. For output, the tool gives a YES or NO decision on the susceptibility of the structure to internal electrostatic breakdown and if necessary, calculates the required changes to bring the system below the breakdown threshold. A complementary programme of laboratory irradiations has been carried out to validate the tool. The results for Epoxy-fibreglass samples show that the code models electric field realistically for a wide variety of shields, dielectric thicknesses and electron spectra. Results for Teflon samples indicate that some further experimentation is required and the radiation-induced conductivity aspects of the code have not been validated.

MUFOLD-SS: New deep inception-inside-inception networks for protein secondary structure prediction.

PubMed

Fang, Chao; Shang, Yi; Xu, Dong

2018-05-01

Protein secondary structure prediction can provide important information for protein 3D structure prediction and protein functions. Deep learning offers a new opportunity to significantly improve prediction accuracy. In this article, a new deep neural network architecture, named the Deep inception-inside-inception (Deep3I) network, is proposed for protein secondary structure prediction and implemented as a software tool MUFOLD-SS. The input to MUFOLD-SS is a carefully designed feature matrix corresponding to the primary amino acid sequence of a protein, which consists of a rich set of information derived from individual amino acid, as well as the context of the protein sequence. Specifically, the feature matrix is a composition of physio-chemical properties of amino acids, PSI-BLAST profile, and HHBlits profile. MUFOLD-SS is composed of a sequence of nested inception modules and maps the input matrix to either eight states or three states of secondary structures. The architecture of MUFOLD-SS enables effective processing of local and global interactions between amino acids in making accurate prediction. In extensive experiments on multiple datasets, MUFOLD-SS outperformed the best existing methods and other deep neural networks significantly. MUFold-SS can be downloaded from http://dslsrv8.cs.missouri.edu/~cf797/MUFoldSS/download.html. © 2018 Wiley Periodicals, Inc.

H++ 3.0: automating pK prediction and the preparation of biomolecular structures for atomistic molecular modeling and simulations.

PubMed

Anandakrishnan, Ramu; Aguilar, Boris; Onufriev, Alexey V

2012-07-01

The accuracy of atomistic biomolecular modeling and simulation studies depend on the accuracy of the input structures. Preparing these structures for an atomistic modeling task, such as molecular dynamics (MD) simulation, can involve the use of a variety of different tools for: correcting errors, adding missing atoms, filling valences with hydrogens, predicting pK values for titratable amino acids, assigning predefined partial charges and radii to all atoms, and generating force field parameter/topology files for MD. Identifying, installing and effectively using the appropriate tools for each of these tasks can be difficult for novice and time-consuming for experienced users. H++ (http://biophysics.cs.vt.edu/) is a free open-source web server that automates the above key steps in the preparation of biomolecular structures for molecular modeling and simulations. H++ also performs extensive error and consistency checking, providing error/warning messages together with the suggested corrections. In addition to numerous minor improvements, the latest version of H++ includes several new capabilities and options: fix erroneous (flipped) side chain conformations for HIS, GLN and ASN, include a ligand in the input structure, process nucleic acid structures and generate a solvent box with specified number of common ions for explicit solvent MD.

Knowledge-based prediction of protein backbone conformation using a structural alphabet.

PubMed

Vetrivel, Iyanar; Mahajan, Swapnil; Tyagi, Manoj; Hoffmann, Lionel; Sanejouand, Yves-Henri; Srinivasan, Narayanaswamy; de Brevern, Alexandre G; Cadet, Frédéric; Offmann, Bernard

2017-01-01

Libraries of structural prototypes that abstract protein local structures are known as structural alphabets and have proven to be very useful in various aspects of protein structure analyses and predictions. One such library, Protein Blocks, is composed of 16 standard 5-residues long structural prototypes. This form of analyzing proteins involves drafting its structure as a string of Protein Blocks. Predicting the local structure of a protein in terms of protein blocks is the general objective of this work. A new approach, PB-kPRED is proposed towards this aim. It involves (i) organizing the structural knowledge in the form of a database of pentapeptide fragments extracted from all protein structures in the PDB and (ii) applying a knowledge-based algorithm that does not rely on any secondary structure predictions and/or sequence alignment profiles, to scan this database and predict most probable backbone conformations for the protein local structures. Though PB-kPRED uses the structural information from homologues in preference, if available. The predictions were evaluated rigorously on 15,544 query proteins representing a non-redundant subset of the PDB filtered at 30% sequence identity cut-off. We have shown that the kPRED method was able to achieve mean accuracies ranging from 40.8% to 66.3% depending on the availability of homologues. The impact of the different strategies for scanning the database on the prediction was evaluated and is discussed. Our results highlight the usefulness of the method in the context of proteins without any known structural homologues. A scoring function that gives a good estimate of the accuracy of prediction was further developed. This score estimates very well the accuracy of the algorithm (R2 of 0.82). An online version of the tool is provided freely for non-commercial usage at http://www.bo-protscience.fr/kpred/.

Comprehensive assessment and performance improvement of effector protein predictors for bacterial secretion systems III, IV and VI.

PubMed

An, Yi; Wang, Jiawei; Li, Chen; Leier, André; Marquez-Lago, Tatiana; Wilksch, Jonathan; Zhang, Yang; Webb, Geoffrey I; Song, Jiangning; Lithgow, Trevor

2018-01-01

Bacterial effector proteins secreted by various protein secretion systems play crucial roles in host-pathogen interactions. In this context, computational tools capable of accurately predicting effector proteins of the various types of bacterial secretion systems are highly desirable. Existing computational approaches use different machine learning (ML) techniques and heterogeneous features derived from protein sequences and/or structural information. These predictors differ not only in terms of the used ML methods but also with respect to the used curated data sets, the features selection and their prediction performance. Here, we provide a comprehensive survey and benchmarking of currently available tools for the prediction of effector proteins of bacterial types III, IV and VI secretion systems (T3SS, T4SS and T6SS, respectively). We review core algorithms, feature selection techniques, tool availability and applicability and evaluate the prediction performance based on carefully curated independent test data sets. In an effort to improve predictive performance, we constructed three ensemble models based on ML algorithms by integrating the output of all individual predictors reviewed. Our benchmarks demonstrate that these ensemble models outperform all the reviewed tools for the prediction of effector proteins of T3SS and T4SS. The webserver of the proposed ensemble methods for T3SS and T4SS effector protein prediction is freely available at http://tbooster.erc.monash.edu/index.jsp. We anticipate that this survey will serve as a useful guide for interested users and that the new ensemble predictors will stimulate research into host-pathogen relationships and inspiration for the development of new bioinformatics tools for predicting effector proteins of T3SS, T4SS and T6SS. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Combining self- and cross-docking as benchmark tools: the performance of DockBench in the D3R Grand Challenge 2

NASA Astrophysics Data System (ADS)

Salmaso, Veronica; Sturlese, Mattia; Cuzzolin, Alberto; Moro, Stefano

2018-01-01

Molecular docking is a powerful tool in the field of computer-aided molecular design. In particular, it is the technique of choice for the prediction of a ligand pose within its target binding site. A multitude of docking methods is available nowadays, whose performance may vary depending on the data set. Therefore, some non-trivial choices should be made before starting a docking simulation. In the same framework, the selection of the target structure to use could be challenging, since the number of available experimental structures is increasing. Both issues have been explored within this work. The pose prediction of a pool of 36 compounds provided by D3R Grand Challenge 2 organizers was preceded by a pipeline to choose the best protein/docking-method couple for each blind ligand. An integrated benchmark approach including ligand shape comparison and cross-docking evaluations was implemented inside our DockBench software. The results are encouraging and show that bringing attention to the choice of the docking simulation fundamental components improves the results of the binding mode predictions.

Heterogeneous Structure of Stem Cells Dynamics: Statistical Models and Quantitative Predictions

PubMed Central

Bogdan, Paul; Deasy, Bridget M.; Gharaibeh, Burhan; Roehrs, Timo; Marculescu, Radu

2014-01-01

Understanding stem cell (SC) population dynamics is essential for developing models that can be used in basic science and medicine, to aid in predicting cells fate. These models can be used as tools e.g. in studying patho-physiological events at the cellular and tissue level, predicting (mal)functions along the developmental course, and personalized regenerative medicine. Using time-lapsed imaging and statistical tools, we show that the dynamics of SC populations involve a heterogeneous structure consisting of multiple sub-population behaviors. Using non-Gaussian statistical approaches, we identify the co-existence of fast and slow dividing subpopulations, and quiescent cells, in stem cells from three species. The mathematical analysis also shows that, instead of developing independently, SCs exhibit a time-dependent fractal behavior as they interact with each other through molecular and tactile signals. These findings suggest that more sophisticated models of SC dynamics should view SC populations as a collective and avoid the simplifying homogeneity assumption by accounting for the presence of more than one dividing sub-population, and their multi-fractal characteristics. PMID:24769917

Designers' unified cost model

NASA Technical Reports Server (NTRS)

Freeman, W.; Ilcewicz, L.; Swanson, G.; Gutowski, T.

1992-01-01

The Structures Technology Program Office (STPO) at NASA LaRC has initiated development of a conceptual and preliminary designers' cost prediction model. The model will provide a technically sound method for evaluating the relative cost of different composite structural designs, fabrication processes, and assembly methods that can be compared to equivalent metallic parts or assemblies. The feasibility of developing cost prediction software in a modular form for interfacing with state-of-the-art preliminary design tools and computer aided design programs is being evaluated. The goal of this task is to establish theoretical cost functions that relate geometric design features to summed material cost and labor content in terms of process mechanics and physics. The output of the designers' present analytical tools will be input for the designers' cost prediction model to provide the designer with a database and deterministic cost methodology that allows one to trade and synthesize designs with both cost and weight as objective functions for optimization. This paper presents the team members, approach, goals, plans, and progress to date for development of COSTADE (Cost Optimization Software for Transport Aircraft Design Evaluation).

RNApdbee 2.0: multifunctional tool for RNA structure annotation.

PubMed

Zok, Tomasz; Antczak, Maciej; Zurkowski, Michal; Popenda, Mariusz; Blazewicz, Jacek; Adamiak, Ryszard W; Szachniuk, Marta

2018-04-30

In the field of RNA structural biology and bioinformatics, an access to correctly annotated RNA structure is of crucial importance, especially in the secondary and 3D structure predictions. RNApdbee webserver, introduced in 2014, primarily aimed to address the problem of RNA secondary structure extraction from the PDB files. Its new version, RNApdbee 2.0, is a highly advanced multifunctional tool for RNA structure annotation, revealing the relationship between RNA secondary and 3D structure given in the PDB or PDBx/mmCIF format. The upgraded version incorporates new algorithms for recognition and classification of high-ordered pseudoknots in large RNA structures. It allows analysis of isolated base pairs impact on RNA structure. It can visualize RNA secondary structures-including that of quadruplexes-with depiction of non-canonical interactions. It also annotates motifs to ease identification of stems, loops and single-stranded fragments in the input RNA structure. RNApdbee 2.0 is implemented as a publicly available webserver with an intuitive interface and can be freely accessed at http://rnapdbee.cs.put.poznan.pl/.

A strategy to improve the identification reliability of the chemical constituents by high-resolution mass spectrometry-based isomer structure prediction combined with a quantitative structure retention relationship analysis: Phthalide compounds in Chuanxiong as a test case.

PubMed

Zhang, Qingqing; Huo, Mengqi; Zhang, Yanling; Qiao, Yanjiang; Gao, Xiaoyan

2018-06-01

High-resolution mass spectrometry (HRMS) provides a powerful tool for the rapid analysis and identification of compounds in herbs. However, the diversity and large differences in the content of the chemical constituents in herbal medicines, especially isomerisms, are a great challenge for mass spectrometry-based structural identification. In the current study, a new strategy for the structural characterization of potential new phthalide compounds was proposed by isomer structure predictions combined with a quantitative structure-retention relationship (QSRR) analysis using phthalide compounds in Chuanxiong as an example. This strategy consists of three steps. First, the structures of phthalide compounds were reasonably predicted on the basis of the structure features and MS/MS fragmentation patterns: (1) the collected raw HRMS data were preliminarily screened by an in-house database; (2) the MS/MS fragmentation patterns of the analogous compounds were summarized; (3) the reported phthalide compounds were identified, and the structures of the isomers were reasonably predicted. Second, the QSRR model was established and verified using representative phthalide compound standards. Finally, the retention times of the predicted isomers were calculated by the QSRR model, and the structures of these peaks were rationally characterized by matching retention times of the detected chromatographic peaks and the predicted isomers. A multiple linear regression QSRR model in which 6 physicochemical variables were screened was built using 23 phthalide standards. The retention times of the phthalide isomers in Chuanxiong were well predicted by the QSRR model combined with reasonable structure predictions (R 2 =0.955). A total of 81 peaks were detected from Chuanxiong and assigned to reasonable structures, and 26 potential new phthalide compounds were structurally characterized. This strategy can improve the identification efficiency and reliability of homologues in complex materials. Copyright © 2018 Elsevier B.V. All rights reserved.

Protein structural similarity search by Ramachandran codes

PubMed Central

Lo, Wei-Cheng; Huang, Po-Jung; Chang, Chih-Hung; Lyu, Ping-Chiang

2007-01-01

Background Protein structural data has increased exponentially, such that fast and accurate tools are necessary to access structure similarity search. To improve the search speed, several methods have been designed to reduce three-dimensional protein structures to one-dimensional text strings that are then analyzed by traditional sequence alignment methods; however, the accuracy is usually sacrificed and the speed is still unable to match sequence similarity search tools. Here, we aimed to improve the linear encoding methodology and develop efficient search tools that can rapidly retrieve structural homologs from large protein databases. Results We propose a new linear encoding method, SARST (Structural similarity search Aided by Ramachandran Sequential Transformation). SARST transforms protein structures into text strings through a Ramachandran map organized by nearest-neighbor clustering and uses a regenerative approach to produce substitution matrices. Then, classical sequence similarity search methods can be applied to the structural similarity search. Its accuracy is similar to Combinatorial Extension (CE) and works over 243,000 times faster, searching 34,000 proteins in 0.34 sec with a 3.2-GHz CPU. SARST provides statistically meaningful expectation values to assess the retrieved information. It has been implemented into a web service and a stand-alone Java program that is able to run on many different platforms. Conclusion As a database search method, SARST can rapidly distinguish high from low similarities and efficiently retrieve homologous structures. It demonstrates that the easily accessible linear encoding methodology has the potential to serve as a foundation for efficient protein structural similarity search tools. These search tools are supposed applicable to automated and high-throughput functional annotations or predictions for the ever increasing number of published protein structures in this post-genomic era. PMID:17716377

Well-characterized sequence features of eukaryote genomes and implications for ab initio gene prediction.

PubMed

Huang, Ying; Chen, Shi-Yi; Deng, Feilong

2016-01-01

In silico analysis of DNA sequences is an important area of computational biology in the post-genomic era. Over the past two decades, computational approaches for ab initio prediction of gene structure from genome sequence alone have largely facilitated our understanding on a variety of biological questions. Although the computational prediction of protein-coding genes has already been well-established, we are also facing challenges to robustly find the non-coding RNA genes, such as miRNA and lncRNA. Two main aspects of ab initio gene prediction include the computed values for describing sequence features and used algorithm for training the discriminant function, and by which different combinations are employed into various bioinformatic tools. Herein, we briefly review these well-characterized sequence features in eukaryote genomes and applications to ab initio gene prediction. The main purpose of this article is to provide an overview to beginners who aim to develop the related bioinformatic tools.

RosettaHoles: rapid assessment of protein core packing for structure prediction, refinement, design, and validation.

PubMed

Sheffler, Will; Baker, David

2009-01-01

We present a novel method called RosettaHoles for visual and quantitative assessment of underpacking in the protein core. RosettaHoles generates a set of spherical cavity balls that fill the empty volume between atoms in the protein interior. For visualization, the cavity balls are aggregated into contiguous overlapping clusters and small cavities are discarded, leaving an uncluttered representation of the unfilled regions of space in a structure. For quantitative analysis, the cavity ball data are used to estimate the probability of observing a given cavity in a high-resolution crystal structure. RosettaHoles provides excellent discrimination between real and computationally generated structures, is predictive of incorrect regions in models, identifies problematic structures in the Protein Data Bank, and promises to be a useful validation tool for newly solved experimental structures.

RosettaHoles: Rapid assessment of protein core packing for structure prediction, refinement, design, and validation

PubMed Central

Sheffler, Will; Baker, David

2009-01-01

We present a novel method called RosettaHoles for visual and quantitative assessment of underpacking in the protein core. RosettaHoles generates a set of spherical cavity balls that fill the empty volume between atoms in the protein interior. For visualization, the cavity balls are aggregated into contiguous overlapping clusters and small cavities are discarded, leaving an uncluttered representation of the unfilled regions of space in a structure. For quantitative analysis, the cavity ball data are used to estimate the probability of observing a given cavity in a high-resolution crystal structure. RosettaHoles provides excellent discrimination between real and computationally generated structures, is predictive of incorrect regions in models, identifies problematic structures in the Protein Data Bank, and promises to be a useful validation tool for newly solved experimental structures. PMID:19177366

Intelligent seismic risk mitigation system on structure building

NASA Astrophysics Data System (ADS)

Suryanita, R.; Maizir, H.; Yuniorto, E.; Jingga, H.

2018-01-01

Indonesia located on the Pacific Ring of Fire, is one of the highest-risk seismic zone in the world. The strong ground motion might cause catastrophic collapse of the building which leads to casualties and property damages. Therefore, it is imperative to properly design the structural response of building against seismic hazard. Seismic-resistant building design process requires structural analysis to be performed to obtain the necessary building responses. However, the structural analysis could be very difficult and time consuming. This study aims to predict the structural response includes displacement, velocity, and acceleration of multi-storey building with the fixed floor plan using Artificial Neural Network (ANN) method based on the 2010 Indonesian seismic hazard map. By varying the building height, soil condition, and seismic location in 47 cities in Indonesia, 6345 data sets were obtained and fed into the ANN model for the learning process. The trained ANN can predict the displacement, velocity, and acceleration responses with up to 96% of predicted rate. The trained ANN architecture and weight factors were later used to build a simple tool in Visual Basic program which possesses the features for prediction of structural response as mentioned previously.

Quantitative Protein Topography Analysis and High-Resolution Structure Prediction Using Hydroxyl Radical Labeling and Tandem-Ion Mass Spectrometry (MS)*

PubMed Central

Kaur, Parminder; Kiselar, Janna; Yang, Sichun; Chance, Mark R.

2015-01-01

Hydroxyl radical footprinting based MS for protein structure assessment has the goal of understanding ligand induced conformational changes and macromolecular interactions, for example, protein tertiary and quaternary structure, but the structural resolution provided by typical peptide-level quantification is limiting. In this work, we present experimental strategies using tandem-MS fragmentation to increase the spatial resolution of the technique to the single residue level to provide a high precision tool for molecular biophysics research. Overall, in this study we demonstrated an eightfold increase in structural resolution compared with peptide level assessments. In addition, to provide a quantitative analysis of residue based solvent accessibility and protein topography as a basis for high-resolution structure prediction; we illustrate strategies of data transformation using the relative reactivity of side chains as a normalization strategy and predict side-chain surface area from the footprinting data. We tested the methods by examination of Ca+2-calmodulin showing highly significant correlations between surface area and side-chain contact predictions for individual side chains and the crystal structure. Tandem ion based hydroxyl radical footprinting-MS provides quantitative high-resolution protein topology information in solution that can fill existing gaps in structure determination for large proteins and macromolecular complexes. PMID:25687570

Boundary Layer Transition Results From STS-114

NASA Technical Reports Server (NTRS)

Berry, Scott A.; Horvath, Thomas J.; Cassady, Amy M.; Kirk, Benjamin S.; Wang, K. C.; Hyatt, Andrew J.

2006-01-01

The tool for predicting the onset of boundary layer transition from damage to and/or repair of the thermal protection system developed in support of Shuttle Return to Flight is compared to the STS-114 flight results. The Boundary Layer Transition (BLT) Tool is part of a suite of tools that analyze the aerothermodynamic environment of the local thermal protection system to allow informed disposition of damage for making recommendations to fly as is or to repair. Using mission specific trajectory information and details of each damage site or repair, the expected time of transition onset is predicted to help determine the proper aerothermodynamic environment to use in the subsequent thermal and stress analysis of the local structure. The boundary layer transition criteria utilized for the tool was developed from ground-based measurements to account for the effect of both protuberances and cavities and has been calibrated against flight data. Computed local boundary layer edge conditions provided the means to correlate the experimental results and then to extrapolate to flight. During STS-114, the BLT Tool was utilized and was part of the decision making process to perform an extravehicular activity to remove the large gap fillers. The role of the BLT Tool during this mission, along with the supporting information that was acquired for the on-orbit analysis, is reviewed. Once the large gap fillers were removed, all remaining damage sites were cleared for reentry as is. Post-flight analysis of the transition onset time revealed excellent agreement with BLT Tool predictions.

Risk Assessments by Female Victims of Intimate Partner Violence: Predictors of Risk Perceptions and Comparison to an Actuarial Measure

ERIC Educational Resources Information Center

Connor-Smith, Jennifer K.; Henning, Kris; Moore, Stephanie; Holdford, Robert

2011-01-01

Recent studies support the validity of both structured risk assessment tools and victim perceptions as predictors of risk for repeat intimate partner violence (IPV). Combining structured risk assessments and victim risk assessments leads to better predictions of repeat violence than either alone, suggesting that the two forms of assessment provide…

General Platform for Systematic Quantitative Evaluation of Small-Molecule Permeability in Bacteria

PubMed Central

2015-01-01

The chemical features that impact small-molecule permeability across bacterial membranes are poorly understood, and the resulting lack of tools to predict permeability presents a major obstacle to the discovery and development of novel antibiotics. Antibacterials are known to have vastly different structural and physicochemical properties compared to nonantiinfective drugs, as illustrated herein by principal component analysis (PCA). To understand how these properties influence bacterial permeability, we have developed a systematic approach to evaluate the penetration of diverse compounds into bacteria with distinct cellular envelopes. Intracellular compound accumulation is quantitated using LC-MS/MS, then PCA and Pearson pairwise correlations are used to identify structural and physicochemical parameters that correlate with accumulation. An initial study using 10 sulfonyladenosines in Escherichia coli, Bacillus subtilis, and Mycobacterium smegmatis has identified nonobvious correlations between chemical structure and permeability that differ among the various bacteria. Effects of cotreatment with efflux pump inhibitors were also investigated. This sets the stage for use of this platform in larger prospective analyses of diverse chemotypes to identify global relationships between chemical structure and bacterial permeability that would enable the development of predictive tools to accelerate antibiotic drug discovery. PMID:25198656

Estimation of Hydrogen-Exchange Protection Factors from MD Simulation Based on Amide Hydrogen Bonding Analysis.

PubMed

Park, In-Hee; Venable, John D; Steckler, Caitlin; Cellitti, Susan E; Lesley, Scott A; Spraggon, Glen; Brock, Ansgar

2015-09-28

Hydrogen exchange (HX) studies have provided critical insight into our understanding of protein folding, structure, and dynamics. More recently, hydrogen exchange mass spectrometry (HX-MS) has become a widely applicable tool for HX studies. The interpretation of the wealth of data generated by HX-MS experiments as well as other HX methods would greatly benefit from the availability of exchange predictions derived from structures or models for comparison with experiment. Most reported computational HX modeling studies have employed solvent-accessible-surface-area based metrics in attempts to interpret HX data on the basis of structures or models. In this study, a computational HX-MS prediction method based on classification of the amide hydrogen bonding modes mimicking the local unfolding model is demonstrated. Analysis of the NH bonding configurations from molecular dynamics (MD) simulation snapshots is used to determine partitioning over bonded and nonbonded NH states and is directly mapped into a protection factor (PF) using a logistics growth function. Predicted PFs are then used for calculating deuteration values of peptides and compared with experimental data. Hydrogen exchange MS data for fatty acid synthase thioesterase (FAS-TE) collected for a range of pHs and temperatures was used for detailed evaluation of the approach. High correlation between prediction and experiment for observable fragment peptides is observed in the FAS-TE and additional benchmarking systems that included various apo/holo proteins for which literature data were available. In addition, it is shown that HX modeling can improve experimental resolution through decomposition of in-exchange curves into rate classes, which correlate with prediction from MD. Successful rate class decompositions provide further evidence that the presented approach captures the underlying physical processes correctly at the single residue level. This assessment is further strengthened in a comparison of residue resolved protection factor predictions for staphylococcal nuclease with NMR data, which was also used to compare prediction performance with other algorithms described in the literature. The demonstrated transferable and scalable MD based HX prediction approach adds significantly to the available tools for HX-MS data interpretation based on available structures and models.

Estimation of Hydrogen-Exchange Protection Factors from MD Simulation Based on Amide Hydrogen Bonding Analysis

PubMed Central

Park, In-Hee; Venable, John D.; Steckler, Caitlin; Cellitti, Susan E.; Lesley, Scott A.; Spraggon, Glen; Brock, Ansgar

2015-01-01

Hydrogen exchange (HX) studies have provided critical insight into our understanding of protein folding, structure and dynamics. More recently, Hydrogen Exchange Mass Spectrometry (HX-MS) has become a widely applicable tool for HX studies. The interpretation of the wealth of data generated by HX-MS experiments as well as other HX methods would greatly benefit from the availability of exchange predictions derived from structures or models for comparison with experiment. Most reported computational HX modeling studies have employed solvent-accessible-surface-area based metrics in attempts to interpret HX data on the basis of structures or models. In this study, a computational HX-MS prediction method based on classification of the amide hydrogen bonding modes mimicking the local unfolding model is demonstrated. Analysis of the NH bonding configurations from Molecular Dynamics (MD) simulation snapshots is used to determine partitioning over bonded and non-bonded NH states and is directly mapped into a protection factor (PF) using a logistics growth function. Predicted PFs are then used for calculating deuteration values of peptides and compared with experimental data. Hydrogen exchange MS data for Fatty acid synthase thioesterase (FAS-TE) collected for a range of pHs and temperatures was used for detailed evaluation of the approach. High correlation between prediction and experiment for observable fragment peptides is observed in the FAS-TE and additional benchmarking systems that included various apo/holo proteins for which literature data were available. In addition, it is shown that HX modeling can improve experimental resolution through decomposition of in-exchange curves into rate classes, which correlate with prediction from MD. Successful rate class decompositions provide further evidence that the presented approach captures the underlying physical processes correctly at the single residue level. This assessment is further strengthened in a comparison of residue resolved protection factor predictions for staphylococcal nuclease with NMR data, which was also used to compare prediction performance with other algorithms described in the literature. The demonstrated transferable and scalable MD based HX prediction approach adds significantly to the available tools for HX-MS data interpretation based on available structures and models. PMID:26241692

Influence of the axial rotation angle on tool mark striations.

PubMed

Garcia, Derrel Louis; Pieterman, René; Baiker, Martin

2017-10-01

A tool's axial rotation influences the geometric properties of a tool mark. The larger the axial rotation angle, the larger the compression of structural details like striations. This complicates comparing tool marks at different axial rotations. Using chisels, tool marks were made from 0° to 75° axial rotation and compared using an automated approach Baiker et al. [10]. In addition, a 3D topographic surface of a chisel was obtained to generate virtual tool marks and to test whether the axial rotation angle of a mark could be predicted. After examination of the tool mark and chisel data-sets it was observed that marks lose information with increasing rotation due to the change in relative distance between geometrical details on the tool and the disappearance of smaller details. The similarity and repeatability were high for comparisons between marks with no difference in axial rotation, but decreasing with increased rotation angle from 0° to 75°. With an increasing difference in the rotation angles, the tool marks had to be corrected to account for the different compression factors between them. For compression up to 7.5%, this was obtained automatically by the tool mark alignment method. For larger compression, manually re-sizing the marks to the uncompressed widths at 0° rotation before the alignment was found suitable for successfully comparing even large differences in axial rotation. The similarity and repeatability were decreasing however, with increasing degree of re-sizing. The quality was assessed by determining the similarity at different detail levels within a tool mark. With an axial rotation up to 75°, tool marks were found to reliably represent structural details down to 100μm. The similarity of structural details below 100μm was dependent on the angle, with the highest similarity at small rotation angles and the lowest similarity at large rotation angles. Filtering to remove the details below 100μm lead to consistently higher similarity between tool marks at all angles and allowed for a comparison of marks up to 75° axial rotation. Finally, generated virtual tool mark profiles with an axial rotation were compared to experimental tool marks. The similarity between virtual and experimental tool marks remained high up to 60° rotation after which it decreased due to the loss in quality in both marks. Predicting the rotation angle is possible under certain conditions up to 45° rotation with an accuracy of 2.667±0.577° rotation. Copyright © 2017 Elsevier B.V. All rights reserved.

SVM-Fold: a tool for discriminative multi-class protein fold and superfamily recognition

PubMed Central

Melvin, Iain; Ie, Eugene; Kuang, Rui; Weston, Jason; Stafford, William Noble; Leslie, Christina

2007-01-01

Background Predicting a protein's structural class from its amino acid sequence is a fundamental problem in computational biology. Much recent work has focused on developing new representations for protein sequences, called string kernels, for use with support vector machine (SVM) classifiers. However, while some of these approaches exhibit state-of-the-art performance at the binary protein classification problem, i.e. discriminating between a particular protein class and all other classes, few of these studies have addressed the real problem of multi-class superfamily or fold recognition. Moreover, there are only limited software tools and systems for SVM-based protein classification available to the bioinformatics community. Results We present a new multi-class SVM-based protein fold and superfamily recognition system and web server called SVM-Fold, which can be found at . Our system uses an efficient implementation of a state-of-the-art string kernel for sequence profiles, called the profile kernel, where the underlying feature representation is a histogram of inexact matching k-mer frequencies. We also employ a novel machine learning approach to solve the difficult multi-class problem of classifying a sequence of amino acids into one of many known protein structural classes. Binary one-vs-the-rest SVM classifiers that are trained to recognize individual structural classes yield prediction scores that are not comparable, so that standard "one-vs-all" classification fails to perform well. Moreover, SVMs for classes at different levels of the protein structural hierarchy may make useful predictions, but one-vs-all does not try to combine these multiple predictions. To deal with these problems, our method learns relative weights between one-vs-the-rest classifiers and encodes information about the protein structural hierarchy for multi-class prediction. In large-scale benchmark results based on the SCOP database, our code weighting approach significantly improves on the standard one-vs-all method for both the superfamily and fold prediction in the remote homology setting and on the fold recognition problem. Moreover, our code weight learning algorithm strongly outperforms nearest-neighbor methods based on PSI-BLAST in terms of prediction accuracy on every structure classification problem we consider. Conclusion By combining state-of-the-art SVM kernel methods with a novel multi-class algorithm, the SVM-Fold system delivers efficient and accurate protein fold and superfamily recognition. PMID:17570145

Advanced composite vertical stabilizer for DC-10 transport aircraft

NASA Technical Reports Server (NTRS)

Stephens, C. O.

1979-01-01

Structural design, tooling, fabrication, and test activities are reported for a program to develop an advanced composite vertical stabilizer (CVS) for the DC 10 Commercial Transport Aircraft. Structural design details are described and the status of structural and weight analyses are reported. A structural weight reduction of 21.7% is currently predicted. Test results are discussed for sine wave stiffened shear webs containing representative of the CVS spar webs and for lightning current transfer and tests on a panel representative of the CVS skins.

Automated eukaryotic gene structure annotation using EVidenceModeler and the Program to Assemble Spliced Alignments

PubMed Central

Haas, Brian J; Salzberg, Steven L; Zhu, Wei; Pertea, Mihaela; Allen, Jonathan E; Orvis, Joshua; White, Owen; Buell, C Robin; Wortman, Jennifer R

2008-01-01

EVidenceModeler (EVM) is presented as an automated eukaryotic gene structure annotation tool that reports eukaryotic gene structures as a weighted consensus of all available evidence. EVM, when combined with the Program to Assemble Spliced Alignments (PASA), yields a comprehensive, configurable annotation system that predicts protein-coding genes and alternatively spliced isoforms. Our experiments on both rice and human genome sequences demonstrate that EVM produces automated gene structure annotation approaching the quality of manual curation. PMID:18190707

Light aircraft crash safety program

NASA Technical Reports Server (NTRS)

Thomson, R. G.; Hayduk, R. J.

1974-01-01

NASA is embarked upon research and development tasks aimed at providing the general aviation industry with a reliable crashworthy airframe design technology. The goals of the NASA program are: reliable analytical techniques for predicting the nonlinear behavior of structures; significant design improvements of airframes; and simulated full-scale crash test data. The analytical tools will include both simplified procedures for estimating energy absorption characteristics and more complex computer programs for analysis of general airframe structures under crash loading conditions. The analytical techniques being developed both in-house and under contract are described, and a comparison of some analytical predictions with experimental results is shown.

Computational Prediction of miRNA Genes from Small RNA Sequencing Data

PubMed Central

Kang, Wenjing; Friedländer, Marc R.

2015-01-01

Next-generation sequencing now for the first time allows researchers to gage the depth and variation of entire transcriptomes. However, now as rare transcripts can be detected that are present in cells at single copies, more advanced computational tools are needed to accurately annotate and profile them. microRNAs (miRNAs) are 22 nucleotide small RNAs (sRNAs) that post-transcriptionally reduce the output of protein coding genes. They have established roles in numerous biological processes, including cancers and other diseases. During miRNA biogenesis, the sRNAs are sequentially cleaved from precursor molecules that have a characteristic hairpin RNA structure. The vast majority of new miRNA genes that are discovered are mined from small RNA sequencing (sRNA-seq), which can detect more than a billion RNAs in a single run. However, given that many of the detected RNAs are degradation products from all types of transcripts, the accurate identification of miRNAs remain a non-trivial computational problem. Here, we review the tools available to predict animal miRNAs from sRNA sequencing data. We present tools for generalist and specialist use cases, including prediction from massively pooled data or in species without reference genome. We also present wet-lab methods used to validate predicted miRNAs, and approaches to computationally benchmark prediction accuracy. For each tool, we reference validation experiments and benchmarking efforts. Last, we discuss the future of the field. PMID:25674563

Measurement and prediction of the thermomechanical response of shape memory alloy hybrid composite beams

NASA Astrophysics Data System (ADS)

Davis, Brian; Turner, Travis L.; Seelecke, Stefan

2005-05-01

Previous work at NASA Langley Research Center (LaRC) involved fabrication and testing of composite beams with embedded, pre-strained shape memory alloy (SMA) ribbons within the beam structures. That study also provided comparison of experimental results with numerical predictions from a research code making use of a new thermoelastic model for shape memory alloy hybrid composite (SMAHC) structures. The previous work showed qualitative validation of the numerical model. However, deficiencies in the experimental-numerical correlation were noted and hypotheses for the discrepancies were given for further investigation. The goal of this work is to refine the experimental measurement and numerical modeling approaches in order to better understand the discrepancies, improve the correlation between prediction and measurement, and provide rigorous quantitative validation of the numerical analysis/design tool. The experimental investigation is refined by a more thorough test procedure and incorporation of higher fidelity measurements such as infrared thermography and projection moire interferometry. The numerical results are produced by a recently commercialized version of the constitutive model as implemented in ABAQUS and are refined by incorporation of additional measured parameters such as geometric imperfection. Thermal buckling, post-buckling, and random responses to thermal and inertial (base acceleration) loads are studied. The results demonstrate the effectiveness of SMAHC structures in controlling static and dynamic responses by adaptive stiffening. Excellent agreement is achieved between the predicted and measured results of the static and dynamic thermomechanical response, thereby providing quantitative validation of the numerical tool.

Measurement and Prediction of the Thermomechanical Response of Shape Memory Alloy Hybrid Composite Beams

NASA Technical Reports Server (NTRS)

Davis, Brian; Turner, Travis L.; Seelecke, Stefan

2005-01-01

Previous work at NASA Langley Research Center (LaRC) involved fabrication and testing of composite beams with embedded, pre-strained shape memory alloy (SMA) ribbons within the beam structures. That study also provided comparison of experimental results with numerical predictions from a research code making use of a new thermoelastic model for shape memory alloy hybrid composite (SMAHC) structures. The previous work showed qualitative validation of the numerical model. However, deficiencies in the experimental-numerical correlation were noted and hypotheses for the discrepancies were given for further investigation. The goal of this work is to refine the experimental measurement and numerical modeling approaches in order to better understand the discrepancies, improve the correlation between prediction and measurement, and provide rigorous quantitative validation of the numerical analysis/design tool. The experimental investigation is refined by a more thorough test procedure and incorporation of higher fidelity measurements such as infrared thermography and projection moire interferometry. The numerical results are produced by a recently commercialized version of the constitutive model as implemented in ABAQUS and are refined by incorporation of additional measured parameters such as geometric imperfection. Thermal buckling, post-buckling, and random responses to thermal and inertial (base acceleration) loads are studied. The results demonstrate the effectiveness of SMAHC structures in controlling static and dynamic responses by adaptive stiffening. Excellent agreement is achieved between the predicted and measured results of the static and dynamic thermomechanical response, thereby providing quantitative validation of the numerical tool.

Protein loop modeling using a new hybrid energy function and its application to modeling in inaccurate structural environments.

PubMed

Park, Hahnbeom; Lee, Gyu Rie; Heo, Lim; Seok, Chaok

2014-01-01

Protein loop modeling is a tool for predicting protein local structures of particular interest, providing opportunities for applications involving protein structure prediction and de novo protein design. Until recently, the majority of loop modeling methods have been developed and tested by reconstructing loops in frameworks of experimentally resolved structures. In many practical applications, however, the protein loops to be modeled are located in inaccurate structural environments. These include loops in model structures, low-resolution experimental structures, or experimental structures of different functional forms. Accordingly, discrepancies in the accuracy of the structural environment assumed in development of the method and that in practical applications present additional challenges to modern loop modeling methods. This study demonstrates a new strategy for employing a hybrid energy function combining physics-based and knowledge-based components to help tackle this challenge. The hybrid energy function is designed to combine the strengths of each energy component, simultaneously maintaining accurate loop structure prediction in a high-resolution framework structure and tolerating minor environmental errors in low-resolution structures. A loop modeling method based on global optimization of this new energy function is tested on loop targets situated in different levels of environmental errors, ranging from experimental structures to structures perturbed in backbone as well as side chains and template-based model structures. The new method performs comparably to force field-based approaches in loop reconstruction in crystal structures and better in loop prediction in inaccurate framework structures. This result suggests that higher-accuracy predictions would be possible for a broader range of applications. The web server for this method is available at http://galaxy.seoklab.org/loop with the PS2 option for the scoring function.

DWARF – a data warehouse system for analyzing protein families

PubMed Central

Fischer, Markus; Thai, Quan K; Grieb, Melanie; Pleiss, Jürgen

2006-01-01

Background The emerging field of integrative bioinformatics provides the tools to organize and systematically analyze vast amounts of highly diverse biological data and thus allows to gain a novel understanding of complex biological systems. The data warehouse DWARF applies integrative bioinformatics approaches to the analysis of large protein families. Description The data warehouse system DWARF integrates data on sequence, structure, and functional annotation for protein fold families. The underlying relational data model consists of three major sections representing entities related to the protein (biochemical function, source organism, classification to homologous families and superfamilies), the protein sequence (position-specific annotation, mutant information), and the protein structure (secondary structure information, superimposed tertiary structure). Tools for extracting, transforming and loading data from public available resources (ExPDB, GenBank, DSSP) are provided to populate the database. The data can be accessed by an interface for searching and browsing, and by analysis tools that operate on annotation, sequence, or structure. We applied DWARF to the family of α/β-hydrolases to host the Lipase Engineering database. Release 2.3 contains 6138 sequences and 167 experimentally determined protein structures, which are assigned to 37 superfamilies 103 homologous families. Conclusion DWARF has been designed for constructing databases of large structurally related protein families and for evaluating their sequence-structure-function relationships by a systematic analysis of sequence, structure and functional annotation. It has been applied to predict biochemical properties from sequence, and serves as a valuable tool for protein engineering. PMID:17094801

Shape: A 3D Modeling Tool for Astrophysics.

PubMed

Steffen, Wolfgang; Koning, Nicholas; Wenger, Stephan; Morisset, Christophe; Magnor, Marcus

2011-04-01

We present a flexible interactive 3D morpho-kinematical modeling application for astrophysics. Compared to other systems, our application reduces the restrictions on the physical assumptions, data type, and amount that is required for a reconstruction of an object's morphology. It is one of the first publicly available tools to apply interactive graphics to astrophysical modeling. The tool allows astrophysicists to provide a priori knowledge about the object by interactively defining 3D structural elements. By direct comparison of model prediction with observational data, model parameters can then be automatically optimized to fit the observation. The tool has already been successfully used in a number of astrophysical research projects.

High-fidelity modeling and impact footprint prediction for vehicle breakup analysis

NASA Astrophysics Data System (ADS)

Ling, Lisa

For decades, vehicle breakup analysis had been performed for space missions that used nuclear heater or power units in order to assess aerospace nuclear safety for potential launch failures leading to inadvertent atmospheric reentry. Such pre-launch risk analysis is imperative to assess possible environmental impacts, obtain launch approval, and for launch contingency planning. In order to accurately perform a vehicle breakup analysis, the analysis tool should include a trajectory propagation algorithm coupled with thermal and structural analyses and influences. Since such a software tool was not available commercially or in the public domain, a basic analysis tool was developed by Dr. Angus McRonald prior to this study. This legacy software consisted of low-fidelity modeling and had the capability to predict vehicle breakup, but did not predict the surface impact point of the nuclear component. Thus the main thrust of this study was to develop and verify the additional dynamics modeling and capabilities for the analysis tool with the objectives to (1) have the capability to predict impact point and footprint, (2) increase the fidelity in the prediction of vehicle breakup, and (3) reduce the effort and time required to complete an analysis. The new functions developed for predicting the impact point and footprint included 3-degrees-of-freedom trajectory propagation, the generation of non-arbitrary entry conditions, sensitivity analysis, and the calculation of impact footprint. The functions to increase the fidelity in the prediction of vehicle breakup included a panel code to calculate the hypersonic aerodynamic coefficients for an arbitrary-shaped body and the modeling of local winds. The function to reduce the effort and time required to complete an analysis included the calculation of node failure criteria. The derivation and development of these new functions are presented in this dissertation, and examples are given to demonstrate the new capabilities and the improvements made, with comparisons between the results obtained from the upgraded analysis tool and the legacy software wherever applicable.

Machine learning classification with confidence: application of transductive conformal predictors to MRI-based diagnostic and prognostic markers in depression.

PubMed

Nouretdinov, Ilia; Costafreda, Sergi G; Gammerman, Alexander; Chervonenkis, Alexey; Vovk, Vladimir; Vapnik, Vladimir; Fu, Cynthia H Y

2011-05-15

There is rapidly accumulating evidence that the application of machine learning classification to neuroimaging measurements may be valuable for the development of diagnostic and prognostic prediction tools in psychiatry. However, current methods do not produce a measure of the reliability of the predictions. Knowing the risk of the error associated with a given prediction is essential for the development of neuroimaging-based clinical tools. We propose a general probabilistic classification method to produce measures of confidence for magnetic resonance imaging (MRI) data. We describe the application of transductive conformal predictor (TCP) to MRI images. TCP generates the most likely prediction and a valid measure of confidence, as well as the set of all possible predictions for a given confidence level. We present the theoretical motivation for TCP, and we have applied TCP to structural and functional MRI data in patients and healthy controls to investigate diagnostic and prognostic prediction in depression. We verify that TCP predictions are as accurate as those obtained with more standard machine learning methods, such as support vector machine, while providing the additional benefit of a valid measure of confidence for each prediction. Copyright © 2010 Elsevier Inc. All rights reserved.

NASA GRC Fatigue Crack Initiation Life Prediction Models

NASA Technical Reports Server (NTRS)

Arya, Vinod K.; Halford, Gary R.

2002-01-01

Metal fatigue has plagued structural components for centuries, and it remains a critical durability issue in today's aerospace hardware. This is true despite vastly improved and advanced materials, increased mechanistic understanding, and development of accurate structural analysis and advanced fatigue life prediction tools. Each advance is quickly taken advantage of to produce safer, more reliable, more cost effective, and better performing products. In other words, as the envelope is expanded, components are then designed to operate just as close to the newly expanded envelope as they were to the initial one. The problem is perennial. The economic importance of addressing structural durability issues early in the design process is emphasized. Tradeoffs with performance, cost, and legislated restrictions are pointed out. Several aspects of structural durability of advanced systems, advanced materials and advanced fatigue life prediction methods are presented. Specific items include the basic elements of durability analysis, conventional designs, barriers to be overcome for advanced systems, high-temperature life prediction for both creep-fatigue and thermomechanical fatigue, mean stress effects, multiaxial stress-strain states, and cumulative fatigue damage accumulation assessment.

A Primer In Advanced Fatigue Life Prediction Methods

NASA Technical Reports Server (NTRS)

Halford, Gary R.

2000-01-01

Metal fatigue has plagued structural components for centuries, and it remains a critical durability issue in today's aerospace hardware. This is true despite vastly improved and advanced materials, increased mechanistic understanding, and development of accurate structural analysis and advanced fatigue life prediction tools. Each advance is quickly taken advantage of to produce safer, more reliable more cost effective, and better performing products. In other words, as the envelop is expanded, components are then designed to operate just as close to the newly expanded envelop as they were to the initial one. The problem is perennial. The economic importance of addressing structural durability issues early in the design process is emphasized. Tradeoffs with performance, cost, and legislated restrictions are pointed out. Several aspects of structural durability of advanced systems, advanced materials and advanced fatigue life prediction methods are presented. Specific items include the basic elements of durability analysis, conventional designs, barriers to be overcome for advanced systems, high-temperature life prediction for both creep-fatigue and thermomechanical fatigue, mean stress effects, multiaxial stress-strain states, and cumulative fatigue damage accumulation assessment.

NASA GRC Fatigue Crack Initiation Life Prediction Models

NASA Astrophysics Data System (ADS)

Arya, Vinod K.; Halford, Gary R.

2002-10-01

Metal fatigue has plagued structural components for centuries, and it remains a critical durability issue in today's aerospace hardware. This is true despite vastly improved and advanced materials, increased mechanistic understanding, and development of accurate structural analysis and advanced fatigue life prediction tools. Each advance is quickly taken advantage of to produce safer, more reliable, more cost effective, and better performing products. In other words, as the envelope is expanded, components are then designed to operate just as close to the newly expanded envelope as they were to the initial one. The problem is perennial. The economic importance of addressing structural durability issues early in the design process is emphasized. Tradeoffs with performance, cost, and legislated restrictions are pointed out. Several aspects of structural durability of advanced systems, advanced materials and advanced fatigue life prediction methods are presented. Specific items include the basic elements of durability analysis, conventional designs, barriers to be overcome for advanced systems, high-temperature life prediction for both creep-fatigue and thermomechanical fatigue, mean stress effects, multiaxial stress-strain states, and cumulative fatigue damage accumulation assessment.

Predicting carcinogenicity of diverse chemicals using probabilistic neural network modeling approaches

DOE Office of Scientific and Technical Information (OSTI.GOV)

Singh, Kunwar P., E-mail: kpsingh_52@yahoo.com; Environmental Chemistry Division, CSIR-Indian Institute of Toxicology Research, Post Box 80, Mahatma Gandhi Marg, Lucknow 226 001; Gupta, Shikha

Robust global models capable of discriminating positive and non-positive carcinogens; and predicting carcinogenic potency of chemicals in rodents were developed. The dataset of 834 structurally diverse chemicals extracted from Carcinogenic Potency Database (CPDB) was used which contained 466 positive and 368 non-positive carcinogens. Twelve non-quantum mechanical molecular descriptors were derived. Structural diversity of the chemicals and nonlinearity in the data were evaluated using Tanimoto similarity index and Brock–Dechert–Scheinkman statistics. Probabilistic neural network (PNN) and generalized regression neural network (GRNN) models were constructed for classification and function optimization problems using the carcinogenicity end point in rat. Validation of the models wasmore » performed using the internal and external procedures employing a wide series of statistical checks. PNN constructed using five descriptors rendered classification accuracy of 92.09% in complete rat data. The PNN model rendered classification accuracies of 91.77%, 80.70% and 92.08% in mouse, hamster and pesticide data, respectively. The GRNN constructed with nine descriptors yielded correlation coefficient of 0.896 between the measured and predicted carcinogenic potency with mean squared error (MSE) of 0.44 in complete rat data. The rat carcinogenicity model (GRNN) applied to the mouse and hamster data yielded correlation coefficient and MSE of 0.758, 0.71 and 0.760, 0.46, respectively. The results suggest for wide applicability of the inter-species models in predicting carcinogenic potency of chemicals. Both the PNN and GRNN (inter-species) models constructed here can be useful tools in predicting the carcinogenicity of new chemicals for regulatory purposes. - Graphical abstract: Figure (a) shows classification accuracies (positive and non-positive carcinogens) in rat, mouse, hamster, and pesticide data yielded by optimal PNN model. Figure (b) shows generalization and predictive abilities of the interspecies GRNN model to predict the carcinogenic potency of diverse chemicals. - Highlights: • Global robust models constructed for carcinogenicity prediction of diverse chemicals. • Tanimoto/BDS test revealed structural diversity of chemicals and nonlinearity in data. • PNN/GRNN successfully predicted carcinogenicity/carcinogenic potency of chemicals. • Developed interspecies PNN/GRNN models for carcinogenicity prediction. • Proposed models can be used as tool to predict carcinogenicity of new chemicals.« less

Modulation/demodulation techniques for satellite communications. Part 2: Advanced techniques. The linear channel

NASA Technical Reports Server (NTRS)

Omura, J. K.; Simon, M. K.

1982-01-01

A theory is presented for deducing and predicting the performance of transmitter/receivers for bandwidth efficient modulations suitable for use on the linear satellite channel. The underlying principle used is the development of receiver structures based on the maximum-likelihood decision rule. The application of the performance prediction tools, e.g., channel cutoff rate and bit error probability transfer function bounds to these modulation/demodulation techniques.

Evaluation of Load Analysis Methods for NASAs GIII Adaptive Compliant Trailing Edge Project

NASA Technical Reports Server (NTRS)

Cruz, Josue; Miller, Eric J.

2016-01-01

The Air Force Research Laboratory (AFRL), NASA Armstrong Flight Research Center (AFRC), and FlexSys Inc. (Ann Arbor, Michigan) have collaborated to flight test the Adaptive Compliant Trailing Edge (ACTE) flaps. These flaps were installed on a Gulfstream Aerospace Corporation (GAC) GIII aircraft and tested at AFRC at various deflection angles over a range of flight conditions. External aerodynamic and inertial load analyses were conducted with the intention to ensure that the change in wing loads due to the deployed ACTE flap did not overload the existing baseline GIII wing box structure. The objective of this paper was to substantiate the analysis tools used for predicting wing loads at AFRC. Computational fluid dynamics (CFD) models and distributed mass inertial models were developed for predicting the loads on the wing. The analysis tools included TRANAIR (full potential) and CMARC (panel) models. Aerodynamic pressure data from the analysis codes were validated against static pressure port data collected in-flight. Combined results from the CFD predictions and the inertial load analysis were used to predict the normal force, bending moment, and torque loads on the wing. Wing loads obtained from calibrated strain gages installed on the wing were used for substantiation of the load prediction tools. The load predictions exhibited good agreement compared to the flight load results obtained from calibrated strain gage measurements.

Assessing therapeutic relevance of biologically interesting, ampholytic substances based on their physicochemical and spectral characteristics with chemometric tools

NASA Astrophysics Data System (ADS)

Judycka, U.; Jagiello, K.; Bober, L.; Błażejowski, J.; Puzyn, T.

2018-06-01

Chemometric tools were applied to investigate the biological behaviour of ampholytic substances in relation to their physicochemical and spectral properties. Results of the Principal Component Analysis suggest that size of molecules and their electronic and spectral characteristics are the key properties required to predict therapeutic relevance of the compounds examined. These properties were used for developing the structure-activity classification model. The classification model allows assessing the therapeutic behaviour of ampholytic substances on the basis of solely values of descriptors that can be obtained computationally. Thus, the prediction is possible without necessity of carrying out time-consuming and expensive laboratory tests, which is its main advantage.

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.

FINDSITE-metal: Integrating evolutionary information and machine learning for structure-based metal binding site prediction at the proteome level

PubMed Central

Brylinski, Michal; Skolnick, Jeffrey

2010-01-01

The rapid accumulation of gene sequences, many of which are hypothetical proteins with unknown function, has stimulated the development of accurate computational tools for protein function prediction with evolution/structure-based approaches showing considerable promise. In this paper, we present FINDSITE-metal, a new threading-based method designed specifically to detect metal binding sites in modeled protein structures. Comprehensive benchmarks using different quality protein structures show that weakly homologous protein models provide sufficient structural information for quite accurate annotation by FINDSITE-metal. Combining structure/evolutionary information with machine learning results in highly accurate metal binding annotations; for protein models constructed by TASSER, whose average Cα RMSD from the native structure is 8.9 Å, 59.5% (71.9%) of the best of top five predicted metal locations are within 4 Å (8 Å) from a bound metal in the crystal structure. For most of the targets, multiple metal binding sites are detected with the best predicted binding site at rank 1 and within the top 2 ranks in 65.6% and 83.1% of the cases, respectively. Furthermore, for iron, copper, zinc, calcium and magnesium ions, the binding metal can be predicted with high, typically 70-90%, accuracy. FINDSITE-metal also provides a set of confidence indexes that help assess the reliability of predictions. Finally, we describe the proteome-wide application of FINDSITE-metal that quantifies the metal binding complement of the human proteome. FINDSITE-metal is freely available to the academic community at http://cssb.biology.gatech.edu/findsite-metal/. PMID:21287609

PDB@: an offline toolkit for exploration and analysis of PDB files.

PubMed

Mani, Udayakumar; Ravisankar, Sadhana; Ramakrishnan, Sai Mukund

2013-12-01

Protein Data Bank (PDB) is a freely accessible archive of the 3-D structural data of biological molecules. Structure based studies offers a unique vantage point in inferring the properties of a protein molecule from structural data. This is too big a task to be done manually. Moreover, there is no single tool, software or server that comprehensively analyses all structure-based properties. The objective of the present work is to develop an offline computational toolkit, PDB@ containing in-built algorithms that help categorizing the structural properties of a protein molecule. The user has the facility to view and edit the PDB file to his need. Some features of the present work are unique in itself and others are an improvement over existing tools. Also, the representation of protein properties in both graphical and textual formats helps in predicting all the necessary details of a protein molecule on a single platform.

The designer of the 90's: A live demonstration

NASA Technical Reports Server (NTRS)

Green, Tommy L.; Jordan, Basil M., Jr.; Oglesby, Timothy L.

1989-01-01

A survey of design tools to be used by the aircraft designer is given. Structural reliability, maintainability, cost and predictability, and acoustics expert systems are discussed, as well as scheduling, drawing, engineering systems, sizing functions, and standard parts and materials data bases.

Atomic Oxygen Erosion Yield Prediction for Spacecraft Polymers in Low Earth Orbit

NASA Technical Reports Server (NTRS)

Banks, Bruce A.; Backus, Jane A.; Manno, Michael V.; Waters, Deborah L.; Cameron, Kevin C.; deGroh, Kim K.

2009-01-01

The ability to predict the atomic oxygen erosion yield of polymers based on their chemistry and physical properties has been only partially successful because of a lack of reliable low Earth orbit (LEO) erosion yield data. Unfortunately, many of the early experiments did not utilize dehydrated mass loss measurements for erosion yield determination, and the resulting mass loss due to atomic oxygen exposure may have been compromised because samples were often not in consistent states of dehydration during the pre-flight and post-flight mass measurements. This is a particular problem for short duration mission exposures or low erosion yield materials. However, as a result of the retrieval of the Polymer Erosion and Contamination Experiment (PEACE) flown as part of the Materials International Space Station Experiment 2 (MISSE 2), the erosion yields of 38 polymers and pyrolytic graphite were accurately measured. The experiment was exposed to the LEO environment for 3.95 years from August 16, 2001 to July 30, 2005 and was successfully retrieved during a space walk on July 30, 2005 during Discovery s STS-114 Return to Flight mission. The 40 different materials tested (including Kapton H fluence witness samples) were selected specifically to represent a variety of polymers used in space as well as a wide variety of polymer chemical structures. The MISSE 2 PEACE Polymers experiment used carefully dehydrated mass measurements, as well as accurate density measurements to obtain accurate erosion yield data for high-fluence (8.43 1021 atoms/sq cm). The resulting data was used to develop an erosion yield predictive tool with a correlation coefficient of 0.895 and uncertainty of +/-6.3 10(exp -25)cu cm/atom. The predictive tool utilizes the chemical structures and physical properties of polymers to predict in-space atomic oxygen erosion yields. A predictive tool concept (September 2009 version) is presented which represents an improvement over an earlier (December 2008) version.

Damage level prediction of non-reshaped berm breakwater using ANN, SVM and ANFIS models

NASA Astrophysics Data System (ADS)

Mandal, Sukomal; Rao, Subba; N., Harish; Lokesha

2012-06-01

The damage analysis of coastal structure is very important as it involves many design parameters to be considered for the better and safe design of structure. In the present study experimental data for non-reshaped berm breakwater are collected from Marine Structures Laboratory, Department of Applied Mechanics and Hydraulics, NITK, Surathkal, India. Soft computing techniques like Artificial Neural Network (ANN), Support Vector Machine (SVM) and Adaptive Neuro Fuzzy Inference system (ANFIS) models are constructed using experimental data sets to predict the damage level of non-reshaped berm breakwater. The experimental data are used to train ANN, SVM and ANFIS models and results are determined in terms of statistical measures like mean square error, root mean square error, correla-tion coefficient and scatter index. The result shows that soft computing techniques i.e., ANN, SVM and ANFIS can be efficient tools in predicting damage levels of non reshaped berm breakwater.

In Silico Prediction of Chemical Toxicity for Drug Design Using Machine Learning Methods and Structural Alerts

NASA Astrophysics Data System (ADS)

Yang, Hongbin; Sun, Lixia; Li, Weihua; Liu, Guixia; Tang, Yun

2018-02-01

For a drug, safety is always the most important issue, including a variety of toxicities and adverse drug effects, which should be evaluated in preclinical and clinical trial phases. This review article at first simply introduced the computational methods used in prediction of chemical toxicity for drug design, including machine learning methods and structural alerts. Machine learning methods have been widely applied in qualitative classification and quantitative regression studies, while structural alerts can be regarded as a complementary tool for lead optimization. The emphasis of this article was put on the recent progress of predictive models built for various toxicities. Available databases and web servers were also provided. Though the methods and models are very helpful for drug design, there are still some challenges and limitations to be improved for drug safety assessment in the future.

Identification of Extracellular Segments by Mass Spectrometry Improves Topology Prediction of Transmembrane Proteins.

PubMed

Langó, Tamás; Róna, Gergely; Hunyadi-Gulyás, Éva; Turiák, Lilla; Varga, Julia; Dobson, László; Várady, György; Drahos, László; Vértessy, Beáta G; Medzihradszky, Katalin F; Szakács, Gergely; Tusnády, Gábor E

2017-02-13

Transmembrane proteins play crucial role in signaling, ion transport, nutrient uptake, as well as in maintaining the dynamic equilibrium between the internal and external environment of cells. Despite their important biological functions and abundance, less than 2% of all determined structures are transmembrane proteins. Given the persisting technical difficulties associated with high resolution structure determination of transmembrane proteins, additional methods, including computational and experimental techniques remain vital in promoting our understanding of their topologies, 3D structures, functions and interactions. Here we report a method for the high-throughput determination of extracellular segments of transmembrane proteins based on the identification of surface labeled and biotin captured peptide fragments by LC/MS/MS. We show that reliable identification of extracellular protein segments increases the accuracy and reliability of existing topology prediction algorithms. Using the experimental topology data as constraints, our improved prediction tool provides accurate and reliable topology models for hundreds of human transmembrane proteins.

In Silico Prediction of Chemical Toxicity for Drug Design Using Machine Learning Methods and Structural Alerts

PubMed Central

Yang, Hongbin; Sun, Lixia; Li, Weihua; Liu, Guixia; Tang, Yun

2018-01-01

During drug development, safety is always the most important issue, including a variety of toxicities and adverse drug effects, which should be evaluated in preclinical and clinical trial phases. This review article at first simply introduced the computational methods used in prediction of chemical toxicity for drug design, including machine learning methods and structural alerts. Machine learning methods have been widely applied in qualitative classification and quantitative regression studies, while structural alerts can be regarded as a complementary tool for lead optimization. The emphasis of this article was put on the recent progress of predictive models built for various toxicities. Available databases and web servers were also provided. Though the methods and models are very helpful for drug design, there are still some challenges and limitations to be improved for drug safety assessment in the future. PMID:29515993

Numerical analysis of the performance of rock weirs: Effects of structure configuration on local hydraulics

USGS Publications Warehouse

Holmquist-Johnson, C. L.

2009-01-01

River spanning rock structures are being constructed for water delivery as well as to enable fish passage at barriers and provide or improve the aquatic habitat for endangered fish species. Current design methods are based upon anecdotal information applicable to a narrow range of channel conditions. The complex flow patterns and performance of rock weirs is not well understood. Without accurate understanding of their hydraulics, designers cannot address the failure mechanisms of these structures. Flow characteristics such as jets, near bed velocities, recirculation, eddies, and plunging flow govern scour pool development. These detailed flow patterns can be replicated using a 3D numerical model. Numerical studies inexpensively simulate a large number of cases resulting in an increased range of applicability in order to develop design tools and predictive capability for analysis and design. The analysis and results of the numerical modeling, laboratory modeling, and field data provide a process-based method for understanding how structure geometry affects flow characteristics, scour development, fish passage, water delivery, and overall structure stability. Results of the numerical modeling allow designers to utilize results of the analysis to determine the appropriate geometry for generating desirable flow parameters. The end product of this research will develop tools and guidelines for more robust structure design or retrofits based upon predictable engineering and hydraulic performance criteria. ?? 2009 ASCE.

De Novo Chromosome Structure Prediction

NASA Astrophysics Data System (ADS)

di Pierro, Michele; Cheng, Ryan R.; Lieberman-Aiden, Erez; Wolynes, Peter G.; Onuchic, Jose'n.

Chromatin consists of DNA and hundreds of proteins that interact with the genetic material. In vivo, chromatin folds into nonrandom structures. The physical mechanism leading to these characteristic conformations, however, remains poorly understood. We recently introduced MiChroM, a model that generates chromosome conformations by using the idea that chromatin can be subdivided into types based on its biochemical interactions. Here we extend and complete our previous finding by showing that structural chromatin types can be inferred from ChIP-Seq data. Chromatin types, which are distinct from DNA sequence, are partially epigenetically controlled and change during cell differentiation, thus constituting a link between epigenetics, chromosomal organization, and cell development. We show that, for GM12878 lymphoblastoid cells we are able to predict accurate chromosome structures with the only input of genomic data. The degree of accuracy achieved by our prediction supports the viability of the proposed physical mechanism of chromatin folding and makes the computational model a powerful tool for future investigations.

FireProt: web server for automated design of thermostable proteins

PubMed Central

Musil, Milos; Stourac, Jan; Brezovsky, Jan; Prokop, Zbynek; Zendulka, Jaroslav; Martinek, Tomas

2017-01-01

Abstract There is a continuous interest in increasing proteins stability to enhance their usability in numerous biomedical and biotechnological applications. A number of in silico tools for the prediction of the effect of mutations on protein stability have been developed recently. However, only single-point mutations with a small effect on protein stability are typically predicted with the existing tools and have to be followed by laborious protein expression, purification, and characterization. Here, we present FireProt, a web server for the automated design of multiple-point thermostable mutant proteins that combines structural and evolutionary information in its calculation core. FireProt utilizes sixteen tools and three protein engineering strategies for making reliable protein designs. The server is complemented with interactive, easy-to-use interface that allows users to directly analyze and optionally modify designed thermostable mutants. FireProt is freely available at http://loschmidt.chemi.muni.cz/fireprot. PMID:28449074

A novel adjuvant to the resident selection process: the hartman value profile.

PubMed

Cone, Jeffrey D; Byrum, C Stephen; Payne, Wyatt G; Smith, David J

2012-01-01

The goal of resident selection is twofold: (1) select candidates who will be successful residents and eventually successful practitioners and (2) avoid selecting candidates who will be unsuccessful residents and/or eventually unsuccessful practitioners. Traditional tools used to select residents have well-known limitations. The Hartman Value Profile (HVP) is a proven adjuvant tool to predicting future performance in candidates for advanced positions in the corporate setting. No literature exists to indicate use of the HVP for resident selection. The HVP evaluates the structure and the dynamics of an individual value system. Given the potential impact, we implemented its use beginning in 2007 as an adjuvant tool to the traditional selection process. Experience gained from incorporating the HVP into the residency selection process suggests that it may add objectivity and refinement in predicting resident performance. Further evaluation is warranted with longer follow-up times.

A Novel Adjuvant to the Resident Selection Process: the Hartman Value Profile

PubMed Central

Cone, Jeffrey D.; Byrum, C. Stephen; Payne, Wyatt G.; Smith, David J.

2012-01-01

Objectives: The goal of resident selection is twofold: (1) select candidates who will be successful residents and eventually successful practitioners and (2) avoid selecting candidates who will be unsuccessful residents and/or eventually unsuccessful practitioners. Traditional tools used to select residents have well-known limitations. The Hartman Value Profile (HVP) is a proven adjuvant tool to predicting future performance in candidates for advanced positions in the corporate setting. Methods: No literature exists to indicate use of the HVP for resident selection. Results: The HVP evaluates the structure and the dynamics of an individual value system. Given the potential impact, we implemented its use beginning in 2007 as an adjuvant tool to the traditional selection process. Conclusions: Experience gained from incorporating the HVP into the residency selection process suggests that it may add objectivity and refinement in predicting resident performance. Further evaluation is warranted with longer follow-up times. PMID:22720114

Spring-back simulation of unidirectional carbon/epoxy L- shaped laminate composites manufactured through autoclave processing

NASA Astrophysics Data System (ADS)

Nasir, M. N. M.; Mezeix, L.; Aminanda, Y.; Seman, M. A.; Rivai, A.; Ali, K. M.

2016-02-01

This paper presents an original method in predicting the spring-back for composite aircraft structures using non-linear Finite Element Analysis (FEA) and is an extension of the previous accompanying study on flat geometry samples. Firstly, unidirectional prepreg lay-up samples are fabricated on moulds with different corner angles (30°, 45° and 90°) and the effect on spring-back deformation are observed. Then, the FEA model that was developed in the previous study on flat samples is utilized. The model maintains the physical mechanisms of spring-back such as ply stretching and tool-part interface properties with the additional mechanism in the corner effect and geometrical changes in the tool, part and the tool-part interface components. The comparative study between the experimental data and FEA results show that the FEA model predicts adequately the spring-back deformation within the range of corner angle tested.

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

PubMed

von Grotthuss, Marcin; Plewczynski, Dariusz; Ginalski, Krzysztof; Rychlewski, Leszek; Shakhnovich, Eugene I

2006-02-06

The number of protein structures from structural genomics centers dramatically increases in the Protein Data Bank (PDB). Many of these structures are functionally unannotated because they have no sequence similarity to proteins of known function. However, it is possible to successfully infer function using only structural similarity. Here we present the PDB-UF database, a web-accessible collection of predictions of enzymatic properties using structure-function relationship. The assignments were conducted for three-dimensional protein structures of unknown function that come from structural genomics initiatives. We show that 4 hypothetical proteins (with PDB accession codes: 1VH0, 1NS5, 1O6D, and 1TO0), for which standard BLAST tools such as PSI-BLAST or RPS-BLAST failed to assign any function, are probably methyltransferase enzymes. We suggest that the structure-based prediction of an EC number should be conducted having the different similarity score cutoff for different protein folds. Moreover, performing the annotation using two different algorithms can reduce the rate of false positive assignments. We believe, that the presented web-based repository will help to decrease the number of protein structures that have functions marked as "unknown" in the PDB file. http://paradox.harvard.edu/PDB-UF and http://bioinfo.pl/PDB-UF.

The PathoYeastract database: an information system for the analysis of gene and genomic transcription regulation in pathogenic yeasts.

PubMed

Monteiro, Pedro Tiago; Pais, Pedro; Costa, Catarina; Manna, Sauvagya; Sá-Correia, Isabel; Teixeira, Miguel Cacho

2017-01-04

We present the PATHOgenic YEAst Search for Transcriptional Regulators And Consensus Tracking (PathoYeastract - http://pathoyeastract.org) database, a tool for the analysis and prediction of transcription regulatory associations at the gene and genomic levels in the pathogenic yeasts Candida albicans and C. glabrata Upon data retrieval from hundreds of publications, followed by curation, the database currently includes 28 000 unique documented regulatory associations between transcription factors (TF) and target genes and 107 DNA binding sites, considering 134 TFs in both species. Following the structure used for the YEASTRACT database, PathoYeastract makes available bioinformatics tools that enable the user to exploit the existing information to predict the TFs involved in the regulation of a gene or genome-wide transcriptional response, while ranking those TFs in order of their relative importance. Each search can be filtered based on the selection of specific environmental conditions, experimental evidence or positive/negative regulatory effect. Promoter analysis tools and interactive visualization tools for the representation of TF regulatory networks are also provided. The PathoYeastract database further provides simple tools for the prediction of gene and genomic regulation based on orthologous regulatory associations described for other yeast species, a comparative genomics setup for the study of cross-species evolution of regulatory networks. © The Author(s) 2016. Published by Oxford University Press on behalf of Nucleic Acids Research.

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.

Prediction Of pKa From Chemical Structure Using Free And Open-Source Tools

EPA Science Inventory

The ionization state of a chemical, reflected in pKa values, affects lipophilicity, solubility, protein binding and the ability of a chemical to cross the plasma membrane. These properties govern the pharmacokinetic parameters such as absorption, distribution, metabolism, excreti...

CFD Validation with Experiment and Verification with Physics of a Propellant Damping Device

NASA Technical Reports Server (NTRS)

Yang, H. Q.; Peugeot, John

2011-01-01

This paper will document our effort in validating a coupled fluid-structure interaction CFD tool in predicting a damping device performance in the laboratory condition. Consistently good comparisons of "blind" CFD predictions against experimental data under various operation conditions, design parameters, and cryogenic environment will be presented. The power of the coupled CFD-structures interaction code in explaining some unexpected phenomena of the device observed during the technology development will be illustrated. The evolution of the damper device design inside the LOX tank will be used to demonstrate the contribution of the tool in understanding, optimization and implementation of LOX damper in Ares I vehicle. It is due to the present validation effort, the LOX damper technology has matured to TRL 5. The present effort has also contributed to the transition of the technology from an early conceptual observation to the baseline design of thrust oscillation mitigation for the Ares I within a 10 month period.

Software analysis handbook: Software complexity analysis and software reliability estimation and prediction

NASA Technical Reports Server (NTRS)

Lee, Alice T.; Gunn, Todd; Pham, Tuan; Ricaldi, Ron

1994-01-01

This handbook documents the three software analysis processes the Space Station Software Analysis team uses to assess space station software, including their backgrounds, theories, tools, and analysis procedures. Potential applications of these analysis results are also presented. The first section describes how software complexity analysis provides quantitative information on code, such as code structure and risk areas, throughout the software life cycle. Software complexity analysis allows an analyst to understand the software structure, identify critical software components, assess risk areas within a software system, identify testing deficiencies, and recommend program improvements. Performing this type of analysis during the early design phases of software development can positively affect the process, and may prevent later, much larger, difficulties. The second section describes how software reliability estimation and prediction analysis, or software reliability, provides a quantitative means to measure the probability of failure-free operation of a computer program, and describes the two tools used by JSC to determine failure rates and design tradeoffs between reliability, costs, performance, and schedule.

Engineering bacterial translation initiation - Do we have all the tools we need?

PubMed

Vigar, Justin R J; Wieden, Hans-Joachim

2017-11-01

Reliable tools that allow precise and predictable control over gene expression are critical for the success of nearly all bioengineering applications. Translation initiation is the most regulated phase during protein biosynthesis, and is therefore a promising target for exerting control over gene expression. At the translational level, the copy number of a protein can be fine-tuned by altering the interaction between the translation initiation region of an mRNA and the ribosome. These interactions can be controlled by modulating the mRNA structure using numerous approaches, including small molecule ligands, RNAs, or RNA-binding proteins. A variety of naturally occurring regulatory elements have been repurposed, facilitating advances in synthetic gene regulation strategies. The pursuit of a comprehensive understanding of mechanisms governing translation initiation provides the framework for future engineering efforts. Here we outline state-of-the-art strategies used to predictably control translation initiation in bacteria. We also discuss current limitations in the field and future goals. Due to its function as the rate-determining step, initiation is the ideal point to exert effective translation regulation. Several engineering tools are currently available to rationally design the initiation characteristics of synthetic mRNAs. However, improvements are required to increase the predictability, effectiveness, and portability of these tools. Predictable and reliable control over translation initiation will allow greater predictability when designing, constructing, and testing genetic circuits. The ability to build more complex circuits predictably will advance synthetic biology and contribute to our fundamental understanding of the underlying principles of these processes. "This article is part of a Special Issue entitled "Biochemistry of Synthetic Biology - Recent Developments" Guest Editor: Dr. Ilka Heinemann and Dr. Patrick O'Donoghue. Copyright © 2017 Elsevier B.V. All rights reserved.

XenoSite server: a web-available site of metabolism prediction tool.

PubMed

Matlock, Matthew K; Hughes, Tyler B; Swamidass, S Joshua

2015-04-01

Cytochrome P450 enzymes (P450s) are metabolic enzymes that process the majority of FDA-approved, small-molecule drugs. Understanding how these enzymes modify molecule structure is key to the development of safe, effective drugs. XenoSite server is an online implementation of the XenoSite, a recently published computational model for P450 metabolism. XenoSite predicts which atomic sites of a molecule--sites of metabolism (SOMs)--are modified by P450s. XenoSite server accepts input in common chemical file formats including SDF and SMILES and provides tools for visualizing the likelihood that each atomic site is a site of metabolism for a variety of important P450s, as well as a flat file download of SOM predictions. XenoSite server is available at http://swami.wustl.edu/xenosite. © The Author 2014. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

POLYVIEW-MM: web-based platform for animation and analysis of molecular simulations

PubMed Central

Porollo, Aleksey; Meller, Jaroslaw

2010-01-01

Molecular simulations offer important mechanistic and functional clues in studies of proteins and other macromolecules. However, interpreting the results of such simulations increasingly requires tools that can combine information from multiple structural databases and other web resources, and provide highly integrated and versatile analysis tools. Here, we present a new web server that integrates high-quality animation of molecular motion (MM) with structural and functional analysis of macromolecules. The new tool, dubbed POLYVIEW-MM, enables animation of trajectories generated by molecular dynamics and related simulation techniques, as well as visualization of alternative conformers, e.g. obtained as a result of protein structure prediction methods or small molecule docking. To facilitate structural analysis, POLYVIEW-MM combines interactive view and analysis of conformational changes using Jmol and its tailored extensions, publication quality animation using PyMol, and customizable 2D summary plots that provide an overview of MM, e.g. in terms of changes in secondary structure states and relative solvent accessibility of individual residues in proteins. Furthermore, POLYVIEW-MM integrates visualization with various structural annotations, including automated mapping of known inter-action sites from structural homologs, mapping of cavities and ligand binding sites, transmembrane regions and protein domains. URL: http://polyview.cchmc.org/conform.html. PMID:20504857

Robustness of atomistic Gō models in predicting native-like folding intermediates

NASA Astrophysics Data System (ADS)

Estácio, S. G.; Fernandes, C. S.; Krobath, H.; Faísca, P. F. N.; Shakhnovich, E. I.

2012-08-01

Gō models are exceedingly popular tools in computer simulations of protein folding. These models are native-centric, i.e., they are directly constructed from the protein's native structure. Therefore, it is important to understand up to which extent the atomistic details of the native structure dictate the folding behavior exhibited by Gō models. Here we address this challenge by performing exhaustive discrete molecular dynamics simulations of a Gō potential combined with a full atomistic protein representation. In particular, we investigate the robustness of this particular type of Gō models in predicting the existence of intermediate states in protein folding. We focus on the N47G mutational form of the Spc-SH3 folding domain (x-ray structure) and compare its folding pathway with that of alternative native structures produced in silico. Our methodological strategy comprises equilibrium folding simulations, structural clustering, and principal component analysis.

Discrete Element Modelling of Floating Debris

NASA Astrophysics Data System (ADS)

Mahaffey, Samantha; Liang, Qiuhua; Parkin, Geoff; Large, Andy; Rouainia, Mohamed

2016-04-01

Flash flooding is characterised by high velocity flows which impact vulnerable catchments with little warning time and as such, result in complex flow dynamics which are difficult to replicate through modelling. The impacts of flash flooding can be made yet more severe by the transport of both natural and anthropogenic debris, ranging from tree trunks to vehicles, wheelie bins and even storage containers, the effects of which have been clearly evident during recent UK flooding. This cargo of debris can have wide reaching effects and result in actual flood impacts which diverge from those predicted. A build-up of debris may lead to partial channel blockage and potential flow rerouting through urban centres. Build-up at bridges and river structures also leads to increased hydraulic loading which may result in damage and possible structural failure. Predicting the impacts of debris transport; however, is difficult as conventional hydrodynamic modelling schemes do not intrinsically include floating debris within their calculations. Subsequently a new tool has been developed using an emerging approach, which incorporates debris transport through the coupling of two existing modelling techniques. A 1D hydrodynamic modelling scheme has here been coupled with a 2D discrete element scheme to form a new modelling tool which predicts the motion and flow-interaction of floating debris. Hydraulic forces arising from flow around the object are applied to instigate its motion. Likewise, an equivalent opposing force is applied to fluid cells, enabling backwater effects to be simulated. Shock capturing capabilities make the tool applicable to predicting the complex flow dynamics associated with flash flooding. The modelling scheme has been applied to experimental case studies where cylindrical wooden dowels are transported by a dam-break wave. These case studies enable validation of the tool's shock capturing capabilities and the coupling technique applied between the two numerical schemes. The results show that the tool is able to adequately replicate water depth and depth-averaged velocity of a dam-break wave, as well as velocity and displacement of floating cylindrical elements, thus validating its shock capturing capabilities and the coupling technique applied for this simple test case. Future development of the tool will incorporate a 2D hydrodynamic scheme and a 3D discrete element scheme in order to model the more complex processes associated with debris transport.

A Micromechanics-Based Method for Multiscale Fatigue Prediction

NASA Astrophysics Data System (ADS)

Moore, John Allan

An estimated 80% of all structural failures are due to mechanical fatigue, often resulting in catastrophic, dangerous and costly failure events. However, an accurate model to predict fatigue remains an elusive goal. One of the major challenges is that fatigue is intrinsically a multiscale process, which is dependent on a structure's geometric design as well as its material's microscale morphology. The following work begins with a microscale study of fatigue nucleation around non- metallic inclusions. Based on this analysis, a novel multiscale method for fatigue predictions is developed. This method simulates macroscale geometries explicitly while concurrently calculating the simplified response of microscale inclusions. Thus, providing adequate detail on multiple scales for accurate fatigue life predictions. The methods herein provide insight into the multiscale nature of fatigue, while also developing a tool to aid in geometric design and material optimization for fatigue critical devices such as biomedical stents and artificial heart valves.

Convenient QSAR model for predicting the complexation of structurally diverse compounds with beta-cyclodextrins.

PubMed

Pérez-Garrido, Alfonso; Morales Helguera, Aliuska; Abellán Guillén, Adela; Cordeiro, M Natália D S; Garrido Escudero, Amalio

2009-01-15

This paper reports a QSAR study for predicting the complexation of a large and heterogeneous variety of substances (233 organic compounds) with beta-cyclodextrins (beta-CDs). Several different theoretical molecular descriptors, calculated solely from the molecular structure of the compounds under investigation, and an efficient variable selection procedure, like the Genetic Algorithm, led to models with satisfactory global accuracy and predictivity. But the best-final QSAR model is based on Topological descriptors meanwhile offering a reasonable interpretation. This QSAR model was able to explain ca. 84% of the variance in the experimental activity, and displayed very good internal cross-validation statistics and predictivity on external data. It shows that the driving forces for CD complexation are mainly hydrophobic and steric (van der Waals) interactions. Thus, the results of our study provide a valuable tool for future screening and priority testing of beta-CDs guest molecules.

Overview of Boundary Layer Transition Research in Support of Orbiter Return To Flight

NASA Technical Reports Server (NTRS)

Berry, Scott A.; Horvath, Thomas J.; Greene, Francis A.; Kinder, Gerald R.; Wang, K. C.

2006-01-01

A predictive tool for estimating the onset of boundary layer transition resulting from damage to and/or repair of the thermal protection system was developed in support of Shuttle Return to Flight. The boundary layer transition tool is part of a suite of tools that analyze the aerothermodynamic environment to the local thermal protection system to allow informed disposition of damage for making recommendations to fly as is or to repair. Using mission specific trajectory information and details of each damage site or repair, the expected time (and thus Mach number) at transition onset is predicted to help define the aerothermodynamic environment to use in the subsequent thermal and stress analysis of the local thermal protection system and structure. The boundary layer transition criteria utilized for the tool was developed from ground-based measurements to account for the effect of both protuberances and cavities and has been calibrated against select flight data. Computed local boundary layer edge conditions were used to correlate the results, specifically the momentum thickness Reynolds number over the edge Mach number and the boundary layer thickness. For the initial Return to Flight mission, STS-114, empirical curve coefficients of 27, 100, and 900 were selected to predict transition onset for protuberances based on height, and cavities based on depth and length, respectively.

Cardiovascular risk of patients with gout seen at rheumatology clinics following a structured assessment.

PubMed

Andrés, Mariano; Bernal, José Antonio; Sivera, Francisca; Quilis, Neus; Carmona, Loreto; Vela, Paloma; Pascual, Eliseo

2017-07-01

Gout-associated cardiovascular (CV) risk relates to comorbidities and crystal-led inflammation. The aim was to estimate the CV risk by prediction tools in new patients with gout and to assess whether ultrasonographic carotid changes are present in patients without high CV risk. Cross-sectional study. Consecutive new patients with crystal-proven gout underwent a structured CV consultation, including CV events, risk factors and two risk prediction tools-the Systematic COronary Evaluation (SCORE) and the Framingham Heart Study (FHS). CV risk was stratified according to current European guidelines. Carotid ultrasound (cUS) was performed in patients with less than very high CV risk. The presence of carotid plaques was studied depending on the SCORE and FHS by the area under the curve (AUC) of receiver operating curves. 237 new patients with gout were recruited. CV stratification by scores showed a predominance of very high (95 patients, 40.1%) and moderate (72 patients, 30.5%) risk levels. cUS was performed in 142 patients, finding atheroma plaques in 66 (46.5%, 95% CI 37.8 to 54.2). Following cUS findings, patients classified as very high risk increased from 40.1% up to 67.9% (161/237 patients). SCORE and FHS predicted moderately (AUC 0.711 and 0.683, respectively) the presence of atheroma plaques at cUS. The majority of patients presenting with gout may be at very high CV risk, indicating the need for initiating optimal prevention strategies at this stage. Risk prediction tools appear to underestimate the presence of carotid plaque in patients with gout. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/.

A simplified method of evaluating the stress wave environment of internal equipment

NASA Technical Reports Server (NTRS)

Colton, J. D.; Desmond, T. P.

1979-01-01

A simplified method called the transfer function technique (TFT) was devised for evaluating the stress wave environment in a structure containing internal equipment. The TFT consists of following the initial in-plane stress wave that propagates through a structure subjected to a dynamic load and characterizing how the wave is altered as it is transmitted through intersections of structural members. As a basis for evaluating the TFT, impact experiments and detailed stress wave analyses were performed for structures with two or three, or more members. Transfer functions that relate the wave transmitted through an intersection to the incident wave were deduced from the predicted wave response. By sequentially applying these transfer functions to a structure with several intersections, it was found that the environment produced by the initial stress wave propagating through the structure can be approximated well. The TFT can be used as a design tool or as an analytical tool to determine whether a more detailed wave analysis is warranted.

GeneBuilder: interactive in silico prediction of gene structure.

PubMed

Milanesi, L; D'Angelo, D; Rogozin, I B

1999-01-01

Prediction of gene structure in newly sequenced DNA becomes very important in large genome sequencing projects. This problem is complicated due to the exon-intron structure of eukaryotic genes and because gene expression is regulated by many different short nucleotide domains. In order to be able to analyse the full gene structure in different organisms, it is necessary to combine information about potential functional signals (promoter region, splice sites, start and stop codons, 3' untranslated region) together with the statistical properties of coding sequences (coding potential), information about homologous proteins, ESTs and repeated elements. We have developed the GeneBuilder system which is based on prediction of functional signals and coding regions by different approaches in combination with similarity searches in proteins and EST databases. The potential gene structure models are obtained by using a dynamic programming method. The program permits the use of several parameters for gene structure prediction and refinement. During gene model construction, selecting different exon homology levels with a protein sequence selected from a list of homologous proteins can improve the accuracy of the gene structure prediction. In the case of low homology, GeneBuilder is still able to predict the gene structure. The GeneBuilder system has been tested by using the standard set (Burset and Guigo, Genomics, 34, 353-367, 1996) and the performances are: 0.89 sensitivity and 0.91 specificity at the nucleotide level. The total correlation coefficient is 0.88. The GeneBuilder system is implemented as a part of the WebGene a the URL: http://www.itba.mi. cnr.it/webgene and TRADAT (TRAncription Database and Analysis Tools) launcher URL: http://www.itba.mi.cnr.it/tradat.

MemBrain: An Easy-to-Use Online Webserver for Transmembrane Protein Structure Prediction

NASA Astrophysics Data System (ADS)

Yin, Xi; Yang, Jing; Xiao, Feng; Yang, Yang; Shen, Hong-Bin

2018-03-01

Membrane proteins are an important kind of proteins embedded in the membranes of cells and play crucial roles in living organisms, such as ion channels, transporters, receptors. Because it is difficult to determinate the membrane protein's structure by wet-lab experiments, accurate and fast amino acid sequence-based computational methods are highly desired. In this paper, we report an online prediction tool called MemBrain, whose input is the amino acid sequence. MemBrain consists of specialized modules for predicting transmembrane helices, residue-residue contacts and relative accessible surface area of α-helical membrane proteins. MemBrain achieves a prediction accuracy of 97.9% of A TMH, 87.1% of A P, 3.2 ± 3.0 of N-score, 3.1 ± 2.8 of C-score. MemBrain-Contact obtains 62%/64.1% prediction accuracy on training and independent dataset on top L/5 contact prediction, respectively. And MemBrain-Rasa achieves Pearson correlation coefficient of 0.733 and its mean absolute error of 13.593. These prediction results provide valuable hints for revealing the structure and function of membrane proteins. MemBrain web server is free for academic use and available at www.csbio.sjtu.edu.cn/bioinf/MemBrain/. [Figure not available: see fulltext.

The general alcoholics anonymous tools of recovery: the adoption of 12-step practices and beliefs.

PubMed

Greenfield, Brenna L; Tonigan, J Scott

2013-09-01

Working the 12 steps is widely prescribed for Alcoholics Anonymous (AA) members although the relative merits of different methods for measuring step work have received minimal attention and even less is known about how step work predicts later substance use. The current study (1) compared endorsements of step work on an face-valid or direct measure, the Alcoholics Anonymous Inventory (AAI), with an indirect measure of step work, the General Alcoholics Anonymous Tools of Recovery (GAATOR); (2) evaluated the underlying factor structure of the GAATOR and changes in step work over time; (3) examined changes in the endorsement of step work over time; and (4) investigated how, if at all, 12-step work predicted later substance use. New AA affiliates (N = 130) completed assessments at intake, 3, 6, and 9 months. Significantly more participants endorsed step work on the GAATOR than on the AAI for nine of the 12 steps. An exploratory factor analysis revealed a two-factor structure for the GAATOR comprising behavioral step work and spiritual step work. Behavioral step work did not change over time, but was predicted by having a sponsor, while Spiritual step work decreased over time and increases were predicted by attending 12-step meetings or treatment. Behavioral step work did not prospectively predict substance use. In contrast, spiritual step work predicted percent days abstinent. Behavioral step work and spiritual step work appear to be conceptually distinct components of step work that have distinct predictors and unique impacts on outcomes. PsycINFO Database Record (c) 2013 APA, all rights reserved.

20180318 - Prediction Of pKa From Chemical Structure Using Free And Open-Source Tools (ACS Spring)

EPA Science Inventory

The ionization state of a chemical, reflected in pKa values, affects lipophilicity, solubility, protein binding and the ability of a chemical to cross the plasma membrane. These properties govern the pharmacokinetic parameters such as absorption, distribution, metabolism, excreti...

Turbine Engine Hot Section Technology, 1987

NASA Technical Reports Server (NTRS)

1987-01-01

Presentations were made concerning the development of design analysis tools for combustor liners, turbine vanes, and turbine blades. Presentations were divided into six sections: instrumentation, combustion, turbine heat transfer, structural analysis, fatigue and fracture, surface protective coatings, constitutive behavior of materials, stress-strain response and life prediction methods.

Preliminary design methods for fiber reinforced composite structures employing a personal computer

NASA Technical Reports Server (NTRS)

Eastlake, C. N.

1986-01-01

The objective of this project was to develop a user-friendly interactive computer program to be used as an analytical tool by structural designers. Its intent was to do preliminary, approximate stress analysis to help select or verify sizing choices for composite structural members. The approach to the project was to provide a subroutine which uses classical lamination theory to predict an effective elastic modulus for a laminate of arbitrary material and ply orientation. This effective elastic modulus can then be used in a family of other subroutines which employ the familiar basic structural analysis methods for isotropic materials. This method is simple and convenient to use but only approximate, as is appropriate for a preliminary design tool which will be subsequently verified by more sophisticated analysis. Additional subroutines have been provided to calculate laminate coefficient of thermal expansion and to calculate ply-by-ply strains within a laminate.

Automated use of mutagenesis data in structure prediction.

PubMed

Nanda, Vikas; DeGrado, William F

2005-05-15

In the absence of experimental structural determination, numerous methods are available to indirectly predict or probe the structure of a target molecule. Genetic modification of a protein sequence is a powerful tool for identifying key residues involved in binding reactions or protein stability. Mutagenesis data is usually incorporated into the modeling process either through manual inspection of model compatibility with empirical data, or through the generation of geometric constraints linking sensitive residues to a binding interface. We present an approach derived from statistical studies of lattice models for introducing mutation information directly into the fitness score. The approach takes into account the phenotype of mutation (neutral or disruptive) and calculates the energy for a given structure over an ensemble of sequences. The structure prediction procedure searches for the optimal conformation where neutral sequences either have no impact or improve stability and disruptive sequences reduce stability relative to wild type. We examine three types of sequence ensembles: information from saturation mutagenesis, scanning mutagenesis, and homologous proteins. Incorporating multiple sequences into a statistical ensemble serves to energetically separate the native state and misfolded structures. As a result, the prediction of structure with a poor force field is sufficiently enhanced by mutational information to improve accuracy. Furthermore, by separating misfolded conformations from the target score, the ensemble energy serves to speed up conformational search algorithms such as Monte Carlo-based methods. Copyright 2005 Wiley-Liss, Inc.

Crystal Structure Prediction via Deep Learning.

PubMed

Ryan, Kevin; Lengyel, Jeff; Shatruk, Michael

2018-06-06

We demonstrate the application of deep neural networks as a machine-learning tool for the analysis of a large collection of crystallographic data contained in the crystal structure repositories. Using input data in the form of multi-perspective atomic fingerprints, which describe coordination topology around unique crystallographic sites, we show that the neural-network model can be trained to effectively distinguish chemical elements based on the topology of their crystallographic environment. The model also identifies structurally similar atomic sites in the entire dataset of ~50000 crystal structures, essentially uncovering trends that reflect the periodic table of elements. The trained model was used to analyze templates derived from the known binary and ternary crystal structures in order to predict the likelihood to form new compounds that could be generated by placing elements into these structural templates in combinatorial fashion. Statistical analysis of predictive performance of the neural-network model, which was applied to a test set of structures never seen by the model during training, indicates its ability to predict known elemental compositions with a high likelihood of success. In ~30% of cases, the known compositions were found among top-10 most likely candidates proposed by the model. These results suggest that the approach developed in this work can be used to effectively guide the synthetic efforts in the discovery of new materials, especially in the case of systems composed of 3 or more chemical elements.

Predicting residue-wise contact orders in proteins by support vector regression.

PubMed

Song, Jiangning; Burrage, Kevin

2006-10-03

The residue-wise contact order (RWCO) describes the sequence separations between the residues of interest and its contacting residues in a protein sequence. It is a new kind of one-dimensional protein structure that represents the extent of long-range contacts and is considered as a generalization of contact order. Together with secondary structure, accessible surface area, the B factor, and contact number, RWCO provides comprehensive and indispensable important information to reconstructing the protein three-dimensional structure from a set of one-dimensional structural properties. Accurately predicting RWCO values could have many important applications in protein three-dimensional structure prediction and protein folding rate prediction, and give deep insights into protein sequence-structure relationships. We developed a novel approach to predict residue-wise contact order values in proteins based on support vector regression (SVR), starting from primary amino acid sequences. We explored seven different sequence encoding schemes to examine their effects on the prediction performance, including local sequence in the form of PSI-BLAST profiles, local sequence plus amino acid composition, local sequence plus molecular weight, local sequence plus secondary structure predicted by PSIPRED, local sequence plus molecular weight and amino acid composition, local sequence plus molecular weight and predicted secondary structure, and local sequence plus molecular weight, amino acid composition and predicted secondary structure. When using local sequences with multiple sequence alignments in the form of PSI-BLAST profiles, we could predict the RWCO distribution with a Pearson correlation coefficient (CC) between the predicted and observed RWCO values of 0.55, and root mean square error (RMSE) of 0.82, based on a well-defined dataset with 680 protein sequences. Moreover, by incorporating global features such as molecular weight and amino acid composition we could further improve the prediction performance with the CC to 0.57 and an RMSE of 0.79. In addition, combining the predicted secondary structure by PSIPRED was found to significantly improve the prediction performance and could yield the best prediction accuracy with a CC of 0.60 and RMSE of 0.78, which provided at least comparable performance compared with the other existing methods. The SVR method shows a prediction performance competitive with or at least comparable to the previously developed linear regression-based methods for predicting RWCO values. In contrast to support vector classification (SVC), SVR is very good at estimating the raw value profiles of the samples. The successful application of the SVR approach in this study reinforces the fact that support vector regression is a powerful tool in extracting the protein sequence-structure relationship and in estimating the protein structural profiles from amino acid sequences.

Tools and Data Services from the NASA Earth Satellite Observations for Remote Sensing Commercial Applications

NASA Technical Reports Server (NTRS)

Vicente, Gilberto

2005-01-01

Several commercial applications of remote sensing data, such as water resources management, environmental monitoring, climate prediction, agriculture, forestry, preparation for and migration of extreme weather events, require access to vast amounts of archived high quality data, software tools and services for data manipulation and information extraction. These on the other hand require gaining detailed understanding of the data's internal structure and physical implementation of data reduction, combination and data product production. The time-consuming task must be undertaken before the core investigation can begin and is an especially difficult challenge when science objectives require users to deal with large multi-sensor data sets of different formats, structures, and resolutions.

Structural constraints on the three-dimensional geometry of simple viruses: case studies of a new predictive tool

PubMed Central

Keef, Thomas; Wardman, Jessica P.; Ranson, Neil A.; Stockley, Peter G.; Twarock, Reidun

2013-01-01

Understanding the fundamental principles of virus architecture is one of the most important challenges in biology and medicine. Crick and Watson were the first to propose that viruses exhibit symmetry in the organization of their protein containers for reasons of genetic economy. Based on this, Caspar and Klug introduced quasi-equivalence theory to predict the relative locations of the coat proteins within these containers and classified virus structure in terms of T-numbers. Here it is shown that quasi-equivalence is part of a wider set of structural constraints on virus structure. These constraints can be formulated using an extension of the underlying symmetry group and this is demonstrated with a number of case studies. This new concept in virus biology provides for the first time predictive information on the structural constraints on coat protein and genome topography, and reveals a previously unrecognized structural interdependence of the shapes and sizes of different viral components. It opens up the possibility of distinguishing the structures of different viruses with the same T-number, suggesting a refined viral structure classification scheme. It can moreover be used as a basis for models of virus function, e.g. to characterize the start and end configurations of a structural transition important for infection. PMID:23403965

Structural constraints on the three-dimensional geometry of simple viruses: case studies of a new predictive tool.

PubMed

Keef, Thomas; Wardman, Jessica P; Ranson, Neil A; Stockley, Peter G; Twarock, Reidun

2013-03-01

Understanding the fundamental principles of virus architecture is one of the most important challenges in biology and medicine. Crick and Watson were the first to propose that viruses exhibit symmetry in the organization of their protein containers for reasons of genetic economy. Based on this, Caspar and Klug introduced quasi-equivalence theory to predict the relative locations of the coat proteins within these containers and classified virus structure in terms of T-numbers. Here it is shown that quasi-equivalence is part of a wider set of structural constraints on virus structure. These constraints can be formulated using an extension of the underlying symmetry group and this is demonstrated with a number of case studies. This new concept in virus biology provides for the first time predictive information on the structural constraints on coat protein and genome topography, and reveals a previously unrecognized structural interdependence of the shapes and sizes of different viral components. It opens up the possibility of distinguishing the structures of different viruses with the same T-number, suggesting a refined viral structure classification scheme. It can moreover be used as a basis for models of virus function, e.g. to characterize the start and end configurations of a structural transition important for infection.

Lessons from making the Structural Classification of Proteins (SCOP) and their implications for protein structure modelling.

PubMed

Andreeva, Antonina

2016-06-15

The Structural Classification of Proteins (SCOP) database has facilitated the development of many tools and algorithms and it has been successfully used in protein structure prediction and large-scale genome annotations. During the development of SCOP, numerous exceptions were found to topological rules, along with complex evolutionary scenarios and peculiarities in proteins including the ability to fold into alternative structures. This article reviews cases of structural variations observed for individual proteins and among groups of homologues, knowledge of which is essential for protein structure modelling. © 2016 The Author(s). published by Portland Press Limited on behalf of the Biochemical Society.

A coarse-grained model for DNA origami.

PubMed

Reshetnikov, Roman V; Stolyarova, Anastasia V; Zalevsky, Arthur O; Panteleev, Dmitry Y; Pavlova, Galina V; Klinov, Dmitry V; Golovin, Andrey V; Protopopova, Anna D

2018-02-16

Modeling tools provide a valuable support for DNA origami design. However, current solutions have limited application for conformational analysis of the designs. In this work we present a tool for a thorough study of DNA origami structure and dynamics. The tool is based on a novel coarse-grained model dedicated to geometry optimization and conformational analysis of DNA origami. We explored the ability of the model to predict dynamic behavior, global shapes, and fine details of two single-layer systems designed in hexagonal and square lattices using atomic force microscopy, Förster resonance energy transfer spectroscopy, and all-atom molecular dynamic simulations for validation of the results. We also examined the performance of the model for multilayer systems by simulation of DNA origami with published cryo-electron microscopy and atomic force microscopy structures. A good agreement between the simulated and experimental data makes the model suitable for conformational analysis of DNA origami objects. The tool is available at http://vsb.fbb.msu.ru/cosm as a web-service and as a standalone version.

A coarse-grained model for DNA origami

PubMed Central

Stolyarova, Anastasia V; Zalevsky, Arthur O; Panteleev, Dmitry Y; Pavlova, Galina V; Klinov, Dmitry V; Golovin, Andrey V; Protopopova, Anna D

2018-01-01

Abstract Modeling tools provide a valuable support for DNA origami design. However, current solutions have limited application for conformational analysis of the designs. In this work we present a tool for a thorough study of DNA origami structure and dynamics. The tool is based on a novel coarse-grained model dedicated to geometry optimization and conformational analysis of DNA origami. We explored the ability of the model to predict dynamic behavior, global shapes, and fine details of two single-layer systems designed in hexagonal and square lattices using atomic force microscopy, Förster resonance energy transfer spectroscopy, and all-atom molecular dynamic simulations for validation of the results. We also examined the performance of the model for multilayer systems by simulation of DNA origami with published cryo-electron microscopy and atomic force microscopy structures. A good agreement between the simulated and experimental data makes the model suitable for conformational analysis of DNA origami objects. The tool is available at http://vsb.fbb.msu.ru/cosm as a web-service and as a standalone version. PMID:29267876

Predicting full-field dynamic strain on a three-bladed wind turbine using three dimensional point tracking and expansion techniques

NASA Astrophysics Data System (ADS)

Baqersad, Javad; Niezrecki, Christopher; Avitabile, Peter

2014-03-01

As part of a project to predict the full-field dynamic strain in rotating structures (e.g. wind turbines and helicopter blades), an experimental measurement was performed on a wind turbine attached to a 500-lb steel block and excited using a mechanical shaker. In this paper, the dynamic displacement of several optical targets mounted to a turbine placed in a semi-built-in configuration was measured by using three-dimensional point tracking. Using an expansion algorithm in conjunction with a finite element model of the blades, the measured displacements were expanded to all finite element degrees of freedom. The calculated displacements were applied to the finite element model to extract dynamic strain on the surface as well as within the interior points of the structure. To validate the technique for dynamic strain prediction, the physical strain at eight locations on the blades was measured during excitation using strain-gages. The expansion was performed by using both structural modes of an individual cantilevered blade and using modes of the entire structure (three-bladed wind turbine and the fixture) and the predicted strain was compared to the physical strain-gage measurements. The results demonstrate the ability of the technique to predict full-field dynamic strain from limited sets of measurements and can be used as a condition based monitoring tool to help provide damage prognosis of structures during operation.

Compilation of mRNA Polyadenylation Signals in Arabidopsis Revealed a New Signal Element and Potential Secondary Structures1[w

PubMed Central

Loke, Johnny C.; Stahlberg, Eric A.; Strenski, David G.; Haas, Brian J.; Wood, Paul Chris; Li, Qingshun Quinn

2005-01-01

Using a novel program, SignalSleuth, and a database containing authenticated polyadenylation [poly(A)] sites, we analyzed the composition of mRNA poly(A) signals in Arabidopsis (Arabidopsis thaliana), and reevaluated previously described cis-elements within the 3′-untranslated (UTR) regions, including near upstream elements and far upstream elements. As predicted, there are absences of high-consensus signal patterns. The AAUAAA signal topped the near upstream elements patterns and was found within the predicted location to only approximately 10% of 3′-UTRs. More importantly, we identified a new set, named cleavage elements, of poly(A) signals flanking both sides of the cleavage site. These cis-elements were not previously revealed by conventional mutagenesis and are contemplated as a cluster of signals for cleavage site recognition. Moreover, a single-nucleotide profile scan on the 3′-UTR regions unveiled a distinct arrangement of alternate stretches of U and A nucleotides, which led to a prediction of the formation of secondary structures. Using an RNA secondary structure prediction program, mFold, we identified three main types of secondary structures on the sequences analyzed. Surprisingly, these observed secondary structures were all interrupted in previously constructed mutations in these regions. These results will enable us to revise the current model of plant poly(A) signals and to develop tools to predict 3′-ends for gene annotation. PMID:15965016

A genetic algorithm approach in interface and surface structure optimization

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zhang, Jian

The thesis is divided into two parts. In the first part a global optimization method is developed for the interface and surface structures optimization. Two prototype systems are chosen to be studied. One is Si[001] symmetric tilted grain boundaries and the other is Ag/Au induced Si(111) surface. It is found that Genetic Algorithm is very efficient in finding lowest energy structures in both cases. Not only existing structures in the experiments can be reproduced, but also many new structures can be predicted using Genetic Algorithm. Thus it is shown that Genetic Algorithm is a extremely powerful tool for the materialmore » structures predictions. The second part of the thesis is devoted to the explanation of an experimental observation of thermal radiation from three-dimensional tungsten photonic crystal structures. The experimental results seems astounding and confusing, yet the theoretical models in the paper revealed the physics insight behind the phenomena and can well reproduced the experimental results.« less

Special Focus

PubMed Central

Nawrocki, Eric P.; Burge, Sarah W.

2013-01-01

The development of RNA bioinformatic tools began more than 30 y ago with the description of the Nussinov and Zuker dynamic programming algorithms for single sequence RNA secondary structure prediction. Since then, many tools have been developed for various RNA sequence analysis problems such as homology search, multiple sequence alignment, de novo RNA discovery, read-mapping, and many more. In this issue, we have collected a sampling of reviews and original research that demonstrate some of the many ways bioinformatics is integrated with current RNA biology research. PMID:23948768

Sequence Alignment to Predict Across Species Susceptibility ...

EPA Pesticide Factsheets

Conservation of a molecular target across species can be used as a line-of-evidence to predict the likelihood of chemical susceptibility. The web-based Sequence Alignment to Predict Across Species Susceptibility (SeqAPASS) tool was developed to simplify, streamline, and quantitatively assess protein sequence/structural similarity across taxonomic groups as a means to predict relative intrinsic susceptibility. The intent of the tool is to allow for evaluation of any potential protein target, so it is amenable to variable degrees of protein characterization, depending on available information about the chemical/protein interaction and the molecular target itself. To allow for flexibility in the analysis, a layered strategy was adopted for the tool. The first level of the SeqAPASS analysis compares primary amino acid sequences to a query sequence, calculating a metric for sequence similarity (including detection of candidate orthologs), the second level evaluates sequence similarity within selected domains (e.g., ligand-binding domain, DNA binding domain), and the third level of analysis compares individual amino acid residue positions identified as being of importance for protein conformation and/or ligand binding upon chemical perturbation. Each level of the SeqAPASS analysis provides increasing evidence to apply toward rapid, screening-level assessments of probable cross species susceptibility. Such analyses can support prioritization of chemicals for further ev

High-throughput interpretation of gene structure changes in human and nonhuman resequencing data, using ACE

PubMed Central

Majoros, William H.; Campbell, Michael S.; Holt, Carson; DeNardo, Erin K.; Ware, Doreen; Allen, Andrew S.; Yandell, Mark; Reddy, Timothy E.

2017-01-01

Abstract Motivation: The accurate interpretation of genetic variants is critical for characterizing genotype–phenotype associations. Because the effects of genetic variants can depend strongly on their local genomic context, accurate genome annotations are essential. Furthermore, as some variants have the potential to disrupt or alter gene structure, variant interpretation efforts stand to gain from the use of individualized annotations that account for differences in gene structure between individuals or strains. Results: We describe a suite of software tools for identifying possible functional changes in gene structure that may result from sequence variants. ACE (‘Assessing Changes to Exons’) converts phased genotype calls to a collection of explicit haplotype sequences, maps transcript annotations onto them, detects gene-structure changes and their possible repercussions, and identifies several classes of possible loss of function. Novel transcripts predicted by ACE are commonly supported by spliced RNA-seq reads, and can be used to improve read alignment and transcript quantification when an individual-specific genome sequence is available. Using publicly available RNA-seq data, we show that ACE predictions confirm earlier results regarding the quantitative effects of nonsense-mediated decay, and we show that predicted loss-of-function events are highly concordant with patterns of intolerance to mutations across the human population. ACE can be readily applied to diverse species including animals and plants, making it a broadly useful tool for use in eukaryotic population-based resequencing projects, particularly for assessing the joint impact of all variants at a locus. Availability and Implementation: ACE is written in open-source C ++ and Perl and is available from geneprediction.org/ACE Contact: myandell@genetics.utah.edu or tim.reddy@duke.edu Supplementary information: Supplementary information is available at Bioinformatics online. PMID:28011790

High-throughput interpretation of gene structure changes in human and nonhuman resequencing data, using ACE.

PubMed

Majoros, William H; Campbell, Michael S; Holt, Carson; DeNardo, Erin K; Ware, Doreen; Allen, Andrew S; Yandell, Mark; Reddy, Timothy E

2017-05-15

The accurate interpretation of genetic variants is critical for characterizing genotype-phenotype associations. Because the effects of genetic variants can depend strongly on their local genomic context, accurate genome annotations are essential. Furthermore, as some variants have the potential to disrupt or alter gene structure, variant interpretation efforts stand to gain from the use of individualized annotations that account for differences in gene structure between individuals or strains. We describe a suite of software tools for identifying possible functional changes in gene structure that may result from sequence variants. ACE ('Assessing Changes to Exons') converts phased genotype calls to a collection of explicit haplotype sequences, maps transcript annotations onto them, detects gene-structure changes and their possible repercussions, and identifies several classes of possible loss of function. Novel transcripts predicted by ACE are commonly supported by spliced RNA-seq reads, and can be used to improve read alignment and transcript quantification when an individual-specific genome sequence is available. Using publicly available RNA-seq data, we show that ACE predictions confirm earlier results regarding the quantitative effects of nonsense-mediated decay, and we show that predicted loss-of-function events are highly concordant with patterns of intolerance to mutations across the human population. ACE can be readily applied to diverse species including animals and plants, making it a broadly useful tool for use in eukaryotic population-based resequencing projects, particularly for assessing the joint impact of all variants at a locus. ACE is written in open-source C ++ and Perl and is available from geneprediction.org/ACE. myandell@genetics.utah.edu or tim.reddy@duke.edu. Supplementary information is available at Bioinformatics online. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com

Composite Cure Process Modeling and Simulations using COMPRO(Registered Trademark) and Validation of Residual Strains using Fiber Optics Sensors

NASA Technical Reports Server (NTRS)

Sreekantamurthy, Thammaiah; Hudson, Tyler B.; Hou, Tan-Hung; Grimsley, Brian W.

2016-01-01

Composite cure process induced residual strains and warping deformations in composite components present significant challenges in the manufacturing of advanced composite structure. As a part of the Manufacturing Process and Simulation initiative of the NASA Advanced Composite Project (ACP), research is being conducted on the composite cure process by developing an understanding of the fundamental mechanisms by which the process induced factors influence the residual responses. In this regard, analytical studies have been conducted on the cure process modeling of composite structural parts with varied physical, thermal, and resin flow process characteristics. The cure process simulation results were analyzed to interpret the cure response predictions based on the underlying physics incorporated into the modeling tool. In the cure-kinetic analysis, the model predictions on the degree of cure, resin viscosity and modulus were interpreted with reference to the temperature distribution in the composite panel part and tool setup during autoclave or hot-press curing cycles. In the fiber-bed compaction simulation, the pore pressure and resin flow velocity in the porous media models, and the compaction strain responses under applied pressure were studied to interpret the fiber volume fraction distribution predictions. In the structural simulation, the effect of temperature on the resin and ply modulus, and thermal coefficient changes during curing on predicted mechanical strains and chemical cure shrinkage strains were studied to understand the residual strains and stress response predictions. In addition to computational analysis, experimental studies were conducted to measure strains during the curing of laminated panels by means of optical fiber Bragg grating sensors (FBGs) embedded in the resin impregnated panels. The residual strain measurements from laboratory tests were then compared with the analytical model predictions. The paper describes the cure process procedures and residual strain predications, and discusses pertinent experimental results from the validation studies.

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.

Predictive Modeling of Estrogen Receptor Binding Agents Using Advanced Cheminformatics Tools and Massive Public Data.

PubMed

Ribay, Kathryn; Kim, Marlene T; Wang, Wenyi; Pinolini, Daniel; Zhu, Hao

2016-03-01

Estrogen receptors (ERα) are a critical target for drug design as well as a potential source of toxicity when activated unintentionally. Thus, evaluating potential ERα binding agents is critical in both drug discovery and chemical toxicity areas. Using computational tools, e.g., Quantitative Structure-Activity Relationship (QSAR) models, can predict potential ERα binding agents before chemical synthesis. The purpose of this project was to develop enhanced predictive models of ERα binding agents by utilizing advanced cheminformatics tools that can integrate publicly available bioassay data. The initial ERα binding agent data set, consisting of 446 binders and 8307 non-binders, was obtained from the Tox21 Challenge project organized by the NIH Chemical Genomics Center (NCGC). After removing the duplicates and inorganic compounds, this data set was used to create a training set (259 binders and 259 non-binders). This training set was used to develop QSAR models using chemical descriptors. The resulting models were then used to predict the binding activity of 264 external compounds, which were available to us after the models were developed. The cross-validation results of training set [Correct Classification Rate (CCR) = 0.72] were much higher than the external predictivity of the unknown compounds (CCR = 0.59). To improve the conventional QSAR models, all compounds in the training set were used to search PubChem and generate a profile of their biological responses across thousands of bioassays. The most important bioassays were prioritized to generate a similarity index that was used to calculate the biosimilarity score between each two compounds. The nearest neighbors for each compound within the set were then identified and its ERα binding potential was predicted by its nearest neighbors in the training set. The hybrid model performance (CCR = 0.94 for cross validation; CCR = 0.68 for external prediction) showed significant improvement over the original QSAR models, particularly for the activity cliffs that induce prediction errors. The results of this study indicate that the response profile of chemicals from public data provides useful information for modeling and evaluation purposes. The public big data resources should be considered along with chemical structure information when predicting new compounds, such as unknown ERα binding agents.

Improved Helicopter Rotor Performance Prediction through Loose and Tight CFD/CSD Coupling

NASA Astrophysics Data System (ADS)

Ickes, Jacob C.

Helicopters and other Vertical Take-Off or Landing (VTOL) vehicles exhibit an interesting combination of structural dynamic and aerodynamic phenomena which together drive the rotor performance. The combination of factors involved make simulating the rotor a challenging and multidisciplinary effort, and one which is still an active area of interest in the industry because of the money and time it could save during design. Modern tools allow the prediction of rotorcraft physics from first principles. Analysis of the rotor system with this level of accuracy provides the understanding necessary to improve its performance. There has historically been a divide between the comprehensive codes which perform aeroelastic rotor simulations using simplified aerodynamic models, and the very computationally intensive Navier-Stokes Computational Fluid Dynamics (CFD) solvers. As computer resources become more available, efforts have been made to replace the simplified aerodynamics of the comprehensive codes with the more accurate results from a CFD code. The objective of this work is to perform aeroelastic rotorcraft analysis using first-principles simulations for both fluids and structural predictions using tools available at the University of Toledo. Two separate codes are coupled together in both loose coupling (data exchange on a periodic interval) and tight coupling (data exchange each time step) schemes. To allow the coupling to be carried out in a reliable and efficient way, a Fluid-Structure Interaction code was developed which automatically performs primary functions of loose and tight coupling procedures. Flow phenomena such as transonics, dynamic stall, locally reversed flow on a blade, and Blade-Vortex Interaction (BVI) were simulated in this work. Results of the analysis show aerodynamic load improvement due to the inclusion of the CFD-based airloads in the structural dynamics analysis of the Computational Structural Dynamics (CSD) code. Improvements came in the form of improved peak/trough magnitude prediction, better phase prediction of these locations, and a predicted signal with a frequency content more like the flight test data than the CSD code acting alone. Additionally, a tight coupling analysis was performed as a demonstration of the capability and unique aspects of such an analysis. This work shows that away from the center of the flight envelope, the aerodynamic modeling of the CSD code can be replaced with a more accurate set of predictions from a CFD code with an improvement in the aerodynamic results. The better predictions come at substantially increased computational costs between 1,000 and 10,000 processor-hours.

Automatic prediction of protein domains from sequence information using a hybrid learning system.

PubMed

Nagarajan, Niranjan; Yona, Golan

2004-06-12

We describe a novel method for detecting the domain structure of a protein from sequence information alone. The method is based on analyzing multiple sequence alignments that are derived from a database search. Multiple measures are defined to quantify the domain information content of each position along the sequence and are combined into a single predictor using a neural network. The output is further smoothed and post-processed using a probabilistic model to predict the most likely transition positions between domains. The method was assessed using the domain definitions in SCOP and CATH for proteins of known structure and was compared with several other existing methods. Our method performs well both in terms of accuracy and sensitivity. It improves significantly over the best methods available, even some of the semi-manual ones, while being fully automatic. Our method can also be used to suggest and verify domain partitions based on structural data. A few examples of predicted domain definitions and alternative partitions, as suggested by our method, are also discussed. An online domain-prediction server is available at http://biozon.org/tools/domains/

OPERA: A free and open source QSAR tool for predicting physicochemical properties and environmental fate endpoints

EPA Science Inventory

Collecting the chemical structures and data for necessary QSAR modeling is facilitated by available public databases and open data. However, QSAR model performance is dependent on the quality of data and modeling methodology used. This study developed robust QSAR models for physi...

Constitutive and numerical modeling of soil and soil-pile interaction for 3D applications and Kealakaha stream bridge case study.

DOT National Transportation Integrated Search

2011-12-01

This study is concerned with developing new modeling tools for predicting the response of the new Kealakaha : Stream Bridge to static and dynamic loads, including seismic shaking. The bridge will span 220 meters, with the : deck structure being curve...

OPERA: A QSAR tool for physicochemical properties and environmental fate predictions (ACS Spring meeting)

EPA Science Inventory

The collection of chemical structures and associated experimental data for QSAR modeling is facilitated by the increasing number and size of public databases. However, the performance of QSAR models highly depends on the quality of the data used and the modeling methodology. The ...

Active Control Technology at NASA Langley Research Center

NASA Technical Reports Server (NTRS)

Antcliff, Richard R.; McGowan, Anna-Marie R.

2000-01-01

NASA Langley has a long history of attacking important technical opportunities from a broad base of supporting disciplines. The research and development at Langley in this subject area range from the test tube to the test flight. The information covered here will range from the development of innovative new materials, sensors and actuators, to the incorporation of smart sensors and actuators in practical devices, to the optimization of the location of these devices, to, finally, a wide variety of applications of these devices utilizing Langley's facilities and expertise. Advanced materials are being developed for sensors and actuators, as well as polymers for integrating smart devices into composite structures. Contributions reside in three key areas: computational materials; advanced piezoelectric materials; and integrated composite structures. The computational materials effort is focused on developing predictive tools for the efficient design of new materials with the appropriate combination of properties for next generation smart airframe systems. Research in the area of advanced piezoelectrics includes optimizing the efficiency, force output, use temperature, and energy transfer between the structure and device for both ceramic and polymeric materials. For structural health monitoring, advanced non-destructive techniques including fiber optics are being developed for detection of delaminations, cracks and environmental deterioration in aircraft structures. The computational materials effort is focused on developing predictive tools for the efficient design of new materials with the appropriate combination of properties for next generation smart airframe system. Innovative fabrication techniques processing structural composites with sensor and actuator integration are being developed.

Probabilistic Structural Analysis Methods (PSAM) for select space propulsion system structural components

NASA Technical Reports Server (NTRS)

Cruse, T. A.

1987-01-01

The objective is the development of several modular structural analysis packages capable of predicting the probabilistic response distribution for key structural variables such as maximum stress, natural frequencies, transient response, etc. The structural analysis packages are to include stochastic modeling of loads, material properties, geometry (tolerances), and boundary conditions. The solution is to be in terms of the cumulative probability of exceedance distribution (CDF) and confidence bounds. Two methods of probability modeling are to be included as well as three types of structural models - probabilistic finite-element method (PFEM); probabilistic approximate analysis methods (PAAM); and probabilistic boundary element methods (PBEM). The purpose in doing probabilistic structural analysis is to provide the designer with a more realistic ability to assess the importance of uncertainty in the response of a high performance structure. Probabilistic Structural Analysis Method (PSAM) tools will estimate structural safety and reliability, while providing the engineer with information on the confidence that should be given to the predicted behavior. Perhaps most critically, the PSAM results will directly provide information on the sensitivity of the design response to those variables which are seen to be uncertain.

Probabilistic Structural Analysis Methods for select space propulsion system structural components (PSAM)

NASA Technical Reports Server (NTRS)

Cruse, T. A.; Burnside, O. H.; Wu, Y.-T.; Polch, E. Z.; Dias, J. B.

1988-01-01

The objective is the development of several modular structural analysis packages capable of predicting the probabilistic response distribution for key structural variables such as maximum stress, natural frequencies, transient response, etc. The structural analysis packages are to include stochastic modeling of loads, material properties, geometry (tolerances), and boundary conditions. The solution is to be in terms of the cumulative probability of exceedance distribution (CDF) and confidence bounds. Two methods of probability modeling are to be included as well as three types of structural models - probabilistic finite-element method (PFEM); probabilistic approximate analysis methods (PAAM); and probabilistic boundary element methods (PBEM). The purpose in doing probabilistic structural analysis is to provide the designer with a more realistic ability to assess the importance of uncertainty in the response of a high performance structure. Probabilistic Structural Analysis Method (PSAM) tools will estimate structural safety and reliability, while providing the engineer with information on the confidence that should be given to the predicted behavior. Perhaps most critically, the PSAM results will directly provide information on the sensitivity of the design response to those variables which are seen to be uncertain.

A Computational Approach From Gene to Structure Analysis of the Human ABCA4 Transporter Involved in Genetic Retinal Diseases.

PubMed

Trezza, Alfonso; Bernini, Andrea; Langella, Andrea; Ascher, David B; Pires, Douglas E V; Sodi, Andrea; Passerini, Ilaria; Pelo, Elisabetta; Rizzo, Stanislao; Niccolai, Neri; Spiga, Ottavia

2017-10-01

The aim of this article is to report the investigation of the structural features of ABCA4, a protein associated with a genetic retinal disease. A new database collecting knowledge of ABCA4 structure may facilitate predictions about the possible functional consequences of gene mutations observed in clinical practice. In order to correlate structural and functional effects of the observed mutations, the structure of mouse P-glycoprotein was used as a template for homology modeling. The obtained structural information and genetic data are the basis of our relational database (ABCA4Database). Sequence variability among all ABCA4-deposited entries was calculated and reported as Shannon entropy score at the residue level. The three-dimensional model of ABCA4 structure was used to locate the spatial distribution of the observed variable regions. Our predictions from structural in silico tools were able to accurately link the functional effects of mutations to phenotype. The development of the ABCA4Database gathers all the available genetic and structural information, yielding a global view of the molecular basis of some retinal diseases. ABCA4 modeled structure provides a molecular basis on which to analyze protein sequence mutations related to genetic retinal disease in order to predict the risk of retinal disease across all possible ABCA4 mutations. Additionally, our ABCA4 predicted structure is a good starting point for the creation of a new data analysis model, appropriate for precision medicine, in order to develop a deeper knowledge network of the disease and to improve the management of patients.

XLinkDB 2.0: integrated, large-scale structural analysis of protein crosslinking data

PubMed Central

Schweppe, Devin K.; Zheng, Chunxiang; Chavez, Juan D.; Navare, Arti T.; Wu, Xia; Eng, Jimmy K.; Bruce, James E.

2016-01-01

Motivation: Large-scale chemical cross-linking with mass spectrometry (XL-MS) analyses are quickly becoming a powerful means for high-throughput determination of protein structural information and protein–protein interactions. Recent studies have garnered thousands of cross-linked interactions, yet the field lacks an effective tool to compile experimental data or access the network and structural knowledge for these large scale analyses. We present XLinkDB 2.0 which integrates tools for network analysis, Protein Databank queries, modeling of predicted protein structures and modeling of docked protein structures. The novel, integrated approach of XLinkDB 2.0 enables the holistic analysis of XL-MS protein interaction data without limitation to the cross-linker or analytical system used for the analysis. Availability and Implementation: XLinkDB 2.0 can be found here, including documentation and help: http://xlinkdb.gs.washington.edu/. Contact: jimbruce@uw.edu Supplementary information: Supplementary data are available at Bioinformatics online. PMID:27153666

Web tools for predictive toxicology model building.

PubMed

Jeliazkova, Nina

2012-07-01

The development and use of web tools in chemistry has accumulated more than 15 years of history already. Powered by the advances in the Internet technologies, the current generation of web systems are starting to expand into areas, traditional for desktop applications. The web platforms integrate data storage, cheminformatics and data analysis tools. The ease of use and the collaborative potential of the web is compelling, despite the challenges. The topic of this review is a set of recently published web tools that facilitate predictive toxicology model building. The focus is on software platforms, offering web access to chemical structure-based methods, although some of the frameworks could also provide bioinformatics or hybrid data analysis functionalities. A number of historical and current developments are cited. In order to provide comparable assessment, the following characteristics are considered: support for workflows, descriptor calculations, visualization, modeling algorithms, data management and data sharing capabilities, availability of GUI or programmatic access and implementation details. The success of the Web is largely due to its highly decentralized, yet sufficiently interoperable model for information access. The expected future convergence between cheminformatics and bioinformatics databases provides new challenges toward management and analysis of large data sets. The web tools in predictive toxicology will likely continue to evolve toward the right mix of flexibility, performance, scalability, interoperability, sets of unique features offered, friendly user interfaces, programmatic access for advanced users, platform independence, results reproducibility, curation and crowdsourcing utilities, collaborative sharing and secure access.

Modularity of Protein Folds as a Tool for Template-Free Modeling of Structures.

PubMed

Vallat, Brinda; Madrid-Aliste, Carlos; Fiser, Andras

2015-08-01

Predicting the three-dimensional structure of proteins from their amino acid sequences remains a challenging problem in molecular biology. While the current structural coverage of proteins is almost exclusively provided by template-based techniques, the modeling of the rest of the protein sequences increasingly require template-free methods. However, template-free modeling methods are much less reliable and are usually applicable for smaller proteins, leaving much space for improvement. We present here a novel computational method that uses a library of supersecondary structure fragments, known as Smotifs, to model protein structures. The library of Smotifs has saturated over time, providing a theoretical foundation for efficient modeling. The method relies on weak sequence signals from remotely related protein structures to create a library of Smotif fragments specific to the target protein sequence. This Smotif library is exploited in a fragment assembly protocol to sample decoys, which are assessed by a composite scoring function. Since the Smotif fragments are larger in size compared to the ones used in other fragment-based methods, the proposed modeling algorithm, SmotifTF, can employ an exhaustive sampling during decoy assembly. SmotifTF successfully predicts the overall fold of the target proteins in about 50% of the test cases and performs competitively when compared to other state of the art prediction methods, especially when sequence signal to remote homologs is diminishing. Smotif-based modeling is complementary to current prediction methods and provides a promising direction in addressing the structure prediction problem, especially when targeting larger proteins for modeling.

Inspection of the Math Model Tools for On-Orbit Assessment of Impact Damage Report

NASA Technical Reports Server (NTRS)

Harris, Charles E.; Raju, Ivatury S.; Piascik, Robert S> ; KramerWhite, Julie A.; KramerWhite, Julie A.; Labbe, Steve G.; Rotter, Hank A.

2007-01-01

In Spring of 2005, the NASA Engineering Safety Center (NESC) was engaged by the Space Shuttle Program (SSP) to peer review the suite of analytical tools being developed to support the determination of impact and damage tolerance of the Orbiter Thermal Protection Systems (TPS). The NESC formed an independent review team with the core disciplines of materials, flight sciences, structures, mechanical analysis and thermal analysis. The Math Model Tools reviewed included damage prediction and stress analysis, aeroheating analysis, and thermal analysis tools. Some tools are physics-based and other tools are empirically-derived. Each tool was created for a specific use and timeframe, including certification, real-time pre-launch assessments. In addition, the tools are used together in an integrated strategy for assessing the ramifications of impact damage to tile and RCC. The NESC teams conducted a peer review of the engineering data package for each Math Model Tool. This report contains the summary of the team observations and recommendations from these reviews.

Antibody-protein interactions: benchmark datasets and prediction tools evaluation

PubMed Central

Ponomarenko, Julia V; Bourne, Philip E

2007-01-01

Background The ability to predict antibody binding sites (aka antigenic determinants or B-cell epitopes) for a given protein is a precursor to new vaccine design and diagnostics. Among the various methods of B-cell epitope identification X-ray crystallography is one of the most reliable methods. Using these experimental data computational methods exist for B-cell epitope prediction. As the number of structures of antibody-protein complexes grows, further interest in prediction methods using 3D structure is anticipated. This work aims to establish a benchmark for 3D structure-based epitope prediction methods. Results Two B-cell epitope benchmark datasets inferred from the 3D structures of antibody-protein complexes were defined. The first is a dataset of 62 representative 3D structures of protein antigens with inferred structural epitopes. The second is a dataset of 82 structures of antibody-protein complexes containing different structural epitopes. Using these datasets, eight web-servers developed for antibody and protein binding sites prediction have been evaluated. In no method did performance exceed a 40% precision and 46% recall. The values of the area under the receiver operating characteristic curve for the evaluated methods were about 0.6 for ConSurf, DiscoTope, and PPI-PRED methods and above 0.65 but not exceeding 0.70 for protein-protein docking methods when the best of the top ten models for the bound docking were considered; the remaining methods performed close to random. The benchmark datasets are included as a supplement to this paper. Conclusion It may be possible to improve epitope prediction methods through training on datasets which include only immune epitopes and through utilizing more features characterizing epitopes, for example, the evolutionary conservation score. Notwithstanding, overall poor performance may reflect the generality of antigenicity and hence the inability to decipher B-cell epitopes as an intrinsic feature of the protein. It is an open question as to whether ultimately discriminatory features can be found. PMID:17910770

Sustainability of transport structures - some aspects of the nonlinear reliability assessment

NASA Astrophysics Data System (ADS)

Pukl, Radomír; Sajdlová, Tereza; Strauss, Alfred; Lehký, David; Novák, Drahomír

2017-09-01

Efficient techniques for both nonlinear numerical analysis of concrete structures and advanced stochastic simulation methods have been combined in order to offer an advanced tool for assessment of realistic behaviour, failure and safety assessment of transport structures. The utilized approach is based on randomization of the non-linear finite element analysis of the structural models. Degradation aspects such as carbonation of concrete can be accounted in order predict durability of the investigated structure and its sustainability. Results can serve as a rational basis for the performance and sustainability assessment based on advanced nonlinear computer analysis of the structures of transport infrastructure such as bridges or tunnels. In the stochastic simulation the input material parameters obtained from material tests including their randomness and uncertainty are represented as random variables or fields. Appropriate identification of material parameters is crucial for the virtual failure modelling of structures and structural elements. Inverse analysis using artificial neural networks and virtual stochastic simulations approach is applied to determine the fracture mechanical parameters of the structural material and its numerical model. Structural response, reliability and sustainability have been investigated on different types of transport structures made from various materials using the above mentioned methodology and tools.

Research and application of borehole structure optimization based on pre-drill risk assessment

NASA Astrophysics Data System (ADS)

Zhang, Guohui; Liu, Xinyun; Chenrong; Hugui; Yu, Wenhua; Sheng, Yanan; Guan, Zhichuan

2017-11-01

Borehole structure design based on pre-drill risk assessment and considering risks related to drilling operation is the pre-condition for safe and smooth drilling operation. Major risks of drilling operation include lost circulation, blowout, sidewall collapsing, sticking and failure of drilling tools etc. In the study, studying data from neighboring wells was used to calculate the profile of formation pressure with credibility in the target well, then the borehole structure design for the target well assessment by using the drilling risk assessment to predict engineering risks before drilling. Finally, the prediction results were used to optimize borehole structure design to prevent such drilling risks. The newly-developed technique provides a scientific basis for lowering probability and frequency of drilling engineering risks, and shortening time required to drill a well, which is of great significance for safe and high-efficient drilling.

Structural Analysis of Composite Flywheels: an Integrated NDE and FEM Approach

NASA Technical Reports Server (NTRS)

Abdul-Aziz, Ali; Baaklini, George; Trudell, Jeffrey

2001-01-01

A structural assessment by integrating finite-element methods (FEM) and a nondestructive evaluation (NDE) of two flywheel rotor assemblies is presented. Composite rotor A is pancake-like with a solid hub design, and composite rotor B is cylindrical with a hollow hub design. Detailed analyses under combined centrifugal and interference-fit loading are performed. Two- and three-dimensional stress analyses and two-dimensional fracture mechanics analyses are conducted. A comparison of the structural analysis results obtained with those extracted via NDE findings is reported. Contact effects due to press-fit conditions are evaluated. Stress results generated from the finite-element analyses were corroborated with the analytical solution. Cracks due to rotational loading up to 48,000 rpm for rotor A and 34,000 rpm for rotor B were successfully imaged with NDE and predicted with FEM and fracture mechanics analyses. A procedure that extends current structural analysis to a life prediction tool is also defined.

Prediction of atmospheric degradation data for POPs by gene expression programming.

PubMed

Luan, F; Si, H Z; Liu, H T; Wen, Y Y; Zhang, X Y

2008-01-01

Quantitative structure-activity relationship models for the prediction of the mean and the maximum atmospheric degradation half-life values of persistent organic pollutants were developed based on the linear heuristic method (HM) and non-linear gene expression programming (GEP). Molecular descriptors, calculated from the structures alone, were used to represent the characteristics of the compounds. HM was used both to pre-select the whole descriptor sets and to build the linear model. GEP yielded satisfactory prediction results: the square of the correlation coefficient r(2) was 0.80 and 0.81 for the mean and maximum half-life values of the test set, and the root mean square errors were 0.448 and 0.426, respectively. The results of this work indicate that the GEP is a very promising tool for non-linear approximations.

Energy landscapes for machine learning

NASA Astrophysics Data System (ADS)

Ballard, Andrew J.; Das, Ritankar; Martiniani, Stefano; Mehta, Dhagash; Sagun, Levent; Stevenson, Jacob D.; Wales, David J.

Machine learning techniques are being increasingly used as flexible non-linear fitting and prediction tools in the physical sciences. Fitting functions that exhibit multiple solutions as local minima can be analysed in terms of the corresponding machine learning landscape. Methods to explore and visualise molecular potential energy landscapes can be applied to these machine learning landscapes to gain new insight into the solution space involved in training and the nature of the corresponding predictions. In particular, we can define quantities analogous to molecular structure, thermodynamics, and kinetics, and relate these emergent properties to the structure of the underlying landscape. This Perspective aims to describe these analogies with examples from recent applications, and suggest avenues for new interdisciplinary research.

Kinact: a computational approach for predicting activating missense mutations in protein kinases.

PubMed

Rodrigues, Carlos H M; Ascher, David B; Pires, Douglas E V

2018-05-21

Protein phosphorylation is tightly regulated due to its vital role in many cellular processes. While gain of function mutations leading to constitutive activation of protein kinases are known to be driver events of many cancers, the identification of these mutations has proven challenging. Here we present Kinact, a novel machine learning approach for predicting kinase activating missense mutations using information from sequence and structure. By adapting our graph-based signatures, Kinact represents both structural and sequence information, which are used as evidence to train predictive models. We show the combination of structural and sequence features significantly improved the overall accuracy compared to considering either primary or tertiary structure alone, highlighting their complementarity. Kinact achieved a precision of 87% and 94% and Area Under ROC Curve of 0.89 and 0.92 on 10-fold cross-validation, and on blind tests, respectively, outperforming well established tools (P < 0.01). We further show that Kinact performs equally well on homology models built using templates with sequence identity as low as 33%. Kinact is freely available as a user-friendly web server at http://biosig.unimelb.edu.au/kinact/.

Life Predicted in a Probabilistic Design Space for Brittle Materials With Transient Loads

NASA Technical Reports Server (NTRS)

Nemeth, Noel N.; Palfi, Tamas; Reh, Stefan

2005-01-01

Analytical techniques have progressively become more sophisticated, and now we can consider the probabilistic nature of the entire space of random input variables on the lifetime reliability of brittle structures. This was demonstrated with NASA s CARES/Life (Ceramic Analysis and Reliability Evaluation of Structures/Life) code combined with the commercially available ANSYS/Probabilistic Design System (ANSYS/PDS), a probabilistic analysis tool that is an integral part of the ANSYS finite-element analysis program. ANSYS/PDS allows probabilistic loads, component geometry, and material properties to be considered in the finite-element analysis. CARES/Life predicts the time dependent probability of failure of brittle material structures under generalized thermomechanical loading--such as that found in a turbine engine hot-section. Glenn researchers coupled ANSYS/PDS with CARES/Life to assess the effects of the stochastic variables of component geometry, loading, and material properties on the predicted life of the component for fully transient thermomechanical loading and cyclic loading.

Individual differences in children's innovative problem-solving are not predicted by divergent thinking or executive functions

PubMed Central

2016-01-01

Recent studies of children's tool innovation have revealed that there is variation in children's success in middle-childhood. In two individual differences studies, we sought to identify personal characteristics that might predict success on an innovation task. In Study 1, we found that although measures of divergent thinking were related to each other they did not predict innovation success. In Study 2, we measured executive functioning including: inhibition, working memory, attentional flexibility and ill-structured problem-solving. None of these measures predicted innovation, but, innovation was predicted by children's performance on a receptive vocabulary scale that may function as a proxy for general intelligence. We did not find evidence that children's innovation was predicted by specific personal characteristics. PMID:26926280

Review on failure prediction techniques of composite single lap joint

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ab Ghani, A.F., E-mail: ahmadfuad@utem.edu.my; Rivai, Ahmad, E-mail: ahmadrivai@utem.edu.my

2016-03-29

Adhesive bonding is the most appropriate joining method in construction of composite structures. The use of reliable design and prediction technique will produce better performance of bonded joints. Several papers from recent papers and journals have been reviewed and synthesized to understand the current state of the art in this area. It is done by studying the most relevant analytical solutions for composite adherends with start of reviewing the most fundamental ones involving beam/plate theory. It is then extended to review single lap joint non linearity and failure prediction and finally on the failure prediction on composite single lap joint.more » The review also encompasses the finite element modelling part as tool to predict the elastic response of composite single lap joint and failure prediction numerically.« less

Accelerated probabilistic inference of RNA structure evolution

PubMed Central

Holmes, Ian

2005-01-01

Background Pairwise stochastic context-free grammars (Pair SCFGs) are powerful tools for evolutionary analysis of RNA, including simultaneous RNA sequence alignment and secondary structure prediction, but the associated algorithms are intensive in both CPU and memory usage. The same problem is faced by other RNA alignment-and-folding algorithms based on Sankoff's 1985 algorithm. It is therefore desirable to constrain such algorithms, by pre-processing the sequences and using this first pass to limit the range of structures and/or alignments that can be considered. Results We demonstrate how flexible classes of constraint can be imposed, greatly reducing the computational costs while maintaining a high quality of structural homology prediction. Any score-attributed context-free grammar (e.g. energy-based scoring schemes, or conditionally normalized Pair SCFGs) is amenable to this treatment. It is now possible to combine independent structural and alignment constraints of unprecedented general flexibility in Pair SCFG alignment algorithms. We outline several applications to the bioinformatics of RNA sequence and structure, including Waterman-Eggert N-best alignments and progressive multiple alignment. We evaluate the performance of the algorithm on test examples from the RFAM database. Conclusion A program, Stemloc, that implements these algorithms for efficient RNA sequence alignment and structure prediction is available under the GNU General Public License. PMID:15790387

IsoCleft Finder – a web-based tool for the detection and analysis of protein binding-site geometric and chemical similarities

PubMed Central

Najmanovich, Rafael

2013-01-01

IsoCleft Finder is a web-based tool for the detection of local geometric and chemical similarities between potential small-molecule binding cavities and a non-redundant dataset of ligand-bound known small-molecule binding-sites. The non-redundant dataset developed as part of this study is composed of 7339 entries representing unique Pfam/PDB-ligand (hetero group code) combinations with known levels of cognate ligand similarity. The query cavity can be uploaded by the user or detected automatically by the system using existing PDB entries as well as user-provided structures in PDB format. In all cases, the user can refine the definition of the cavity interactively via a browser-based Jmol 3D molecular visualization interface. Furthermore, users can restrict the search to a subset of the dataset using a cognate-similarity threshold. Local structural similarities are detected using the IsoCleft software and ranked according to two criteria (number of atoms in common and Tanimoto score of local structural similarity) and the associated Z-score and p-value measures of statistical significance. The results, including predicted ligands, target proteins, similarity scores, number of atoms in common, etc., are shown in a powerful interactive graphical interface. This interface permits the visualization of target ligands superimposed on the query cavity and additionally provides a table of pairwise ligand topological similarities. Similarities between top scoring ligands serve as an additional tool to judge the quality of the results obtained. We present several examples where IsoCleft Finder provides useful functional information. IsoCleft Finder results are complementary to existing approaches for the prediction of protein function from structure, rational drug design and x-ray crystallography. IsoCleft Finder can be found at: http://bcb.med.usherbrooke.ca/isocleftfinder. PMID:24555058

Mapping genetic variations to three-dimensional protein structures to enhance variant interpretation: a proposed framework.

PubMed

Glusman, Gustavo; Rose, Peter W; Prlić, Andreas; Dougherty, Jennifer; Duarte, José M; Hoffman, Andrew S; Barton, Geoffrey J; Bendixen, Emøke; Bergquist, Timothy; Bock, Christian; Brunk, Elizabeth; Buljan, Marija; Burley, Stephen K; Cai, Binghuang; Carter, Hannah; Gao, JianJiong; Godzik, Adam; Heuer, Michael; Hicks, Michael; Hrabe, Thomas; Karchin, Rachel; Leman, Julia Koehler; Lane, Lydie; Masica, David L; Mooney, Sean D; Moult, John; Omenn, Gilbert S; Pearl, Frances; Pejaver, Vikas; Reynolds, Sheila M; Rokem, Ariel; Schwede, Torsten; Song, Sicheng; Tilgner, Hagen; Valasatava, Yana; Zhang, Yang; Deutsch, Eric W

2017-12-18

The translation of personal genomics to precision medicine depends on the accurate interpretation of the multitude of genetic variants observed for each individual. However, even when genetic variants are predicted to modify a protein, their functional implications may be unclear. Many diseases are caused by genetic variants affecting important protein features, such as enzyme active sites or interaction interfaces. The scientific community has catalogued millions of genetic variants in genomic databases and thousands of protein structures in the Protein Data Bank. Mapping mutations onto three-dimensional (3D) structures enables atomic-level analyses of protein positions that may be important for the stability or formation of interactions; these may explain the effect of mutations and in some cases even open a path for targeted drug development. To accelerate progress in the integration of these data types, we held a two-day Gene Variation to 3D (GVto3D) workshop to report on the latest advances and to discuss unmet needs. The overarching goal of the workshop was to address the question: what can be done together as a community to advance the integration of genetic variants and 3D protein structures that could not be done by a single investigator or laboratory? Here we describe the workshop outcomes, review the state of the field, and propose the development of a framework with which to promote progress in this arena. The framework will include a set of standard formats, common ontologies, a common application programming interface to enable interoperation of the resources, and a Tool Registry to make it easy to find and apply the tools to specific analysis problems. Interoperability will enable integration of diverse data sources and tools and collaborative development of variant effect prediction methods.

Dynamic analysis of space structures including elastic, multibody, and control behavior

NASA Technical Reports Server (NTRS)

Pinson, Larry; Soosaar, Keto

1989-01-01

The problem is to develop analysis methods, modeling stategies, and simulation tools to predict with assurance the on-orbit performance and integrity of large complex space structures that cannot be verified on the ground. The problem must incorporate large reliable structural models, multi-body flexible dynamics, multi-tier controller interaction, environmental models including 1g and atmosphere, various on-board disturbances, and linkage to mission-level performance codes. All areas are in serious need of work, but the weakest link is multi-body flexible dynamics.

PAT: predictor for structured units and its application for the optimization of target molecules for the generation of synthetic antibodies.

PubMed

Jeon, Jouhyun; Arnold, Roland; Singh, Fateh; Teyra, Joan; Braun, Tatjana; Kim, Philip M

2016-04-01

The identification of structured units in a protein sequence is an important first step for most biochemical studies. Importantly for this study, the identification of stable structured region is a crucial first step to generate novel synthetic antibodies. While many approaches to find domains or predict structured regions exist, important limitations remain, such as the optimization of domain boundaries and the lack of identification of non-domain structured units. Moreover, no integrated tool exists to find and optimize structural domains within protein sequences. Here, we describe a new tool, PAT ( http://www.kimlab.org/software/pat ) that can efficiently identify both domains (with optimized boundaries) and non-domain putative structured units. PAT automatically analyzes various structural properties, evaluates the folding stability, and reports possible structural domains in a given protein sequence. For reliability evaluation of PAT, we applied PAT to identify antibody target molecules based on the notion that soluble and well-defined protein secondary and tertiary structures are appropriate target molecules for synthetic antibodies. PAT is an efficient and sensitive tool to identify structured units. A performance analysis shows that PAT can characterize structurally well-defined regions in a given sequence and outperforms other efforts to define reliable boundaries of domains. Specially, PAT successfully identifies experimentally confirmed target molecules for antibody generation. PAT also offers the pre-calculated results of 20,210 human proteins to accelerate common queries. PAT can therefore help to investigate large-scale structured domains and improve the success rate for synthetic antibody generation.

Streamflow prediction using multi-site rainfall obtained from hydroclimatic teleconnection

NASA Astrophysics Data System (ADS)

Kashid, S. S.; Ghosh, Subimal; Maity, Rajib

2010-12-01

SummarySimultaneous variations in weather and climate over widely separated regions are commonly known as "hydroclimatic teleconnections". Rainfall and runoff patterns, over continents, are found to be significantly teleconnected, with large-scale circulation patterns, through such hydroclimatic teleconnections. Though such teleconnections exist in nature, it is very difficult to model them, due to their inherent complexity. Statistical techniques and Artificial Intelligence (AI) tools gain popularity in modeling hydroclimatic teleconnection, based on their ability, in capturing the complicated relationship between the predictors (e.g. sea surface temperatures) and predictand (e.g., rainfall). Genetic Programming is such an AI tool, which is capable of capturing nonlinear relationship, between predictor and predictand, due to its flexible functional structure. In the present study, gridded multi-site weekly rainfall is predicted from El Niño Southern Oscillation (ENSO) indices, Equatorial Indian Ocean Oscillation (EQUINOO) indices, Outgoing Longwave Radiation (OLR) and lag rainfall at grid points, over the catchment, using Genetic Programming. The predicted rainfall is further used in a Genetic Programming model to predict streamflows. The model is applied for weekly forecasting of streamflow in Mahanadi River, India, and satisfactory performance is observed.

CaFE: a tool for binding affinity prediction using end-point free energy methods.

PubMed

Liu, Hui; Hou, Tingjun

2016-07-15

Accurate prediction of binding free energy is of particular importance to computational biology and structure-based drug design. Among those methods for binding affinity predictions, the end-point approaches, such as MM/PBSA and LIE, have been widely used because they can achieve a good balance between prediction accuracy and computational cost. Here we present an easy-to-use pipeline tool named Calculation of Free Energy (CaFE) to conduct MM/PBSA and LIE calculations. Powered by the VMD and NAMD programs, CaFE is able to handle numerous static coordinate and molecular dynamics trajectory file formats generated by different molecular simulation packages and supports various force field parameters. CaFE source code and documentation are freely available under the GNU General Public License via GitHub at https://github.com/huiliucode/cafe_plugin It is a VMD plugin written in Tcl and the usage is platform-independent. tingjunhou@zju.edu.cn. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

A Model Based Approach to Increase the Part Accuracy in Robot Based Incremental Sheet Metal Forming

DOE Office of Scientific and Technical Information (OSTI.GOV)

Meier, Horst; Laurischkat, Roman; Zhu Junhong

One main influence on the dimensional accuracy in robot based incremental sheet metal forming results from the compliance of the involved robot structures. Compared to conventional machine tools the low stiffness of the robot's kinematic results in a significant deviation of the planned tool path and therefore in a shape of insufficient quality. To predict and compensate these deviations offline, a model based approach, consisting of a finite element approach, to simulate the sheet forming, and a multi body system, modeling the compliant robot structure, has been developed. This paper describes the implementation and experimental verification of the multi bodymore » system model and its included compensation method.« less

Prognostic and Prediction Tools in Bladder Cancer: A Comprehensive Review of the Literature.

PubMed

Kluth, Luis A; Black, Peter C; Bochner, Bernard H; Catto, James; Lerner, Seth P; Stenzl, Arnulf; Sylvester, Richard; Vickers, Andrew J; Xylinas, Evanguelos; Shariat, Shahrokh F

2015-08-01

This review focuses on risk assessment and prediction tools for bladder cancer (BCa). To review the current knowledge on risk assessment and prediction tools to enhance clinical decision making and counseling of patients with BCa. A literature search in English was performed using PubMed in July 2013. Relevant risk assessment and prediction tools for BCa were selected. More than 1600 publications were retrieved. Special attention was given to studies that investigated the clinical benefit of a prediction tool. Most prediction tools for BCa focus on the prediction of disease recurrence and progression in non-muscle-invasive bladder cancer or disease recurrence and survival after radical cystectomy. Although these tools are helpful, recent prediction tools aim to address a specific clinical problem, such as the prediction of organ-confined disease and lymph node metastasis to help identify patients who might benefit from neoadjuvant chemotherapy. Although a large number of prediction tools have been reported in recent years, many of them lack external validation. Few studies have investigated the clinical utility of any given model as measured by its ability to improve clinical decision making. There is a need for novel biomarkers to improve the accuracy and utility of prediction tools for BCa. Decision tools hold the promise of facilitating the shared decision process, potentially improving clinical outcomes for BCa patients. Prediction models need external validation and assessment of clinical utility before they can be incorporated into routine clinical care. We looked at models that aim to predict outcomes for patients with bladder cancer (BCa). We found a large number of prediction models that hold the promise of facilitating treatment decisions for patients with BCa. However, many models are missing confirmation in a different patient cohort, and only a few studies have tested the clinical utility of any given model as measured by its ability to improve clinical decision making. Copyright © 2015 European Association of Urology. Published by Elsevier B.V. All rights reserved.

Density functional theory in the solid state

PubMed Central

Hasnip, Philip J.; Refson, Keith; Probert, Matt I. J.; Yates, Jonathan R.; Clark, Stewart J.; Pickard, Chris J.

2014-01-01

Density functional theory (DFT) has been used in many fields of the physical sciences, but none so successfully as in the solid state. From its origins in condensed matter physics, it has expanded into materials science, high-pressure physics and mineralogy, solid-state chemistry and more, powering entire computational subdisciplines. Modern DFT simulation codes can calculate a vast range of structural, chemical, optical, spectroscopic, elastic, vibrational and thermodynamic phenomena. The ability to predict structure–property relationships has revolutionized experimental fields, such as vibrational and solid-state NMR spectroscopy, where it is the primary method to analyse and interpret experimental spectra. In semiconductor physics, great progress has been made in the electronic structure of bulk and defect states despite the severe challenges presented by the description of excited states. Studies are no longer restricted to known crystallographic structures. DFT is increasingly used as an exploratory tool for materials discovery and computational experiments, culminating in ex nihilo crystal structure prediction, which addresses the long-standing difficult problem of how to predict crystal structure polymorphs from nothing but a specified chemical composition. We present an overview of the capabilities of solid-state DFT simulations in all of these topics, illustrated with recent examples using the CASTEP computer program. PMID:24516184

The General Alcoholics Anonymous Tools of Recovery: The Adoption of 12-Step Practices and Beliefs

PubMed Central

Greenfield, Brenna L.; Tonigan, J. Scott

2013-01-01

Working the 12 steps is widely prescribed for Alcoholics Anonymous (AA) members although the relative merits of different methods for measuring step-work have received minimal attention and even less is known about how step-work predicts later substance use. The current study (1) compared endorsements of step-work on an face-valid or direct measure, the Alcoholics Anonymous Inventory (AAI), with an indirect measure of step-work, the General Alcoholics Anonymous Tools of Recovery (GAATOR), (2) evaluated the underlying factor structure of the GAATOR and changes in step-work over time, (3) examined changes in the endorsement of step-work over time, and (4) investigated how, if at all, 12-step-work predicted later substance use. New AA affiliates (N = 130) completed assessments at intake, 3, 6, and 9 months. Significantly more participants endorsed step-work on the GAATOR than on the AAI for nine of the 12 steps. An exploratory factor analysis revealed a two-factor structure for the GAATOR comprising Behavioral Step-Work and Spiritual Step-Work. Behavioral Step-Work did not change over time, but was predicted by having a sponsor, while Spiritual Step-Work decreased over time and increases were predicted by attending 12-step meetings or treatment. Behavioral Step-Work did not prospectively predict substance use. In contrast, Spiritual Step-Work predicted percent days abstinent, an effect that is consistent with recent work on the mediating effects of spiritual growth, AA, and increased abstinence. Behavioral and Spiritual Step-Work appear to be conceptually distinct components of step-work that have distinct predictors and unique impacts on outcomes. PMID:22867293

Computational Prediction of Electron Ionization Mass Spectra to Assist in GC/MS Compound Identification.

PubMed

Allen, Felicity; Pon, Allison; Greiner, Russ; Wishart, David

2016-08-02

We describe a tool, competitive fragmentation modeling for electron ionization (CFM-EI) that, given a chemical structure (e.g., in SMILES or InChI format), computationally predicts an electron ionization mass spectrum (EI-MS) (i.e., the type of mass spectrum commonly generated by gas chromatography mass spectrometry). The predicted spectra produced by this tool can be used for putative compound identification, complementing measured spectra in reference databases by expanding the range of compounds able to be considered when availability of measured spectra is limited. The tool extends CFM-ESI, a recently developed method for computational prediction of electrospray tandem mass spectra (ESI-MS/MS), but unlike CFM-ESI, CFM-EI can handle odd-electron ions and isotopes and incorporates an artificial neural network. Tests on EI-MS data from the NIST database demonstrate that CFM-EI is able to model fragmentation likelihoods in low-resolution EI-MS data, producing predicted spectra whose dot product scores are significantly better than full enumeration "bar-code" spectra. CFM-EI also outperformed previously reported results for MetFrag, MOLGEN-MS, and Mass Frontier on one compound identification task. It also outperformed MetFrag in a range of other compound identification tasks involving a much larger data set, containing both derivatized and nonderivatized compounds. While replicate EI-MS measurements of chemical standards are still a more accurate point of comparison, CFM-EI's predictions provide a much-needed alternative when no reference standard is available for measurement. CFM-EI is available at https://sourceforge.net/projects/cfm-id/ for download and http://cfmid.wishartlab.com as a web service.

The Index Offence Representation Scales; a predictive clinical tool in the management of dangerous, violent patients with personality disorder?

PubMed

McGauley, Gill; Ferris, Scott; Marin-Avellan, Luisa; Fonagy, Peter

2013-10-01

Forensic mental health professionals attach considerable importance to their patient's description of his or her index offence. Despite this, there is no systematic approach to examining and formulating the patient's offence narrative. To use the index offence narratives and capacity to mentalize of violent offender-patients with personality disorder to develop a tool to predict their progress and to evaluate that tool. In a prospective, cohort study, the index offence narratives of 66 violent high security hospital patients with personality disorder were obtained from a semi-structured interview and used to generate the Index Offence Representational Scales (IORS). The predictive validity of these scales was investigated across a range of outcome variables, controlling for the association between initial and final value of the dependent variable. The degree to which patients held internal representations of interpersonal violence and malevolence, as measured by the IORS, predicted subsequent violent behaviour. In contrast to their actual aggressive behaviour, these patients rated themselves as having fewer symptoms on the Symptom Checklist-90-R (SCL-90-R) and fewer problems in interpersonal relationships on the Inventory of Interpersonal Problems. A more empathic victim representation on the IORS predicted better engagement with treatment. The IORS show promise for helping clinicians formulate the early institutional pathway of seriously violent people with personality disorder, particularly with respect to their overt aggression and prosocial engagement. Replication studies are, however, indicated. Copyright © 2013 John Wiley & Sons, Ltd.

A New Analysis Tool Assessment for Rotordynamic Modeling of Gas Foil Bearings

NASA Technical Reports Server (NTRS)

Howard, Samuel A.; SanAndres, Luis

2010-01-01

Gas foil bearings offer several advantages over traditional bearing types that make them attractive for use in high-speed turbomachinery. They can operate at very high temperatures, require no lubrication supply (oil pumps, seals, etc.), exhibit very long life with no maintenance, and once operating airborne, have very low power loss. The use of gas foil bearings in high-speed turbomachinery has been accelerating in recent years, although the pace has been slow. One of the contributing factors to the slow growth has been a lack of analysis tools, benchmarked to measurements, to predict gas foil bearing behavior in rotating machinery. To address this shortcoming, NASA Glenn Research Center (GRC) has supported the development of analytical tools to predict gas foil bearing performance. One of the codes has the capability to predict rotordynamic coefficients, power loss, film thickness, structural deformation, and more. The current paper presents an assessment of the predictive capability of the code, named XLGFBTH (Texas A&M University). A test rig at GRC is used as a simulated case study to compare rotordynamic analysis using output from the code to actual rotor response as measured in the test rig. The test rig rotor is supported on two gas foil journal bearings manufactured at GRC, with all pertinent geometry disclosed. The resulting comparison shows that the rotordynamic coefficients calculated using XLGFBTH represent the dynamics of the system reasonably well, especially as they pertain to predicting critical speeds.

Search for 5'-leader regulatory RNA structures based on gene annotation aided by the RiboGap database.

PubMed

Naghdi, Mohammad Reza; Smail, Katia; Wang, Joy X; Wade, Fallou; Breaker, Ronald R; Perreault, Jonathan

2017-03-15

The discovery of noncoding RNAs (ncRNAs) and their importance for gene regulation led us to develop bioinformatics tools to pursue the discovery of novel ncRNAs. Finding ncRNAs de novo is challenging, first due to the difficulty of retrieving large numbers of sequences for given gene activities, and second due to exponential demands on calculation needed for comparative genomics on a large scale. Recently, several tools for the prediction of conserved RNA secondary structure were developed, but many of them are not designed to uncover new ncRNAs, or are too slow for conducting analyses on a large scale. Here we present various approaches using the database RiboGap as a primary tool for finding known ncRNAs and for uncovering simple sequence motifs with regulatory roles. This database also can be used to easily extract intergenic sequences of eubacteria and archaea to find conserved RNA structures upstream of given genes. We also show how to extend analysis further to choose the best candidate ncRNAs for experimental validation. Copyright © 2017 Elsevier Inc. All rights reserved.

Flow Structures and Noise Produced by a Heated Rectangular Nozzle with a Third Stream and Aft Deck

NASA Astrophysics Data System (ADS)

Ruscher, Christopher; Gogineni, Sivaram; Kiel, Barry

2015-11-01

Jet noise is a huge issue that affects both civilian and military aviation and is a two-fold problem. Near-field noise causes hearing damage and is of great concern to the Navy. Far-field noise is also a concern for military and civilian aircraft. For military jets, the trend has shown that newer and more advanced planes are louder than their predecessors. Most of these planes are designed keeping the performance as the main driver in mind while the jet noise becomes an afterthought. To remedy this and to aid the design process, we propose to create a joint noise and performance prediction tool. To create this tool, one must understand how the near-field flow structures generate noise and how they are related to far-field noise. In the current work, we considered rectangular, three-stream nozzle with an aft deck and investigated the flow structures such as corner vortices, shocks and their impact on the noise generation mechanism. We have also used state-of-the-art data analytical tools such as wavelets, POD, and stochastic estimations.

Computational methods in drug discovery

PubMed Central

Leelananda, Sumudu P

2016-01-01

The process for drug discovery and development is challenging, time consuming and expensive. Computer-aided drug discovery (CADD) tools can act as a virtual shortcut, assisting in the expedition of this long process and potentially reducing the cost of research and development. Today CADD has become an effective and indispensable tool in therapeutic development. The human genome project has made available a substantial amount of sequence data that can be used in various drug discovery projects. Additionally, increasing knowledge of biological structures, as well as increasing computer power have made it possible to use computational methods effectively in various phases of the drug discovery and development pipeline. The importance of in silico tools is greater than ever before and has advanced pharmaceutical research. Here we present an overview of computational methods used in different facets of drug discovery and highlight some of the recent successes. In this review, both structure-based and ligand-based drug discovery methods are discussed. Advances in virtual high-throughput screening, protein structure prediction methods, protein–ligand docking, pharmacophore modeling and QSAR techniques are reviewed. PMID:28144341

Computational methods in drug discovery.

PubMed

Leelananda, Sumudu P; Lindert, Steffen

2016-01-01

The process for drug discovery and development is challenging, time consuming and expensive. Computer-aided drug discovery (CADD) tools can act as a virtual shortcut, assisting in the expedition of this long process and potentially reducing the cost of research and development. Today CADD has become an effective and indispensable tool in therapeutic development. The human genome project has made available a substantial amount of sequence data that can be used in various drug discovery projects. Additionally, increasing knowledge of biological structures, as well as increasing computer power have made it possible to use computational methods effectively in various phases of the drug discovery and development pipeline. The importance of in silico tools is greater than ever before and has advanced pharmaceutical research. Here we present an overview of computational methods used in different facets of drug discovery and highlight some of the recent successes. In this review, both structure-based and ligand-based drug discovery methods are discussed. Advances in virtual high-throughput screening, protein structure prediction methods, protein-ligand docking, pharmacophore modeling and QSAR techniques are reviewed.

NIR monitoring of in-service wood structures

Treesearch

Michela Zanetti; Timothy G. Rials; Douglas Rammer

2005-01-01

Near infrared spectroscopy (NIRS) was used to study a set of Southern Yellow Pine boards exposed to natural weathering for different periods of exposure time. This non-destructive spectroscopic technique is a very powerful tool to predict the weathering of wood when used in combination with multivariate analysis (Principal Component Analysis, PCA, and Projection to...

Mining and Modeling Real-World Networks: Patterns, Anomalies, and Tools

ERIC Educational Resources Information Center

Akoglu, Leman

2012-01-01

Large real-world graph (a.k.a network, relational) data are omnipresent, in online media, businesses, science, and the government. Analysis of these massive graphs is crucial, in order to extract descriptive and predictive knowledge with many commercial, medical, and environmental applications. In addition to its general structure, knowing what…

Simulating the Effects of Alternative Forest Management Strategies on Landscape Structure

Treesearch

Eric J. Gustafson; Thomas Crow

1996-01-01

Quantitative, spatial tools are needed to assess the long-term spatial consequences of alternative management strategies for land use planning and resource management. We constructed a timber harvest allocation model (HARVEST) that provides a visual and quantitative means to predict the spatial pattern of forest openings produced by alternative harvest strategies....

Monte Carlo Simulation to Estimate Likelihood of Direct Lightning Strikes

NASA Technical Reports Server (NTRS)

Mata, Carlos; Medelius, Pedro

2008-01-01

A software tool has been designed to quantify the lightning exposure at launch sites of the stack at the pads under different configurations. In order to predict lightning strikes to generic structures, this model uses leaders whose origins (in the x-y plane) are obtained from a 2D random, normal distribution.

A review of fracture mechanics life technology

NASA Technical Reports Server (NTRS)

Besuner, P. M.; Harris, D. O.; Thomas, J. M.

1986-01-01

Lifetime prediction technology for structural components subjected to cyclic loads is examined. The central objectives of the project are: (1) to report the current state of the art, and (2) recommend future development of fracture mechanics-based analytical tools for modeling subcritical fatigue crack growth in structures. Of special interest is the ability to apply these tools to practical engineering problems and the developmental steps necessary to bring vital technologies to this stage. The authors conducted a survey of published literature and numerous discussions with experts in the field of fracture mechanics life technology. One of the key points made is that fracture mechanics analyses of crack growth often involve consideration of fatigue and fracture under extreme conditions. Therefore, inaccuracies in predicting component lifetime will be dominated by inaccuracies in environment and fatigue crack growth relations, stress intensity factor solutions, and methods used to model given loads and stresses. Suggestions made for reducing these inaccuracies include development of improved models of subcritical crack growth, research efforts aimed at better characterizing residual and assembly stresses that can be introduced during fabrication, and more widespread and uniform use of the best existing methods.

Combining MOSCED with molecular simulation free energy calculations or electronic structure calculations to develop an efficient tool for solvent formulation and selection

NASA Astrophysics Data System (ADS)

Cox, Courtney E.; Phifer, Jeremy R.; Ferreira da Silva, Larissa; Gonçalves Nogueira, Gabriel; Ley, Ryan T.; O'Loughlin, Elizabeth J.; Pereira Barbosa, Ana Karolyne; Rygelski, Brett T.; Paluch, Andrew S.

2017-02-01

Solubility parameter based methods have long been a valuable tool for solvent formulation and selection. Of these methods, the MOdified Separation of Cohesive Energy Density (MOSCED) has recently been shown to correlate well the equilibrium solubility of multifunctional non-electrolyte solids. However, before it can be applied to a novel solute, a limited amount of reference solubility data is required to regress the necessary MOSCED parameters. Here we demonstrate for the solutes methylparaben, ethylparaben, propylparaben, butylparaben, lidocaine and ephedrine how conventional molecular simulation free energy calculations or electronic structure calculations in a continuum solvent, here the SMD or SM8 solvation model, can instead be used to generate the necessary reference data, resulting in a predictive flavor of MOSCED. Adopting the melting point temperature and enthalpy of fusion of these compounds from experiment, we are able to predict equilibrium solubilities. We find the method is able to well correlate the (mole fraction) equilibrium solubility in non-aqueous solvents over four orders of magnitude with good quantitative agreement.

Combining MOSCED with molecular simulation free energy calculations or electronic structure calculations to develop an efficient tool for solvent formulation and selection.

PubMed

Cox, Courtney E; Phifer, Jeremy R; Ferreira da Silva, Larissa; Gonçalves Nogueira, Gabriel; Ley, Ryan T; O'Loughlin, Elizabeth J; Pereira Barbosa, Ana Karolyne; Rygelski, Brett T; Paluch, Andrew S

2017-02-01

Solubility parameter based methods have long been a valuable tool for solvent formulation and selection. Of these methods, the MOdified Separation of Cohesive Energy Density (MOSCED) has recently been shown to correlate well the equilibrium solubility of multifunctional non-electrolyte solids. However, before it can be applied to a novel solute, a limited amount of reference solubility data is required to regress the necessary MOSCED parameters. Here we demonstrate for the solutes methylparaben, ethylparaben, propylparaben, butylparaben, lidocaine and ephedrine how conventional molecular simulation free energy calculations or electronic structure calculations in a continuum solvent, here the SMD or SM8 solvation model, can instead be used to generate the necessary reference data, resulting in a predictive flavor of MOSCED. Adopting the melting point temperature and enthalpy of fusion of these compounds from experiment, we are able to predict equilibrium solubilities. We find the method is able to well correlate the (mole fraction) equilibrium solubility in non-aqueous solvents over four orders of magnitude with good quantitative agreement.

A review of fracture mechanics life technology

NASA Technical Reports Server (NTRS)

Thomas, J. M.; Besuner, P. M.; Harris, D. O.

1985-01-01

Current lifetime prediction technology for structural components subjected to cyclic loads was reviewed. The central objectives of the project were to report the current state of and recommend future development of fracture mechanics-based analytical tools for modeling and forecasting subcritical fatigue crack growth in structures. Of special interest to NASA was the ability to apply these tools to practical engineering problems and the developmental steps necessary to bring vital technologies to this stage. A survey of published literature and numerous discussions with experts in the field of fracture mechanics life technology were conducted. One of the key points made is that fracture mechanics analyses of crack growth often involve consideration of fatigue and fracture under extreme conditions. Therefore, inaccuracies in predicting component lifetime will be dominated by inaccuracies in environment and fatigue crack growth relations, stress intensity factor solutions, and methods used to model given loads and stresses. Suggestions made for reducing these inaccuracies include: development of improved models of subcritical crack growth, research efforts aimed at better characterizing residual and assembly stresses that can be introduced during fabrication, and more widespread and uniform use of the best existing methods.

Predicting Gene Structure Changes Resulting from Genetic Variants via Exon Definition Features.

PubMed

Majoros, William H; Holt, Carson; Campbell, Michael S; Ware, Doreen; Yandell, Mark; Reddy, Timothy E

2018-04-25

Genetic variation that disrupts gene function by altering gene splicing between individuals can substantially influence traits and disease. In those cases, accurately predicting the effects of genetic variation on splicing can be highly valuable for investigating the mechanisms underlying those traits and diseases. While methods have been developed to generate high quality computational predictions of gene structures in reference genomes, the same methods perform poorly when used to predict the potentially deleterious effects of genetic changes that alter gene splicing between individuals. Underlying that discrepancy in predictive ability are the common assumptions by reference gene finding algorithms that genes are conserved, well-formed, and produce functional proteins. We describe a probabilistic approach for predicting recent changes to gene structure that may or may not conserve function. The model is applicable to both coding and noncoding genes, and can be trained on existing gene annotations without requiring curated examples of aberrant splicing. We apply this model to the problem of predicting altered splicing patterns in the genomes of individual humans, and we demonstrate that performing gene-structure prediction without relying on conserved coding features is feasible. The model predicts an unexpected abundance of variants that create de novo splice sites, an observation supported by both simulations and empirical data from RNA-seq experiments. While these de novo splice variants are commonly misinterpreted by other tools as coding or noncoding variants of little or no effect, we find that in some cases they can have large effects on splicing activity and protein products, and we propose that they may commonly act as cryptic factors in disease. The software is available from geneprediction.org/SGRF. bmajoros@duke.edu. Supplementary information is available at Bioinformatics online.

Time-efficient simulations of tight-binding electronic structures with Intel Xeon PhiTM many-core processors

NASA Astrophysics Data System (ADS)

Ryu, Hoon; Jeong, Yosang; Kang, Ji-Hoon; Cho, Kyu Nam

2016-12-01

Modelling of multi-million atomic semiconductor structures is important as it not only predicts properties of physically realizable novel materials, but can accelerate advanced device designs. This work elaborates a new Technology-Computer-Aided-Design (TCAD) tool for nanoelectronics modelling, which uses a sp3d5s∗ tight-binding approach to describe multi-million atomic structures, and simulate electronic structures with high performance computing (HPC), including atomic effects such as alloy and dopant disorders. Being named as Quantum simulation tool for Advanced Nanoscale Devices (Q-AND), the tool shows nice scalability on traditional multi-core HPC clusters implying the strong capability of large-scale electronic structure simulations, particularly with remarkable performance enhancement on latest clusters of Intel Xeon PhiTM coprocessors. A review of the recent modelling study conducted to understand an experimental work of highly phosphorus-doped silicon nanowires, is presented to demonstrate the utility of Q-AND. Having been developed via Intel Parallel Computing Center project, Q-AND will be open to public to establish a sound framework of nanoelectronics modelling with advanced HPC clusters of a many-core base. With details of the development methodology and exemplary study of dopant electronics, this work will present a practical guideline for TCAD development to researchers in the field of computational nanoelectronics.

CNNH_PSS: protein 8-class secondary structure prediction by convolutional neural network with highway.

PubMed

Zhou, Jiyun; Wang, Hongpeng; Zhao, Zhishan; Xu, Ruifeng; Lu, Qin

2018-05-08

Protein secondary structure is the three dimensional form of local segments of proteins and its prediction is an important problem in protein tertiary structure prediction. Developing computational approaches for protein secondary structure prediction is becoming increasingly urgent. We present a novel deep learning based model, referred to as CNNH_PSS, by using multi-scale CNN with highway. In CNNH_PSS, any two neighbor convolutional layers have a highway to deliver information from current layer to the output of the next one to keep local contexts. As lower layers extract local context while higher layers extract long-range interdependencies, the highways between neighbor layers allow CNNH_PSS to have ability to extract both local contexts and long-range interdependencies. We evaluate CNNH_PSS on two commonly used datasets: CB6133 and CB513. CNNH_PSS outperforms the multi-scale CNN without highway by at least 0.010 Q8 accuracy and also performs better than CNF, DeepCNF and SSpro8, which cannot extract long-range interdependencies, by at least 0.020 Q8 accuracy, demonstrating that both local contexts and long-range interdependencies are indeed useful for prediction. Furthermore, CNNH_PSS also performs better than GSM and DCRNN which need extra complex model to extract long-range interdependencies. It demonstrates that CNNH_PSS not only cost less computer resource, but also achieves better predicting performance. CNNH_PSS have ability to extracts both local contexts and long-range interdependencies by combing multi-scale CNN and highway network. The evaluations on common datasets and comparisons with state-of-the-art methods indicate that CNNH_PSS is an useful and efficient tool for protein secondary structure prediction.

Structure prediction of Fe(II) 2-oxoglutarate dioxygenase from a psychrophilic yeast Glaciozyma antarctica PI12

NASA Astrophysics Data System (ADS)

Yusof, Nik Yusnoraini; Bakar, Farah Diba Abu; Mahadi, Nor Muhammad; Raih, Mohd Firdaus; Murad, Abdul Munir Abdul

2015-09-01

A cDNA encoding Fe(II) 2-oxoglutarate (2OG) dependent dioxygenases was isolated from psychrophilic yeast, Glaciozyma antarctica PI12. We have successfully amplified 1,029 bp cDNA sequence that encodes 342 amino acid with predicted molecular weight 38 kDa. The prediction protein was analysed using various bioinformatics tools to explore the properties of the protein. Based on a BLAST search analysis, the Fe2OX amino acid sequence showed 61% identity to the sequence of oxoglutarate/iron-dependent oxygenase from Rhodosporidium toruloides NP11. SignalP prediction showed that the Fe2OX protein contains no putative signal peptide, which suggests that this enzyme most probably localised intracellularly.The structure of Fe2OX was predicted by homology modelling using MODELLER9v11. The model with the lowest objective function was selected from hundred models generated using MODELLER9v11. Analysis of the structure revealed the longer loop at Fe2OX from G.antarctica that might be responsible for the flexibility of the structure, which contributes to its adaptation to low temperatures. Fe2OX hold a highly conserved Fe(II) binding HXD/E…H triad motif. The binding site for 2-oxoglutarate was found conserved for Arg280 among reported studies, however the Phe268 was found to be different in Fe2OX.

BRAKER1: Unsupervised RNA-Seq-Based Genome Annotation with GeneMark-ET and AUGUSTUS.

PubMed

Hoff, Katharina J; Lange, Simone; Lomsadze, Alexandre; Borodovsky, Mark; Stanke, Mario

2016-03-01

Gene finding in eukaryotic genomes is notoriously difficult to automate. The task is to design a work flow with a minimal set of tools that would reach state-of-the-art performance across a wide range of species. GeneMark-ET is a gene prediction tool that incorporates RNA-Seq data into unsupervised training and subsequently generates ab initio gene predictions. AUGUSTUS is a gene finder that usually requires supervised training and uses information from RNA-Seq reads in the prediction step. Complementary strengths of GeneMark-ET and AUGUSTUS provided motivation for designing a new combined tool for automatic gene prediction. We present BRAKER1, a pipeline for unsupervised RNA-Seq-based genome annotation that combines the advantages of GeneMark-ET and AUGUSTUS. As input, BRAKER1 requires a genome assembly file and a file in bam-format with spliced alignments of RNA-Seq reads to the genome. First, GeneMark-ET performs iterative training and generates initial gene structures. Second, AUGUSTUS uses predicted genes for training and then integrates RNA-Seq read information into final gene predictions. In our experiments, we observed that BRAKER1 was more accurate than MAKER2 when it is using RNA-Seq as sole source for training and prediction. BRAKER1 does not require pre-trained parameters or a separate expert-prepared training step. BRAKER1 is available for download at http://bioinf.uni-greifswald.de/bioinf/braker/ and http://exon.gatech.edu/GeneMark/ katharina.hoff@uni-greifswald.de or borodovsky@gatech.edu Supplementary data are available at Bioinformatics online. © The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Structural Prediction and In Silico Physicochemical Characterization for Mouse Caltrin I and Bovine Caltrin Proteins

PubMed Central

Grasso, Ernesto J.; Sottile, Adolfo E.; Coronel, Carlos E.

2016-01-01

It is known that caltrin (calcium transport inhibitor) protein binds to sperm cells during ejaculation and inhibits extracellular Ca2+ uptake. Although the sequence and some biological features of mouse caltrin I and bovine caltrin are known, their physicochemical properties and tertiary structure are mainly unknown. We predicted the 3D structures of mouse caltrin I and bovine caltrin by molecular homology modeling and threading. Surface electrostatic potentials and electric fields were calculated using the Poisson–Boltzmann equation. Several different bioinformatics tools and available web servers were used to thoroughly analyze the physicochemical characteristics of both proteins, such as their Kyte and Doolittle hydropathy scores and helical wheel projections. The results presented in this work significantly aid further understanding of the molecular mechanisms of caltrin proteins modulating physiological processes associated with fertilization. PMID:27812283

Pattern search in multi-structure data: a framework for the next-generation evidence-based medicine

NASA Astrophysics Data System (ADS)

Sukumar, Sreenivas R.; Ainsworth, Keela C.

2014-03-01

With the impetus towards personalized and evidence-based medicine, the need for a framework to analyze/interpret quantitative measurements (blood work, toxicology, etc.) with qualitative descriptions (specialist reports after reading images, bio-medical knowledgebase, etc.) to predict diagnostic risks is fast emerging. Addressing this need, we pose and answer the following questions: (i) How can we jointly analyze and explore measurement data in context with qualitative domain knowledge? (ii) How can we search and hypothesize patterns (not known apriori) from such multi-structure data? (iii) How can we build predictive models by integrating weakly-associated multi-relational multi-structure data? We propose a framework towards answering these questions. We describe a software solution that leverages hardware for scalable in-memory analytics and applies next-generation semantic query tools on medical data.

Development of a 3D finite element acoustic model to predict the sound reduction index of stud based double-leaf walls

NASA Astrophysics Data System (ADS)

Arjunan, A.; Wang, C. J.; Yahiaoui, K.; Mynors, D. J.; Morgan, T.; Nguyen, V. B.; English, M.

2014-11-01

Building standards incorporating quantitative acoustical criteria to ensure adequate sound insulation are now being implemented. Engineers are making great efforts to design acoustically efficient double-wall structures. Accordingly, efficient simulation models to predict the acoustic insulation of double-leaf wall structures are needed. This paper presents the development of a numerical tool that can predict the frequency dependent sound reduction index R of stud based double-leaf walls at one-third-octave band frequency range. A fully vibro-acoustic 3D model consisting of two rooms partitioned using a double-leaf wall, considering the structure and acoustic fluid coupling incorporating the existing fluid and structural solvers are presented. The validity of the finite element (FE) model is assessed by comparison with experimental test results carried out in a certified laboratory. Accurate representation of the structural damping matrix to effectively predict the R values are studied. The possibilities of minimising the simulation time using a frequency dependent mesh model was also investigated. The FEA model presented in this work is capable of predicting the weighted sound reduction index Rw along with A-weighted pink noise C and A-weighted urban noise Ctr within an error of 1 dB. The model developed can also be used to analyse the acoustically induced frequency dependent geometrical behaviour of the double-leaf wall components to optimise them for best acoustic performance. The FE modelling procedure reported in this paper can be extended to other building components undergoing fluid-structure interaction (FSI) to evaluate their acoustic insulation.

Prediction of redox-sensitive cysteines using sequential distance and other sequence-based features.

PubMed

Sun, Ming-An; Zhang, Qing; Wang, Yejun; Ge, Wei; Guo, Dianjing

2016-08-24

Reactive oxygen species can modify the structure and function of proteins and may also act as important signaling molecules in various cellular processes. Cysteine thiol groups of proteins are particularly susceptible to oxidation. Meanwhile, their reversible oxidation is of critical roles for redox regulation and signaling. Recently, several computational tools have been developed for predicting redox-sensitive cysteines; however, those methods either only focus on catalytic redox-sensitive cysteines in thiol oxidoreductases, or heavily depend on protein structural data, thus cannot be widely used. In this study, we analyzed various sequence-based features potentially related to cysteine redox-sensitivity, and identified three types of features for efficient computational prediction of redox-sensitive cysteines. These features are: sequential distance to the nearby cysteines, PSSM profile and predicted secondary structure of flanking residues. After further feature selection using SVM-RFE, we developed Redox-Sensitive Cysteine Predictor (RSCP), a SVM based classifier for redox-sensitive cysteine prediction using primary sequence only. Using 10-fold cross-validation on RSC758 dataset, the accuracy, sensitivity, specificity, MCC and AUC were estimated as 0.679, 0.602, 0.756, 0.362 and 0.727, respectively. When evaluated using 10-fold cross-validation with BALOSCTdb dataset which has structure information, the model achieved performance comparable to current structure-based method. Further validation using an independent dataset indicates it is robust and of relatively better accuracy for predicting redox-sensitive cysteines from non-enzyme proteins. In this study, we developed a sequence-based classifier for predicting redox-sensitive cysteines. The major advantage of this method is that it does not rely on protein structure data, which ensures more extensive application compared to other current implementations. Accurate prediction of redox-sensitive cysteines not only enhances our understanding about the redox sensitivity of cysteine, it may also complement the proteomics approach and facilitate further experimental investigation of important redox-sensitive cysteines.

StrBioLib: a Java library for development of custom computational structural biology applications.

PubMed

Chandonia, John-Marc

2007-08-01

StrBioLib is a library of Java classes useful for developing software for computational structural biology research. StrBioLib contains classes to represent and manipulate protein structures, biopolymer sequences, sets of biopolymer sequences, and alignments between biopolymers based on either sequence or structure. Interfaces are provided to interact with commonly used bioinformatics applications, including (psi)-blast, modeller, muscle and Primer3, and tools are provided to read and write many file formats used to represent bioinformatic data. The library includes a general-purpose neural network object with multiple training algorithms, the Hooke and Jeeves non-linear optimization algorithm, and tools for efficient C-style string parsing and formatting. StrBioLib is the basis for the Pred2ary secondary structure prediction program, is used to build the astral compendium for sequence and structure analysis, and has been extensively tested through use in many smaller projects. Examples and documentation are available at the site below. StrBioLib may be obtained under the terms of the GNU LGPL license from http://strbio.sourceforge.net/

Computational Methods in Drug Discovery

PubMed Central

Sliwoski, Gregory; Kothiwale, Sandeepkumar; Meiler, Jens

2014-01-01

Computer-aided drug discovery/design methods have played a major role in the development of therapeutically important small molecules for over three decades. These methods are broadly classified as either structure-based or ligand-based methods. Structure-based methods are in principle analogous to high-throughput screening in that both target and ligand structure information is imperative. Structure-based approaches include ligand docking, pharmacophore, and ligand design methods. The article discusses theory behind the most important methods and recent successful applications. Ligand-based methods use only ligand information for predicting activity depending on its similarity/dissimilarity to previously known active ligands. We review widely used ligand-based methods such as ligand-based pharmacophores, molecular descriptors, and quantitative structure-activity relationships. In addition, important tools such as target/ligand data bases, homology modeling, ligand fingerprint methods, etc., necessary for successful implementation of various computer-aided drug discovery/design methods in a drug discovery campaign are discussed. Finally, computational methods for toxicity prediction and optimization for favorable physiologic properties are discussed with successful examples from literature. PMID:24381236

DOE Office of Scientific and Technical Information (OSTI.GOV)

Frolov, T.; Setyawan, W.; Kurtz, R. J.

We report a computational discovery of novel grain boundary structures and multiple grain boundary phases in elemental bcc tungsten. While grain boundary structures created by the - surface method as a union of two perfect half crystals have been studied extensively, it is known that the method has limitations and does not always predict the correct ground states. Here, we use a newly developed computational tool, based on evolutionary algorithms, to perform a grand-canonical search of high-angle symmetric tilt boundary in tungsten, and we find new ground states and multiple phases that cannot be described using the conventional structural unitmore » model. We use MD simulations to demonstrate that the new structures can coexist at finite temperature in a closed system, confirming these are examples of different GB phases. The new ground state is confirmed by first-principles calculations.Evolutionary grand-canonical search predicts novel grain boundary structures and multiple grain boundary phases in elemental body-centered cubic (bcc) metals represented by tungsten, tantalum and molybdenum.« less

Energy Economics of Farm Biogas in Cold Climates

DOE Office of Scientific and Technical Information (OSTI.GOV)

Pillay, Pragasen; Grimberg, Stefan; Powers, Susan E

Anaerobic digestion of farm and dairy waste has been shown to be capital intensive. One way to improve digester economics is to co-digest high-energy substrates together with the dairy manure. Cheese whey for example represents a high-energy substrate that is generated during cheese manufacture. There are currently no quantitative tools available that predict performance of co-digestion farm systems. The goal of this project was to develop a mathematical tool that would (1) predict the impact of co-digestion and (2) determine the best use of the generated biogas for a cheese manufacturing plant. Two models were developed that separately could bemore » used to meet both goals of the project. Given current pricing structures of the most economical use of the generated biogas at the cheese manufacturing plant was as a replacement of fuel oil to generate heat. The developed digester model accurately predicted the performance of 26 farm digesters operating in the North Eastern U.S.« less

Designer's unified cost model

NASA Technical Reports Server (NTRS)

Freeman, William T.; Ilcewicz, L. B.; Swanson, G. D.; Gutowski, T.

1992-01-01

A conceptual and preliminary designers' cost prediction model has been initiated. The model will provide a technically sound method for evaluating the relative cost of different composite structural designs, fabrication processes, and assembly methods that can be compared to equivalent metallic parts or assemblies. The feasibility of developing cost prediction software in a modular form for interfacing with state of the art preliminary design tools and computer aided design programs is being evaluated. The goal of this task is to establish theoretical cost functions that relate geometric design features to summed material cost and labor content in terms of process mechanics and physics. The output of the designers' present analytical tools will be input for the designers' cost prediction model to provide the designer with a data base and deterministic cost methodology that allows one to trade and synthesize designs with both cost and weight as objective functions for optimization. The approach, goals, plans, and progress is presented for development of COSTADE (Cost Optimization Software for Transport Aircraft Design Evaluation).

The Cementitious Barriers Partnership (CBP) Software Toolbox Capabilities in Assessing the Degradation of Cementitious Barriers - 13487

DOE Office of Scientific and Technical Information (OSTI.GOV)

Flach, G.P.; Burns, H.H.; Langton, C.

2013-07-01

The Cementitious Barriers Partnership (CBP) Project is a multi-disciplinary, multi-institutional collaboration supported by the U.S. Department of Energy (US DOE) Office of Tank Waste and Nuclear Materials Management. The CBP program has developed a set of integrated tools (based on state-of-the-art models and leaching test methods) that help improve understanding and predictions of the long-term structural, hydraulic and chemical performance of cementitious barriers used in nuclear applications. Tools selected for and developed under this program have been used to evaluate and predict the behavior of cementitious barriers used in near-surface engineered waste disposal systems for periods of performance up tomore » 100 years and longer for operating facilities and longer than 1000 years for waste disposal. The CBP Software Toolbox has produced tangible benefits to the DOE Performance Assessment (PA) community. A review of prior DOE PAs has provided a list of potential opportunities for improving cementitious barrier performance predictions through the use of the CBP software tools. These opportunities include: 1) impact of atmospheric exposure to concrete and grout before closure, such as accelerated slag and Tc-99 oxidation, 2) prediction of changes in K{sub d}/mobility as a function of time that result from changing pH and redox conditions, 3) concrete degradation from rebar corrosion due to carbonation, 4) early age cracking from drying and/or thermal shrinkage and 5) degradation due to sulfate attack. The CBP has already had opportunity to provide near-term, tangible support to ongoing DOE-EM PAs such as the Savannah River Saltstone Disposal Facility (SDF) by providing a sulfate attack analysis that predicts the extent and damage that sulfate ingress will have on the concrete vaults over extended time (i.e., > 1000 years). This analysis is one of the many technical opportunities in cementitious barrier performance that can be addressed by the DOE-EM sponsored CBP software tools. Modification of the existing tools can provide many opportunities to bring defense in depth in prediction of the performance of cementitious barriers over time. (authors)« less

Computational modeling of human oral bioavailability: what will be next?

PubMed

Cabrera-Pérez, Miguel Ángel; Pham-The, Hai

2018-06-01

The oral route is the most convenient way of administrating drugs. Therefore, accurate determination of oral bioavailability is paramount during drug discovery and development. Quantitative structure-property relationship (QSPR), rule-of-thumb (RoT) and physiologically based-pharmacokinetic (PBPK) approaches are promising alternatives to the early oral bioavailability prediction. Areas covered: The authors give insight into the factors affecting bioavailability, the fundamental theoretical framework and the practical aspects of computational methods for predicting this property. They also give their perspectives on future computational models for estimating oral bioavailability. Expert opinion: Oral bioavailability is a multi-factorial pharmacokinetic property with its accurate prediction challenging. For RoT and QSPR modeling, the reliability of datasets, the significance of molecular descriptor families and the diversity of chemometric tools used are important factors that define model predictability and interpretability. Likewise, for PBPK modeling the integrity of the pharmacokinetic data, the number of input parameters, the complexity of statistical analysis and the software packages used are relevant factors in bioavailability prediction. Although these approaches have been utilized independently, the tendency to use hybrid QSPR-PBPK approaches together with the exploration of ensemble and deep-learning systems for QSPR modeling of oral bioavailability has opened new avenues for development promising tools for oral bioavailability prediction.

Prediction of boiling points of organic compounds by QSPR tools.

PubMed

Dai, Yi-min; Zhu, Zhi-ping; Cao, Zhong; Zhang, Yue-fei; Zeng, Ju-lan; Li, Xun

2013-07-01

The novel electro-negativity topological descriptors of YC, WC were derived from molecular structure by equilibrium electro-negativity of atom and relative bond length of molecule. The quantitative structure-property relationships (QSPR) between descriptors of YC, WC as well as path number parameter P3 and the normal boiling points of 80 alkanes, 65 unsaturated hydrocarbons and 70 alcohols were obtained separately. The high-quality prediction models were evidenced by coefficient of determination (R(2)), the standard error (S), average absolute errors (AAE) and predictive parameters (Qext(2),RCV(2),Rm(2)). According to the regression equations, the influences of the length of carbon backbone, the size, the degree of branching of a molecule and the role of functional groups on the normal boiling point were analyzed. Comparison results with reference models demonstrated that novel topological descriptors based on the equilibrium electro-negativity of atom and the relative bond length were useful molecular descriptors for predicting the normal boiling points of organic compounds. Copyright © 2013 Elsevier Inc. All rights reserved.

Embedding Multilevel Survival Analysis of Dyadic Social Interaction in Structural Equation Models: Hazard Rates as Both Outcomes and Predictors

PubMed Central

Snyder, James

2014-01-01

Objective Demonstrate multivariate multilevel survival analysis within a larger structural equation model. Test the 3 hypotheses that when confronted by a negative parent, child rates of angry, sad/fearful, and positive emotion will increase, decrease, and stay the same, respectively, for antisocial compared with normal children. This same pattern will predict increases in future antisocial behavior. Methods Parent–child dyads were videotaped in the fall of kindergarten in the laboratory and antisocial behavior ratings were obtained in the fall of kindergarten and third grade. Results Kindergarten antisocial predicted less child sad/fear and child positive but did not predict child anger given parent negative. Less child positive and more child neutral given parent negative predicted increases in third-grade antisocial behavior. Conclusions The model is a useful analytic tool for studying rates of social behavior. Lack of positive affect or excess neutral affect may be a new risk factor for child antisocial behavior. PMID:24133296

Immunoinformatic Analysis of Crimean Congo Hemorrhagic Fever Virus Glycoproteins and Epitope Prediction for Synthetic Peptide Vaccine.

PubMed

Tipu, Hamid Nawaz

2016-02-01

To determine the Crimean Congo Hemorrhagic Fever (CCHF) virus M segement glycoprotein's immunoinformatic parameters, and identify Human Leukocyte Antigen (HLA) class I binders as candidates for synthetic peptide vaccines. Cross-sectional study. Combined Military Hospital, Khuzdar Cantt, in May 2015. Data acquisition, antigenicity prediction, secondary and tertiary structure prediction, residue analysis were done using immunoinformatics tools. HLAclass I binders in glycoprotein's sequence were identified at nanomer length using NetMHC 3.4 and mapped onto tertiary structure. Docking was done for strongest binder against its corresponding allele with CABS-dock. HLAA*0101, 0201, 0301, 2402, 2601 and B*0702, 0801, 2705, 3901, 4001, 5801, 1501 were analyzed against two glycoprotein components of the virus. Atotal of 35 nanomers from GP1, and 3 from GP2 were identified. HLAB*0702 bound maximum number of peptides (6), while HLAB*4001 showed strongest binding affinity. HLAspecific glycoproteins epitope prediction can help identify synthetic peptide vaccine candidates.

QSAR models for predicting octanol/water and organic carbon/water partition coefficients of polychlorinated biphenyls.

PubMed

Yu, S; Gao, S; Gan, Y; Zhang, Y; Ruan, X; Wang, Y; Yang, L; Shi, J

2016-04-01

Quantitative structure-property relationship modelling can be a valuable alternative method to replace or reduce experimental testing. In particular, some endpoints such as octanol-water (KOW) and organic carbon-water (KOC) partition coefficients of polychlorinated biphenyls (PCBs) are easier to predict and various models have been already developed. In this paper, two different methods, which are multiple linear regression based on the descriptors generated using Dragon software and hologram quantitative structure-activity relationships, were employed to predict suspended particulate matter (SPM) derived log KOC and generator column, shake flask and slow stirring method derived log KOW values of 209 PCBs. The predictive ability of the derived models was validated using a test set. The performances of all these models were compared with EPI Suite™ software. The results indicated that the proposed models were robust and satisfactory, and could provide feasible and promising tools for the rapid assessment of the SPM derived log KOC and generator column, shake flask and slow stirring method derived log KOW values of PCBs.

Violence risk prediction. Clinical and actuarial measures and the role of the Psychopathy Checklist.

PubMed

Dolan, M; Doyle, M

2000-10-01

Violence risk prediction is a priority issue for clinicians working with mentally disordered offenders. To review the current status of violence risk prediction research. Literature search (Medline). Key words: violence, risk prediction, mental disorder. Systematic/structured risk assessment approaches may enhance the accuracy of clinical prediction of violent outcomes. Data on the predictive validity of available clinical risk assessment tools are based largely on American and North American studies and further validation is required in British samples. The Psychopathy Checklist appears to be a key predictor of violent recidivism in a variety of settings. Violence risk prediction is an inexact science and as such will continue to provoke debate. Clinicians clearly need to be able to demonstrate the rationale behind their decisions on violence risk and much can be learned from recent developments in research on violence risk prediction.

Meta-analysis diagnostic accuracy of SNP-based pathogenicity detection tools: a case of UTG1A1 gene mutations.

PubMed

Galehdari, Hamid; Saki, Najmaldin; Mohammadi-Asl, Javad; Rahim, Fakher

2013-01-01

Crigler-Najjar syndrome (CNS) type I and type II are usually inherited as autosomal recessive conditions that result from mutations in the UGT1A1 gene. The main objective of the present review is to summarize results of all available evidence on the accuracy of SNP-based pathogenicity detection tools compared to published clinical result for the prediction of in nsSNPs that leads to disease using prediction performance method. A comprehensive search was performed to find all mutations related to CNS. Database searches included dbSNP, SNPdbe, HGMD, Swissvar, ensemble, and OMIM. All the mutation related to CNS was extracted. The pathogenicity prediction was done using SNP-based pathogenicity detection tools include SIFT, PHD-SNP, PolyPhen2, fathmm, Provean, and Mutpred. Overall, 59 different SNPs related to missense mutations in the UGT1A1 gene, were reviewed. Comparing the diagnostic OR, PolyPhen2 and Mutpred have the highest detection 4.983 (95% CI: 1.24 - 20.02) in both, following by SIFT (diagnostic OR: 3.25, 95% CI: 1.07 - 9.83). The highest MCC of SNP-based pathogenicity detection tools, was belong to SIFT (34.19%) followed by Provean, PolyPhen2, and Mutpred (29.99%, 29.89%, and 29.89%, respectively). Hence the highest SNP-based pathogenicity detection tools ACC, was fit to SIFT (62.71%) followed by PolyPhen2, and Mutpred (61.02%, in both). Our results suggest that some of the well-established SNP-based pathogenicity detection tools can appropriately reflect the role of a disease-associated SNP in both local and global structures.

Hierarchical kernel mixture models for the prediction of AIDS disease progression using HIV structural gp120 profiles

PubMed Central

2010-01-01

Changes to the glycosylation profile on HIV gp120 can influence viral pathogenesis and alter AIDS disease progression. The characterization of glycosylation differences at the sequence level is inadequate as the placement of carbohydrates is structurally complex. However, no structural framework is available to date for the study of HIV disease progression. In this study, we propose a novel machine-learning based framework for the prediction of AIDS disease progression in three stages (RP, SP, and LTNP) using the HIV structural gp120 profile. This new intelligent framework proves to be accurate and provides an important benchmark for predicting AIDS disease progression computationally. The model is trained using a novel HIV gp120 glycosylation structural profile to detect possible stages of AIDS disease progression for the target sequences of HIV+ individuals. The performance of the proposed model was compared to seven existing different machine-learning models on newly proposed gp120-Benchmark_1 dataset in terms of error-rate (MSE), accuracy (CCI), stability (STD), and complexity (TBM). The novel framework showed better predictive performance with 67.82% CCI, 30.21 MSE, 0.8 STD, and 2.62 TBM on the three stages of AIDS disease progression of 50 HIV+ individuals. This framework is an invaluable bioinformatics tool that will be useful to the clinical assessment of viral pathogenesis. PMID:21143806

Using CV-GLUE procedure in analysis of wetland model predictive uncertainty.

PubMed

Huang, Chun-Wei; Lin, Yu-Pin; Chiang, Li-Chi; Wang, Yung-Chieh

2014-07-01

This study develops a procedure that is related to Generalized Likelihood Uncertainty Estimation (GLUE), called the CV-GLUE procedure, for assessing the predictive uncertainty that is associated with different model structures with varying degrees of complexity. The proposed procedure comprises model calibration, validation, and predictive uncertainty estimation in terms of a characteristic coefficient of variation (characteristic CV). The procedure first performed two-stage Monte-Carlo simulations to ensure predictive accuracy by obtaining behavior parameter sets, and then the estimation of CV-values of the model outcomes, which represent the predictive uncertainties for a model structure of interest with its associated behavior parameter sets. Three commonly used wetland models (the first-order K-C model, the plug flow with dispersion model, and the Wetland Water Quality Model; WWQM) were compared based on data that were collected from a free water surface constructed wetland with paddy cultivation in Taipei, Taiwan. The results show that the first-order K-C model, which is simpler than the other two models, has greater predictive uncertainty. This finding shows that predictive uncertainty does not necessarily increase with the complexity of the model structure because in this case, the more simplistic representation (first-order K-C model) of reality results in a higher uncertainty in the prediction made by the model. The CV-GLUE procedure is suggested to be a useful tool not only for designing constructed wetlands but also for other aspects of environmental management. Copyright © 2014 Elsevier Ltd. All rights reserved.

Light Water Reactor Sustainability Program: Survey of Models for Concrete Degradation

DOE Office of Scientific and Technical Information (OSTI.GOV)

Spencer, Benjamin W.; Huang, Hai

Concrete is widely used in the construction of nuclear facilities because of its structural strength and its ability to shield radiation. The use of concrete in nuclear facilities for containment and shielding of radiation and radioactive materials has made its performance crucial for the safe operation of the facility. As such, when life extension is considered for nuclear power plants, it is critical to have predictive tools to address concerns related to aging processes of concrete structures and the capacity of structures subjected to age-related degradation. The goal of this report is to review and document the main aging mechanismsmore » of concern for concrete structures in nuclear power plants (NPPs) and the models used in simulations of concrete aging and structural response of degraded concrete structures. This is in preparation for future work to develop and apply models for aging processes and response of aged NPP concrete structures in the Grizzly code. To that end, this report also provides recommendations for developing more robust predictive models for aging effects of performance of concrete.« less

Propagating annotations of molecular networks using in silico fragmentation

PubMed Central

da Silva, Ricardo R.; Wang, Mingxun; Fox, Evan; Balunas, Marcy J.; Klassen, Jonathan L.; Dorrestein, Pieter C.

2018-01-01

The annotation of small molecules is one of the most challenging and important steps in untargeted mass spectrometry analysis, as most of our biological interpretations rely on structural annotations. Molecular networking has emerged as a structured way to organize and mine data from untargeted tandem mass spectrometry (MS/MS) experiments and has been widely applied to propagate annotations. However, propagation is done through manual inspection of MS/MS spectra connected in the spectral networks and is only possible when a reference library spectrum is available. One of the alternative approaches used to annotate an unknown fragmentation mass spectrum is through the use of in silico predictions. One of the challenges of in silico annotation is the uncertainty around the correct structure among the predicted candidate lists. Here we show how molecular networking can be used to improve the accuracy of in silico predictions through propagation of structural annotations, even when there is no match to a MS/MS spectrum in spectral libraries. This is accomplished through creating a network consensus of re-ranked structural candidates using the molecular network topology and structural similarity to improve in silico annotations. The Network Annotation Propagation (NAP) tool is accessible through the GNPS web-platform https://gnps.ucsd.edu/ProteoSAFe/static/gnps-theoretical.jsp. PMID:29668671

Propagating annotations of molecular networks using in silico fragmentation.

PubMed

da Silva, Ricardo R; Wang, Mingxun; Nothias, Louis-Félix; van der Hooft, Justin J J; Caraballo-Rodríguez, Andrés Mauricio; Fox, Evan; Balunas, Marcy J; Klassen, Jonathan L; Lopes, Norberto Peporine; Dorrestein, Pieter C

2018-04-01

The annotation of small molecules is one of the most challenging and important steps in untargeted mass spectrometry analysis, as most of our biological interpretations rely on structural annotations. Molecular networking has emerged as a structured way to organize and mine data from untargeted tandem mass spectrometry (MS/MS) experiments and has been widely applied to propagate annotations. However, propagation is done through manual inspection of MS/MS spectra connected in the spectral networks and is only possible when a reference library spectrum is available. One of the alternative approaches used to annotate an unknown fragmentation mass spectrum is through the use of in silico predictions. One of the challenges of in silico annotation is the uncertainty around the correct structure among the predicted candidate lists. Here we show how molecular networking can be used to improve the accuracy of in silico predictions through propagation of structural annotations, even when there is no match to a MS/MS spectrum in spectral libraries. This is accomplished through creating a network consensus of re-ranked structural candidates using the molecular network topology and structural similarity to improve in silico annotations. The Network Annotation Propagation (NAP) tool is accessible through the GNPS web-platform https://gnps.ucsd.edu/ProteoSAFe/static/gnps-theoretical.jsp.

Molecular beacon sequence design algorithm.

PubMed

Monroe, W Todd; Haselton, Frederick R

2003-01-01

A method based on Web-based tools is presented to design optimally functioning molecular beacons. Molecular beacons, fluorogenic hybridization probes, are a powerful tool for the rapid and specific detection of a particular nucleic acid sequence. However, their synthesis costs can be considerable. Since molecular beacon performance is based on its sequence, it is imperative to rationally design an optimal sequence before synthesis. The algorithm presented here uses simple Microsoft Excel formulas and macros to rank candidate sequences. This analysis is carried out using mfold structural predictions along with other free Web-based tools. For smaller laboratories where molecular beacons are not the focus of research, the public domain algorithm described here may be usefully employed to aid in molecular beacon design.

Development and Overview of CPAS Sasquatch Airdrop Landing Location Predictor Software

NASA Technical Reports Server (NTRS)

Bledsoe, Kristin J.; Bernatovich, Michael A.

2015-01-01

The Capsule Parachute Assembly System (CPAS) is the parachute system for NASA's Orion spacecraft. CPAS is currently in the Engineering Development Unit (EDU) phase of testing. The test program consists of numerous drop tests, wherein a test article rigged with parachutes is extracted from an aircraft. During such tests, range safety is paramount, as is the recoverability of the parachutes and test article. It is crucial to establish a release point from the aircraft that will ensure that the article and all items released from it during flight will land in a designated safe area. The Sasquatch footprint tool was developed to determine this safe release point and to predict the probable landing locations (footprints) of the payload and all released objects. In 2012, a new version of Sasquatch, called Sasquatch Polygons, was developed that significantly upgraded the capabilities of the footprint tool. Key improvements were an increase in the accuracy of the predictions, and the addition of an interface with the Debris Tool (DT), an in-flight debris avoidance tool for use on the test observation helicopter. Additional enhancements include improved data presentation for communication with test personnel and a streamlined code structure. This paper discusses the development, validation, and performance of Sasquatch Polygons, as well as its differences from the original Sasquatch footprint tool.

Developing eThread pipeline using SAGA-pilot abstraction for large-scale structural bioinformatics.

PubMed

Ragothaman, Anjani; Boddu, Sairam Chowdary; Kim, Nayong; Feinstein, Wei; Brylinski, Michal; Jha, Shantenu; Kim, Joohyun

2014-01-01

While most of computational annotation approaches are sequence-based, threading methods are becoming increasingly attractive because of predicted structural information that could uncover the underlying function. However, threading tools are generally compute-intensive and the number of protein sequences from even small genomes such as prokaryotes is large typically containing many thousands, prohibiting their application as a genome-wide structural systems biology tool. To leverage its utility, we have developed a pipeline for eThread--a meta-threading protein structure modeling tool, that can use computational resources efficiently and effectively. We employ a pilot-based approach that supports seamless data and task-level parallelism and manages large variation in workload and computational requirements. Our scalable pipeline is deployed on Amazon EC2 and can efficiently select resources based upon task requirements. We present runtime analysis to characterize computational complexity of eThread and EC2 infrastructure. Based on results, we suggest a pathway to an optimized solution with respect to metrics such as time-to-solution or cost-to-solution. Our eThread pipeline can scale to support a large number of sequences and is expected to be a viable solution for genome-scale structural bioinformatics and structure-based annotation, particularly, amenable for small genomes such as prokaryotes. The developed pipeline is easily extensible to other types of distributed cyberinfrastructure.

Developing eThread Pipeline Using SAGA-Pilot Abstraction for Large-Scale Structural Bioinformatics

PubMed Central

Ragothaman, Anjani; Feinstein, Wei; Jha, Shantenu; Kim, Joohyun

2014-01-01

While most of computational annotation approaches are sequence-based, threading methods are becoming increasingly attractive because of predicted structural information that could uncover the underlying function. However, threading tools are generally compute-intensive and the number of protein sequences from even small genomes such as prokaryotes is large typically containing many thousands, prohibiting their application as a genome-wide structural systems biology tool. To leverage its utility, we have developed a pipeline for eThread—a meta-threading protein structure modeling tool, that can use computational resources efficiently and effectively. We employ a pilot-based approach that supports seamless data and task-level parallelism and manages large variation in workload and computational requirements. Our scalable pipeline is deployed on Amazon EC2 and can efficiently select resources based upon task requirements. We present runtime analysis to characterize computational complexity of eThread and EC2 infrastructure. Based on results, we suggest a pathway to an optimized solution with respect to metrics such as time-to-solution or cost-to-solution. Our eThread pipeline can scale to support a large number of sequences and is expected to be a viable solution for genome-scale structural bioinformatics and structure-based annotation, particularly, amenable for small genomes such as prokaryotes. The developed pipeline is easily extensible to other types of distributed cyberinfrastructure. PMID:24995285

Finite element modeling as a tool for predicting the fracture behavior of robocast scaffolds.

PubMed

Miranda, Pedro; Pajares, Antonia; Guiberteau, Fernando

2008-11-01

The use of finite element modeling to calculate the stress fields in complex scaffold structures and thus predict their mechanical behavior during service (e.g., as load-bearing bone implants) is evaluated. The method is applied to identifying the fracture modes and estimating the strength of robocast hydroxyapatite and beta-tricalcium phosphate scaffolds, consisting of a three-dimensional lattice of interpenetrating rods. The calculations are performed for three testing configurations: compression, tension and shear. Different testing orientations relative to the calcium phosphate rods are considered for each configuration. The predictions for the compressive configurations are compared to experimental data from uniaxial compression tests.

Quantitative structure-activity relationships for predicting potential ecological hazard of organic chemicals for use in regulatory risk assessments.

PubMed

Comber, Mike H I; Walker, John D; Watts, Chris; Hermens, Joop

2003-08-01

The use of quantitative structure-activity relationships (QSARs) for deriving the predicted no-effect concentration of discrete organic chemicals for the purposes of conducting a regulatory risk assessment in Europe and the United States is described. In the United States, under the Toxic Substances Control Act (TSCA), the TSCA Interagency Testing Committee and the U.S. Environmental Protection Agency (U.S. EPA) use SARs to estimate the hazards of existing and new chemicals. Within the Existing Substances Regulation in Europe, QSARs may be used for data evaluation, test strategy indications, and the identification and filling of data gaps. To illustrate where and when QSARs may be useful and when their use is more problematic, an example, methyl tertiary-butyl ether (MTBE), is given and the predicted and experimental data are compared. Improvements needed for new QSARs and tools for developing and using QSARs are discussed.

Surrogate Modeling of High-Fidelity Fracture Simulations for Real-Time Residual Strength Predictions

NASA Technical Reports Server (NTRS)

Spear, Ashley D.; Priest, Amanda R.; Veilleux, Michael G.; Ingraffea, Anthony R.; Hochhalter, Jacob D.

2011-01-01

A surrogate model methodology is described for predicting in real time the residual strength of flight structures with discrete-source damage. Starting with design of experiment, an artificial neural network is developed that takes as input discrete-source damage parameters and outputs a prediction of the structural residual strength. Target residual strength values used to train the artificial neural network are derived from 3D finite element-based fracture simulations. A residual strength test of a metallic, integrally-stiffened panel is simulated to show that crack growth and residual strength are determined more accurately in discrete-source damage cases by using an elastic-plastic fracture framework rather than a linear-elastic fracture mechanics-based method. Improving accuracy of the residual strength training data would, in turn, improve accuracy of the surrogate model. When combined, the surrogate model methodology and high-fidelity fracture simulation framework provide useful tools for adaptive flight technology.

Static and fatigue testing of full-scale fuselage panels fabricated using a Therm-X(R) process

NASA Technical Reports Server (NTRS)

Dinicola, Albert J.; Kassapoglou, Christos; Chou, Jack C.

1992-01-01

Large, curved, integrally stiffened composite panels representative of an aircraft fuselage structure were fabricated using a Therm-X process, an alternative concept to conventional two-sided hard tooling and contour vacuum bagging. Panels subsequently were tested under pure shear loading in both static and fatigue regimes to assess the adequacy of the manufacturing process, the effectiveness of damage tolerant design features co-cured with the structure, and the accuracy of finite element and closed-form predictions of postbuckling capability and failure load. Test results indicated the process yielded panels of high quality and increased damage tolerance through suppression of common failure modes such as skin-stiffener separation and frame-stiffener corner failure. Finite element analyses generally produced good predictions of postbuckled shape, and a global-local modelling technique yielded failure load predictions that were within 7% of the experimental mean.

Computational prediction of muon stopping sites using ab initio random structure searching (AIRSS)

NASA Astrophysics Data System (ADS)

Liborio, Leandro; Sturniolo, Simone; Jochym, Dominik

2018-04-01

The stopping site of the muon in a muon-spin relaxation experiment is in general unknown. There are some techniques that can be used to guess the muon stopping site, but they often rely on approximations and are not generally applicable to all cases. In this work, we propose a purely theoretical method to predict muon stopping sites in crystalline materials from first principles. The method is based on a combination of ab initio calculations, random structure searching, and machine learning, and it has successfully predicted the MuT and MuBC stopping sites of muonium in Si, diamond, and Ge, as well as the muonium stopping site in LiF, without any recourse to experimental results. The method makes use of Soprano, a Python library developed to aid ab initio computational crystallography, that was publicly released and contains all the software tools necessary to reproduce our analysis.

Surrogate Modeling of High-Fidelity Fracture Simulations for Real-Time Residual Strength Predictions

NASA Technical Reports Server (NTRS)

Spear, Ashley D.; Priest, Amanda R.; Veilleux, Michael G.; Ingraffea, Anthony R.; Hochhalter, Jacob D.

2011-01-01

A surrogate model methodology is described for predicting, during flight, the residual strength of aircraft structures that sustain discrete-source damage. Starting with design of experiment, an artificial neural network is developed that takes as input discrete-source damage parameters and outputs a prediction of the structural residual strength. Target residual strength values used to train the artificial neural network are derived from 3D finite element-based fracture simulations. Two ductile fracture simulations are presented to show that crack growth and residual strength are determined more accurately in discrete-source damage cases by using an elastic-plastic fracture framework rather than a linear-elastic fracture mechanics-based method. Improving accuracy of the residual strength training data does, in turn, improve accuracy of the surrogate model. When combined, the surrogate model methodology and high fidelity fracture simulation framework provide useful tools for adaptive flight technology.

Functional insights from proteome-wide structural modeling of Treponema pallidum subspecies pallidum, the causative agent of syphilis.

PubMed

Houston, Simon; Lithgow, Karen Vivien; Osbak, Kara Krista; Kenyon, Chris Richard; Cameron, Caroline E

2018-05-16

Syphilis continues to be a major global health threat with 11 million new infections each year, and a global burden of 36 million cases. The causative agent of syphilis, Treponema pallidum subspecies pallidum, is a highly virulent bacterium, however the molecular mechanisms underlying T. pallidum pathogenesis remain to be definitively identified. This is due to the fact that T. pallidum is currently uncultivatable, inherently fragile and thus difficult to work with, and phylogenetically distinct with no conventional virulence factor homologs found in other pathogens. In fact, approximately 30% of its predicted protein-coding genes have no known orthologs or assigned functions. Here we employed a structural bioinformatics approach using Phyre2-based tertiary structure modeling to improve our understanding of T. pallidum protein function on a proteome-wide scale. Phyre2-based tertiary structure modeling generated high-confidence predictions for 80% of the T. pallidum proteome (780/978 predicted proteins). Tertiary structure modeling also inferred the same function as primary structure-based annotations from genome sequencing pipelines for 525/605 proteins (87%), which represents 54% (525/978) of all T. pallidum proteins. Of the 175 T. pallidum proteins modeled with high confidence that were not assigned functions in the previously annotated published proteome, 167 (95%) were able to be assigned predicted functions. Twenty-one of the 175 hypothetical proteins modeled with high confidence were also predicted to exhibit significant structural similarity with proteins experimentally confirmed to be required for virulence in other pathogens. Phyre2-based structural modeling is a powerful bioinformatics tool that has provided insight into the potential structure and function of the majority of T. pallidum proteins and helped validate the primary structure-based annotation of more than 50% of all T. pallidum proteins with high confidence. This work represents the first T. pallidum proteome-wide structural modeling study and is one of few studies to apply this approach for the functional annotation of a whole proteome.

LECTINPred: web Server that Uses Complex Networks of Protein Structure for Prediction of Lectins with Potential Use as Cancer Biomarkers or in Parasite Vaccine Design.

PubMed

Munteanu, Cristian R; Pedreira, Nieves; Dorado, Julián; Pazos, Alejandro; Pérez-Montoto, Lázaro G; Ubeira, Florencio M; González-Díaz, Humberto

2014-04-01

Lectins (Ls) play an important role in many diseases such as different types of cancer, parasitic infections and other diseases. Interestingly, the Protein Data Bank (PDB) contains +3000 protein 3D structures with unknown function. Thus, we can in principle, discover new Ls mining non-annotated structures from PDB or other sources. However, there are no general models to predict new biologically relevant Ls based on 3D chemical structures. We used the MARCH-INSIDE software to calculate the Markov-Shannon 3D electrostatic entropy parameters for the complex networks of protein structure of 2200 different protein 3D structures, including 1200 Ls. We have performed a Linear Discriminant Analysis (LDA) using these parameters as inputs in order to seek a new Quantitative Structure-Activity Relationship (QSAR) model, which is able to discriminate 3D structure of Ls from other proteins. We implemented this predictor in the web server named LECTINPred, freely available at http://bio-aims.udc.es/LECTINPred.php. This web server showed the following goodness-of-fit statistics: Sensitivity=96.7 % (for Ls), Specificity=87.6 % (non-active proteins), and Accuracy=92.5 % (for all proteins), considering altogether both the training and external prediction series. In mode 2, users can carry out an automatic retrieval of protein structures from PDB. We illustrated the use of this server, in operation mode 1, performing a data mining of PDB. We predicted Ls scores for +2000 proteins with unknown function and selected the top-scored ones as possible lectins. In operation mode 2, LECTINPred can also upload 3D structural models generated with structure-prediction tools like LOMETS or PHYRE2. The new Ls are expected to be of relevance as cancer biomarkers or useful in parasite vaccine design. © 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

A thermal sensation prediction tool for use by the profession

DOE Office of Scientific and Technical Information (OSTI.GOV)

Fountain, M.E.; Huizenga, C.

1997-12-31

As part of a recent ASHRAE research project (781-RP), a thermal sensation prediction tool has been developed. This paper introduces the tool, describes the component thermal sensation models, and presents examples of how the tool can be used in practice. Since the main end product of the HVAC industry is the comfort of occupants indoors, tools for predicting occupant thermal response can be an important asset to designers of indoor climate control systems. The software tool presented in this paper incorporates several existing models for predicting occupant comfort.

In silico prediction of a disease-associated STIL mutant and its affect on the recruitment of centromere protein J (CENPJ).

PubMed

Kumar, Ambuj; Rajendran, Vidya; Sethumadhavan, Rao; Purohit, Rituraj

2012-01-01

Human STIL (SCL/TAL1 interrupting locus) protein maintains centriole stability and spindle pole localisation. It helps in recruitment of CENPJ (Centromere protein J)/CPAP (centrosomal P4.1-associated protein) and other centrosomal proteins. Mutations in STIL protein are reported in several disorders, especially in deregulation of cell cycle cascades. In this work, we examined the non-synonymous single nucleotide polymorphisms (nsSNPs) reported in STIL protein for their disease association. Different SNP prediction tools were used to predict disease-associated nsSNPs. Our evaluation technique predicted rs147744459 (R242C) as a highly deleterious disease-associated nsSNP and its interaction behaviour with CENPJ protein. Molecular modelling, docking and molecular dynamics simulation were conducted to examine the structural consequences of the predicted disease-associated mutation. By molecular dynamic simulation we observed structural consequences of R242C mutation which affects interaction of STIL and CENPJ functional domains. The result obtained in this study will provide a biophysical insight into future investigations of pathological nsSNPs using a computational platform.

Computer predictions on Rh-based double perovskites with unusual electronic and magnetic properties

NASA Astrophysics Data System (ADS)

Halder, Anita; Nafday, Dhani; Sanyal, Prabuddha; Saha-Dasgupta, Tanusri

2018-03-01

In search for new magnetic materials, we make computer prediction of structural, electronic and magnetic properties of yet-to-be synthesized Rh-based double perovskite compounds, Sr(Ca)2BRhO6 (B=Cr, Mn, Fe). We use combination of evolutionary algorithm, density functional theory, and statistical-mechanical tool for this purpose. We find that the unusual valence of Rh5+ may be stabilized in these compounds through formation of oxygen ligand hole. Interestingly, while the Cr-Rh and Mn-Rh compounds are predicted to be ferromagnetic half-metals, the Fe-Rh compounds are found to be rare examples of antiferromagnetic and metallic transition-metal oxide with three-dimensional electronic structure. The computed magnetic transition temperatures of the predicted compounds, obtained from finite temperature Monte Carlo study of the first principles-derived model Hamiltonian, are found to be reasonably high. The prediction of favorable growth condition of the compounds, reported in our study, obtained through extensive thermodynamic analysis should be useful for future synthesize of this interesting class of materials with intriguing properties.

Designing drugs on the internet? Free web tools and services supporting medicinal chemistry.

PubMed

Ertl, Peter; Jelfs, Stephen

2007-01-01

The drug discovery process is supported by a multitude of freely available tools on the Internet. This paper summarizes some of the databases and tools that are of particular interest to medicinal chemistry. These include numerous data collections that provide access to valuable chemical data resources, allowing complex queries of compound structures, associated physicochemical properties and biological activities to be performed and, in many cases, providing links to commercial chemical suppliers. Further applications are available for searching protein-ligand complexes and identifying important binding interactions that occur. This is particularly useful for understanding the molecular recognition of ligands in the lead optimization process. The Internet also provides access to databases detailing metabolic pathways and transformations which can provide insight into disease mechanism, identify new targets entities or the potential off-target effects of a drug candidate. Furthermore, sophisticated online cheminformatics tools are available for processing chemical structures, predicting properties, and generating 2D or 3D structure representations--often required prior to more advanced analyses. The Internet provides a wealth of valuable resources that, if fully exploited, can greatly benefit the drug discovery community. In this paper, we provide an overview of some of the more important of these and, in particular, the freely accessible resources that are currently available.

A computational study of coherent structures in the wakes of two-dimensional bluff bodies

NASA Astrophysics Data System (ADS)

Pearce, Jeffrey Alan

1988-08-01

The periodic shedding of vortices from bluff bodies was first recognized in the late 1800's. Currently, there is great interest concerning the effect of vortex shedding on structures and on vehicle stability. In the design of bluff structures which will be exposed to a flow, knowledge of the shedding frequency and the amplitude of the aerodynamic forces is critical. The ability to computationally predict parameters associated with periodic vortex shedding is thus a valuable tool. In this study, the periodic shedding of vortices from several bluff body geometries is predicted. The study is conducted with a two-dimensional finite-difference code employed on various grid sizes. The effects of the grid size and time step on the accuracy of the solution are addressed. Strouhal numbers and aerodynamic force coefficients are computed for all of the bodies considered and compared with previous experimental results. Results indicate that the finite-difference code is capable of predicting periodic vortex shedding for all of the geometries tested. Refinement of the finite-difference grid was found to give little improvement in the prediction; however, the choice of time step size was shown to be critical. Predictions of Strouhal numbers were generally accurate, and the calculated aerodynamic forces generally exhibited behavior consistent with previous studies.

Prediction of Protein-Protein Interaction Sites by Random Forest Algorithm with mRMR and IFS

PubMed Central

Li, Bi-Qing; Feng, Kai-Yan; Chen, Lei; Huang, Tao; Cai, Yu-Dong

2012-01-01

Prediction of protein-protein interaction (PPI) sites is one of the most challenging problems in computational biology. Although great progress has been made by employing various machine learning approaches with numerous characteristic features, the problem is still far from being solved. In this study, we developed a novel predictor based on Random Forest (RF) algorithm with the Minimum Redundancy Maximal Relevance (mRMR) method followed by incremental feature selection (IFS). We incorporated features of physicochemical/biochemical properties, sequence conservation, residual disorder, secondary structure and solvent accessibility. We also included five 3D structural features to predict protein-protein interaction sites and achieved an overall accuracy of 0.672997 and MCC of 0.347977. Feature analysis showed that 3D structural features such as Depth Index (DPX) and surface curvature (SC) contributed most to the prediction of protein-protein interaction sites. It was also shown via site-specific feature analysis that the features of individual residues from PPI sites contribute most to the determination of protein-protein interaction sites. It is anticipated that our prediction method will become a useful tool for identifying PPI sites, and that the feature analysis described in this paper will provide useful insights into the mechanisms of interaction. PMID:22937126

Use of read-across and computer-based predictive analysis for the safety assessment of PEG cocamines.

PubMed

Skare, Julie A; Blackburn, Karen; Wu, Shengde; Re, Thomas A; Duche, Daniel; Ringeissen, Stephanie; Bjerke, Donald L; Srinivasan, Viny; Eisenmann, Carol

2015-04-01

In the European Union animal testing has been eliminated for cosmetic ingredients while the US Cosmetic Ingredient Review Expert Panel may request data from animal studies. The use of read-across and predictive toxicology provides a path for filling data gaps without additional animal testing. The PEG cocamines are tertiary amines with an alkyl group derived from coconut fatty acids and two PEG chains of varying length. Toxicology data gaps for the PEG cocamines can be addressed by read-across based on structure-activity relationship using the framework described by Wu et al. (2010) for identifying suitable structural analogs. Data for structural analogs supports the conclusion that the PEG cocamines are non-genotoxic and not expected to exhibit systemic or developmental/reproductive toxicity with use in cosmetics. Due to lack of reliable dermal sensitization data for suitable analogs, this endpoint was addressed using predictive software (TIMES SS) as a first step (Laboratory of Mathematical Chemistry). The prediction for PEG cocamines was the same as that for PEGs, which have been concluded to not present a significant concern for dermal sensitization. This evaluation for PEG cocamines demonstrates the utility of read-across and predictive toxicology tools to assess the safety of cosmetic ingredients. Copyright © 2015 Elsevier Inc. All rights reserved.

A Comparison of Predictive Thermo and Water Solvation Property Prediction Tools and Experimental Data for Selected Traditional Chemical Warfare Agents and Simulants II: COSMO RS and COSMOTherm

DTIC Science & Technology

2017-04-01

A COMPARISON OF PREDICTIVE THERMO AND WATER SOLVATION PROPERTY PREDICTION TOOLS AND EXPERIMENTAL DATA FOR...4. TITLE AND SUBTITLE A Comparison of Predictive Thermo and Water Solvation Property Prediction Tools and Experimental Data for Selected...1  2.  EXPERIMENTAL PROCEDURE

The development of a tool to predict team performance.

PubMed

Sinclair, M A; Siemieniuch, C E; Haslam, R A; Henshaw, M J D C; Evans, L

2012-01-01

The paper describes the development of a tool to predict quantitatively the success of a team when executing a process. The tool was developed for the UK defence industry, though it may be useful in other domains. It is expected to be used by systems engineers in initial stages of systems design, when concepts are still fluid, including the structure of the team(s) which are expected to be operators within the system. It enables answers to be calculated for questions such as "What happens if I reduce team size?" and "Can I reduce the qualifications necessary to execute this process and still achieve the required level of success?". The tool has undergone verification and validation; it predicts fairly well and shows promise. An unexpected finding is that the tool creates a good a priori argument for significant attention to Human Factors Integration in systems projects. The simulations show that if a systems project takes full account of human factors integration (selection, training, process design, interaction design, culture, etc.) then the likelihood of team success will be in excess of 0.95. As the project derogates from this state, the likelihood of team success will drop as low as 0.05. If the team has good internal communications and good individuals in key roles, the likelihood of success rises towards 0.25. Even with a team comprising the best individuals, p(success) will not be greater than 0.35. It is hoped that these results will be useful for human factors professionals involved in systems design. Copyright © 2011 Elsevier Ltd and The Ergonomics Society. All rights reserved.

Support vector machine prediction of enzyme function with conjoint triad feature and hierarchical context.

PubMed

Wang, Yong-Cui; Wang, Yong; Yang, Zhi-Xia; Deng, Nai-Yang

2011-06-20

Enzymes are known as the largest class of proteins and their functions are usually annotated by the Enzyme Commission (EC), which uses a hierarchy structure, i.e., four numbers separated by periods, to classify the function of enzymes. Automatically categorizing enzyme into the EC hierarchy is crucial to understand its specific molecular mechanism. In this paper, we introduce two key improvements in predicting enzyme function within the machine learning framework. One is to introduce the efficient sequence encoding methods for representing given proteins. The second one is to develop a structure-based prediction method with low computational complexity. In particular, we propose to use the conjoint triad feature (CTF) to represent the given protein sequences by considering not only the composition of amino acids but also the neighbor relationships in the sequence. Then we develop a support vector machine (SVM)-based method, named as SVMHL (SVM for hierarchy labels), to output enzyme function by fully considering the hierarchical structure of EC. The experimental results show that our SVMHL with the CTF outperforms SVMHL with the amino acid composition (AAC) feature both in predictive accuracy and Matthew's correlation coefficient (MCC). In addition, SVMHL with the CTF obtains the accuracy and MCC ranging from 81% to 98% and 0.82 to 0.98 when predicting the first three EC digits on a low-homologous enzyme dataset. We further demonstrate that our method outperforms the methods which do not take account of hierarchical relationship among enzyme categories and alternative methods which incorporate prior knowledge about inter-class relationships. Our structure-based prediction model, SVMHL with the CTF, reduces the computational complexity and outperforms the alternative approaches in enzyme function prediction. Therefore our new method will be a useful tool for enzyme function prediction community.

Predicting recreational fishing use of offshore petroleum platforms in the Central Gulf of Mexico

DOE Office of Scientific and Technical Information (OSTI.GOV)

Gordon, W.R. Jr.

1987-01-01

This study is based on the premise that properly sited artificial reefs for optimal human recreational use, a predictive model based upon the marine travel patterns and behavior of marine recreational fishermen, is needed. This research used data gathered from a previous study that addressed the recreational fishing use of offshore oil and gas structures (Ditton and Auyong 1984); on-site data were also collected. The primary research objective was to generate a predictive model that can be applied to artificial-reef development efforts elsewhere. This study investigated the recreational-user patterns of selected petroleum platforms structures in the Central Gulf of Mexico.more » The petroleum structures offshore from the Louisiana coastline provide a unique research tool. Although intended to facilitate the exploration and recovery of hydrocarbons, petroleum platforms also serve as defacto artificial reefs, providing habitat for numerous species of fish and other marine life. Petroleum platforms were found to be the principal fishing destinations within the study area. On-site findings reveal that marine recreational fishermen were as mobile on water, as they are on land. On-site findings were used to assist in the development of a predictive model.« less

AUTO-MUTE 2.0: A Portable Framework with Enhanced Capabilities for Predicting Protein Functional Consequences upon Mutation.

PubMed

Masso, Majid; Vaisman, Iosif I

2014-01-01

The AUTO-MUTE 2.0 stand-alone software package includes a collection of programs for predicting functional changes to proteins upon single residue substitutions, developed by combining structure-based features with trained statistical learning models. Three of the predictors evaluate changes to protein stability upon mutation, each complementing a distinct experimental approach. Two additional classifiers are available, one for predicting activity changes due to residue replacements and the other for determining the disease potential of mutations associated with nonsynonymous single nucleotide polymorphisms (nsSNPs) in human proteins. These five command-line driven tools, as well as all the supporting programs, complement those that run our AUTO-MUTE web-based server. Nevertheless, all the codes have been rewritten and substantially altered for the new portable software, and they incorporate several new features based on user feedback. Included among these upgrades is the ability to perform three highly requested tasks: to run "big data" batch jobs; to generate predictions using modified protein data bank (PDB) structures, and unpublished personal models prepared using standard PDB file formatting; and to utilize NMR structure files that contain multiple models.

Predictability of catastrophic events: Material rupture, earthquakes, turbulence, financial crashes, and human birth

PubMed Central

Sornette, Didier

2002-01-01

We propose that catastrophic events are “outliers” with statistically different properties than the rest of the population and result from mechanisms involving amplifying critical cascades. We describe a unifying approach for modeling and predicting these catastrophic events or “ruptures,” that is, sudden transitions from a quiescent state to a crisis. Such ruptures involve interactions between structures at many different scales. Applications and the potential for prediction are discussed in relation to the rupture of composite materials, great earthquakes, turbulence, and abrupt changes of weather regimes, financial crashes, and human parturition (birth). Future improvements will involve combining ideas and tools from statistical physics and artificial/computational intelligence, to identify and classify possible universal structures that occur at different scales, and to develop application-specific methodologies to use these structures for prediction of the “crises” known to arise in each application of interest. We live on a planet and in a society with intermittent dynamics rather than a state of equilibrium, and so there is a growing and urgent need to sensitize students and citizens to the importance and impacts of ruptures in their multiple forms. PMID:11875205

TANGLE: Two-Level Support Vector Regression Approach for Protein Backbone Torsion Angle Prediction from Primary Sequences

PubMed Central

Song, Jiangning; Tan, Hao; Wang, Mingjun; Webb, Geoffrey I.; Akutsu, Tatsuya

2012-01-01

Protein backbone torsion angles (Phi) and (Psi) involve two rotation angles rotating around the Cα-N bond (Phi) and the Cα-C bond (Psi). Due to the planarity of the linked rigid peptide bonds, these two angles can essentially determine the backbone geometry of proteins. Accordingly, the accurate prediction of protein backbone torsion angle from sequence information can assist the prediction of protein structures. In this study, we develop a new approach called TANGLE (Torsion ANGLE predictor) to predict the protein backbone torsion angles from amino acid sequences. TANGLE uses a two-level support vector regression approach to perform real-value torsion angle prediction using a variety of features derived from amino acid sequences, including the evolutionary profiles in the form of position-specific scoring matrices, predicted secondary structure, solvent accessibility and natively disordered region as well as other global sequence features. When evaluated based on a large benchmark dataset of 1,526 non-homologous proteins, the mean absolute errors (MAEs) of the Phi and Psi angle prediction are 27.8° and 44.6°, respectively, which are 1% and 3% respectively lower than that using one of the state-of-the-art prediction tools ANGLOR. Moreover, the prediction of TANGLE is significantly better than a random predictor that was built on the amino acid-specific basis, with the p-value<1.46e-147 and 7.97e-150, respectively by the Wilcoxon signed rank test. As a complementary approach to the current torsion angle prediction algorithms, TANGLE should prove useful in predicting protein structural properties and assisting protein fold recognition by applying the predicted torsion angles as useful restraints. TANGLE is freely accessible at http://sunflower.kuicr.kyoto-u.ac.jp/~sjn/TANGLE/. PMID:22319565

G4RNA screener web server: User focused interface for RNA G-quadruplex prediction.

PubMed

Garant, Jean-Michel; Perreault, Jean-Pierre; Scott, Michelle S

2018-06-06

Though RNA G-quadruplexes became a focus of study over a decade ago, the main challenge associated with the identification of new potential G-quadruplexes remains a bottleneck step. It slows the study of these non-canonical structures in nucleic acids, and thus the understanding of their significance. The G4RNA screener is an accurate tool for the prediction of RNA G-quadruplexes but its deployment has brought to light an issue with its accessibility to G-quadruplex experts and biologists. G4RNA screener web server is a platform that provides a much needed interface to manage the input, parameters and result display of the main command-line ready tool. It is accessible at http://scottgroup.med.usherbrooke.ca/G4RNA_screener/. Copyright © 2018. Published by Elsevier B.V.

Validation of Nurse Practitioner Primary Care Organizational Climate Questionnaire: A New Tool to Study Nurse Practitioner Practice Settings.

PubMed

Poghosyan, Lusine; Chaplin, William F; Shaffer, Jonathan A

2017-04-01

Favorable organizational climate in primary care settings is necessary to expand the nurse practitioner (NP) workforce and promote their practice. Only one NP-specific tool, the Nurse Practitioner Primary Care Organizational Climate Questionnaire (NP-PCOCQ), measures NP organizational climate. We confirmed NP-PCOCQ's factor structure and established its predictive validity. A crosssectional survey design was used to collect data from 314 NPs in Massachusetts in 2012. Confirmatory factor analysis and regression models were used. The 4-factor model characterized NP-PCOCQ. The NP-PCOCQ score predicted job satisfaction (beta = .36; p < .001) and intent to leave job (odds ratio = .28; p = .011). NP-PCOCQ can be used by researchers to produce new evidence and by administrators to assess organizational climate in their clinics. Further testing of NP-PCOCQ is needed.

From data to the decision: A software architecture to integrate predictive modelling in clinical settings.

PubMed

Martinez-Millana, A; Fernandez-Llatas, C; Sacchi, L; Segagni, D; Guillen, S; Bellazzi, R; Traver, V

2015-08-01

The application of statistics and mathematics over large amounts of data is providing healthcare systems with new tools for screening and managing multiple diseases. Nonetheless, these tools have many technical and clinical limitations as they are based on datasets with concrete characteristics. This proposition paper describes a novel architecture focused on providing a validation framework for discrimination and prediction models in the screening of Type 2 diabetes. For that, the architecture has been designed to gather different data sources under a common data structure and, furthermore, to be controlled by a centralized component (Orchestrator) in charge of directing the interaction flows among data sources, models and graphical user interfaces. This innovative approach aims to overcome the data-dependency of the models by providing a validation framework for the models as they are used within clinical settings.

A CFD/CSD Interaction Methodology for Aircraft Wings

NASA Technical Reports Server (NTRS)

Bhardwaj, Manoj K.

1997-01-01

With advanced subsonic transports and military aircraft operating in the transonic regime, it is becoming important to determine the effects of the coupling between aerodynamic loads and elastic forces. Since aeroelastic effects can contribute significantly to the design of these aircraft, there is a strong need in the aerospace industry to predict these aero-structure interactions computationally. To perform static aeroelastic analysis in the transonic regime, high fidelity computational fluid dynamics (CFD) analysis tools must be used in conjunction with high fidelity computational structural fluid dynamics (CSD) analysis tools due to the nonlinear behavior of the aerodynamics in the transonic regime. There is also a need to be able to use a wide variety of CFD and CSD tools to predict these aeroelastic effects in the transonic regime. Because source codes are not always available, it is necessary to couple the CFD and CSD codes without alteration of the source codes. In this study, an aeroelastic coupling procedure is developed which will perform static aeroelastic analysis using any CFD and CSD code with little code integration. The aeroelastic coupling procedure is demonstrated on an F/A-18 Stabilator using NASTD (an in-house McDonnell Douglas CFD code) and NASTRAN. In addition, the Aeroelastic Research Wing (ARW-2) is used for demonstration of the aeroelastic coupling procedure by using ENSAERO (NASA Ames Research Center CFD code) and a finite element wing-box code (developed as part of this research).

Applications of Protein Thermodynamic Database for Understanding Protein Mutant Stability and Designing Stable Mutants.

PubMed

Gromiha, M Michael; Anoosha, P; Huang, Liang-Tsung

2016-01-01

Protein stability is the free energy difference between unfolded and folded states of a protein, which lies in the range of 5-25 kcal/mol. Experimentally, protein stability is measured with circular dichroism, differential scanning calorimetry, and fluorescence spectroscopy using thermal and denaturant denaturation methods. These experimental data have been accumulated in the form of a database, ProTherm, thermodynamic database for proteins and mutants. It also contains sequence and structure information of a protein, experimental methods and conditions, and literature information. Different features such as search, display, and sorting options and visualization tools have been incorporated in the database. ProTherm is a valuable resource for understanding/predicting the stability of proteins and it can be accessed at http://www.abren.net/protherm/ . ProTherm has been effectively used to examine the relationship among thermodynamics, structure, and function of proteins. We describe the recent progress on the development of methods for understanding/predicting protein stability, such as (1) general trends on mutational effects on stability, (2) relationship between the stability of protein mutants and amino acid properties, (3) applications of protein three-dimensional structures for predicting their stability upon point mutations, (4) prediction of protein stability upon single mutations from amino acid sequence, and (5) prediction methods for addressing double mutants. A list of online resources for predicting has also been provided.

Improved method for predicting protein fold patterns with ensemble classifiers.

PubMed

Chen, W; Liu, X; Huang, Y; Jiang, Y; Zou, Q; Lin, C

2012-01-27

Protein folding is recognized as a critical problem in the field of biophysics in the 21st century. Predicting protein-folding patterns is challenging due to the complex structure of proteins. In an attempt to solve this problem, we employed ensemble classifiers to improve prediction accuracy. In our experiments, 188-dimensional features were extracted based on the composition and physical-chemical property of proteins and 20-dimensional features were selected using a coupled position-specific scoring matrix. Compared with traditional prediction methods, these methods were superior in terms of prediction accuracy. The 188-dimensional feature-based method achieved 71.2% accuracy in five cross-validations. The accuracy rose to 77% when we used a 20-dimensional feature vector. These methods were used on recent data, with 54.2% accuracy. Source codes and dataset, together with web server and software tools for prediction, are available at: http://datamining.xmu.edu.cn/main/~cwc/ProteinPredict.html.

Secondary structural analyses of ITS1 in Paramecium.

PubMed

Hoshina, Ryo

2010-01-01

The nuclear ribosomal RNA gene operon is interrupted by internal transcribed spacer (ITS) 1 and ITS2. Although the secondary structure of ITS2 has been widely investigated, less is known about ITS1 and its structure. In this study, the secondary structure of ITS1 sequences for Paramecium and other ciliates was predicted. Each Paramecium ITS1 forms an open loop with three helices, A through C. Helix B was highly conserved among Paramecium, and similar helices were found in other ciliates. A phylogenetic analysis using the ITS1 sequences showed high-resolution, implying that ITS1 is a good tool for species-level analyses.

Vibrations and structureborne noise in space station

NASA Technical Reports Server (NTRS)

Vaicaitis, R.

1985-01-01

Theoretical models were developed capable of predicting structural response and noise transmission to random point mechanical loads. Fiber reinforced composite and aluminum materials were considered. Cylindrical shells and circular plates were taken as typical representatives of structural components for space station habitability modules. Analytical formulations include double wall and single wall constructions. Pressurized and unpressurized models were considered. Parametric studies were conducted to determine the effect on structural response and noise transmission due to fiber orientation, point load location, damping in the core and the main load carrying structure, pressurization, interior acoustic absorption, etc. These analytical models could serve as preliminary tools for assessing noise related problems, for space station applications.

A Multidimensional Screening Tool for Preschoolers with Externalizing Behavior: Factor Structure and Factorial Invariance

ERIC Educational Resources Information Center

Vancraeyveldt, Caroline; Verschueren, Karine; Wouters, Sofie; Van Craeyevelt, Sanne; Colpin, Hilde

2014-01-01

Externalizing behavior (EB) in preschool has been found to predict maladjustment later in life. Therefore, it is important to identify children most at risk for continuing EB beyond preschool. To date, a number of questionnaires are available for teachers to assist in identifying those children. A frequently overlooked aspect in this screening…

Protein flexibility in the light of structural alphabets

PubMed Central

Craveur, Pierrick; Joseph, Agnel P.; Esque, Jeremy; Narwani, Tarun J.; Noël, Floriane; Shinada, Nicolas; Goguet, Matthieu; Leonard, Sylvain; Poulain, Pierre; Bertrand, Olivier; Faure, Guilhem; Rebehmed, Joseph; Ghozlane, Amine; Swapna, Lakshmipuram S.; Bhaskara, Ramachandra M.; Barnoud, Jonathan; Téletchéa, Stéphane; Jallu, Vincent; Cerny, Jiri; Schneider, Bohdan; Etchebest, Catherine; Srinivasan, Narayanaswamy; Gelly, Jean-Christophe; de Brevern, Alexandre G.

2015-01-01

Protein structures are valuable tools to understand protein function. Nonetheless, proteins are often considered as rigid macromolecules while their structures exhibit specific flexibility, which is essential to complete their functions. Analyses of protein structures and dynamics are often performed with a simplified three-state description, i.e., the classical secondary structures. More precise and complete description of protein backbone conformation can be obtained using libraries of small protein fragments that are able to approximate every part of protein structures. These libraries, called structural alphabets (SAs), have been widely used in structure analysis field, from definition of ligand binding sites to superimposition of protein structures. SAs are also well suited to analyze the dynamics of protein structures. Here, we review innovative approaches that investigate protein flexibility based on SAs description. Coupled to various sources of experimental data (e.g., B-factor) and computational methodology (e.g., Molecular Dynamic simulation), SAs turn out to be powerful tools to analyze protein dynamics, e.g., to examine allosteric mechanisms in large set of structures in complexes, to identify order/disorder transition. SAs were also shown to be quite efficient to predict protein flexibility from amino-acid sequence. Finally, in this review, we exemplify the interest of SAs for studying flexibility with different cases of proteins implicated in pathologies and diseases. PMID:26075209

Inspection of the Math Model Tools for On-Orbit Assessment of Impact Damage Report. Version 1.0

NASA Technical Reports Server (NTRS)

Harris, Charles E.; Raju, Ivatury S.; Piascik, Robert S.; Kramer White, Julie; Labbe, Steve G.; Rotter, Hank A.

2005-01-01

In Spring of 2005, the NASA Engineering Safety Center (NESC) was engaged by the Space Shuttle Program (SSP) to peer review the suite of analytical tools being developed to support the determination of impact and damage tolerance of the Orbiter Thermal Protection Systems (TPS). The NESC formed an independent review team with the core disciplines of materials, flight sciences, structures, mechanical analysis and thermal analysis. The Math Model Tools reviewed included damage prediction and stress analysis, aeroheating analysis, and thermal analysis tools. Some tools are physics-based and other tools are empirically-derived. Each tool was created for a specific use and timeframe, including certification, real-time pre-launch assessments, and real-time on-orbit assessments. The tools are used together in an integrated strategy for assessing the ramifications of impact damage to tile and RCC. The NESC teams conducted a peer review of the engineering data package for each Math Model Tool. This report contains the summary of the team observations and recommendations from these reviews.

A generalized analysis of hydrophobic and loop clusters within globular protein sequences

PubMed Central

Eudes, Richard; Le Tuan, Khanh; Delettré, Jean; Mornon, Jean-Paul; Callebaut, Isabelle

2007-01-01

Background Hydrophobic Cluster Analysis (HCA) is an efficient way to compare highly divergent sequences through the implicit secondary structure information directly derived from hydrophobic clusters. However, its efficiency and application are currently limited by the need of user expertise. In order to help the analysis of HCA plots, we report here the structural preferences of hydrophobic cluster species, which are frequently encountered in globular domains of proteins. These species are characterized only by their hydrophobic/non-hydrophobic dichotomy. This analysis has been extended to loop-forming clusters, using an appropriate loop alphabet. Results The structural behavior of hydrophobic cluster species, which are typical of protein globular domains, was investigated within banks of experimental structures, considered at different levels of sequence redundancy. The 294 more frequent hydrophobic cluster species were analyzed with regard to their association with the different secondary structures (frequencies of association with secondary structures and secondary structure propensities). Hydrophobic cluster species are predominantly associated with regular secondary structures, and a large part (60 %) reveals preferences for α-helices or β-strands. Moreover, the analysis of the hydrophobic cluster amino acid composition generally allows for finer prediction of the regular secondary structure associated with the considered cluster within a cluster species. We also investigated the behavior of loop forming clusters, using a "PGDNS" alphabet. These loop clusters do not overlap with hydrophobic clusters and are highly associated with coils. Finally, the structural information contained in the hydrophobic structural words, as deduced from experimental structures, was compared to the PSI-PRED predictions, revealing that β-strands and especially α-helices are generally over-predicted within the limits of typical β and α hydrophobic clusters. Conclusion The dictionary of hydrophobic clusters described here can help the HCA user to interpret and compare the HCA plots of globular protein sequences, as well as provides an original fundamental insight into the structural bricks of protein folds. Moreover, the novel loop cluster analysis brings additional information for secondary structure prediction on the whole sequence through a generalized cluster analysis (GCA), and not only on regular secondary structures. Such information lays the foundations for developing a new and original tool for secondary structure prediction. PMID:17210072

Engineering and validation of a novel lipid thin film for biomembrane modeling in lipophilicity determination of drugs and xenobiotics

PubMed Central

Idowu, Sunday Olakunle; Adeyemo, Morenikeji Ambali; Ogbonna, Udochi Ihechiluru

2009-01-01

Background Determination of lipophilicity as a tool for predicting pharmacokinetic molecular behavior is limited by the predictive power of available experimental models of the biomembrane. There is current interest, therefore, in models that accurately simulate the biomembrane structure and function. A novel bio-device; a lipid thin film, was engineered as an alternative approach to the previous use of hydrocarbon thin films in biomembrane modeling. Results Retention behavior of four structurally diverse model compounds; 4-amino-3,5-dinitrobenzoic acid (ADBA), naproxen (NPX), nabumetone (NBT) and halofantrine (HF), representing 4 broad classes of varying molecular polarities and aqueous solubility behavior, was investigated on the lipid film, liquid paraffin, and octadecylsilane layers. Computational, thermodynamic and image analysis confirms the peculiar amphiphilic configuration of the lipid film. Effect of solute-type, layer-type and variables interactions on retention behavior was delineated by 2-way analysis of variance (ANOVA) and quantitative structure property relationships (QSPR). Validation of the lipid film was implemented by statistical correlation of a unique chromatographic metric with Log P (octanol/water) and several calculated molecular descriptors of bulk and solubility properties. Conclusion The lipid film signifies a biomimetic artificial biological interface capable of both hydrophobic and specific electrostatic interactions. It captures the hydrophilic-lipophilic balance (HLB) in the determination of lipophilicity of molecules unlike the pure hydrocarbon film of the prior art. The potentials and performance of the bio-device gives the promise of its utility as a predictive analytic tool for early-stage drug discovery science. PMID:19735551

GDAP: a web tool for genome-wide protein disulfide bond prediction.

PubMed

O'Connor, Brian D; Yeates, Todd O

2004-07-01

The Genomic Disulfide Analysis Program (GDAP) provides web access to computationally predicted protein disulfide bonds for over one hundred microbial genomes, including both bacterial and achaeal species. In the GDAP process, sequences of unknown structure are mapped, when possible, to known homologous Protein Data Bank (PDB) structures, after which specific distance criteria are applied to predict disulfide bonds. GDAP also accepts user-supplied protein sequences and subsequently queries the PDB sequence database for the best matches, scans for possible disulfide bonds and returns the results to the client. These predictions are useful for a variety of applications and have previously been used to show a dramatic preference in certain thermophilic archaea and bacteria for disulfide bonds within intracellular proteins. Given the central role these stabilizing, covalent bonds play in such organisms, the predictions available from GDAP provide a rich data source for designing site-directed mutants with more stable thermal profiles. The GDAP web application is a gateway to this information and can be used to understand the role disulfide bonds play in protein stability both in these unusual organisms and in sequences of interest to the individual researcher. The prediction server can be accessed at http://www.doe-mbi.ucla.edu/Services/GDAP.

Sparse RNA folding revisited: space-efficient minimum free energy structure prediction.

PubMed

Will, Sebastian; Jabbari, Hosna

2016-01-01

RNA secondary structure prediction by energy minimization is the central computational tool for the analysis of structural non-coding RNAs and their interactions. Sparsification has been successfully applied to improve the time efficiency of various structure prediction algorithms while guaranteeing the same result; however, for many such folding problems, space efficiency is of even greater concern, particularly for long RNA sequences. So far, space-efficient sparsified RNA folding with fold reconstruction was solved only for simple base-pair-based pseudo-energy models. Here, we revisit the problem of space-efficient free energy minimization. Whereas the space-efficient minimization of the free energy has been sketched before, the reconstruction of the optimum structure has not even been discussed. We show that this reconstruction is not possible in trivial extension of the method for simple energy models. Then, we present the time- and space-efficient sparsified free energy minimization algorithm SparseMFEFold that guarantees MFE structure prediction. In particular, this novel algorithm provides efficient fold reconstruction based on dynamically garbage-collected trace arrows. The complexity of our algorithm depends on two parameters, the number of candidates Z and the number of trace arrows T; both are bounded by [Formula: see text], but are typically much smaller. The time complexity of RNA folding is reduced from [Formula: see text] to [Formula: see text]; the space complexity, from [Formula: see text] to [Formula: see text]. Our empirical results show more than 80 % space savings over RNAfold [Vienna RNA package] on the long RNAs from the RNA STRAND database (≥2500 bases). The presented technique is intentionally generalizable to complex prediction algorithms; due to their high space demands, algorithms like pseudoknot prediction and RNA-RNA-interaction prediction are expected to profit even stronger than "standard" MFE folding. SparseMFEFold is free software, available at http://www.bioinf.uni-leipzig.de/~will/Software/SparseMFEFold.

SFESA: a web server for pairwise alignment refinement by secondary structure shifts.

PubMed

Tong, Jing; Pei, Jimin; Grishin, Nick V

2015-09-03

Protein sequence alignment is essential for a variety of tasks such as homology modeling and active site prediction. Alignment errors remain the main cause of low-quality structure models. A bioinformatics tool to refine alignments is needed to make protein alignments more accurate. We developed the SFESA web server to refine pairwise protein sequence alignments. Compared to the previous version of SFESA, which required a set of 3D coordinates for a protein, the new server will search a sequence database for the closest homolog with an available 3D structure to be used as a template. For each alignment block defined by secondary structure elements in the template, SFESA evaluates alignment variants generated by local shifts and selects the best-scoring alignment variant. A scoring function that combines the sequence score of profile-profile comparison and the structure score of template-derived contact energy is used for evaluation of alignments. PROMALS pairwise alignments refined by SFESA are more accurate than those produced by current advanced alignment methods such as HHpred and CNFpred. In addition, SFESA also improves alignments generated by other software. SFESA is a web-based tool for alignment refinement, designed for researchers to compute, refine, and evaluate pairwise alignments with a combined sequence and structure scoring of alignment blocks. To our knowledge, the SFESA web server is the only tool that refines alignments by evaluating local shifts of secondary structure elements. The SFESA web server is available at http://prodata.swmed.edu/sfesa.

Occipital cortical thickness in very low birth weight born adolescents predicts altered neural specialization of visual semantic category related neural networks.

PubMed

Klaver, Peter; Latal, Beatrice; Martin, Ernst

2015-01-01

Very low birth weight (VLBW) premature born infants have a high risk to develop visual perceptual and learning deficits as well as widespread functional and structural brain abnormalities during infancy and childhood. Whether and how prematurity alters neural specialization within visual neural networks is still unknown. We used functional and structural brain imaging to examine the visual semantic system of VLBW born (<1250 g, gestational age 25-32 weeks) adolescents (13-15 years, n = 11, 3 males) and matched term born control participants (13-15 years, n = 11, 3 males). Neurocognitive assessment revealed no group differences except for lower scores on an adaptive visuomotor integration test. All adolescents were scanned while viewing pictures of animals and tools and scrambled versions of these pictures. Both groups demonstrated animal and tool category related neural networks. Term born adolescents showed tool category related neural activity, i.e. tool pictures elicited more activity than animal pictures, in temporal and parietal brain areas. Animal category related activity was found in the occipital, temporal and frontal cortex. VLBW born adolescents showed reduced tool category related activity in the dorsal visual stream compared with controls, specifically the left anterior intraparietal sulcus, and enhanced animal category related activity in the left middle occipital gyrus and right lingual gyrus. Lower birth weight of VLBW adolescents correlated with larger thickness of the pericalcarine gyrus in the occipital cortex and smaller surface area of the superior temporal gyrus in the lateral temporal cortex. Moreover, larger thickness of the pericalcarine gyrus and smaller surface area of the superior temporal gyrus correlated with reduced tool category related activity in the parietal cortex. Together, our data suggest that very low birth weight predicts alterations of higher order visual semantic networks, particularly in the dorsal stream. The differences in neural specialization may be associated with aberrant cortical development of areas in the visual system that develop early in childhood. Copyright © 2014 Elsevier Ltd. All rights reserved.

Prediction of Path Deviation in Robot Based Incremental Sheet Metal Forming by Means of a New Solid-Shell Finite Element Technology and a Finite Elastoplastic Model with Combined Hardening

NASA Astrophysics Data System (ADS)

Kiliclar, Yalin; Laurischkat, Roman; Vladimirov, Ivaylo N.; Reese, Stefanie

2011-08-01

The presented project deals with a robot based incremental sheet metal forming process, which is called roboforming and has been developed at the Chair of Production Systems. It is characterized by flexible shaping using a freely programmable path-synchronous movement of two industrial robots. The final shape is produced by the incremental infeed of the forming tool in depth direction and its movement along the part contour in lateral direction. However, the resulting geometries formed in roboforming deviate several millimeters from the reference geometry. This results from the compliance of the involved machine structures and the springback effects of the workpiece. The project aims to predict these deviations caused by resiliences and to carry out a compensative path planning based on this prediction. Therefore a planning tool is implemented which compensates the robots's compliance and the springback effects of the sheet metal. The forming process is simulated by means of a finite element analysis using a material model developed at the Institute of Applied Mechanics (IFAM). It is based on the multiplicative split of the deformation gradient in the context of hyperelasticity and combines nonlinear kinematic and isotropic hardening. Low-order finite elements used to simulate thin sheet structures, such as used for the experiments, have the major problem of locking, a nonphysical stiffening effect. For an efficient finite element analysis a special solid-shell finite element formulation based on reduced integration with hourglass stabilization has been developed. To circumvent different locking effects, the enhanced assumed strain (EAS) and the assumed natural strain (ANS) concepts are included in this formulation. Having such powerful tools available we obtain more accurate geometries.

Modeling protein structure at near atomic resolutions with Gorgon.

PubMed

Baker, Matthew L; Abeysinghe, Sasakthi S; Schuh, Stephen; Coleman, Ross A; Abrams, Austin; Marsh, Michael P; Hryc, Corey F; Ruths, Troy; Chiu, Wah; Ju, Tao

2011-05-01

Electron cryo-microscopy (cryo-EM) has played an increasingly important role in elucidating the structure and function of macromolecular assemblies in near native solution conditions. Typically, however, only non-atomic resolution reconstructions have been obtained for these large complexes, necessitating computational tools for integrating and extracting structural details. With recent advances in cryo-EM, maps at near-atomic resolutions have been achieved for several macromolecular assemblies from which models have been manually constructed. In this work, we describe a new interactive modeling toolkit called Gorgon targeted at intermediate to near-atomic resolution density maps (10-3.5 Å), particularly from cryo-EM. Gorgon's de novo modeling procedure couples sequence-based secondary structure prediction with feature detection and geometric modeling techniques to generate initial protein backbone models. Beyond model building, Gorgon is an extensible interactive visualization platform with a variety of computational tools for annotating a wide variety of 3D volumes. Examples from cryo-EM maps of Rotavirus and Rice Dwarf Virus are used to demonstrate its applicability to modeling protein structure. Copyright © 2011 Elsevier Inc. All rights reserved.

RNAmutants: a web server to explore the mutational landscape of RNA secondary structures

PubMed Central

Waldispühl, Jerome; Devadas, Srinivas; Berger, Bonnie; Clote, Peter

2009-01-01

The history and mechanism of molecular evolution in DNA have been greatly elucidated by contributions from genetics, probability theory and bioinformatics—indeed, mathematical developments such as Kimura's neutral theory, Kingman's coalescent theory and efficient software such as BLAST, ClustalW, Phylip, etc., provide the foundation for modern population genetics. In contrast to DNA, the function of most noncoding RNA depends on tertiary structure, experimentally known to be largely determined by secondary structure, for which dynamic programming can efficiently compute the minimum free energy secondary structure. For this reason, understanding the effect of pointwise mutations in RNA secondary structure could reveal fundamental properties of structural RNA molecules and improve our understanding of molecular evolution of RNA. The web server RNAmutants provides several efficient tools to compute the ensemble of low-energy secondary structures for all k-mutants of a given RNA sequence, where k is bounded by a user-specified upper bound. As we have previously shown, these tools can be used to predict putative deleterious mutations and to analyze regulatory sequences from the hepatitis C and human immunodeficiency genomes. Web server is available at http://bioinformatics.bc.edu/clotelab/RNAmutants/, and downloadable binaries at http://rnamutants.csail.mit.edu/. PMID:19531740

The multi-copy simultaneous search methodology: a fundamental tool for structure-based drug design.

PubMed

Schubert, Christian R; Stultz, Collin M

2009-08-01

Fragment-based ligand design approaches, such as the multi-copy simultaneous search (MCSS) methodology, have proven to be useful tools in the search for novel therapeutic compounds that bind pre-specified targets of known structure. MCSS offers a variety of advantages over more traditional high-throughput screening methods, and has been applied successfully to challenging targets. The methodology is quite general and can be used to construct functionality maps for proteins, DNA, and RNA. In this review, we describe the main aspects of the MCSS method and outline the general use of the methodology as a fundamental tool to guide the design of de novo lead compounds. We focus our discussion on the evaluation of MCSS results and the incorporation of protein flexibility into the methodology. In addition, we demonstrate on several specific examples how the information arising from the MCSS functionality maps has been successfully used to predict ligand binding to protein targets and RNA.

Comprehensive comparative analysis and identification of RNA-binding protein domains: multi-class classification and feature selection.

PubMed

Jahandideh, Samad; Srinivasasainagendra, Vinodh; Zhi, Degui

2012-11-07

RNA-protein interaction plays an important role in various cellular processes, such as protein synthesis, gene regulation, post-transcriptional gene regulation, alternative splicing, and infections by RNA viruses. In this study, using Gene Ontology Annotated (GOA) and Structural Classification of Proteins (SCOP) databases an automatic procedure was designed to capture structurally solved RNA-binding protein domains in different subclasses. Subsequently, we applied tuned multi-class SVM (TMCSVM), Random Forest (RF), and multi-class ℓ1/ℓq-regularized logistic regression (MCRLR) for analysis and classifying RNA-binding protein domains based on a comprehensive set of sequence and structural features. In this study, we compared prediction accuracy of three different state-of-the-art predictor methods. From our results, TMCSVM outperforms the other methods and suggests the potential of TMCSVM as a useful tool for facilitating the multi-class prediction of RNA-binding protein domains. On the other hand, MCRLR by elucidating importance of features for their contribution in predictive accuracy of RNA-binding protein domains subclasses, helps us to provide some biological insights into the roles of sequences and structures in protein-RNA interactions.

Isolation and in silico analysis of a novel H+-pyrophosphatase gene orthologue from the halophytic grass Leptochloa fusca

NASA Astrophysics Data System (ADS)

Rauf, Muhammad; Saeed, Nasir A.; Habib, Imran; Ahmed, Moddassir; Shahzad, Khurram; Mansoor, Shahid; Ali, Rashid

2017-02-01

Structure prediction can provide information about function and active sites of protein which helps to design new functional proteins. H+-pyrophosphatase is transmembrane protein involved in establishing proton motive force for active transport of Na+ across membrane by Na+/H+ antiporters. A full length novel H+-pyrophosphatase gene was isolated from halophytic grass Leptochloa fusca using RT-PCR and RACE method. Full length LfVP1 gene sequence of 2292 nucleotides encodes protein of 764 amino acids. DNA and protein sequences were used for characterization using bioinformatics tools. Various important potential sites were predicted by PROSITE webserver. Primary structural analysis showed LfVP1 as stable protein and Grand average hydropathy (GRAVY) indicated that LfVP1 protein has good hydrosolubility. Secondary structure analysis showed that LfVP1 protein sequence contains significant proportion of alpha helix and random coil. Protein membrane topology suggested the presence of 14 transmembrane domains and presence of catalytic domain in TM3. Three dimensional structure from LfVP1 protein sequence also indicated the presence of 14 transmembrane domains and hydrophobicity surface model showed amino acid hydrophobicity. Ramachandran plot showed that 98% amino acid residues were predicted in the favored region.

Enhanced CARES Software Enables Improved Ceramic Life Prediction

NASA Technical Reports Server (NTRS)

Janosik, Lesley A.

1997-01-01

The NASA Lewis Research Center has developed award-winning software that enables American industry to establish the reliability and life of brittle material (e.g., ceramic, intermetallic, graphite) structures in a wide variety of 21st century applications. The CARES (Ceramics Analysis and Reliability Evaluation of Structures) series of software is successfully used by numerous engineers in industrial, academic, and government organizations as an essential element of the structural design and material selection processes. The latest version of this software, CARES/Life, provides a general- purpose design tool that predicts the probability of failure of a ceramic component as a function of its time in service. CARES/Life was recently enhanced by adding new modules designed to improve functionality and user-friendliness. In addition, a beta version of the newly-developed CARES/Creep program (for determining the creep life of monolithic ceramic components) has just been released to selected organizations.

Normal Modes Expose Active Sites in Enzymes.

PubMed

Glantz-Gashai, Yitav; Meirson, Tomer; Samson, Abraham O

2016-12-01

Accurate prediction of active sites is an important tool in bioinformatics. Here we present an improved structure based technique to expose active sites that is based on large changes of solvent accessibility accompanying normal mode dynamics. The technique which detects EXPOsure of active SITes through normal modEs is named EXPOSITE. The technique is trained using a small 133 enzyme dataset and tested using a large 845 enzyme dataset, both with known active site residues. EXPOSITE is also tested in a benchmark protein ligand dataset (PLD) comprising 48 proteins with and without bound ligands. EXPOSITE is shown to successfully locate the active site in most instances, and is found to be more accurate than other structure-based techniques. Interestingly, in several instances, the active site does not correspond to the largest pocket. EXPOSITE is advantageous due to its high precision and paves the way for structure based prediction of active site in enzymes.

Normal Modes Expose Active Sites in Enzymes

PubMed Central

Glantz-Gashai, Yitav; Samson, Abraham O.

2016-01-01

Accurate prediction of active sites is an important tool in bioinformatics. Here we present an improved structure based technique to expose active sites that is based on large changes of solvent accessibility accompanying normal mode dynamics. The technique which detects EXPOsure of active SITes through normal modEs is named EXPOSITE. The technique is trained using a small 133 enzyme dataset and tested using a large 845 enzyme dataset, both with known active site residues. EXPOSITE is also tested in a benchmark protein ligand dataset (PLD) comprising 48 proteins with and without bound ligands. EXPOSITE is shown to successfully locate the active site in most instances, and is found to be more accurate than other structure-based techniques. Interestingly, in several instances, the active site does not correspond to the largest pocket. EXPOSITE is advantageous due to its high precision and paves the way for structure based prediction of active site in enzymes. PMID:28002427

Solution x-ray scattering and structure formation in protein dynamics

NASA Astrophysics Data System (ADS)

Nasedkin, Alexandr; Davidsson, Jan; Niemi, Antti J.; Peng, Xubiao

2017-12-01

We propose a computationally effective approach that builds on Landau mean-field theory in combination with modern nonequilibrium statistical mechanics to model and interpret protein dynamics and structure formation in small- to wide-angle x-ray scattering (S/WAXS) experiments. We develop the methodology by analyzing experimental data in the case of Engrailed homeodomain protein as an example. We demonstrate how to interpret S/WAXS data qualitatively with a good precision and over an extended temperature range. We explain experimental observations in terms of protein phase structure, and we make predictions for future experiments and for how to analyze data at different ambient temperature values. We conclude that the approach we propose has the potential to become a highly accurate, computationally effective, and predictive tool for analyzing S/WAXS data. For this, we compare our results with those obtained previously in an all-atom molecular dynamics simulation.

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.

Integrated NDE and FEM characterization of composite rotors

NASA Astrophysics Data System (ADS)

Abdul-Aziz, Ali; Baaklini, George Y.; Trudell, Jeffrey J.

2001-08-01

A structural assessment by integrating finite-element methods (FEM) and a nondestructive evaluation (NDE) of two flywheel rotor assemblies is presented. Composite rotor A is pancake like with a solid hub design, and composite rotor B is cylindrical with a hollow hub design. Detailed analyses under combined centrifugal and interference-fit loading are performed. Two- and three-dimensional stress analyses and two-dimensional fracture mechanics analyses are conducted. A comparison of the structural analysis results obtained with those extracted via NDE findings is reported. Contact effects due to press-fit conditions are evaluated. Stress results generated from the finite-element analyses were corroborated with the analytical solution. Cracks due to rotational loading up to 48 000 rpm for rotor A and 34 000 rpm for rotor B were successfully imaged with NDE and predicted with FEM and fracture mechanics analyses. A procedure that extends current structural analysis to a life prediction tool is also defined.

Modeling the fusion of cylindrical bioink particles in post bioprinting structure formation

NASA Astrophysics Data System (ADS)

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

2015-03-01

Cellular Particle Dynamics (CPD) is an effective computational method to describe the shape evolution and biomechanical relaxation processes in multicellular systems. Thus, CPD is a useful tool to predict the outcome of post-printing structure formation in bioprinting. The predictive power of CPD has been demonstrated for multicellular systems composed of spherical bioink units. Experiments and computer simulations were related through an independently developed theoretical formalism based on continuum mechanics. Here we generalize the CPD formalism to (i) include cylindrical bioink particles often used in specific bioprinting applications, (ii) describe the more realistic experimental situation in which both the length and the volume of the cylindrical bioink units decrease during post-printing structure formation, and (iii) directly connect CPD simulations to the corresponding experiments without the need of the intermediate continuum theory inherently based on simplifying assumptions. Work supported by NSF [PHY-0957914]. Computer time provided by the University of Missouri Bioinformatics Consortium.

An Integrated NDE and FEM Characterization of Composite Rotors

NASA Technical Reports Server (NTRS)

Abdul-Aziz, Ali; Baaklini, George Y.; Trudell, Jeffrey J.

2000-01-01

A structural assessment by integrating finite-element methods (FEM) and a nondestructive evaluation (NDE) of two flywheel rotor assemblies is presented. Composite rotor A is pancake like with a solid hub design, and composite rotor B is cylindrical with a hollow hub design. Detailed analyses under combined centrifugal and interference-fit loading are performed. Two- and three-dimensional stress analyses and two-dimensional fracture mechanics analyses are conducted. A comparison of the structural analysis results obtained with those extracted via NDE findings is reported. Contact effects due to press-fit conditions are evaluated. Stress results generated from the finite-element analyses were corroborated with the analytical solution. Cracks due to rotational loading up to 49 000 rpm for rotor A and 34 000 rpm for rotor B were successfully imaged with NDE and predicted with FEM and fracture mechanics analyses. A procedure that extends current structural analysis to a life prediction tool is also defined.

Towards a generalized energy prediction model for machine tools

PubMed Central

Bhinge, Raunak; Park, Jinkyoo; Law, Kincho H.; Dornfeld, David A.; Helu, Moneer; Rachuri, Sudarsan

2017-01-01

Energy prediction of machine tools can deliver many advantages to a manufacturing enterprise, ranging from energy-efficient process planning to machine tool monitoring. Physics-based, energy prediction models have been proposed in the past to understand the energy usage pattern of a machine tool. However, uncertainties in both the machine and the operating environment make it difficult to predict the energy consumption of the target machine reliably. Taking advantage of the opportunity to collect extensive, contextual, energy-consumption data, we discuss a data-driven approach to develop an energy prediction model of a machine tool in this paper. First, we present a methodology that can efficiently and effectively collect and process data extracted from a machine tool and its sensors. We then present a data-driven model that can be used to predict the energy consumption of the machine tool for machining a generic part. Specifically, we use Gaussian Process (GP) Regression, a non-parametric machine-learning technique, to develop the prediction model. The energy prediction model is then generalized over multiple process parameters and operations. Finally, we apply this generalized model with a method to assess uncertainty intervals to predict the energy consumed to machine any part using a Mori Seiki NVD1500 machine tool. Furthermore, the same model can be used during process planning to optimize the energy-efficiency of a machining process. PMID:28652687

Towards a generalized energy prediction model for machine tools.

PubMed

Bhinge, Raunak; Park, Jinkyoo; Law, Kincho H; Dornfeld, David A; Helu, Moneer; Rachuri, Sudarsan

2017-04-01

Energy prediction of machine tools can deliver many advantages to a manufacturing enterprise, ranging from energy-efficient process planning to machine tool monitoring. Physics-based, energy prediction models have been proposed in the past to understand the energy usage pattern of a machine tool. However, uncertainties in both the machine and the operating environment make it difficult to predict the energy consumption of the target machine reliably. Taking advantage of the opportunity to collect extensive, contextual, energy-consumption data, we discuss a data-driven approach to develop an energy prediction model of a machine tool in this paper. First, we present a methodology that can efficiently and effectively collect and process data extracted from a machine tool and its sensors. We then present a data-driven model that can be used to predict the energy consumption of the machine tool for machining a generic part. Specifically, we use Gaussian Process (GP) Regression, a non-parametric machine-learning technique, to develop the prediction model. The energy prediction model is then generalized over multiple process parameters and operations. Finally, we apply this generalized model with a method to assess uncertainty intervals to predict the energy consumed to machine any part using a Mori Seiki NVD1500 machine tool. Furthermore, the same model can be used during process planning to optimize the energy-efficiency of a machining process.

A scrutiny of tools used for assessment of hospital disaster preparedness in Iran.

PubMed

Heidaranlu, Esmail; Ebadi, Abbas; Ardalan, Ali; Khankeh, Hamidreza

2015-01-01

In emergencies and disasters, hospitals are among the first and most vital organizations involved. To determine preparedness of a hospital to deal with crisis, health system requires tools compatible with the type of crisis. The present study aimed to evaluate the accuracy of tools used for assessment of hospitals preparedness for major emergencies and disasters in Iran. In this review study, all studies conducted on hospital preparedness to deal with disasters in Iran in the interim 2000-2015 were examined. The World Health Organization (WHO) criteria were used to assess focus of studies for entry in this study. Of the 36 articles obtained, 28 articles that met inclusion criteria were analyzed. In accordance with the WHO standards, focus of tools used was examined in three areas (structural, nonstructural, and functional). In nonstructural area, the most focus of preparation tools was on medical gases, and the least focus on office and storeroom furnishings and equipment. In the functional area, the most focus was on operational plan, and the least on business continuity. Half of the tools in domestic studies considered structural safety as indicator of hospital preparedness. The present study showed that tools used contain a few indicators approved by the WHO, especially in the functional area. Moreover, a lack of a standard indigenous tool was evident, especially in the functional area. Thus, to assess hospital disaster preparedness, the national health system requires new tools compatible with scientific tool design principles, to enable a more accurate prediction of hospital preparedness in disasters before they occur.

Bioinformatics Approaches to Classifying Allergens and Predicting Cross-Reactivity

PubMed Central

Schein, Catherine H.; Ivanciuc, Ovidiu; Braun, Werner

2007-01-01

The major advances in understanding why patients respond to several seemingly different stimuli have been through the isolation, sequencing and structural analysis of proteins that induce an IgE response. The most significant finding is that allergenic proteins from very different sources can have nearly identical sequences and structures, and that this similarity can account for clinically observed cross-reactivity. The increasing amount of information on the sequence, structure and IgE epitopes of allergens is now available in several databases and powerful bioinformatics search tools allow user access to relevant information. Here, we provide an overview of these databases and describe state-of-the art bioinformatics tools to identify the common proteins that may be at the root of multiple allergy syndromes. Progress has also been made in quantitatively defining characteristics that discriminate allergens from non-allergens. Search and software tools for this purpose have been developed and implemented in the Structural Database of Allergenic Proteins (SDAP, http://fermi.utmb.edu/SDAP/). SDAP contains information for over 800 allergens and extensive bibliographic references in a relational database with links to other publicly available databases. SDAP is freely available on the Web to clinicians and patients, and can be used to find structural and functional relations among known allergens and to identify potentially cross-reacting antigens. Here we illustrate how these bioinformatics tools can be used to group allergens, and to detect areas that may account for common patterns of IgE binding and cross-reactivity. Such results can be used to guide treatment regimens for allergy sufferers. PMID:17276876

VITAL NMR: Using Chemical Shift Derived Secondary Structure Information for a Limited Set of Amino Acids to Assess Homology Model Accuracy

DOE Office of Scientific and Technical Information (OSTI.GOV)

Brothers, Michael C; Nesbitt, Anna E; Hallock, Michael J

2011-01-01

Homology modeling is a powerful tool for predicting protein structures, whose success depends on obtaining a reasonable alignment between a given structural template and the protein sequence being analyzed. In order to leverage greater predictive power for proteins with few structural templates, we have developed a method to rank homology models based upon their compliance to secondary structure derived from experimental solid-state NMR (SSNMR) data. Such data is obtainable in a rapid manner by simple SSNMR experiments (e.g., (13)C-(13)C 2D correlation spectra). To test our homology model scoring procedure for various amino acid labeling schemes, we generated a library ofmore » 7,474 homology models for 22 protein targets culled from the TALOS+/SPARTA+ training set of protein structures. Using subsets of amino acids that are plausibly assigned by SSNMR, we discovered that pairs of the residues Val, Ile, Thr, Ala and Leu (VITAL) emulate an ideal dataset where all residues are site specifically assigned. Scoring the models with a predicted VITAL site-specific dataset and calculating secondary structure with the Chemical Shift Index resulted in a Pearson correlation coefficient (-0.75) commensurate to the control (-0.77), where secondary structure was scored site specifically for all amino acids (ALL 20) using STRIDE. This method promises to accelerate structure procurement by SSNMR for proteins with unknown folds through guiding the selection of remotely homologous protein templates and assessing model quality.« less

Predicted phototoxicities of carbon nano-material by quantum mechanical calculations.

PubMed

Betowski, Don

2017-08-01

The purpose of this research was to develop a predictive model for the phototoxicity potential of carbon nanomaterials (fullerenols and single-walled carbon nanotubes). This model is based on the quantum mechanical (ab initio) calculations on these carbon-based materials and comparison of the triplet excited states of these materials to published work relating phototoxicity of polynuclear aromatic hydrocarbons (PAH) to their predictive triplet excited state energy. A successful outcome will add another tool to the arsenal of predictive methods for the U.S. EPA program offices as they assess the toxicity of compounds in use or coming into commerce. The basis of this research was obtaining the best quantum mechanical structure of the carbon nanomaterial and was fundamental in determining the triplet excited state energy. The triplet excited state, in turn, is associated with the phototoxicity of the material. This project relies heavily on the interaction of the predictive results (physical chemistry) and the experimental results obtained by biologists and toxicologists. The results of the experiments (toxicity testing) will help refine the predictive model, while the predictions will alert the scientists to red flag compounds. It is hoped that a guidance document for the U.S. EPA will be forthcoming to help determine the toxicity of compounds. This can be a screening tool that would rely on further testing for those compounds found by these predictions to be a phototoxic danger to health and the environment. Copyright © 2017. Published by Elsevier Inc.

Density functional calculations on structural materials for nuclear energy applications and functional materials for photovoltaic energy applications (abstract only).

PubMed

Domain, C; Olsson, P; Becquart, C S; Legris, A; Guillemoles, J F

2008-02-13

Ab initio density functional theory calculations are carried out in order to predict the evolution of structural materials under aggressive working conditions such as cases with exposure to corrosion and irradiation, as well as to predict and investigate the properties of functional materials for photovoltaic energy applications. Structural metallic materials used in nuclear facilities are subjected to irradiation which induces the creation of large amounts of point defects. These defects interact with each other as well as with the different elements constituting the alloys, which leads to modifications of the microstructure and the mechanical properties. VASP (Vienna Ab initio Simulation Package) has been used to determine the properties of point defect clusters and also those of extended defects such as dislocations. The resulting quantities, such as interaction energies and migration energies, are used in larger scale simulation methods in order to build predictive tools. For photovoltaic energy applications, ab initio calculations are used in order to search for new semiconductors and possible element substitutions for existing ones in order to improve their efficiency.

Workflows and performances in the ranking prediction of 2016 D3R Grand Challenge 2: lessons learned from a collaborative effort.

PubMed

Gao, Ying-Duo; Hu, Yuan; Crespo, Alejandro; Wang, Deping; Armacost, Kira A; Fells, James I; Fradera, Xavier; Wang, Hongwu; Wang, Huijun; Sherborne, Brad; Verras, Andreas; Peng, Zhengwei

2018-01-01

The 2016 D3R Grand Challenge 2 includes both pose and affinity or ranking predictions. This article is focused exclusively on affinity predictions submitted to the D3R challenge from a collaborative effort of the modeling and informatics group. Our submissions include ranking of 102 ligands covering 4 different chemotypes against the FXR ligand binding domain structure, and the relative binding affinity predictions of the two designated free energy subsets of 15 and 18 compounds. Using all the complex structures prepared in the same way allowed us to cover many types of workflows and compare their performances effectively. We evaluated typical workflows used in our daily structure-based design modeling support, which include docking scores, force field-based scores, QM/MM, MMGBSA, MD-MMGBSA, and MacroModel interaction energy estimations. The best performing methods for the two free energy subsets are discussed. Our results suggest that affinity ranking still remains very challenging; that the knowledge of more structural information does not necessarily yield more accurate predictions; and that visual inspection and human intervention are considerably important for ranking. Knowledge of the mode of action and protein flexibility along with visualization tools that depict polar and hydrophobic maps are very useful for visual inspection. QM/MM-based workflows were found to be powerful in affinity ranking and are encouraged to be applied more often. The standardized input and output enable systematic analysis and support methodology development and improvement for high level blinded predictions.

Workflows and performances in the ranking prediction of 2016 D3R Grand Challenge 2: lessons learned from a collaborative effort

NASA Astrophysics Data System (ADS)

Gao, Ying-Duo; Hu, Yuan; Crespo, Alejandro; Wang, Deping; Armacost, Kira A.; Fells, James I.; Fradera, Xavier; Wang, Hongwu; Wang, Huijun; Sherborne, Brad; Verras, Andreas; Peng, Zhengwei

2018-01-01

The 2016 D3R Grand Challenge 2 includes both pose and affinity or ranking predictions. This article is focused exclusively on affinity predictions submitted to the D3R challenge from a collaborative effort of the modeling and informatics group. Our submissions include ranking of 102 ligands covering 4 different chemotypes against the FXR ligand binding domain structure, and the relative binding affinity predictions of the two designated free energy subsets of 15 and 18 compounds. Using all the complex structures prepared in the same way allowed us to cover many types of workflows and compare their performances effectively. We evaluated typical workflows used in our daily structure-based design modeling support, which include docking scores, force field-based scores, QM/MM, MMGBSA, MD-MMGBSA, and MacroModel interaction energy estimations. The best performing methods for the two free energy subsets are discussed. Our results suggest that affinity ranking still remains very challenging; that the knowledge of more structural information does not necessarily yield more accurate predictions; and that visual inspection and human intervention are considerably important for ranking. Knowledge of the mode of action and protein flexibility along with visualization tools that depict polar and hydrophobic maps are very useful for visual inspection. QM/MM-based workflows were found to be powerful in affinity ranking and are encouraged to be applied more often. The standardized input and output enable systematic analysis and support methodology development and improvement for high level blinded predictions.

United3D: a protein model quality assessment program that uses two consensus based methods.

PubMed

Terashi, Genki; Oosawa, Makoto; Nakamura, Yuuki; Kanou, Kazuhiko; Takeda-Shitaka, Mayuko

2012-01-01

In protein structure prediction, such as template-based modeling and free modeling (ab initio modeling), the step that assesses the quality of protein models is very important. We have developed a model quality assessment (QA) program United3D that uses an optimized clustering method and a simple Cα atom contact-based potential. United3D automatically estimates the quality scores (Qscore) of predicted protein models that are highly correlated with the actual quality (GDT_TS). The performance of United3D was tested in the ninth Critical Assessment of protein Structure Prediction (CASP9) experiment. In CASP9, United3D showed the lowest average loss of GDT_TS (5.3) among the QA methods participated in CASP9. This result indicates that the performance of United3D to identify the high quality models from the models predicted by CASP9 servers on 116 targets was best among the QA methods that were tested in CASP9. United3D also produced high average Pearson correlation coefficients (0.93) and acceptable Kendall rank correlation coefficients (0.68) between the Qscore and GDT_TS. This performance was competitive with the other top ranked QA methods that were tested in CASP9. These results indicate that United3D is a useful tool for selecting high quality models from many candidate model structures provided by various modeling methods. United3D will improve the accuracy of protein structure prediction.

TBI server: a web server for predicting ion effects in RNA folding.

PubMed

Zhu, Yuhong; He, Zhaojian; Chen, Shi-Jie

2015-01-01

Metal ions play a critical role in the stabilization of RNA structures. Therefore, accurate prediction of the ion effects in RNA folding can have a far-reaching impact on our understanding of RNA structure and function. Multivalent ions, especially Mg²⁺, are essential for RNA tertiary structure formation. These ions can possibly become strongly correlated in the close vicinity of RNA surface. Most of the currently available software packages, which have widespread success in predicting ion effects in biomolecular systems, however, do not explicitly account for the ion correlation effect. Therefore, it is important to develop a software package/web server for the prediction of ion electrostatics in RNA folding by including ion correlation effects. The TBI web server http://rna.physics.missouri.edu/tbi_index.html provides predictions for the total electrostatic free energy, the different free energy components, and the mean number and the most probable distributions of the bound ions. A novel feature of the TBI server is its ability to account for ion correlation and ion distribution fluctuation effects. By accounting for the ion correlation and fluctuation effects, the TBI server is a unique online tool for computing ion-mediated electrostatic properties for given RNA structures. The results can provide important data for in-depth analysis for ion effects in RNA folding including the ion-dependence of folding stability, ion uptake in the folding process, and the interplay between the different energetic components.

Structural details (kinks and non-alpha conformations) in transmembrane helices are intrahelically determined and can be predicted by sequence pattern descriptors.

PubMed

Rigoutsos, Isidore; Riek, Peter; Graham, Robert M; Novotny, Jiri

2003-08-01

One of the promising methods of protein structure prediction involves the use of amino acid sequence-derived patterns. Here we report on the creation of non-degenerate motif descriptors derived through data mining of training sets of residues taken from the transmembrane-spanning segments of polytopic proteins. These residues correspond to short regions in which there is a deviation from the regular alpha-helical character (i.e. pi-helices, 3(10)-helices and kinks). A 'search engine' derived from these motif descriptors correctly identifies, and discriminates amongst instances of the above 'non-canonical' helical motifs contained in the SwissProt/TrEMBL database of protein primary structures. Our results suggest that deviations from alpha-helicity are encoded locally in sequence patterns only about 7-9 residues long and can be determined in silico directly from the amino acid sequence. Delineation of such variations in helical habit is critical to understanding the complex structure-function relationships of polytopic proteins and for drug discovery. The success of our current methodology foretells development of similar prediction tools capable of identifying other structural motifs from sequence alone. The method described here has been implemented and is available on the World Wide Web at http://cbcsrv.watson.ibm.com/Ttkw.html.

Development of structural and material clavicle response corridors under axial compression and three point bending loading for clavicle finite element model validation.

PubMed

Zhang, Qi; Kindig, Matthew; Li, Zuoping; Crandall, Jeff R; Kerrigan, Jason R

2014-08-22

Clavicle injuries were frequently observed in automotive side and frontal crashes. Finite element (FE) models have been developed to understand the injury mechanism, although no clavicle loading response corridors yet exist in the literature to ensure the model response biofidelity. Moreover, the typically developed structural level (e.g., force-deflection) response corridors were shown to be insufficient for verifying the injury prediction capacity of FE model, which usually is based on strain related injury criteria. Therefore, the purpose of this study is to develop both the structural (force vs deflection) and material level (strain vs force) clavicle response corridors for validating FE models for injury risk modeling. 20 Clavicles were loaded to failure under loading conditions representative of side and frontal crashes respectively, half of which in axial compression, and the other half in three point bending. Both structural and material response corridors were developed for each loading condition. FE model that can accurately predict structural response and strain level provides a more useful tool in injury risk modeling and prediction. The corridor development method in this study could also be extended to develop corridors for other components of the human body. Copyright © 2014 Elsevier Ltd. All rights reserved.

Visualisation of variable binding pockets on protein surfaces by probabilistic analysis of related structure sets.

PubMed

Ashford, Paul; Moss, David S; Alex, Alexander; Yeap, Siew K; Povia, Alice; Nobeli, Irene; Williams, Mark A

2012-03-14

Protein structures provide a valuable resource for rational drug design. For a protein with no known ligand, computational tools can predict surface pockets that are of suitable size and shape to accommodate a complementary small-molecule drug. However, pocket prediction against single static structures may miss features of pockets that arise from proteins' dynamic behaviour. In particular, ligand-binding conformations can be observed as transiently populated states of the apo protein, so it is possible to gain insight into ligand-bound forms by considering conformational variation in apo proteins. This variation can be explored by considering sets of related structures: computationally generated conformers, solution NMR ensembles, multiple crystal structures, homologues or homology models. It is non-trivial to compare pockets, either from different programs or across sets of structures. For a single structure, difficulties arise in defining particular pocket's boundaries. For a set of conformationally distinct structures the challenge is how to make reasonable comparisons between them given that a perfect structural alignment is not possible. We have developed a computational method, Provar, that provides a consistent representation of predicted binding pockets across sets of related protein structures. The outputs are probabilities that each atom or residue of the protein borders a predicted pocket. These probabilities can be readily visualised on a protein using existing molecular graphics software. We show how Provar simplifies comparison of the outputs of different pocket prediction algorithms, of pockets across multiple simulated conformations and between homologous structures. We demonstrate the benefits of use of multiple structures for protein-ligand and protein-protein interface analysis on a set of complexes and consider three case studies in detail: i) analysis of a kinase superfamily highlights the conserved occurrence of surface pockets at the active and regulatory sites; ii) a simulated ensemble of unliganded Bcl2 structures reveals extensions of a known ligand-binding pocket not apparent in the apo crystal structure; iii) visualisations of interleukin-2 and its homologues highlight conserved pockets at the known receptor interfaces and regions whose conformation is known to change on inhibitor binding. Through post-processing of the output of a variety of pocket prediction software, Provar provides a flexible approach to the analysis and visualization of the persistence or variability of pockets in sets of related protein structures.

Accurate secondary structure prediction and fold recognition for circular dichroism spectroscopy

PubMed Central

Micsonai, András; Wien, Frank; Kernya, Linda; Lee, Young-Ho; Goto, Yuji; Réfrégiers, Matthieu; Kardos, József

2015-01-01

Circular dichroism (CD) spectroscopy is a widely used technique for the study of protein structure. Numerous algorithms have been developed for the estimation of the secondary structure composition from the CD spectra. These methods often fail to provide acceptable results on α/β-mixed or β-structure–rich proteins. The problem arises from the spectral diversity of β-structures, which has hitherto been considered as an intrinsic limitation of the technique. The predictions are less reliable for proteins of unusual β-structures such as membrane proteins, protein aggregates, and amyloid fibrils. Here, we show that the parallel/antiparallel orientation and the twisting of the β-sheets account for the observed spectral diversity. We have developed a method called β-structure selection (BeStSel) for the secondary structure estimation that takes into account the twist of β-structures. This method can reliably distinguish parallel and antiparallel β-sheets and accurately estimates the secondary structure for a broad range of proteins. Moreover, the secondary structure components applied by the method are characteristic to the protein fold, and thus the fold can be predicted to the level of topology in the CATH classification from a single CD spectrum. By constructing a web server, we offer a general tool for a quick and reliable structure analysis using conventional CD or synchrotron radiation CD (SRCD) spectroscopy for the protein science research community. The method is especially useful when X-ray or NMR techniques fail. Using BeStSel on data collected by SRCD spectroscopy, we investigated the structure of amyloid fibrils of various disease-related proteins and peptides. PMID:26038575

RSRE: RNA structural robustness evaluator

PubMed Central

Shu, Wenjie; Zheng, Zhiqiang; Wang, Shengqi

2007-01-01

Biological robustness, defined as the ability to maintain stable functioning in the face of various perturbations, is an important and fundamental topic in current biology, and has become a focus of numerous studies in recent years. Although structural robustness has been explored in several types of RNA molecules, the origins of robustness are still controversial. Computational analysis results are needed to make up for the lack of evidence of robustness in natural biological systems. The RNA structural robustness evaluator (RSRE) web server presented here provides a freely available online tool to quantitatively evaluate the structural robustness of RNA based on the widely accepted definition of neutrality. Several classical structure comparison methods are employed; five randomization methods are implemented to generate control sequences; sub-optimal predicted structures can be optionally utilized to mitigate the uncertainty of secondary structure prediction. With a user-friendly interface, the web application is easy to use. Intuitive illustrations are provided along with the original computational results to facilitate analysis. The RSRE will be helpful in the wide exploration of RNA structural robustness and will catalyze our understanding of RNA evolution. The RSRE web server is freely available at http://biosrv1.bmi.ac.cn/RSRE/ or http://biotech.bmi.ac.cn/RSRE/. PMID:17567615

Understanding Clinicians' Use of Cues When Assessing the Future Risk of Violence: A Clinical Judgement Analysis in the Psychiatric Setting.

PubMed

Brown, Barbara; Rakow, Tim

2016-01-01

Research is sparse on how clinicians' judgement informs their violence risk assessments. Yet, determining preferences for which risk factors are used, and how they are weighted and combined, is important to understanding such assessments. This study investigated clinicians' use of static and dynamic cues when assessing risk in individual patients and for dynamic cues considered in the recent and distant past. Clinicians provided three violence risk assessments for 41 separate hypothetical cases of hospitalized patients, each defined by eight cues (e.g., psychopathy and past violence severity/frequency). A clinical judgement analysis, using regression analysis of judgements for multiple cases, created linear models reflecting the major influences on each individual clinician's judgement. Risk assessments could be successfully predicted by between one and four cues, and there was close agreement between different clinicians' models regarding which cues were relevant for a given assessment. However, which cues were used varied between assessments: history of recent violence predicted assessments of in-hospital risk, whereas violence in the distant past predicted the assessed risk in the community. Crucially, several factors included in actuarial/structured risk assessment tools had little influence on clinicians' assessments. Our findings point to the adaptivity in clinicians' violence risk assessments, with a preference for relying on information consistent with the setting for which the assessment applies. The implication is that clinicians are open to using different structured assessment tools for different kinds of risk assessment, although they may seek greater flexibility in their assessments than some structured risk assessment tools afford (e.g., discounting static risk factors). Across three separate violence risk assessments, clinicians' risk assessments were more strongly influenced by dynamic cues that can vary over time (e.g., level of violence) than by static cues that are fixed for a given individual (e.g., a diagnosis of psychopathy). The variation in the factors affecting risk assessments for different settings (i.e., in hospital versus in the community) was greater than the variability between clinicians for such judgements. The findings imply a preference for risk assessment strategies that offer flexibility: either using different risk assessment tools for different purposes and settings or employing a single tool that allows for different inputs into the risk assessment depending upon the nature of the assessment. The appropriateness of these clinical intuitions about violence risk that are implied by our findings warrants further investigation. Copyright © 2015 John Wiley & Sons, Ltd.

Prediction of miRNA targets.

PubMed

Oulas, Anastasis; Karathanasis, Nestoras; Louloupi, Annita; Pavlopoulos, Georgios A; Poirazi, Panayiota; Kalantidis, Kriton; Iliopoulos, Ioannis

2015-01-01

Computational methods for miRNA target prediction are currently undergoing extensive review and evaluation. There is still a great need for improvement of these tools and bioinformatics approaches are looking towards high-throughput experiments in order to validate predictions. The combination of large-scale techniques with computational tools will not only provide greater credence to computational predictions but also lead to the better understanding of specific biological questions. Current miRNA target prediction tools utilize probabilistic learning algorithms, machine learning methods and even empirical biologically defined rules in order to build models based on experimentally verified miRNA targets. Large-scale protein downregulation assays and next-generation sequencing (NGS) are now being used to validate methodologies and compare the performance of existing tools. Tools that exhibit greater correlation between computational predictions and protein downregulation or RNA downregulation are considered the state of the art. Moreover, efficiency in prediction of miRNA targets that are concurrently verified experimentally provides additional validity to computational predictions and further highlights the competitive advantage of specific tools and their efficacy in extracting biologically significant results. In this review paper, we discuss the computational methods for miRNA target prediction and provide a detailed comparison of methodologies and features utilized by each specific tool. Moreover, we provide an overview of current state-of-the-art high-throughput methods used in miRNA target prediction.

OISI dynamic end-to-end modeling tool

NASA Astrophysics Data System (ADS)

Kersten, Michael; Weidler, Alexander; Wilhelm, Rainer; Johann, Ulrich A.; Szerdahelyi, Laszlo

2000-07-01

The OISI Dynamic end-to-end modeling tool is tailored to end-to-end modeling and dynamic simulation of Earth- and space-based actively controlled optical instruments such as e.g. optical stellar interferometers. `End-to-end modeling' is meant to denote the feature that the overall model comprises besides optical sub-models also structural, sensor, actuator, controller and disturbance sub-models influencing the optical transmission, so that the system- level instrument performance due to disturbances and active optics can be simulated. This tool has been developed to support performance analysis and prediction as well as control loop design and fine-tuning for OISI, Germany's preparatory program for optical/infrared spaceborne interferometry initiated in 1994 by Dornier Satellitensysteme GmbH in Friedrichshafen.

Metabolic network flux analysis for engineering plant systems.

PubMed

Shachar-Hill, Yair

2013-04-01

Metabolic network flux analysis (NFA) tools have proven themselves to be powerful aids to metabolic engineering of microbes by providing quantitative insights into the flows of material and energy through cellular systems. The development and application of NFA tools to plant systems has advanced in recent years and are yielding significant insights and testable predictions. Plants present substantial opportunities for the practical application of NFA but they also pose serious challenges related to the complexity of plant metabolic networks and to deficiencies in our knowledge of their structure and regulation. By considering the tools available and selected examples, this article attempts to assess where and how NFA is most likely to have a real impact on plant biotechnology. Copyright © 2013 Elsevier Ltd. All rights reserved.

Internet Databases of the Properties, Enzymatic Reactions, and Metabolism of Small Molecules—Search Options and Applications in Food Science

PubMed Central

Minkiewicz, Piotr; Darewicz, Małgorzata; Iwaniak, Anna; Bucholska, Justyna; Starowicz, Piotr; Czyrko, Emilia

2016-01-01

Internet databases of small molecules, their enzymatic reactions, and metabolism have emerged as useful tools in food science. Database searching is also introduced as part of chemistry or enzymology courses for food technology students. Such resources support the search for information about single compounds and facilitate the introduction of secondary analyses of large datasets. Information can be retrieved from databases by searching for the compound name or structure, annotating with the help of chemical codes or drawn using molecule editing software. Data mining options may be enhanced by navigating through a network of links and cross-links between databases. Exemplary databases reviewed in this article belong to two classes: tools concerning small molecules (including general and specialized databases annotating food components) and tools annotating enzymes and metabolism. Some problems associated with database application are also discussed. Data summarized in computer databases may be used for calculation of daily intake of bioactive compounds, prediction of metabolism of food components, and their biological activity as well as for prediction of interactions between food component and drugs. PMID:27929431

Internet Databases of the Properties, Enzymatic Reactions, and Metabolism of Small Molecules-Search Options and Applications in Food Science.

PubMed

Minkiewicz, Piotr; Darewicz, Małgorzata; Iwaniak, Anna; Bucholska, Justyna; Starowicz, Piotr; Czyrko, Emilia

2016-12-06

Internet databases of small molecules, their enzymatic reactions, and metabolism have emerged as useful tools in food science. Database searching is also introduced as part of chemistry or enzymology courses for food technology students. Such resources support the search for information about single compounds and facilitate the introduction of secondary analyses of large datasets. Information can be retrieved from databases by searching for the compound name or structure, annotating with the help of chemical codes or drawn using molecule editing software. Data mining options may be enhanced by navigating through a network of links and cross-links between databases. Exemplary databases reviewed in this article belong to two classes: tools concerning small molecules (including general and specialized databases annotating food components) and tools annotating enzymes and metabolism. Some problems associated with database application are also discussed. Data summarized in computer databases may be used for calculation of daily intake of bioactive compounds, prediction of metabolism of food components, and their biological activity as well as for prediction of interactions between food component and drugs.

Modeling NIF experimental designs with adaptive mesh refinement and Lagrangian hydrodynamics

NASA Astrophysics Data System (ADS)

Koniges, A. E.; Anderson, R. W.; Wang, P.; Gunney, B. T. N.; Becker, R.; Eder, D. C.; MacGowan, B. J.; Schneider, M. B.

2006-06-01

Incorporation of adaptive mesh refinement (AMR) into Lagrangian hydrodynamics algorithms allows for the creation of a highly powerful simulation tool effective for complex target designs with three-dimensional structure. We are developing an advanced modeling tool that includes AMR and traditional arbitrary Lagrangian-Eulerian (ALE) techniques. Our goal is the accurate prediction of vaporization, disintegration and fragmentation in National Ignition Facility (NIF) experimental target elements. Although our focus is on minimizing the generation of shrapnel in target designs and protecting the optics, the general techniques are applicable to modern advanced targets that include three-dimensional effects such as those associated with capsule fill tubes. Several essential computations in ordinary radiation hydrodynamics need to be redesigned in order to allow for AMR to work well with ALE, including algorithms associated with radiation transport. Additionally, for our goal of predicting fragmentation, we include elastic/plastic flow into our computations. We discuss the integration of these effects into a new ALE-AMR simulation code. Applications of this newly developed modeling tool as well as traditional ALE simulations in two and three dimensions are applied to NIF early-light target designs.

A multivariate prediction model for Rho-dependent termination of transcription.

PubMed

Nadiras, Cédric; Eveno, Eric; Schwartz, Annie; Figueroa-Bossi, Nara; Boudvillain, Marc

2018-06-21

Bacterial transcription termination proceeds via two main mechanisms triggered either by simple, well-conserved (intrinsic) nucleic acid motifs or by the motor protein Rho. Although bacterial genomes can harbor hundreds of termination signals of either type, only intrinsic terminators are reliably predicted. Computational tools to detect the more complex and diversiform Rho-dependent terminators are lacking. To tackle this issue, we devised a prediction method based on Orthogonal Projections to Latent Structures Discriminant Analysis [OPLS-DA] of a large set of in vitro termination data. Using previously uncharacterized genomic sequences for biochemical evaluation and OPLS-DA, we identified new Rho-dependent signals and quantitative sequence descriptors with significant predictive value. Most relevant descriptors specify features of transcript C>G skewness, secondary structure, and richness in regularly-spaced 5'CC/UC dinucleotides that are consistent with known principles for Rho-RNA interaction. Descriptors collectively warrant OPLS-DA predictions of Rho-dependent termination with a ∼85% success rate. Scanning of the Escherichia coli genome with the OPLS-DA model identifies significantly more termination-competent regions than anticipated from transcriptomics and predicts that regions intrinsically refractory to Rho are primarily located in open reading frames. Altogether, this work delineates features important for Rho activity and describes the first method able to predict Rho-dependent terminators in bacterial genomes.

Common features of microRNA target prediction tools

PubMed Central

Peterson, Sarah M.; Thompson, Jeffrey A.; Ufkin, Melanie L.; Sathyanarayana, Pradeep; Liaw, Lucy; Congdon, Clare Bates

2014-01-01

The human genome encodes for over 1800 microRNAs (miRNAs), which are short non-coding RNA molecules that function to regulate gene expression post-transcriptionally. Due to the potential for one miRNA to target multiple gene transcripts, miRNAs are recognized as a major mechanism to regulate gene expression and mRNA translation. Computational prediction of miRNA targets is a critical initial step in identifying miRNA:mRNA target interactions for experimental validation. The available tools for miRNA target prediction encompass a range of different computational approaches, from the modeling of physical interactions to the incorporation of machine learning. This review provides an overview of the major computational approaches to miRNA target prediction. Our discussion highlights three tools for their ease of use, reliance on relatively updated versions of miRBase, and range of capabilities, and these are DIANA-microT-CDS, miRanda-mirSVR, and TargetScan. In comparison across all miRNA target prediction tools, four main aspects of the miRNA:mRNA target interaction emerge as common features on which most target prediction is based: seed match, conservation, free energy, and site accessibility. This review explains these features and identifies how they are incorporated into currently available target prediction tools. MiRNA target prediction is a dynamic field with increasing attention on development of new analysis tools. This review attempts to provide a comprehensive assessment of these tools in a manner that is accessible across disciplines. Understanding the basis of these prediction methodologies will aid in user selection of the appropriate tools and interpretation of the tool output. PMID:24600468

Common features of microRNA target prediction tools.

PubMed

Peterson, Sarah M; Thompson, Jeffrey A; Ufkin, Melanie L; Sathyanarayana, Pradeep; Liaw, Lucy; Congdon, Clare Bates

2014-01-01

The human genome encodes for over 1800 microRNAs (miRNAs), which are short non-coding RNA molecules that function to regulate gene expression post-transcriptionally. Due to the potential for one miRNA to target multiple gene transcripts, miRNAs are recognized as a major mechanism to regulate gene expression and mRNA translation. Computational prediction of miRNA targets is a critical initial step in identifying miRNA:mRNA target interactions for experimental validation. The available tools for miRNA target prediction encompass a range of different computational approaches, from the modeling of physical interactions to the incorporation of machine learning. This review provides an overview of the major computational approaches to miRNA target prediction. Our discussion highlights three tools for their ease of use, reliance on relatively updated versions of miRBase, and range of capabilities, and these are DIANA-microT-CDS, miRanda-mirSVR, and TargetScan. In comparison across all miRNA target prediction tools, four main aspects of the miRNA:mRNA target interaction emerge as common features on which most target prediction is based: seed match, conservation, free energy, and site accessibility. This review explains these features and identifies how they are incorporated into currently available target prediction tools. MiRNA target prediction is a dynamic field with increasing attention on development of new analysis tools. This review attempts to provide a comprehensive assessment of these tools in a manner that is accessible across disciplines. Understanding the basis of these prediction methodologies will aid in user selection of the appropriate tools and interpretation of the tool output.

Comparison of Performance Predictions for New Low-Thrust Trajectory Tools

NASA Technical Reports Server (NTRS)

Polsgrove, Tara; Kos, Larry; Hopkins, Randall; Crane, Tracie

2006-01-01

Several low thrust trajectory optimization tools have been developed over the last 3% years by the Low Thrust Trajectory Tools development team. This toolset includes both low-medium fidelity and high fidelity tools which allow the analyst to quickly research a wide mission trade space and perform advanced mission design. These tools were tested using a set of reference trajectories that exercised each tool s unique capabilities. This paper compares the performance predictions of the various tools against several of the reference trajectories. The intent is to verify agreement between the high fidelity tools and to quantify the performance prediction differences between tools of different fidelity levels.

Predictive models in cancer management: A guide for clinicians.

PubMed

Kazem, Mohammed Ali

2017-04-01

Predictive tools in cancer management are used to predict different outcomes including survival probability or risk of recurrence. The uptake of these tools by clinicians involved in cancer management has not been as common as other clinical tools, which may be due to the complexity of some of these tools or a lack of understanding of how they can aid decision-making in particular clinical situations. The aim of this article is to improve clinicians' knowledge and understanding of predictive tools used in cancer management, including how they are built, how they can be applied to medical practice, and what their limitations may be. Literature review was conducted to investigate the role of predictive tools in cancer management. All predictive models share similar characteristics, but depending on the type of the tool its ability to predict an outcome will differ. Each type has its own pros and cons, and its generalisability will depend on the cohort used to build the tool. These factors will affect the clinician's decision whether to apply the model to their cohort or not. Before a model is used in clinical practice, it is important to appreciate how the model is constructed, what its use may add over and above traditional decision-making tools, and what problems or limitations may be associated with it. Understanding all the above is an important step for any clinician who wants to decide whether or not use predictive tools in their practice. Copyright © 2016 Royal College of Surgeons of Edinburgh (Scottish charity number SC005317) and Royal College of Surgeons in Ireland. Published by Elsevier Ltd. All rights reserved.

Patient-Reported Outcomes After Radiation Therapy in Men With Prostate Cancer: A Systematic Review of Prognostic Tool Accuracy and Validity

DOE Office of Scientific and Technical Information (OSTI.GOV)

O'Callaghan, Michael E., E-mail: elspeth.raymond@health.sa.gov.au; Freemasons Foundation Centre for Men's Health, University of Adelaide; Urology Unit, Repatriation General Hospital, SA Health, Flinders Centre for Innovation in Cancer

Purpose: To identify, through a systematic review, all validated tools used for the prediction of patient-reported outcome measures (PROMs) in patients being treated with radiation therapy for prostate cancer, and provide a comparative summary of accuracy and generalizability. Methods and Materials: PubMed and EMBASE were searched from July 2007. Title/abstract screening, full text review, and critical appraisal were undertaken by 2 reviewers, whereas data extraction was performed by a single reviewer. Eligible articles had to provide a summary measure of accuracy and undertake internal or external validation. Tools were recommended for clinical implementation if they had been externally validated and foundmore » to have accuracy ≥70%. Results: The search strategy identified 3839 potential studies, of which 236 progressed to full text review and 22 were included. From these studies, 50 tools predicted gastrointestinal/rectal symptoms, 29 tools predicted genitourinary symptoms, 4 tools predicted erectile dysfunction, and no tools predicted quality of life. For patients treated with external beam radiation therapy, 3 tools could be recommended for the prediction of rectal toxicity, gastrointestinal toxicity, and erectile dysfunction. For patients treated with brachytherapy, 2 tools could be recommended for the prediction of urinary retention and erectile dysfunction. Conclusions: A large number of tools for the prediction of PROMs in prostate cancer patients treated with radiation therapy have been developed. Only a small minority are accurate and have been shown to be generalizable through external validation. This review provides an accessible catalogue of tools that are ready for clinical implementation as well as which should be prioritized for validation.« less

Use of historic metabolic biotransformation data as a means of anticipating metabolic sites using MetaPrint2D and Bioclipse.

PubMed

Carlsson, Lars; Spjuth, Ola; Adams, Samuel; Glen, Robert C; Boyer, Scott

2010-07-01

Predicting metabolic sites is important in the drug discovery process to aid in rapid compound optimisation. No interactive tool exists and most of the useful tools are quite expensive. Here a fast and reliable method to analyse ligands and visualise potential metabolic sites is presented which is based on annotated metabolic data, described by circular fingerprints. The method is available via the graphical workbench Bioclipse, which is equipped with advanced features in cheminformatics. Due to the speed of predictions (less than 50 ms per molecule), scientists can get real time decision support when editing chemical structures. Bioclipse is a rich client, which means that all calculations are performed on the local computer and do not require network connection. Bioclipse and MetaPrint2D are free for all users, released under open source licenses, and available from http://www.bioclipse.net.

GWFASTA: server for FASTA search in eukaryotic and microbial genomes.

PubMed

Issac, Biju; Raghava, G P S

2002-09-01

Similarity searches are a powerful method for solving important biological problems such as database scanning, evolutionary studies, gene prediction, and protein structure prediction. FASTA is a widely used sequence comparison tool for rapid database scanning. Here we describe the GWFASTA server that was developed to assist the FASTA user in similarity searches against partially and/or completely sequenced genomes. GWFASTA consists of more than 60 microbial genomes, eight eukaryote genomes, and proteomes of annotatedgenomes. Infact, it provides the maximum number of databases for similarity searching from a single platform. GWFASTA allows the submission of more than one sequence as a single query for a FASTA search. It also provides integrated post-processing of FASTA output, including compositional analysis of proteins, multiple sequences alignment, and phylogenetic analysis. Furthermore, it summarizes the search results organism-wise for prokaryotes and chromosome-wise for eukaryotes. Thus, the integration of different tools for sequence analyses makes GWFASTA a powerful toolfor biologists.

[Multivariate Adaptive Regression Splines (MARS), an alternative for the analysis of time series].

PubMed

Vanegas, Jairo; Vásquez, Fabián

Multivariate Adaptive Regression Splines (MARS) is a non-parametric modelling method that extends the linear model, incorporating nonlinearities and interactions between variables. It is a flexible tool that automates the construction of predictive models: selecting relevant variables, transforming the predictor variables, processing missing values and preventing overshooting using a self-test. It is also able to predict, taking into account structural factors that might influence the outcome variable, thereby generating hypothetical models. The end result could identify relevant cut-off points in data series. It is rarely used in health, so it is proposed as a tool for the evaluation of relevant public health indicators. For demonstrative purposes, data series regarding the mortality of children under 5 years of age in Costa Rica were used, comprising the period 1978-2008. Copyright © 2016 SESPAS. Publicado por Elsevier España, S.L.U. All rights reserved.

Sequence Stratigraphy of the Dakota Sandstone, Eastern San Juan Basin, New Mexico, and its Relationship to Reservoir Compartmentalization

DOE Office of Scientific and Technical Information (OSTI.GOV)

Varney, Peter J.

2002-04-23

This research established the Dakota-outcrop sequence stratigraphy in part of the eastern San Juan Basin, New Mexico, and relates reservoir quality lithologies in depositional sequences to structure and reservoir compartmentalization in the South Lindrith Field area. The result was a predictive tool that will help guide further exploration and development.

Integral Airframe Structures (IAS): Validated Feasibility Study of Integrally Stiffened Metallic Fuselage Panels for Reducing Manufacturing Costs

NASA Technical Reports Server (NTRS)

Munroe, J.; Wilkins, K.; Gruber, M.; Domack, Marcia S. (Technical Monitor)

2000-01-01

The Integral Airframe Structures (IAS) program investigated the feasibility of using "integrally stiffened" construction for commercial transport fuselage structure. The objective of the program was to demonstrate structural performance and weight equal to current "built-up" structure with lower manufacturing cost. Testing evaluated mechanical properties, structural details, joint performance, repair, static compression, and two-bay crack residual strength panels. Alloys evaluated included 7050-T7451 plate, 7050-T74511 extrusion, 6013-T6511x extrusion, and 7475-T7351 plate. Structural performance was evaluated with a large 7475-T7351 pressure test that included the arrest of a two-bay longitudinal crack, and a measure of residual strength for a two-bay crack centered on a broken frame. Analysis predictions for the two-bay longitudinal crack panel correlated well with the test results. Analysis activity conducted by the IAS team strongly indicates that current analysis tools predict integral structural behavior as accurately as built-up structure. The cost study results indicated that, compared to built-up fabrication methods, high-speed machining structure from aluminum plate would yield a recurring cost savings of 61%. Part count dropped from 78 individual parts on a baseline panel to just 7 parts for machined IAS structure.

On the virtues of automated quantitative structure-activity relationship: the new kid on the block.

PubMed

de Oliveira, Marcelo T; Katekawa, Edson

2018-02-01

Quantitative structure-activity relationship (QSAR) has proved to be an invaluable tool in medicinal chemistry. Data availability at unprecedented levels through various databases have collaborated to a resurgence in the interest for QSAR. In this context, rapid generation of quality predictive models is highly desirable for hit identification and lead optimization. We showcase the application of an automated QSAR approach, which randomly selects multiple training/test sets and utilizes machine-learning algorithms to generate predictive models. Results demonstrate that AutoQSAR produces models of improved or similar quality to those generated by practitioners in the field but in just a fraction of the time. Despite the potential of the concept to the benefit of the community, the AutoQSAR opportunity has been largely undervalued.

Review: To be or not to be an identifiable model. Is this a relevant question in animal science modelling?

PubMed

Muñoz-Tamayo, R; Puillet, L; Daniel, J B; Sauvant, D; Martin, O; Taghipoor, M; Blavy, P

2018-04-01

What is a good (useful) mathematical model in animal science? For models constructed for prediction purposes, the question of model adequacy (usefulness) has been traditionally tackled by statistical analysis applied to observed experimental data relative to model-predicted variables. However, little attention has been paid to analytic tools that exploit the mathematical properties of the model equations. For example, in the context of model calibration, before attempting a numerical estimation of the model parameters, we might want to know if we have any chance of success in estimating a unique best value of the model parameters from available measurements. This question of uniqueness is referred to as structural identifiability; a mathematical property that is defined on the sole basis of the model structure within a hypothetical ideal experiment determined by a setting of model inputs (stimuli) and observable variables (measurements). Structural identifiability analysis applied to dynamic models described by ordinary differential equations (ODEs) is a common practice in control engineering and system identification. This analysis demands mathematical technicalities that are beyond the academic background of animal science, which might explain the lack of pervasiveness of identifiability analysis in animal science modelling. To fill this gap, in this paper we address the analysis of structural identifiability from a practitioner perspective by capitalizing on the use of dedicated software tools. Our objectives are (i) to provide a comprehensive explanation of the structural identifiability notion for the community of animal science modelling, (ii) to assess the relevance of identifiability analysis in animal science modelling and (iii) to motivate the community to use identifiability analysis in the modelling practice (when the identifiability question is relevant). We focus our study on ODE models. By using illustrative examples that include published mathematical models describing lactation in cattle, we show how structural identifiability analysis can contribute to advancing mathematical modelling in animal science towards the production of useful models and, moreover, highly informative experiments via optimal experiment design. Rather than attempting to impose a systematic identifiability analysis to the modelling community during model developments, we wish to open a window towards the discovery of a powerful tool for model construction and experiment design.

Longitudinal Aerodynamic Modeling of the Adaptive Compliant Trailing Edge Flaps on a GIII Airplane and Comparisons to Flight Data

NASA Technical Reports Server (NTRS)

Smith, Mark S.; Bui, Trong T.; Garcia, Christian A.; Cumming, Stephen B.

2016-01-01

A pair of compliant trailing edge flaps was flown on a modified GIII airplane. Prior to flight test, multiple analysis tools of various levels of complexity were used to predict the aerodynamic effects of the flaps. Vortex lattice, full potential flow, and full Navier-Stokes aerodynamic analysis software programs were used for prediction, in addition to another program that used empirical data. After the flight-test series, lift and pitching moment coefficient increments due to the flaps were estimated from flight data and compared to the results of the predictive tools. The predicted lift increments matched flight data well for all predictive tools for small flap deflections. All tools over-predicted lift increments for large flap deflections. The potential flow and Navier-Stokes programs predicted pitching moment coefficient increments better than the other tools.

Reliability analysis of composite structures

NASA Technical Reports Server (NTRS)

Kan, Han-Pin

1992-01-01

A probabilistic static stress analysis methodology has been developed to estimate the reliability of a composite structure. Closed form stress analysis methods are the primary analytical tools used in this methodology. These structural mechanics methods are used to identify independent variables whose variations significantly affect the performance of the structure. Once these variables are identified, scatter in their values is evaluated and statistically characterized. The scatter in applied loads and the structural parameters are then fitted to appropriate probabilistic distribution functions. Numerical integration techniques are applied to compute the structural reliability. The predicted reliability accounts for scatter due to variability in material strength, applied load, fabrication and assembly processes. The influence of structural geometry and mode of failure are also considerations in the evaluation. Example problems are given to illustrate various levels of analytical complexity.

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

PubMed Central

2011-01-01

Background Protein is an important molecule that performs a wide range of functions in biological systems. Recently, the protein folding attracts much more attention since the function of protein can be generally derived from its molecular structure. The GOR algorithm is one of the most successful computational methods and has been widely used as an efficient analysis tool to predict secondary structure from protein sequence. However, the execution time is still intolerable with the steep growth in protein database. Recently, FPGA chips have emerged as one promising application accelerator to accelerate bioinformatics algorithms by exploiting fine-grained custom design. Results In this paper, we propose a complete fine-grained parallel hardware implementation on FPGA to accelerate the GOR-IV package for 2D protein structure prediction. To improve computing efficiency, we partition the parameter table into small segments and access them in parallel. We aggressively exploit data reuse schemes to minimize the need for loading data from external memory. The whole computation structure is carefully pipelined to overlap the sequence loading, computing and back-writing operations as much as possible. We implemented a complete GOR desktop system based on an FPGA chip XC5VLX330. Conclusions The experimental results show a speedup factor of more than 430x over the original GOR-IV version and 110x speedup over the optimized version with multi-thread SIMD implementation running on a PC platform with AMD Phenom 9650 Quad CPU for 2D protein structure prediction. However, the power consumption is only about 30% of that of current general-propose CPUs. PMID:21342582

Identification and structural characterization of serobactins, a suite of lipopeptide siderophores produced by the grass endophyte Herbaspirillum seropedicae.

PubMed

Rosconi, Federico; Davyt, Danilo; Martínez, Verónica; Martínez, Marcela; Abin-Carriquiry, Juan Andrés; Zane, Hannah; Butler, Alison; de Souza, Emanuel M; Fabiano, Elena

2013-03-01

Herbaspirillum seropedicae Z67 is a diazotrophic endophyte able to colonize the interior of many economically relevant crops such as rice, wheat, corn and sorghum. Structures of siderophores produced by bacterial endophytes have not yet been elucidated. The aim of this work was to identify and characterize the siderophores produced by this bacterium. In a screening for mutants unable to produce siderophores we found a mutant that had a transposon insertion in a non-ribosomal peptide synthase (NRPS) gene coding for a putative siderophore biosynthetic enzyme. The chemical structure of the siderophore was predicted using computational genomic tools. The predicted structure was confirmed by chemical analysis. We found that siderophores produced by H. seropedicae Z67 are a suite of amphiphilic lipopeptides, named serobactin A, B and C, which vary by the length of the fatty acid chain. We also demonstrated the biological activity of serobactins as nutritional iron sources for H. seropedicae. These are the first structurally described siderophores produced by endophytic bacteria. © 2012 Society for Applied Microbiology and Blackwell Publishing Ltd.

bcl::Cluster : A method for clustering biological molecules coupled with visualization in the Pymol Molecular Graphics System.

PubMed

Alexander, Nathan; Woetzel, Nils; Meiler, Jens

2011-02-01

Clustering algorithms are used as data analysis tools in a wide variety of applications in Biology. Clustering has become especially important in protein structure prediction and virtual high throughput screening methods. In protein structure prediction, clustering is used to structure the conformational space of thousands of protein models. In virtual high throughput screening, databases with millions of drug-like molecules are organized by structural similarity, e.g. common scaffolds. The tree-like dendrogram structure obtained from hierarchical clustering can provide a qualitative overview of the results, which is important for focusing detailed analysis. However, in practice it is difficult to relate specific components of the dendrogram directly back to the objects of which it is comprised and to display all desired information within the two dimensions of the dendrogram. The current work presents a hierarchical agglomerative clustering method termed bcl::Cluster. bcl::Cluster utilizes the Pymol Molecular Graphics System to graphically depict dendrograms in three dimensions. This allows simultaneous display of relevant biological molecules as well as additional information about the clusters and the members comprising them.

Systematic analysis of snake neurotoxins' functional classification using a data warehousing approach.

PubMed

Siew, Joyce Phui Yee; Khan, Asif M; Tan, Paul T J; Koh, Judice L Y; Seah, Seng Hong; Koo, Chuay Yeng; Chai, Siaw Ching; Armugam, Arunmozhiarasi; Brusic, Vladimir; Jeyaseelan, Kandiah

2004-12-12

Sequence annotations, functional and structural data on snake venom neurotoxins (svNTXs) are scattered across multiple databases and literature sources. Sequence annotations and structural data are available in the public molecular databases, while functional data are almost exclusively available in the published articles. There is a need for a specialized svNTXs database that contains NTX entries, which are organized, well annotated and classified in a systematic manner. We have systematically analyzed svNTXs and classified them using structure-function groups based on their structural, functional and phylogenetic properties. Using conserved motifs in each phylogenetic group, we built an intelligent module for the prediction of structural and functional properties of unknown NTXs. We also developed an annotation tool to aid the functional prediction of newly identified NTXs as an additional resource for the venom research community. We created a searchable online database of NTX proteins sequences (http://research.i2r.a-star.edu.sg/Templar/DB/snake_neurotoxin). This database can also be found under Swiss-Prot Toxin Annotation Project website (http://www.expasy.org/sprot/).

RF-Phos: A Novel General Phosphorylation Site Prediction Tool Based on Random Forest.

PubMed

Ismail, Hamid D; Jones, Ahoi; Kim, Jung H; Newman, Robert H; Kc, Dukka B

2016-01-01

Protein phosphorylation is one of the most widespread regulatory mechanisms in eukaryotes. Over the past decade, phosphorylation site prediction has emerged as an important problem in the field of bioinformatics. Here, we report a new method, termed Random Forest-based Phosphosite predictor 2.0 (RF-Phos 2.0), to predict phosphorylation sites given only the primary amino acid sequence of a protein as input. RF-Phos 2.0, which uses random forest with sequence and structural features, is able to identify putative sites of phosphorylation across many protein families. In side-by-side comparisons based on 10-fold cross validation and an independent dataset, RF-Phos 2.0 compares favorably to other popular mammalian phosphosite prediction methods, such as PhosphoSVM, GPS2.1, and Musite.

Model representations of kerogen structures: An insight from density functional theory calculations and spectroscopic measurements

DOE PAGES

Weck, Philippe F.; Kim, Eunja; Wang, Yifeng; ...

2017-08-01

Molecular structures of kerogen control hydrocarbon production in unconventional reservoirs. Significant progress has been made in developing model representations of various kerogen structures. These models have been widely used for the prediction of gas adsorption and migration in shale matrix. However, using density functional perturbation theory (DFPT) calculations and vibrational spectroscopic measurements, we here show that a large gap may still remain between the existing model representations and actual kerogen structures, therefore calling for new model development. Using DFPT, we calculated Fourier transform infrared (FTIR) spectra for six most widely used kerogen structure models. The computed spectra were then systematicallymore » compared to the FTIR absorption spectra collected for kerogen samples isolated from Mancos, Woodford and Marcellus formations representing a wide range of kerogen origin and maturation conditions. Limited agreement between the model predictions and the measurements highlights that the existing kerogen models may still miss some key features in structural representation. A combination of DFPT calculations with spectroscopic measurements may provide a useful diagnostic tool for assessing the adequacy of a proposed structural model as well as for future model development. This approach may eventually help develop comprehensive infrared (IR)-fingerprints for tracing kerogen evolution.« less

Model representations of kerogen structures: An insight from density functional theory calculations and spectroscopic measurements.

PubMed

Weck, Philippe F; Kim, Eunja; Wang, Yifeng; Kruichak, Jessica N; Mills, Melissa M; Matteo, Edward N; Pellenq, Roland J-M

2017-08-01

Molecular structures of kerogen control hydrocarbon production in unconventional reservoirs. Significant progress has been made in developing model representations of various kerogen structures. These models have been widely used for the prediction of gas adsorption and migration in shale matrix. However, using density functional perturbation theory (DFPT) calculations and vibrational spectroscopic measurements, we here show that a large gap may still remain between the existing model representations and actual kerogen structures, therefore calling for new model development. Using DFPT, we calculated Fourier transform infrared (FTIR) spectra for six most widely used kerogen structure models. The computed spectra were then systematically compared to the FTIR absorption spectra collected for kerogen samples isolated from Mancos, Woodford and Marcellus formations representing a wide range of kerogen origin and maturation conditions. Limited agreement between the model predictions and the measurements highlights that the existing kerogen models may still miss some key features in structural representation. A combination of DFPT calculations with spectroscopic measurements may provide a useful diagnostic tool for assessing the adequacy of a proposed structural model as well as for future model development. This approach may eventually help develop comprehensive infrared (IR)-fingerprints for tracing kerogen evolution.

Model representations of kerogen structures: An insight from density functional theory calculations and spectroscopic measurements

DOE Office of Scientific and Technical Information (OSTI.GOV)

Weck, Philippe F.; Kim, Eunja; Wang, Yifeng

Molecular structures of kerogen control hydrocarbon production in unconventional reservoirs. Significant progress has been made in developing model representations of various kerogen structures. These models have been widely used for the prediction of gas adsorption and migration in shale matrix. However, using density functional perturbation theory (DFPT) calculations and vibrational spectroscopic measurements, we here show that a large gap may still remain between the existing model representations and actual kerogen structures, therefore calling for new model development. Using DFPT, we calculated Fourier transform infrared (FTIR) spectra for six most widely used kerogen structure models. The computed spectra were then systematicallymore » compared to the FTIR absorption spectra collected for kerogen samples isolated from Mancos, Woodford and Marcellus formations representing a wide range of kerogen origin and maturation conditions. Limited agreement between the model predictions and the measurements highlights that the existing kerogen models may still miss some key features in structural representation. A combination of DFPT calculations with spectroscopic measurements may provide a useful diagnostic tool for assessing the adequacy of a proposed structural model as well as for future model development. This approach may eventually help develop comprehensive infrared (IR)-fingerprints for tracing kerogen evolution.« less

Programmable Hydrogels for Cell Encapsulation and Neo-Tissue Growth to Enable Personalized Tissue Engineering.

PubMed

Bryant, Stephanie J; Vernerey, Franck J

2018-01-01

Biomimetic and biodegradable synthetic hydrogels are emerging as a promising platform for cell encapsulation and tissue engineering. Notably, synthetic-based hydrogels offer highly programmable macroscopic properties (e.g., mechanical, swelling and transport properties) and degradation profiles through control over several tunable parameters (e.g., the initial network structure, degradation kinetics and behavior, and polymer properties). One component to success is the ability to maintain structural integrity as the hydrogel transitions to neo-tissue. This seamless transition is complicated by the fact that cellular activity is highly variable among donors. Thus, computational models provide an important tool in tissue engineering due to their unique ability to explore the coupled processes of hydrogel degradation and neo-tissue growth across multiple length scales. In addition, such models provide new opportunities to develop predictive computational tools to overcome the challenges with designing hydrogels for different donors. In this report, programmable properties of synthetic-based hydrogels and their relation to the hydrogel's structural properties and their evolution with degradation are reviewed. This is followed by recent progress on the development of computational models that describe hydrogel degradation with neo-tissue growth when cells are encapsulated in a hydrogel. Finally, the potential for predictive models to enable patient-specific hydrogel designs for personalized tissue engineering is discussed. © 2017 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Aerodynamic-structural model of offwind yacht sails

NASA Astrophysics Data System (ADS)

Mairs, Christopher M.

An aerodynamic-structural model of offwind yacht sails was created that is useful in predicting sail forces. Two sails were examined experimentally and computationally at several wind angles to explore a variety of flow regimes. The accuracy of the numerical solutions was measured by comparing to experimental results. The two sails examined were a Code 0 and a reaching asymmetric spinnaker. During experiment, balance, wake, and sail shape data were recorded for both sails in various configurations. Two computational steps were used to evaluate the computational model. First, an aerodynamic flow model that includes viscosity effects was used to examine the experimental flying shapes that were recorded. Second, the aerodynamic model was combined with a nonlinear, structural, finite element analysis (FEA) model. The aerodynamic and structural models were used iteratively to predict final flying shapes of offwind sails, starting with the design shapes. The Code 0 has relatively low camber and is used at small angles of attack. It was examined experimentally and computationally at a single angle of attack in two trim configurations, a baseline and overtrimmed setting. Experimentally, the Code 0 was stable and maintained large flow attachment regions. The digitized flying shapes from experiment were examined in the aerodynamic model. Force area predictions matched experimental results well. When the aerodynamic-structural tool was employed, the predictive capability was slightly worse. The reaching asymmetric spinnaker has higher camber and operates at higher angles of attack than the Code 0. Experimentally and computationally, it was examined at two angles of attack. Like the Code 0, at each wind angle, baseline and overtrimmed settings were examined. Experimentally, sail oscillations and large flow detachment regions were encountered. The computational analysis began by examining the experimental flying shapes in the aerodynamic model. In the baseline setting, the computational force predictions were fair at both wind angles examined. Force predictions were much improved in the overtrimmed setting when the sail was highly stalled and more stable. The same trends in force prediction were seen when employing the aerodynamic-structural model. Predictions were good to fair in the baseline setting but improved in the overtrimmed configuration.

QSRR using evolved artificial neural network for 52 common pharmaceuticals and drugs of abuse in hair from UPLC-TOF-MS.

PubMed

Noorizadeh, Hadi; Farmany, Abbas; Narimani, Hojat; Noorizadeh, Mehrab

2013-05-01

A quantitative structure-retention relationship (QSRR) study based on an artificial neural network (ANN) was carried out for the prediction of the ultra-performance liquid chromatography-Time-of-Flight mass spectrometry (UPLC-TOF-MS) retention time (RT) of a set of 52 pharmaceuticals and drugs of abuse in hair. The genetic algorithm was used as a variable selection tool. A partial least squares (PLS) method was used to select the best descriptors which were used as input neurons in neural network model. For choosing the best predictive model from among comparable models, square correlation coefficient R(2) for the whole set calculated based on leave-group-out predicted values of the training set and model-derived predicted values for the test set compounds is suggested to be a good criterion. Finally, to improve the results, structure-retention relationships were followed by a non-linear approach using artificial neural networks and consequently better results were obtained. This also demonstrates the advantages of ANN. Copyright © 2011 John Wiley & Sons, Ltd.

lncRScan-SVM: A Tool for Predicting Long Non-Coding RNAs Using Support Vector Machine.

PubMed

Sun, Lei; Liu, Hui; Zhang, Lin; Meng, Jia

2015-01-01

Functional long non-coding RNAs (lncRNAs) have been bringing novel insight into biological study, however it is still not trivial to accurately distinguish the lncRNA transcripts (LNCTs) from the protein coding ones (PCTs). As various information and data about lncRNAs are preserved by previous studies, it is appealing to develop novel methods to identify the lncRNAs more accurately. Our method lncRScan-SVM aims at classifying PCTs and LNCTs using support vector machine (SVM). The gold-standard datasets for lncRScan-SVM model training, lncRNA prediction and method comparison were constructed according to the GENCODE gene annotations of human and mouse respectively. By integrating features derived from gene structure, transcript sequence, potential codon sequence and conservation, lncRScan-SVM outperforms other approaches, which is evaluated by several criteria such as sensitivity, specificity, accuracy, Matthews correlation coefficient (MCC) and area under curve (AUC). In addition, several known human lncRNA datasets were assessed using lncRScan-SVM. LncRScan-SVM is an efficient tool for predicting the lncRNAs, and it is quite useful for current lncRNA study.

New computational tools for H/D determination in macromolecular structures from neutron data.

PubMed

Siliqi, Dritan; Caliandro, Rocco; Carrozzini, Benedetta; Cascarano, Giovanni Luca; Mazzone, Annamaria

2010-11-01

Two new computational methods dedicated to neutron crystallography, called n-FreeLunch and DNDM-NDM, have been developed and successfully tested. The aim in developing these methods is to determine hydrogen and deuterium positions in macromolecular structures by using information from neutron density maps. Of particular interest is resolving cases in which the geometrically predicted hydrogen or deuterium positions are ambiguous. The methods are an evolution of approaches that are already applied in X-ray crystallography: extrapolation beyond the observed resolution (known as the FreeLunch procedure) and a difference electron-density modification (DEDM) technique combined with the electron-density modification (EDM) tool (known as DEDM-EDM). It is shown that the two methods are complementary to each other and are effective in finding the positions of H and D atoms in neutron density maps.

Using FT-IR Spectroscopy to Elucidate the Structures of Ablative Polymers

NASA Technical Reports Server (NTRS)

Fan, Wendy

2011-01-01

The composition and structure of an ablative polymer has a multifaceted influence on its thermal, mechanical and ablative properties. Understanding the molecular level information is critical to the optimization of material performance because it helps to establish correlations with the macroscopic properties of the material, the so-called structure-property relationship. Moreover, accurate information of molecular structures is also essential to predict the thermal decomposition pathways as well as to identify decomposition species that are fundamentally important to modeling work. In this presentation, I will describe the use of infrared transmission spectroscopy (FT-IR) as a convenient tool to aid the discovery and development of thermal protection system materials.

A Multiscale, Nonlinear, Modeling Framework Enabling the Design and Analysis of Composite Materials and Structures

NASA Technical Reports Server (NTRS)

Bednarcyk, Brett A.; Arnold, Steven M.

2012-01-01

A framework for the multiscale design and analysis of composite materials and structures is presented. The ImMAC software suite, developed at NASA Glenn Research Center, embeds efficient, nonlinear micromechanics capabilities within higher scale structural analysis methods such as finite element analysis. The result is an integrated, multiscale tool that relates global loading to the constituent scale, captures nonlinearities at this scale, and homogenizes local nonlinearities to predict their effects at the structural scale. Example applications of the multiscale framework are presented for the stochastic progressive failure of a SiC/Ti composite tensile specimen and the effects of microstructural variations on the nonlinear response of woven polymer matrix composites.

Structure of supersonic jet flow and its radiated sound

NASA Technical Reports Server (NTRS)

Mankbadi, Reda R.; Hayer, M. Ehtesham; Povinelli, Louis A.

1994-01-01

The present paper explores the use of large-eddy simulations as a tool for predicting noise from first principles. A high-order numerical scheme is used to perform large-eddy simulations of a supersonic jet flow with emphasis on capturing the time-dependent flow structure representating the sound source. The wavelike nature of this structure under random inflow disturbances is demonstrated. This wavelike structure is then enhanced by taking the inflow disturbances to be purely harmonic. Application of Lighthill's theory to calculate the far-field noise, with the sound source obtained from the calculated time-dependent near field, is demonstrated. Alternative approaches to coupling the near-field sound source to the far-field sound are discussed.

A Multiscale, Nonlinear, Modeling Framework Enabling the Design and Analysis of Composite Materials and Structures

NASA Technical Reports Server (NTRS)

Bednarcyk, Brett A.; Arnold, Steven M.

2011-01-01

A framework for the multiscale design and analysis of composite materials and structures is presented. The ImMAC software suite, developed at NASA Glenn Research Center, embeds efficient, nonlinear micromechanics capabilities within higher scale structural analysis methods such as finite element analysis. The result is an integrated, multiscale tool that relates global loading to the constituent scale, captures nonlinearities at this scale, and homogenizes local nonlinearities to predict their effects at the structural scale. Example applications of the multiscale framework are presented for the stochastic progressive failure of a SiC/Ti composite tensile specimen and the effects of microstructural variations on the nonlinear response of woven polymer matrix composites.

G-LoSA: An efficient computational tool for local structure-centric biological studies and drug design.

PubMed

Lee, Hui Sun; Im, Wonpil

2016-04-01

Molecular recognition by protein mostly occurs in a local region on the protein surface. Thus, an efficient computational method for accurate characterization of protein local structural conservation is necessary to better understand biology and drug design. We present a novel local structure alignment tool, G-LoSA. G-LoSA aligns protein local structures in a sequence order independent way and provides a GA-score, a chemical feature-based and size-independent structure similarity score. Our benchmark validation shows the robust performance of G-LoSA to the local structures of diverse sizes and characteristics, demonstrating its universal applicability to local structure-centric comparative biology studies. In particular, G-LoSA is highly effective in detecting conserved local regions on the entire surface of a given protein. In addition, the applications of G-LoSA to identifying template ligands and predicting ligand and protein binding sites illustrate its strong potential for computer-aided drug design. We hope that G-LoSA can be a useful computational method for exploring interesting biological problems through large-scale comparison of protein local structures and facilitating drug discovery research and development. G-LoSA is freely available to academic users at http://im.compbio.ku.edu/GLoSA/. © 2016 The Protein Society.

MQAPRank: improved global protein model quality assessment by learning-to-rank.

PubMed

Jing, Xiaoyang; Dong, Qiwen

2017-05-25

Protein structure prediction has achieved a lot of progress during the last few decades and a greater number of models for a certain sequence can be predicted. Consequently, assessing the qualities of predicted protein models in perspective is one of the key components of successful protein structure prediction. Over the past years, a number of methods have been developed to address this issue, which could be roughly divided into three categories: single methods, quasi-single methods and clustering (or consensus) methods. Although these methods achieve much success at different levels, accurate protein model quality assessment is still an open problem. Here, we present the MQAPRank, a global protein model quality assessment program based on learning-to-rank. The MQAPRank first sorts the decoy models by using single method based on learning-to-rank algorithm to indicate their relative qualities for the target protein. And then it takes the first five models as references to predict the qualities of other models by using average GDT_TS scores between reference models and other models. Benchmarked on CASP11 and 3DRobot datasets, the MQAPRank achieved better performances than other leading protein model quality assessment methods. Recently, the MQAPRank participated in the CASP12 under the group name FDUBio and achieved the state-of-the-art performances. The MQAPRank provides a convenient and powerful tool for protein model quality assessment with the state-of-the-art performances, it is useful for protein structure prediction and model quality assessment usages.

PredictSNP: Robust and Accurate Consensus Classifier for Prediction of Disease-Related Mutations

PubMed Central

Bendl, Jaroslav; Stourac, Jan; Salanda, Ondrej; Pavelka, Antonin; Wieben, Eric D.; Zendulka, Jaroslav; Brezovsky, Jan; Damborsky, Jiri

2014-01-01

Single nucleotide variants represent a prevalent form of genetic variation. Mutations in the coding regions are frequently associated with the development of various genetic diseases. Computational tools for the prediction of the effects of mutations on protein function are very important for analysis of single nucleotide variants and their prioritization for experimental characterization. Many computational tools are already widely employed for this purpose. Unfortunately, their comparison and further improvement is hindered by large overlaps between the training datasets and benchmark datasets, which lead to biased and overly optimistic reported performances. In this study, we have constructed three independent datasets by removing all duplicities, inconsistencies and mutations previously used in the training of evaluated tools. The benchmark dataset containing over 43,000 mutations was employed for the unbiased evaluation of eight established prediction tools: MAPP, nsSNPAnalyzer, PANTHER, PhD-SNP, PolyPhen-1, PolyPhen-2, SIFT and SNAP. The six best performing tools were combined into a consensus classifier PredictSNP, resulting into significantly improved prediction performance, and at the same time returned results for all mutations, confirming that consensus prediction represents an accurate and robust alternative to the predictions delivered by individual tools. A user-friendly web interface enables easy access to all eight prediction tools, the consensus classifier PredictSNP and annotations from the Protein Mutant Database and the UniProt database. The web server and the datasets are freely available to the academic community at http://loschmidt.chemi.muni.cz/predictsnp. PMID:24453961

Screening and structure-based modeling of T-cell epitopes of Nipah virus proteome: an immunoinformatic approach for designing peptide-based vaccine.

PubMed

Kamthania, Mohit; Sharma, D K

2015-12-01

Identification of Nipah virus (NiV) T-cell-specific antigen is urgently needed for appropriate diagnostic and vaccination. In the present study, prediction and modeling of T-cell epitopes of Nipah virus antigenic proteins nucleocapsid, phosphoprotein, matrix, fusion, glycoprotein, L protein, W protein, V protein and C protein followed by the binding simulation studies of predicted highest binding scorers with their corresponding MHC class I alleles were done. Immunoinformatic tool ProPred1 was used to predict the promiscuous MHC class I epitopes of viral antigenic proteins. The molecular modelings of the epitopes were done by PEPstr server. And alleles structure were predicted by MODELLER 9.10. Molecular dynamics (MD) simulation studies were performed through the NAMD graphical user interface embedded in visual molecular dynamics. Epitopes VPATNSPEL, NPTAVPFTL and LLFVFGPNL of Nucleocapsid, V protein and Fusion protein have considerable binding energy and score with HLA-B7, HLA-B*2705 and HLA-A2MHC class I allele, respectively. These three predicted peptides are highly potential to induce T-cell-mediated immune response and are expected to be useful in designing epitope-based vaccines against Nipah virus after further testing by wet laboratory studies.

Towards the design of novel cuprate-based superconductors

NASA Astrophysics Data System (ADS)

Yee, Chuck-Hou

The rapid maturation of materials databases combined with recent development of theories seeking to quantitatively link chemical properties to superconductivity in the cuprates provide the context to design novel superconductors. In this talk, we describe a framework designed to search for new superconductors, which combines chemical rules-of-thumb, insights of transition temperatures from dynamical mean-field theory, first-principles electronic structure tools, materials databases and structure prediction via evolutionary algorithms. We apply the framework to design a family of copper oxysulfides and evaluate the prospects of superconductivity.

Assimilating Text-Mining & Bio-Informatics Tools to Analyze Cellulase structures

NASA Astrophysics Data System (ADS)

Satyasree, K. P. N. V., Dr; Lalitha Kumari, B., Dr; Jyotsna Devi, K. S. N. V.; Choudri, S. M. Roy; Pratap Joshi, K.

2017-08-01

Text-mining is one of the best potential way of automatically extracting information from the huge biological literature. To exploit its prospective, the knowledge encrypted in the text should be converted to some semantic representation such as entities and relations, which could be analyzed by machines. But large-scale practical systems for this purpose are rare. But text mining could be helpful for generating or validating predictions. Cellulases have abundant applications in various industries. Cellulose degrading enzymes are cellulases and the same producing bacteria - Bacillus subtilis & fungus Pseudomonas putida were isolated from top soil of Guntur Dt. A.P. India. Absolute cultures were conserved on potato dextrose agar medium for molecular studies. In this paper, we presented how well the text mining concepts can be used to analyze cellulase producing bacteria and fungi, their comparative structures are also studied with the aid of well-establised, high quality standard bioinformatic tools such as Bioedit, Swissport, Protparam, EMBOSSwin with which a complete data on Cellulases like structure, constituents of the enzyme has been obtained.

The centrality of RNA for engineering gene expression

PubMed Central

Chappell, James; Takahashi, Melissa K; Meyer, Sarai; Loughrey, David; Watters, Kyle E; Lucks, Julius

2013-01-01

Synthetic biology holds promise as both a framework for rationally engineering biological systems and a way to revolutionize how we fundamentally understand them. Essential to realizing this promise is the development of strategies and tools to reliably and predictably control and characterize sophisticated patterns of gene expression. Here we review the role that RNA can play towards this goal and make a case for why this versatile, designable, and increasingly characterizable molecule is one of the most powerful substrates for engineering gene expression at our disposal. We discuss current natural and synthetic RNA regulators of gene expression acting at key points of control – transcription, mRNA degradation, and translation. We also consider RNA structural probing and computational RNA structure predication tools as a way to study RNA structure and ultimately function. Finally, we discuss how next-generation sequencing methods are being applied to the study of RNA and to the characterization of RNA's many properties throughout the cell. PMID:24124015

Prediction of protein mutant stability using classification and regression tool.

PubMed

Huang, Liang-Tsung; Saraboji, K; Ho, Shinn-Ying; Hwang, Shiow-Fen; Ponnuswamy, M N; Gromiha, M Michael

2007-02-01

Prediction of protein stability upon amino acid substitutions is an important problem in molecular biology and the solving of which would help for designing stable mutants. In this work, we have analyzed the stability of protein mutants using two different datasets of 1396 and 2204 mutants obtained from ProTherm database, respectively for free energy change due to thermal (DeltaDeltaG) and denaturant denaturations (DeltaDeltaG(H(2)O)). We have used a set of 48 physical, chemical energetic and conformational properties of amino acid residues and computed the difference of amino acid properties for each mutant in both sets of data. These differences in amino acid properties have been related to protein stability (DeltaDeltaG and DeltaDeltaG(H(2)O)) and are used to train with classification and regression tool for predicting the stability of protein mutants. Further, we have tested the method with 4 fold, 5 fold and 10 fold cross validation procedures. We found that the physical properties, shape and flexibility are important determinants of protein stability. The classification of mutants based on secondary structure (helix, strand, turn and coil) and solvent accessibility (buried, partially buried, partially exposed and exposed) distinguished the stabilizing/destabilizing mutants at an average accuracy of 81% and 80%, respectively for DeltaDeltaG and DeltaDeltaG(H(2)O). The correlation between the experimental and predicted stability change is 0.61 for DeltaDeltaG and 0.44 for DeltaDeltaG(H(2)O). Further, the free energy change due to the replacement of amino acid residue has been predicted within an average error of 1.08 kcal/mol and 1.37 kcal/mol for thermal and chemical denaturation, respectively. The relative importance of secondary structure and solvent accessibility, and the influence of the dataset on prediction of protein mutant stability have been discussed.

PyPLIF: Python-based Protein-Ligand Interaction Fingerprinting.

PubMed

Radifar, Muhammad; Yuniarti, Nunung; Istyastono, Enade Perdana

2013-01-01

Structure-based virtual screening (SBVS) methods often rely on docking score. The docking score is an over-simplification of the actual ligand-target binding. Its capability to model and predict the actual binding reality is limited. Recently, interaction fingerprinting (IFP) has come and offered us an alternative way to model reality. IFP provides us an alternate way to examine protein-ligand interactions. The docking score indicates the approximate affinity and IFP shows the interaction specificity. IFP is a method to convert three dimensional (3D) protein-ligand interactions into one dimensional (1D) bitstrings. The bitstrings are subsequently employed to compare the protein-ligand interaction predicted by the docking tool against the reference ligand. These comparisons produce scores that can be used to enhance the quality of SBVS campaigns. However, some IFP tools are either proprietary or using a proprietary library, which limits the access to the tools and the development of customized IFP algorithm. Therefore, we have developed PyPLIF, a Python-based open source tool to analyze IFP. In this article, we describe PyPLIF and its application to enhance the quality of SBVS in order to identify antagonists for estrogen α receptor (ERα). PyPLIF is freely available at http://code.google.com/p/pyplif.

Sonic Boom Research at NASA Dryden: Objectives and Flight Results from the Lift and Nozzle Change Effects on Tail Shock (LaNCETS) Project

NASA Technical Reports Server (NTRS)

Moes, Timothy R.

2009-01-01

The principal objective of the Supersonics Project is to develop and validate multidisciplinary physics-based predictive design, analysis and optimization capabilities for supersonic vehicles. For aircraft, the focus will be on eliminating the efficiency, environmental and performance barriers to practical supersonic flight. Previous flight projects found that a shaped sonic boom could propagate all the way to the ground (F-5 SSBD experiment) and validated design tools for forebody shape modifications (F-5 SSBD and Quiet Spike experiments). The current project, Lift and Nozzle Change Effects on Tail Shock (LaNCETS) seeks to obtain flight data to develop and validate design tools for low-boom tail shock modifications. Attempts will be made to alter the shock structure of NASA's NF-15B TN/837 by changing the lift distribution by biasing the canard positions, changing the plume shape by under- and over-expanding the nozzles, and changing the plume shape using thrust vectoring. Additional efforts will measure resulting shocks with a probing aircraft (F-15B TN/836) and use the results to validate and update predictive tools. Preliminary flight results are presented and are available to provide truth data for developing and validating the CFD tools required to design low-boom supersonic aircraft.

ToxiM: A Toxicity Prediction Tool for Small Molecules Developed Using Machine Learning and Chemoinformatics Approaches.

PubMed

Sharma, Ashok K; Srivastava, Gopal N; Roy, Ankita; Sharma, Vineet K

2017-01-01

The experimental methods for the prediction of molecular toxicity are tedious and time-consuming tasks. Thus, the computational approaches could be used to develop alternative methods for toxicity prediction. We have developed a tool for the prediction of molecular toxicity along with the aqueous solubility and permeability of any molecule/metabolite. Using a comprehensive and curated set of toxin molecules as a training set, the different chemical and structural based features such as descriptors and fingerprints were exploited for feature selection, optimization and development of machine learning based classification and regression models. The compositional differences in the distribution of atoms were apparent between toxins and non-toxins, and hence, the molecular features were used for the classification and regression. On 10-fold cross-validation, the descriptor-based, fingerprint-based and hybrid-based classification models showed similar accuracy (93%) and Matthews's correlation coefficient (0.84). The performances of all the three models were comparable (Matthews's correlation coefficient = 0.84-0.87) on the blind dataset. In addition, the regression-based models using descriptors as input features were also compared and evaluated on the blind dataset. Random forest based regression model for the prediction of solubility performed better ( R 2 = 0.84) than the multi-linear regression (MLR) and partial least square regression (PLSR) models, whereas, the partial least squares based regression model for the prediction of permeability (caco-2) performed better ( R 2 = 0.68) in comparison to the random forest and MLR based regression models. The performance of final classification and regression models was evaluated using the two validation datasets including the known toxins and commonly used constituents of health products, which attests to its accuracy. The ToxiM web server would be a highly useful and reliable tool for the prediction of toxicity, solubility, and permeability of small molecules.

ToxiM: A Toxicity Prediction Tool for Small Molecules Developed Using Machine Learning and Chemoinformatics Approaches

PubMed Central

Sharma, Ashok K.; Srivastava, Gopal N.; Roy, Ankita; Sharma, Vineet K.

2017-01-01

The experimental methods for the prediction of molecular toxicity are tedious and time-consuming tasks. Thus, the computational approaches could be used to develop alternative methods for toxicity prediction. We have developed a tool for the prediction of molecular toxicity along with the aqueous solubility and permeability of any molecule/metabolite. Using a comprehensive and curated set of toxin molecules as a training set, the different chemical and structural based features such as descriptors and fingerprints were exploited for feature selection, optimization and development of machine learning based classification and regression models. The compositional differences in the distribution of atoms were apparent between toxins and non-toxins, and hence, the molecular features were used for the classification and regression. On 10-fold cross-validation, the descriptor-based, fingerprint-based and hybrid-based classification models showed similar accuracy (93%) and Matthews's correlation coefficient (0.84). The performances of all the three models were comparable (Matthews's correlation coefficient = 0.84–0.87) on the blind dataset. In addition, the regression-based models using descriptors as input features were also compared and evaluated on the blind dataset. Random forest based regression model for the prediction of solubility performed better (R2 = 0.84) than the multi-linear regression (MLR) and partial least square regression (PLSR) models, whereas, the partial least squares based regression model for the prediction of permeability (caco-2) performed better (R2 = 0.68) in comparison to the random forest and MLR based regression models. The performance of final classification and regression models was evaluated using the two validation datasets including the known toxins and commonly used constituents of health products, which attests to its accuracy. The ToxiM web server would be a highly useful and reliable tool for the prediction of toxicity, solubility, and permeability of small molecules. PMID:29249969

Text Mining for Protein Docking

PubMed Central

Badal, Varsha D.; Kundrotas, Petras J.; Vakser, Ilya A.

2015-01-01

The rapidly growing amount of publicly available information from biomedical research is readily accessible on the Internet, providing a powerful resource for predictive biomolecular modeling. The accumulated data on experimentally determined structures transformed structure prediction of proteins and protein complexes. Instead of exploring the enormous search space, predictive tools can simply proceed to the solution based on similarity to the existing, previously determined structures. A similar major paradigm shift is emerging due to the rapidly expanding amount of information, other than experimentally determined structures, which still can be used as constraints in biomolecular structure prediction. Automated text mining has been widely used in recreating protein interaction networks, as well as in detecting small ligand binding sites on protein structures. Combining and expanding these two well-developed areas of research, we applied the text mining to structural modeling of protein-protein complexes (protein docking). Protein docking can be significantly improved when constraints on the docking mode are available. We developed a procedure that retrieves published abstracts on a specific protein-protein interaction and extracts information relevant to docking. The procedure was assessed on protein complexes from Dockground (http://dockground.compbio.ku.edu). The results show that correct information on binding residues can be extracted for about half of the complexes. The amount of irrelevant information was reduced by conceptual analysis of a subset of the retrieved abstracts, based on the bag-of-words (features) approach. Support Vector Machine models were trained and validated on the subset. The remaining abstracts were filtered by the best-performing models, which decreased the irrelevant information for ~ 25% complexes in the dataset. The extracted constraints were incorporated in the docking protocol and tested on the Dockground unbound benchmark set, significantly increasing the docking success rate. PMID:26650466

AEROELASTIC SIMULATION TOOL FOR INFLATABLE BALLUTE AEROCAPTURE

NASA Technical Reports Server (NTRS)

Liever, P. A.; Sheta, E. F.; Habchi, S. D.

2006-01-01

A multidisciplinary analysis tool is under development for predicting the impact of aeroelastic effects on the functionality of inflatable ballute aeroassist vehicles in both the continuum and rarefied flow regimes. High-fidelity modules for continuum and rarefied aerodynamics, structural dynamics, heat transfer, and computational grid deformation are coupled in an integrated multi-physics, multi-disciplinary computing environment. This flexible and extensible approach allows the integration of state-of-the-art, stand-alone NASA and industry leading continuum and rarefied flow solvers and structural analysis codes into a computing environment in which the modules can run concurrently with synchronized data transfer. Coupled fluid-structure continuum flow demonstrations were conducted on a clamped ballute configuration. The feasibility of implementing a DSMC flow solver in the simulation framework was demonstrated, and loosely coupled rarefied flow aeroelastic demonstrations were performed. A NASA and industry technology survey identified CFD, DSMC and structural analysis codes capable of modeling non-linear shape and material response of thin-film inflated aeroshells. The simulation technology will find direct and immediate applications with NASA and industry in ongoing aerocapture technology development programs.

Predicting protein crystallization propensity from protein sequence

PubMed Central

2011-01-01

The high-throughput structure determination pipelines developed by structural genomics programs offer a unique opportunity for data mining. One important question is how protein properties derived from a primary sequence correlate with the protein’s propensity to yield X-ray quality crystals (crystallizability) and 3D X-ray structures. A set of protein properties were computed for over 1,300 proteins that expressed well but were insoluble, and for ~720 unique proteins that resulted in X-ray structures. The correlation of the protein’s iso-electric point and grand average hydropathy (GRAVY) with crystallizability was analyzed for full length and domain constructs of protein targets. In a second step, several additional properties that can be calculated from the protein sequence were added and evaluated. Using statistical analyses we have identified a set of the attributes correlating with a protein’s propensity to crystallize and implemented a Support Vector Machine (SVM) classifier based on these. We have created applications to analyze and provide optimal boundary information for query sequences and to visualize the data. These tools are available via the web site http://bioinformatics.anl.gov/cgi-bin/tools/pdpredictor. PMID:20177794

The Zeldovich & Adhesion approximations and applications to the local universe

NASA Astrophysics Data System (ADS)

Hidding, Johan; van de Weygaert, Rien; Shandarin, Sergei

2016-10-01

The Zeldovich approximation (ZA) predicts the formation of a web of singularities. While these singularities may only exist in the most formal interpretation of the ZA, they provide a powerful tool for the analysis of initial conditions. We present a novel method to find the skeleton of the resulting cosmic web based on singularities in the primordial deformation tensor and its higher order derivatives. We show that the A 3 lines predict the formation of filaments in a two-dimensional model. We continue with applications of the adhesion model to visualise structures in the local (z < 0.03) universe.

Knotty: Efficient and Accurate Prediction of Complex RNA Pseudoknot Structures.

PubMed

Jabbari, Hosna; Wark, Ian; Montemagno, Carlo; Will, Sebastian

2018-06-01

The computational prediction of RNA secondary structure by free energy minimization has become an important tool in RNA research. However in practice, energy minimization is mostly limited to pseudoknot-free structures or rather simple pseudoknots, not covering many biologically important structures such as kissing hairpins. Algorithms capable of predicting sufficiently complex pseudoknots (for sequences of length n) used to have extreme complexities, e.g. Pknots (Rivas and Eddy, 1999) has O(n6) time and O(n4) space complexity. The algorithm CCJ (Chen et al., 2009) dramatically improves the asymptotic run time for predicting complex pseudoknots (handling almost all relevant pseudoknots, while being slightly less general than Pknots), but this came at the cost of large constant factors in space and time, which strongly limited its practical application (∼200 bases already require 256GB space). We present a CCJ-type algorithm, Knotty, that handles the same comprehensive pseudoknot class of structures as CCJ with improved space complexity of Θ(n3 + Z)-due to the applied technique of sparsification, the number of "candidates", Z, appears to grow significantly slower than n4 on our benchmark set (which include pseudoknotted RNAs up to 400 nucleotides). In terms of run time over this benchmark, Knotty clearly outperforms Pknots and the original CCJ implementation, CCJ 1.0; Knotty's space consumption fundamentally improves over CCJ 1.0, being on a par with the space-economic Pknots. By comparing to CCJ 2.0, our unsparsified Knotty variant, we demonstrate the isolated effect of sparsification. Moreover, Knotty employs the state-of-the-art energy model of "HotKnots DP09", which results in superior prediction accuracy over Pknots. Our software is available at https://github.com/HosnaJabbari/Knotty. will@tbi.unvie.ac.at. Supplementary data are available at Bioinformatics online.

Drug Repositioning by Kernel-Based Integration of Molecular Structure, Molecular Activity, and Phenotype Data

PubMed Central

Wang, Yongcui; Chen, Shilong; Deng, Naiyang; Wang, Yong

2013-01-01

Computational inference of novel therapeutic values for existing drugs, i.e., drug repositioning, offers the great prospect for faster and low-risk drug development. Previous researches have indicated that chemical structures, target proteins, and side-effects could provide rich information in drug similarity assessment and further disease similarity. However, each single data source is important in its own way and data integration holds the great promise to reposition drug more accurately. Here, we propose a new method for drug repositioning, PreDR (Predict Drug Repositioning), to integrate molecular structure, molecular activity, and phenotype data. Specifically, we characterize drug by profiling in chemical structure, target protein, and side-effects space, and define a kernel function to correlate drugs with diseases. Then we train a support vector machine (SVM) to computationally predict novel drug-disease interactions. PreDR is validated on a well-established drug-disease network with 1,933 interactions among 593 drugs and 313 diseases. By cross-validation, we find that chemical structure, drug target, and side-effects information are all predictive for drug-disease relationships. More experimentally observed drug-disease interactions can be revealed by integrating these three data sources. Comparison with existing methods demonstrates that PreDR is competitive both in accuracy and coverage. Follow-up database search and pathway analysis indicate that our new predictions are worthy of further experimental validation. Particularly several novel predictions are supported by clinical trials databases and this shows the significant prospects of PreDR in future drug treatment. In conclusion, our new method, PreDR, can serve as a useful tool in drug discovery to efficiently identify novel drug-disease interactions. In addition, our heterogeneous data integration framework can be applied to other problems. PMID:24244318

A range of complex probabilistic models for RNA secondary structure prediction that includes the nearest-neighbor model and more.

PubMed

Rivas, Elena; Lang, Raymond; Eddy, Sean R

2012-02-01

The standard approach for single-sequence RNA secondary structure prediction uses a nearest-neighbor thermodynamic model with several thousand experimentally determined energy parameters. An attractive alternative is to use statistical approaches with parameters estimated from growing databases of structural RNAs. Good results have been reported for discriminative statistical methods using complex nearest-neighbor models, including CONTRAfold, Simfold, and ContextFold. Little work has been reported on generative probabilistic models (stochastic context-free grammars [SCFGs]) of comparable complexity, although probabilistic models are generally easier to train and to use. To explore a range of probabilistic models of increasing complexity, and to directly compare probabilistic, thermodynamic, and discriminative approaches, we created TORNADO, a computational tool that can parse a wide spectrum of RNA grammar architectures (including the standard nearest-neighbor model and more) using a generalized super-grammar that can be parameterized with probabilities, energies, or arbitrary scores. By using TORNADO, we find that probabilistic nearest-neighbor models perform comparably to (but not significantly better than) discriminative methods. We find that complex statistical models are prone to overfitting RNA structure and that evaluations should use structurally nonhomologous training and test data sets. Overfitting has affected at least one published method (ContextFold). The most important barrier to improving statistical approaches for RNA secondary structure prediction is the lack of diversity of well-curated single-sequence RNA secondary structures in current RNA databases.

A range of complex probabilistic models for RNA secondary structure prediction that includes the nearest-neighbor model and more

PubMed Central

Rivas, Elena; Lang, Raymond; Eddy, Sean R.

2012-01-01

The standard approach for single-sequence RNA secondary structure prediction uses a nearest-neighbor thermodynamic model with several thousand experimentally determined energy parameters. An attractive alternative is to use statistical approaches with parameters estimated from growing databases of structural RNAs. Good results have been reported for discriminative statistical methods using complex nearest-neighbor models, including CONTRAfold, Simfold, and ContextFold. Little work has been reported on generative probabilistic models (stochastic context-free grammars [SCFGs]) of comparable complexity, although probabilistic models are generally easier to train and to use. To explore a range of probabilistic models of increasing complexity, and to directly compare probabilistic, thermodynamic, and discriminative approaches, we created TORNADO, a computational tool that can parse a wide spectrum of RNA grammar architectures (including the standard nearest-neighbor model and more) using a generalized super-grammar that can be parameterized with probabilities, energies, or arbitrary scores. By using TORNADO, we find that probabilistic nearest-neighbor models perform comparably to (but not significantly better than) discriminative methods. We find that complex statistical models are prone to overfitting RNA structure and that evaluations should use structurally nonhomologous training and test data sets. Overfitting has affected at least one published method (ContextFold). The most important barrier to improving statistical approaches for RNA secondary structure prediction is the lack of diversity of well-curated single-sequence RNA secondary structures in current RNA databases. PMID:22194308

Discrete Molecular Dynamics Can Predict Helical Prestructured Motifs in Disordered Proteins

PubMed Central

Han, Kyou-Hoon; Dokholyan, Nikolay V.; Tompa, Péter; Kalmár, Lajos; Hegedűs, Tamás

2014-01-01

Intrinsically disordered proteins (IDPs) lack a stable tertiary structure, but their short binding regions termed Pre-Structured Motifs (PreSMo) can form transient secondary structure elements in solution. Although disordered proteins are crucial in many biological processes and designing strategies to modulate their function is highly important, both experimental and computational tools to describe their conformational ensembles and the initial steps of folding are sparse. Here we report that discrete molecular dynamics (DMD) simulations combined with replica exchange (RX) method efficiently samples the conformational space and detects regions populating α-helical conformational states in disordered protein regions. While the available computational methods predict secondary structural propensities in IDPs based on the observation of protein-protein interactions, our ab initio method rests on physical principles of protein folding and dynamics. We show that RX-DMD predicts α-PreSMos with high confidence confirmed by comparison to experimental NMR data. Moreover, the method also can dissect α-PreSMos in close vicinity to each other and indicate helix stability. Importantly, simulations with disordered regions forming helices in X-ray structures of complexes indicate that a preformed helix is frequently the binding element itself, while in other cases it may have a role in initiating the binding process. Our results indicate that RX-DMD provides a breakthrough in the structural and dynamical characterization of disordered proteins by generating the structural ensembles of IDPs even when experimental data are not available. PMID:24763499

Analysis, prediction, and case studies of early-age cracking in bridge decks

NASA Astrophysics Data System (ADS)

ElSafty, Adel; Graeff, Matthew K.; El-Gharib, Georges; Abdel-Mohti, Ahmed; Mike Jackson, N.

2016-06-01

Early-age cracking can adversely affect strength, serviceability, and durability of concrete bridge decks. Early age is defined as the period after final setting, during which concrete properties change rapidly. Many factors can cause early-age bridge deck cracking including temperature change, hydration, plastic shrinkage, autogenous shrinkage, and drying shrinkage. The cracking may also increase the effect of freeze and thaw cycles and may lead to corrosion of reinforcement. This research paper presents an analysis of causes and factors affecting early-age cracking. It also provides a tool developed to predict the likelihood and initiation of early-age cracking of concrete bridge decks. Understanding the concrete properties is essential so that the developed tool can accurately model the mechanisms contributing to the cracking of concrete bridge decks. The user interface of the implemented computer Excel program enables the user to input the properties of the concrete being monitored. The research study and the developed spreadsheet were used to comprehensively investigate the issue of concrete deck cracking. The spreadsheet is designed to be a user-friendly calculation tool for concrete mixture proportioning, temperature prediction, thermal analysis, and tensile cracking prediction. The study also provides review and makes recommendations on the deck cracking based mainly on the Florida Department of Transportation specifications and Structures Design Guidelines, and Bridge Design Manuals of other states. The results were also compared with that of other commercially available software programs that predict early-age cracking in concrete slabs, concrete pavement, and reinforced concrete bridge decks. The outcome of this study can identify a set of recommendations to limit the deck cracking problem and maintain a longer service life of bridges.

miREE: miRNA recognition elements ensemble

PubMed Central

2011-01-01

Background Computational methods for microRNA target prediction are a fundamental step to understand the miRNA role in gene regulation, a key process in molecular biology. In this paper we present miREE, a novel microRNA target prediction tool. miREE is an ensemble of two parts entailing complementary but integrated roles in the prediction. The Ab-Initio module leverages upon a genetic algorithmic approach to generate a set of candidate sites on the basis of their microRNA-mRNA duplex stability properties. Then, a Support Vector Machine (SVM) learning module evaluates the impact of microRNA recognition elements on the target gene. As a result the prediction takes into account information regarding both miRNA-target structural stability and accessibility. Results The proposed method significantly improves the state-of-the-art prediction tools in terms of accuracy with a better balance between specificity and sensitivity, as demonstrated by the experiments conducted on several large datasets across different species. miREE achieves this result by tackling two of the main challenges of current prediction tools: (1) The reduced number of false positives for the Ab-Initio part thanks to the integration of a machine learning module (2) the specificity of the machine learning part, obtained through an innovative technique for rich and representative negative records generation. The validation was conducted on experimental datasets where the miRNA:mRNA interactions had been obtained through (1) direct validation where even the binding site is provided, or through (2) indirect validation, based on gene expression variations obtained from high-throughput experiments where the specific interaction is not validated in detail and consequently the specific binding site is not provided. Conclusions The coupling of two parts: a sensitive Ab-Initio module and a selective machine learning part capable of recognizing the false positives, leads to an improved balance between sensitivity and specificity. miREE obtains a reasonable trade-off between filtering false positives and identifying targets. miREE tool is available online at http://didattica-online.polito.it/eda/miREE/ PMID:22115078

Quality assessment of protein model-structures based on structural and functional similarities.

PubMed

Konopka, Bogumil M; Nebel, Jean-Christophe; Kotulska, Malgorzata

2012-09-21

Experimental determination of protein 3D structures is expensive, time consuming and sometimes impossible. A gap between number of protein structures deposited in the World Wide Protein Data Bank and the number of sequenced proteins constantly broadens. Computational modeling is deemed to be one of the ways to deal with the problem. Although protein 3D structure prediction is a difficult task, many tools are available. These tools can model it from a sequence or partial structural information, e.g. contact maps. Consequently, biologists have the ability to generate automatically a putative 3D structure model of any protein. However, the main issue becomes evaluation of the model quality, which is one of the most important challenges of structural biology. GOBA--Gene Ontology-Based Assessment is a novel Protein Model Quality Assessment Program. It estimates the compatibility between a model-structure and its expected function. GOBA is based on the assumption that a high quality model is expected to be structurally similar to proteins functionally similar to the prediction target. Whereas DALI is used to measure structure similarity, protein functional similarity is quantified using standardized and hierarchical description of proteins provided by Gene Ontology combined with Wang's algorithm for calculating semantic similarity. Two approaches are proposed to express the quality of protein model-structures. One is a single model quality assessment method, the other is its modification, which provides a relative measure of model quality. Exhaustive evaluation is performed on data sets of model-structures submitted to the CASP8 and CASP9 contests. The validation shows that the method is able to discriminate between good and bad model-structures. The best of tested GOBA scores achieved 0.74 and 0.8 as a mean Pearson correlation to the observed quality of models in our CASP8 and CASP9-based validation sets. GOBA also obtained the best result for two targets of CASP8, and one of CASP9, compared to the contest participants. Consequently, GOBA offers a novel single model quality assessment program that addresses the practical needs of biologists. In conjunction with other Model Quality Assessment Programs (MQAPs), it would prove useful for the evaluation of single protein models.

Bridging the gap between structural bioinformatics and receptor research: the membrane-embedded, ligand-gated, P2X glycoprotein receptor.

PubMed

Mager, Peter P; Weber, Anje; Illes, Peter

2004-01-01

No details on P2X receptor architecture had been known at the atomic resolution level. Using comparative homology-based molecular modelling and threading, it was attempted to predict the three-dimensional structure of P2X receptors. This prediction could not be carried out, however, because important properties of the P2X family differ considerably from that of the potential template proteins. This paper reviews an alternative approach consisting of three research fields: bioinformatics, structural modelling, and a variety of the results of biological experiments. Starting point is the amino acid sequence. Using the sequential data, the first step is a secondary structure prediction. The resulting secondary structure is converted into a three-dimensional geometry. Then, the secondary and tertiary structures are optimized by using the quantum chemistry RHF/3-21G minimal basic set and the all-atom molecular mechanics AMBER96 force field. The fold of the membrane-embedded protein is simulated by a suitable dielectricum. The structure is refined using a conjugate gradient minimizer (Fletcher-Reeves modification of the Polak-Ribiere method). The results of the geometry optimization were checked by a Ramanchandran plot, rotamer analysis, all-atom contact dots, and the C(beta) deviation. As additional tools for the model building, multiple alignment analysis and comparative sequence-function analysis were used. The approach is exemplified on the membrane-embedded, ligand-gated P2X3 receptor subunit, a monovalent-bivalent cation channel-forming glycoprotein that is activated by extracellular adenosine 5'-triphosphate. From these results, a topology of the pore-forming motif of the P2X3 receptor subunit was proposed. It is believed that a fully functional P2X channel requires a precise coupling between (i) two distinct peptide modules, an extracellularly occurring ATP-binding module and a pore module that includes a long transmembrane and short intracellular part, (ii) an interaction surface with membranes, and (iii) hydrogen bonding forces of the residues and hydrated cations. Furthermore, this paper demonstrates the role of quantitative structure-activity relationships (QSARs) in P2X research (calcium ion permeability of the wild-type and after site-directed mutagenesis of the rat P2X2 receptor protein, KN-62 analogs as competitive antagonists of the human P2X7 receptor). EXPERIMENTAL PROOFS: The predictions are experimentally testable and may provide an additional interpretation of experimental observations published in literature. In particular, there is the good agreement of the geometry optimized P2X3 structure with experimentally proposed P2X receptor models obtained by neurophysiological, biochemical, pharmacological, and mutation experiments. Although the rat P2X3 receptor subunit is more complex (397 amino acids) than the KcsA protein (160 amino acids), the overall folds of the peptide backbone atoms are similar. To avoid semantic confusion, it should be noted that "prediction" is defined in a probabilistic sense. Matches to generic rules do not mean "this is true" but rather "this might be true". Only biological and chemical knowledge can determine whether or not these predictions are meaningful. Thus, the results from the computational tools are probabilistic predictions and subject to further experimental verification. The geometry optimized P2X3 receptor subunit is freely available for academic researchers on e-mail request (PDB format).

Thermodynamic database for proteins: features and applications.

PubMed

Gromiha, M Michael; Sarai, Akinori

2010-01-01

We have developed a thermodynamic database for proteins and mutants, ProTherm, which is a collection of a large number of thermodynamic data on protein stability along with the sequence and structure information, experimental methods and conditions, and literature information. This is a valuable resource for understanding/predicting the stability of proteins, and it can be accessible at http://www.gibk26.bse.kyutech.ac.jp/jouhou/Protherm/protherm.html . ProTherm has several features including various search, display, and sorting options and visualization tools. We have analyzed the data in ProTherm to examine the relationship among thermodynamics, structure, and function of proteins. We describe the progress on the development of methods for understanding/predicting protein stability, such as (i) relationship between the stability of protein mutants and amino acid properties, (ii) average assignment method, (iii) empirical energy functions, (iv) torsion, distance, and contact potentials, and (v) machine learning techniques. The list of online resources for predicting protein stability has also been provided.