The Experts below are selected from a list of 27920655 Experts worldwide ranked by ideXlab platform
Andrej Sali - One of the best experts on this subject based on the ideXlab platform.
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ucsf chimera MODELLER and imp an integrated modeling system
Journal of Structural Biology, 2012Co-Authors: Zheng Yang, Ben Webb, Keren Lasker, Dina Schneidmanduhovny, Conrad C Huang, Eric F Pettersen, Thomas D Goddard, Elaine C Meng, Andrej SaliAbstract:Structural modeling of macromolecular complexes greatly benefits from interactive visualization capabilities. Here we present the integration of several modeling tools into UCSF Chimera. These include comparative modeling by MODELLER, simultaneous fitting of multiple components into electron microscopy density maps by IMP MultiFit, computing of small-angle X-ray scattering profiles and fitting of the corresponding experimental profile by IMP FoXS, and assessment of amino acid sidechain conformations based on rotamer probabilities and local interactions by Chimera.
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protein structure modeling with MODELLER
Methods of Molecular Biology, 2008Co-Authors: Narayanan Eswar, Ben Webb, David Eramian, Minyi Shen, Andrej SaliAbstract:Genome sequencing projects have resulted in a rapid increase in the number of known protein sequences. In contrast, only about one-hundredth of these sequences have been characterized using experimental structure determination methods. Computational protein structure modeling techniques have the potential to bridge this sequence-structure gap. This chapter presents an example that illustrates the use of MODELLER to construct a comparative model for a protein with unknown structure. Automation of similar protocols (correction of protcols) has resulted in models of useful accuracy for domains in more than half of all known protein sequences.
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comparative protein structure modeling using MODELLER
Current protocols in protein science, 2007Co-Authors: Narayanan Eswar, Marc A Martirenom, M S Madhusudhan, Ursula Pieper, Ben Webb, David Eramian, Minyi Shen, Andrej SaliAbstract:Functional characterization of a protein sequence is a common goal in biology, and is usually facilitated by having an accurate three-dimensional (3-D) structure of the studied protein. In the absence of an experimentally determined structure, comparative or homology modeling can sometimes provide a useful 3-D model for a protein that is related to at least one known protein structure. Comparative modeling predicts the 3-D structure of a given protein sequence (target) based primarily on its alignment to one or more proteins of known structure (templates). The prediction process consists of fold assignment, target-template alignment, model building, and model evaluation. This unit describes how to calculate comparative models using the program MODELLER and discusses all four steps of comparative modeling, frequently observed errors, and some applications. Modeling lactate dehydrogenase from Trichomonas vaginalis (TvLDH) is described as an example. The download and installation of the MODELLER software is also described. Curr. Protoc. Protein Sci. 50:2.9.1-2.9.31. © 2007 by John Wiley & Sons, Inc. Keywords: MODELLER; protein structure; comparative modeling; structure prediction; protein fold
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comparative protein structure modeling using MODELLER
Current protocols in protein science, 2007Co-Authors: Narayanan Eswar, Marc A Martirenom, M S Madhusudhan, Ursula Pieper, Ben Webb, David Eramian, Minyi Shen, Andrej SaliAbstract:Functional characterization of a protein sequence is a common goal in biology, and is usually facilitated by having an accurate three-dimensional (3-D) structure of the studied protein. In the absence of an experimentally determined structure, comparative or homology modeling can sometimes provide a useful 3-D model for a protein that is related to at least one known protein structure. Comparative modeling predicts the 3-D structure of a given protein sequence (target) based primarily on its alignment to one or more proteins of known structure (templates). The prediction process consists of fold assignment, target-template alignment, model building, and model evaluation. This unit describes how to calculate comparative models using the program MODELLER and discusses all four steps of comparative modeling, frequently observed errors, and some applications. Modeling lactate dehydrogenase from Trichomonas vaginalis (TvLDH) is described as an example. The download and installation of the MODELLER software is also described.
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comparative protein structure modeling using MODELLER
Current protocols in human genetics, 2006Co-Authors: Benjamin Webb, Andrej SaliAbstract:Functional characterization of a protein sequence is one of the most frequent problems in biology. This task is usually facilitated by accurate three-dimensional (3-D) structure of the studied protein. In the absence of an experimentally determined structure, comparative or homology modeling can sometimes provide a useful 3-D model for a protein that is related to at least one known protein structure. Comparative modeling predicts the 3-D structure of a given protein sequence (target) based primarily on its alignment to one or more proteins of known structure (templates). The prediction process consists of fold assignment, target-template alignment, model building, and model evaluation. This unit describes how to calculate comparative models using the program MODELLER and discusses all four steps of comparative modeling, frequently observed errors, and some applications. Modeling lactate dehydrogenase from Trichomonas vaginalis (TvLDH) is described as an example. The download and installation of the MODELLER software is also described.
Siem Jan Koopman - One of the best experts on this subject based on the ideXlab platform.
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structural time series analyser MODELLER and predictor stamp 8 2
2009Co-Authors: Siem Jan Koopman, Andrew Harvey, Jurgen A Doornik, Neil ShephardAbstract:STAMP™ stands for Structural Time series Analyser, MODELLER and Predictor. It is a menu-driven system designed to model, describe and predict time series. It is based on structural time series models. These models are set up in terms of components such as trends, seasonals and cycles, which have a direct interpretation. Estimation is carried out using state space methods and Kalman filtering. STAMP 8.2 for OxMetrics 6 handles time series with missing values. Explanatory variables with time varying coefficients and interventions can be included. Version 8 includes extensions and improvements for Multivariate Models: select components by equation, select regressors and interventions by equation, separate dependence structures for each component, wide choice of variance matrices, higher order multivariate components, missing observations allowed, forecasting, exact likelihood computation, automatic outlier and break detection, fixing parameters is made easy. Among the special features of STAMP are interactive model selection, a wide range of diagnostics, easy creation of model based forecasts, spectral filters, observation weight functions, and batch facilities. The STAMP book introduces structural time series models and the way in which they can be used to model a wide range of series.
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stamp 5 0 structural time series analyser MODELLER and predictor
The Economic Journal, 1996Co-Authors: Siem Jan KoopmanAbstract:Part 1: installation procedure for STAMP. Part 2 Tutorials on structural time series modelling: getting started on simple univariate modelling tutorial on components tutorial on interventions and explanatory variables tutorial on multivariate models applications in macroeconomics and finance. Part 3 STAMP tutorials: the basic skills tutorial on graphics tutorial on data input and output tutorial on data transformation and description tutorial on model building and testing. Part 4 Statistical output: descriptive statistics statistical treatment of models model output output from STAMP model estimation selected model and estimation output summary statistics the sample period test hyperparameters variances and standard deviations cycle and AR(1) covariance matrices (multivariate models) factor loading matrices (multivariate models) transformed hyperparameters and standard errors final state analysis of state regression analysis seasonal tests cycle tests data in logs goodness of fit prediction error variance prediction error mean deviation coefficients of determination information criteria - AIC and BIC components series with components detrended seasonally adjusted individual seasonals data in logs joint components residuals correlogram periodogram and spectrum cumulative statistics and graphs distribution statistics heteroskedasticity. Part 5 STAMP manuals: general information STAMP files the information and binary data files (.IN7/.BN7) spreadsheet files (.XLS,.WKS,.WK1) human-readable files (.DAT) the information and ASCII data files (.IN7/.DAT) the print file (.PRN) results file (.OUT) PCX files (.PCX) algebra file (.ALG) forecast file (.STF) configuration file (STAMP.CFG) STAMP output the results window is full printscreen graphics the graphics window printing graphs graphics modes graphics display configuration limitations of STAMP memory management out of memory memory is low memory fragmentation saving memory command-line options session logging and playback lags the database size variable names missing values details of algebra STAMP menus. Part 6 Appendices: giveman file open configuration save configuration exit DataManager convert edit information file reconstruct information file compress information and binary file information video system setup colours install 800 x 600 x 16 HPP, PSP and NCDC GRAFPLUS user manual DOS extender manual STAMP error messages.
T.j. Samuel - One of the best experts on this subject based on the ideXlab platform.
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Modelling positive consequences: Increased vegetable intakes following modelled enjoyment versus modelled intake
Appetite, 2019Co-Authors: Katherine M. Appleton, E. Barrie, T.j. SamuelAbstract:Abstract Objective Modelling has previously been demonstrated to encourage healthy eating, but the importance of modelling the behaviour versus modelling the positive consequences of the behaviour is unknown. This work investigated the impact of modelling carrot intake (the behaviour) and modelling carrot enjoyment (the positive consequences) on subsequent liking and consumption of carrots and sweetcorn. Methods 155 children aged 7–10 years were randomized to hear a story where fictional characters consumed a picnic with either: no mention of carrot sticks (control) (N = 45); mention of carrot sticks that all characters ate (modelling intake) (N = 60); or mention of carrot sticks that the characters like (modelling enjoyment) (N = 50). Carrot and sweetcorn liking and intake were measured before and after the story during a 5 min task. Results Carrot liking and intake after a story were higher following the story modelling carrot enjoyment compared to the stories not modelling enjoyment (smallest β = 0.16, p = 0.05), and in those with higher pre-story carrot liking and intake (smallest β = 0.25, p Conclusions These findings demonstrate a role for modelling enjoyment to encourage vegetable liking and intake, although effects sizes were small. These findings also suggest a benefit from modelling the positive consequences of a behaviour for encouraging healthy food intake in children, while limited effects were found for modelling the behaviour itself.
Narayanan Eswar - One of the best experts on this subject based on the ideXlab platform.
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protein structure modeling with MODELLER
Methods of Molecular Biology, 2008Co-Authors: Narayanan Eswar, Ben Webb, David Eramian, Minyi Shen, Andrej SaliAbstract:Genome sequencing projects have resulted in a rapid increase in the number of known protein sequences. In contrast, only about one-hundredth of these sequences have been characterized using experimental structure determination methods. Computational protein structure modeling techniques have the potential to bridge this sequence-structure gap. This chapter presents an example that illustrates the use of MODELLER to construct a comparative model for a protein with unknown structure. Automation of similar protocols (correction of protcols) has resulted in models of useful accuracy for domains in more than half of all known protein sequences.
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comparative protein structure modeling using MODELLER
Current protocols in protein science, 2007Co-Authors: Narayanan Eswar, Marc A Martirenom, M S Madhusudhan, Ursula Pieper, Ben Webb, David Eramian, Minyi Shen, Andrej SaliAbstract:Functional characterization of a protein sequence is a common goal in biology, and is usually facilitated by having an accurate three-dimensional (3-D) structure of the studied protein. In the absence of an experimentally determined structure, comparative or homology modeling can sometimes provide a useful 3-D model for a protein that is related to at least one known protein structure. Comparative modeling predicts the 3-D structure of a given protein sequence (target) based primarily on its alignment to one or more proteins of known structure (templates). The prediction process consists of fold assignment, target-template alignment, model building, and model evaluation. This unit describes how to calculate comparative models using the program MODELLER and discusses all four steps of comparative modeling, frequently observed errors, and some applications. Modeling lactate dehydrogenase from Trichomonas vaginalis (TvLDH) is described as an example. The download and installation of the MODELLER software is also described. Curr. Protoc. Protein Sci. 50:2.9.1-2.9.31. © 2007 by John Wiley & Sons, Inc. Keywords: MODELLER; protein structure; comparative modeling; structure prediction; protein fold
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comparative protein structure modeling using MODELLER
Current protocols in protein science, 2007Co-Authors: Narayanan Eswar, Marc A Martirenom, M S Madhusudhan, Ursula Pieper, Ben Webb, David Eramian, Minyi Shen, Andrej SaliAbstract:Functional characterization of a protein sequence is a common goal in biology, and is usually facilitated by having an accurate three-dimensional (3-D) structure of the studied protein. In the absence of an experimentally determined structure, comparative or homology modeling can sometimes provide a useful 3-D model for a protein that is related to at least one known protein structure. Comparative modeling predicts the 3-D structure of a given protein sequence (target) based primarily on its alignment to one or more proteins of known structure (templates). The prediction process consists of fold assignment, target-template alignment, model building, and model evaluation. This unit describes how to calculate comparative models using the program MODELLER and discusses all four steps of comparative modeling, frequently observed errors, and some applications. Modeling lactate dehydrogenase from Trichomonas vaginalis (TvLDH) is described as an example. The download and installation of the MODELLER software is also described.
Ben Webb - One of the best experts on this subject based on the ideXlab platform.
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ucsf chimera MODELLER and imp an integrated modeling system
Journal of Structural Biology, 2012Co-Authors: Zheng Yang, Ben Webb, Keren Lasker, Dina Schneidmanduhovny, Conrad C Huang, Eric F Pettersen, Thomas D Goddard, Elaine C Meng, Andrej SaliAbstract:Structural modeling of macromolecular complexes greatly benefits from interactive visualization capabilities. Here we present the integration of several modeling tools into UCSF Chimera. These include comparative modeling by MODELLER, simultaneous fitting of multiple components into electron microscopy density maps by IMP MultiFit, computing of small-angle X-ray scattering profiles and fitting of the corresponding experimental profile by IMP FoXS, and assessment of amino acid sidechain conformations based on rotamer probabilities and local interactions by Chimera.
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protein structure modeling with MODELLER
Methods of Molecular Biology, 2008Co-Authors: Narayanan Eswar, Ben Webb, David Eramian, Minyi Shen, Andrej SaliAbstract:Genome sequencing projects have resulted in a rapid increase in the number of known protein sequences. In contrast, only about one-hundredth of these sequences have been characterized using experimental structure determination methods. Computational protein structure modeling techniques have the potential to bridge this sequence-structure gap. This chapter presents an example that illustrates the use of MODELLER to construct a comparative model for a protein with unknown structure. Automation of similar protocols (correction of protcols) has resulted in models of useful accuracy for domains in more than half of all known protein sequences.
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comparative protein structure modeling using MODELLER
Current protocols in protein science, 2007Co-Authors: Narayanan Eswar, Marc A Martirenom, M S Madhusudhan, Ursula Pieper, Ben Webb, David Eramian, Minyi Shen, Andrej SaliAbstract:Functional characterization of a protein sequence is a common goal in biology, and is usually facilitated by having an accurate three-dimensional (3-D) structure of the studied protein. In the absence of an experimentally determined structure, comparative or homology modeling can sometimes provide a useful 3-D model for a protein that is related to at least one known protein structure. Comparative modeling predicts the 3-D structure of a given protein sequence (target) based primarily on its alignment to one or more proteins of known structure (templates). The prediction process consists of fold assignment, target-template alignment, model building, and model evaluation. This unit describes how to calculate comparative models using the program MODELLER and discusses all four steps of comparative modeling, frequently observed errors, and some applications. Modeling lactate dehydrogenase from Trichomonas vaginalis (TvLDH) is described as an example. The download and installation of the MODELLER software is also described. Curr. Protoc. Protein Sci. 50:2.9.1-2.9.31. © 2007 by John Wiley & Sons, Inc. Keywords: MODELLER; protein structure; comparative modeling; structure prediction; protein fold
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comparative protein structure modeling using MODELLER
Current protocols in protein science, 2007Co-Authors: Narayanan Eswar, Marc A Martirenom, M S Madhusudhan, Ursula Pieper, Ben Webb, David Eramian, Minyi Shen, Andrej SaliAbstract:Functional characterization of a protein sequence is a common goal in biology, and is usually facilitated by having an accurate three-dimensional (3-D) structure of the studied protein. In the absence of an experimentally determined structure, comparative or homology modeling can sometimes provide a useful 3-D model for a protein that is related to at least one known protein structure. Comparative modeling predicts the 3-D structure of a given protein sequence (target) based primarily on its alignment to one or more proteins of known structure (templates). The prediction process consists of fold assignment, target-template alignment, model building, and model evaluation. This unit describes how to calculate comparative models using the program MODELLER and discusses all four steps of comparative modeling, frequently observed errors, and some applications. Modeling lactate dehydrogenase from Trichomonas vaginalis (TvLDH) is described as an example. The download and installation of the MODELLER software is also described.
