WorldWideScience

Sample records for protein model selection

  1. A semi-nonparametric mixture model for selecting functionally consistent proteins.

    Science.gov (United States)

    Yu, Lianbo; Doerge, Rw

    2010-09-28

    High-throughput technologies have led to a new era of proteomics. Although protein microarray experiments are becoming more common place there are a variety of experimental and statistical issues that have yet to be addressed, and that will carry over to new high-throughput technologies unless they are investigated. One of the largest of these challenges is the selection of functionally consistent proteins. We present a novel semi-nonparametric mixture model for classifying proteins as consistent or inconsistent while controlling the false discovery rate and the false non-discovery rate. The performance of the proposed approach is compared to current methods via simulation under a variety of experimental conditions. We provide a statistical method for selecting functionally consistent proteins in the context of protein microarray experiments, but the proposed semi-nonparametric mixture model method can certainly be generalized to solve other mixture data problems. The main advantage of this approach is that it provides the posterior probability of consistency for each protein.

  2. Selective memory generalization by spatial patterning of protein synthesis.

    Science.gov (United States)

    O'Donnell, Cian; Sejnowski, Terrence J

    2014-04-16

    Protein synthesis is crucial for both persistent synaptic plasticity and long-term memory. De novo protein expression can be restricted to specific neurons within a population, and to specific dendrites within a single neuron. Despite its ubiquity, the functional benefits of spatial protein regulation for learning are unknown. We used computational modeling to study this problem. We found that spatially patterned protein synthesis can enable selective consolidation of some memories but forgetting of others, even for simultaneous events that are represented by the same neural population. Key factors regulating selectivity include the functional clustering of synapses on dendrites, and the sparsity and overlap of neural activity patterns at the circuit level. Based on these findings, we proposed a two-step model for selective memory generalization during REM and slow-wave sleep. The pattern-matching framework we propose may be broadly applicable to spatial protein signaling throughout cortex and hippocampus. Copyright © 2014 Elsevier Inc. All rights reserved.

  3. Protein construct storage: Bayesian variable selection and prediction with mixtures.

    Science.gov (United States)

    Clyde, M A; Parmigiani, G

    1998-07-01

    Determining optimal conditions for protein storage while maintaining a high level of protein activity is an important question in pharmaceutical research. A designed experiment based on a space-filling design was conducted to understand the effects of factors affecting protein storage and to establish optimal storage conditions. Different model-selection strategies to identify important factors may lead to very different answers about optimal conditions. Uncertainty about which factors are important, or model uncertainty, can be a critical issue in decision-making. We use Bayesian variable selection methods for linear models to identify important variables in the protein storage data, while accounting for model uncertainty. We also use the Bayesian framework to build predictions based on a large family of models, rather than an individual model, and to evaluate the probability that certain candidate storage conditions are optimal.

  4. The Protein Model Portal.

    Science.gov (United States)

    Arnold, Konstantin; Kiefer, Florian; Kopp, Jürgen; Battey, James N D; Podvinec, Michael; Westbrook, John D; Berman, Helen M; Bordoli, Lorenza; Schwede, Torsten

    2009-03-01

    Structural Genomics has been successful in determining the structures of many unique proteins in a high throughput manner. Still, the number of known protein sequences is much larger than the number of experimentally solved protein structures. Homology (or comparative) modeling methods make use of experimental protein structures to build models for evolutionary related proteins. Thereby, experimental structure determination efforts and homology modeling complement each other in the exploration of the protein structure space. One of the challenges in using model information effectively has been to access all models available for a specific protein in heterogeneous formats at different sites using various incompatible accession code systems. Often, structure models for hundreds of proteins can be derived from a given experimentally determined structure, using a variety of established methods. This has been done by all of the PSI centers, and by various independent modeling groups. The goal of the Protein Model Portal (PMP) is to provide a single portal which gives access to the various models that can be leveraged from PSI targets and other experimental protein structures. A single interface allows all existing pre-computed models across these various sites to be queried simultaneously, and provides links to interactive services for template selection, target-template alignment, model building, and quality assessment. The current release of the portal consists of 7.6 million model structures provided by different partner resources (CSMP, JCSG, MCSG, NESG, NYSGXRC, JCMM, ModBase, SWISS-MODEL Repository). The PMP is available at http://www.proteinmodelportal.org and from the PSI Structural Genomics Knowledgebase.

  5. A unified conformational selection and induced fit approach to protein-peptide docking.

    Directory of Open Access Journals (Sweden)

    Mikael Trellet

    Full Text Available Protein-peptide interactions are vital for the cell. They mediate, inhibit or serve as structural components in nearly 40% of all macromolecular interactions, and are often associated with diseases, making them interesting leads for protein drug design. In recent years, large-scale technologies have enabled exhaustive studies on the peptide recognition preferences for a number of peptide-binding domain families. Yet, the paucity of data regarding their molecular binding mechanisms together with their inherent flexibility makes the structural prediction of protein-peptide interactions very challenging. This leaves flexible docking as one of the few amenable computational techniques to model these complexes. We present here an ensemble, flexible protein-peptide docking protocol that combines conformational selection and induced fit mechanisms. Starting from an ensemble of three peptide conformations (extended, a-helix, polyproline-II, flexible docking with HADDOCK generates 79.4% of high quality models for bound/unbound and 69.4% for unbound/unbound docking when tested against the largest protein-peptide complexes benchmark dataset available to date. Conformational selection at the rigid-body docking stage successfully recovers the most relevant conformation for a given protein-peptide complex and the subsequent flexible refinement further improves the interface by up to 4.5 Å interface RMSD. Cluster-based scoring of the models results in a selection of near-native solutions in the top three for ∼75% of the successfully predicted cases. This unified conformational selection and induced fit approach to protein-peptide docking should open the route to the modeling of challenging systems such as disorder-order transitions taking place upon binding, significantly expanding the applicability limit of biomolecular interaction modeling by docking.

  6. IRaPPA: information retrieval based integration of biophysical models for protein assembly selection.

    Science.gov (United States)

    Moal, Iain H; Barradas-Bautista, Didier; Jiménez-García, Brian; Torchala, Mieczyslaw; van der Velde, Arjan; Vreven, Thom; Weng, Zhiping; Bates, Paul A; Fernández-Recio, Juan

    2017-06-15

    In order to function, proteins frequently bind to one another and form 3D assemblies. Knowledge of the atomic details of these structures helps our understanding of how proteins work together, how mutations can lead to disease, and facilitates the designing of drugs which prevent or mimic the interaction. Atomic modeling of protein-protein interactions requires the selection of near-native structures from a set of docked poses based on their calculable properties. By considering this as an information retrieval problem, we have adapted methods developed for Internet search ranking and electoral voting into IRaPPA, a pipeline integrating biophysical properties. The approach enhances the identification of near-native structures when applied to four docking methods, resulting in a near-native appearing in the top 10 solutions for up to 50% of complexes benchmarked, and up to 70% in the top 100. IRaPPA has been implemented in the SwarmDock server ( http://bmm.crick.ac.uk/∼SwarmDock/ ), pyDock server ( http://life.bsc.es/pid/pydockrescoring/ ) and ZDOCK server ( http://zdock.umassmed.edu/ ), with code available on request. moal@ebi.ac.uk. Supplementary data are available at Bioinformatics online. © The Author 2017. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com

  7. Targeted Diazotransfer Reagents Enable Selective Modification of Proteins with Azides.

    Science.gov (United States)

    Lohse, Jonas; Swier, Lotteke J Y M; Oudshoorn, Ruben C; Médard, Guillaume; Kuster, Bernhard; Slotboom, Dirk-Jan; Witte, Martin D

    2017-04-19

    In chemical biology, azides are used to chemically manipulate target structures in a bioorthogonal manner for a plethora of applications ranging from target identification to the synthesis of homogeneously modified protein conjugates. While a variety of methods have been established to introduce the azido group into recombinant proteins, a method that directly converts specific amino groups in endogenous proteins is lacking. Here, we report the first biotin-tethered diazotransfer reagent DtBio and demonstrate that it selectively modifies the model proteins streptavidin and avidin and the membrane protein BioY on cell surface. The reagent converts amines in the proximity of the binding pocket to azides and leaves the remaining amino groups in streptavidin untouched. Reagents of this novel class will find use in target identification as well as the selective functionalization and bioorthogonal protection of proteins.

  8. Positive Selection and Centrality in the Yeast and Fly Protein-Protein Interaction Networks

    Directory of Open Access Journals (Sweden)

    Sandip Chakraborty

    2016-01-01

    Full Text Available Proteins within a molecular network are expected to be subject to different selective pressures depending on their relative hierarchical positions. However, it is not obvious what genes within a network should be more likely to evolve under positive selection. On one hand, only mutations at genes with a relatively high degree of control over adaptive phenotypes (such as those encoding highly connected proteins are expected to be “seen” by natural selection. On the other hand, a high degree of pleiotropy at these genes is expected to hinder adaptation. Previous analyses of the human protein-protein interaction network have shown that genes under long-term, recurrent positive selection (as inferred from interspecific comparisons tend to act at the periphery of the network. It is unknown, however, whether these trends apply to other organisms. Here, we show that long-term positive selection has preferentially targeted the periphery of the yeast interactome. Conversely, in flies, genes under positive selection encode significantly more connected and central proteins. These observations are not due to covariation of genes’ adaptability and centrality with confounding factors. Therefore, the distribution of proteins encoded by genes under recurrent positive selection across protein-protein interaction networks varies from one species to another.

  9. Selection of peptides interfering with protein-protein interaction.

    Science.gov (United States)

    Gaida, Annette; Hagemann, Urs B; Mattay, Dinah; Räuber, Christina; Müller, Kristian M; Arndt, Katja M

    2009-01-01

    Cell physiology depends on a fine-tuned network of protein-protein interactions, and misguided interactions are often associated with various diseases. Consequently, peptides, which are able to specifically interfere with such adventitious interactions, are of high interest for analytical as well as medical purposes. One of the most abundant protein interaction domains is the coiled-coil motif, and thus provides a premier target. Coiled coils, which consist of two or more alpha-helices wrapped around each other, have one of the simplest interaction interfaces, yet they are able to confer highly specific homo- and heterotypic interactions involved in virtually any cellular process. While there are several ways to generate interfering peptides, the combination of library design with a powerful selection system seems to be one of the most effective and promising approaches. This chapter guides through all steps of such a process, starting with library options and cloning, detailing suitable selection techniques and ending with purification for further down-stream characterization. Such generated peptides will function as versatile tools to interfere with the natural function of their targets thereby illuminating their down-stream signaling and, in general, promoting understanding of factors leading to specificity and stability in protein-protein interactions. Furthermore, peptides interfering with medically relevant proteins might become important diagnostics and therapeutics.

  10. Directional Darwinian Selection in proteins.

    Science.gov (United States)

    McClellan, David A

    2013-01-01

    Molecular evolution is a very active field of research, with several complementary approaches, including dN/dS, HON90, MM01, and others. Each has documented strengths and weaknesses, and no one approach provides a clear picture of how natural selection works at the molecular level. The purpose of this work is to present a simple new method that uses quantitative amino acid properties to identify and characterize directional selection in proteins. Inferred amino acid replacements are viewed through the prism of a single physicochemical property to determine the amount and direction of change caused by each replacement. This allows the calculation of the probability that the mean change in the single property associated with the amino acid replacements is equal to zero (H0: μ = 0; i.e., no net change) using a simple two-tailed t-test. Example data from calanoid and cyclopoid copepod cytochrome oxidase subunit I sequence pairs are presented to demonstrate how directional selection may be linked to major shifts in adaptive zones, and that convergent evolution at the whole organism level may be the result of convergent protein adaptations. Rather than replace previous methods, this new method further complements existing methods to provide a holistic glimpse of how natural selection shapes protein structure and function over evolutionary time.

  11. DeepQA: Improving the estimation of single protein model quality with deep belief networks

    OpenAIRE

    Cao, Renzhi; Bhattacharya, Debswapna; Hou, Jie; Cheng, Jianlin

    2016-01-01

    Background Protein quality assessment (QA) useful for ranking and selecting protein models has long been viewed as one of the major challenges for protein tertiary structure prediction. Especially, estimating the quality of a single protein model, which is important for selecting a few good models out of a large model pool consisting of mostly low-quality models, is still a largely unsolved problem. Results We introduce a novel single-model quality assessment method DeepQA based on deep belie...

  12. ProSelection: A Novel Algorithm to Select Proper Protein Structure Subsets for in Silico Target Identification and Drug Discovery Research.

    Science.gov (United States)

    Wang, Nanyi; Wang, Lirong; Xie, Xiang-Qun

    2017-11-27

    Molecular docking is widely applied to computer-aided drug design and has become relatively mature in the recent decades. Application of docking in modeling varies from single lead compound optimization to large-scale virtual screening. The performance of molecular docking is highly dependent on the protein structures selected. It is especially challenging for large-scale target prediction research when multiple structures are available for a single target. Therefore, we have established ProSelection, a docking preferred-protein selection algorithm, in order to generate the proper structure subset(s). By the ProSelection algorithm, protein structures of "weak selectors" are filtered out whereas structures of "strong selectors" are kept. Specifically, the structure which has a good statistical performance of distinguishing active ligands from inactive ligands is defined as a strong selector. In this study, 249 protein structures of 14 autophagy-related targets are investigated. Surflex-dock was used as the docking engine to distinguish active and inactive compounds against these protein structures. Both t test and Mann-Whitney U test were used to distinguish the strong from the weak selectors based on the normality of the docking score distribution. The suggested docking score threshold for active ligands (SDA) was generated for each strong selector structure according to the receiver operating characteristic (ROC) curve. The performance of ProSelection was further validated by predicting the potential off-targets of 43 U.S. Federal Drug Administration approved small molecule antineoplastic drugs. Overall, ProSelection will accelerate the computational work in protein structure selection and could be a useful tool for molecular docking, target prediction, and protein-chemical database establishment research.

  13. The importance of protein in leaf selection of folivorous primates.

    Science.gov (United States)

    Ganzhorn, Joerg U; Arrigo-Nelson, Summer J; Carrai, Valentina; Chalise, Mukesh K; Donati, Giuseppe; Droescher, Iris; Eppley, Timothy M; Irwin, Mitchell T; Koch, Flávia; Koenig, Andreas; Kowalewski, Martin M; Mowry, Christopher B; Patel, Erik R; Pichon, Claire; Ralison, Jose; Reisdorff, Christoph; Simmen, Bruno; Stalenberg, Eleanor; Starrs, Danswell; Terboven, Juana; Wright, Patricia C; Foley, William J

    2017-04-01

    Protein limitation has been considered a key factor in hypotheses on the evolution of life history and animal communities, suggesting that animals should prioritize protein in their food choice. This contrasts with the limited support that food selection studies have provided for such a priority in nonhuman primates, particularly for folivores. Here, we suggest that this discrepancy can be resolved if folivores only need to select for high protein leaves when average protein concentration in the habitat is low. To test the prediction, we applied meta-analyses to analyze published and unpublished results of food selection for protein and fiber concentrations from 24 studies (some with multiple species) of folivorous primates. To counter potential methodological flaws, we differentiated between methods analyzing total nitrogen and soluble protein concentrations. We used a meta-analysis to test for the effect of protein on food selection by primates and found a significant effect of soluble protein concentrations, but a non-significant effect for total nitrogen. Furthermore, selection for soluble protein was reinforced in forests where protein was less available. Selection for low fiber content was significant but unrelated to the fiber concentrations in representative leaf samples of a given forest. There was no relationship (either negative or positive) between the concentration of protein and fiber in the food or in representative samples of leaves. Overall our study suggests that protein selection is influenced by the protein availability in the environment, explaining the sometimes contradictory results in previous studies on protein selection. Am. J. Primatol. 79:e22550, 2017. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

  14. Protein-protein interaction networks identify targets which rescue the MPP+ cellular model of Parkinson’s disease

    Science.gov (United States)

    Keane, Harriet; Ryan, Brent J.; Jackson, Brendan; Whitmore, Alan; Wade-Martins, Richard

    2015-11-01

    Neurodegenerative diseases are complex multifactorial disorders characterised by the interplay of many dysregulated physiological processes. As an exemplar, Parkinson’s disease (PD) involves multiple perturbed cellular functions, including mitochondrial dysfunction and autophagic dysregulation in preferentially-sensitive dopamine neurons, a selective pathophysiology recapitulated in vitro using the neurotoxin MPP+. Here we explore a network science approach for the selection of therapeutic protein targets in the cellular MPP+ model. We hypothesised that analysis of protein-protein interaction networks modelling MPP+ toxicity could identify proteins critical for mediating MPP+ toxicity. Analysis of protein-protein interaction networks constructed to model the interplay of mitochondrial dysfunction and autophagic dysregulation (key aspects of MPP+ toxicity) enabled us to identify four proteins predicted to be key for MPP+ toxicity (P62, GABARAP, GBRL1 and GBRL2). Combined, but not individual, knockdown of these proteins increased cellular susceptibility to MPP+ toxicity. Conversely, combined, but not individual, over-expression of the network targets provided rescue of MPP+ toxicity associated with the formation of autophagosome-like structures. We also found that modulation of two distinct proteins in the protein-protein interaction network was necessary and sufficient to mitigate neurotoxicity. Together, these findings validate our network science approach to multi-target identification in complex neurological diseases.

  15. Automated protein structure modeling with SWISS-MODEL Workspace and the Protein Model Portal.

    Science.gov (United States)

    Bordoli, Lorenza; Schwede, Torsten

    2012-01-01

    Comparative protein structure modeling is a computational approach to build three-dimensional structural models for proteins using experimental structures of related protein family members as templates. Regular blind assessments of modeling accuracy have demonstrated that comparative protein structure modeling is currently the most reliable technique to model protein structures. Homology models are often sufficiently accurate to substitute for experimental structures in a wide variety of applications. Since the usefulness of a model for specific application is determined by its accuracy, model quality estimation is an essential component of protein structure prediction. Comparative protein modeling has become a routine approach in many areas of life science research since fully automated modeling systems allow also nonexperts to build reliable models. In this chapter, we describe practical approaches for automated protein structure modeling with SWISS-MODEL Workspace and the Protein Model Portal.

  16. DeepQA: improving the estimation of single protein model quality with deep belief networks.

    Science.gov (United States)

    Cao, Renzhi; Bhattacharya, Debswapna; Hou, Jie; Cheng, Jianlin

    2016-12-05

    Protein quality assessment (QA) useful for ranking and selecting protein models has long been viewed as one of the major challenges for protein tertiary structure prediction. Especially, estimating the quality of a single protein model, which is important for selecting a few good models out of a large model pool consisting of mostly low-quality models, is still a largely unsolved problem. We introduce a novel single-model quality assessment method DeepQA based on deep belief network that utilizes a number of selected features describing the quality of a model from different perspectives, such as energy, physio-chemical characteristics, and structural information. The deep belief network is trained on several large datasets consisting of models from the Critical Assessment of Protein Structure Prediction (CASP) experiments, several publicly available datasets, and models generated by our in-house ab initio method. Our experiments demonstrate that deep belief network has better performance compared to Support Vector Machines and Neural Networks on the protein model quality assessment problem, and our method DeepQA achieves the state-of-the-art performance on CASP11 dataset. It also outperformed two well-established methods in selecting good outlier models from a large set of models of mostly low quality generated by ab initio modeling methods. DeepQA is a useful deep learning tool for protein single model quality assessment and protein structure prediction. The source code, executable, document and training/test datasets of DeepQA for Linux is freely available to non-commercial users at http://cactus.rnet.missouri.edu/DeepQA/ .

  17. Protein structure and ionic selectivity in calcium channels: selectivity filter size, not shape, matters.

    Science.gov (United States)

    Malasics, Attila; Gillespie, Dirk; Nonner, Wolfgang; Henderson, Douglas; Eisenberg, Bob; Boda, Dezso

    2009-12-01

    Calcium channels have highly charged selectivity filters (4 COO(-) groups) that attract cations in to balance this charge and minimize free energy, forcing the cations (Na(+) and Ca(2+)) to compete for space in the filter. A reduced model was developed to better understand the mechanism of ion selectivity in calcium channels. The charge/space competition (CSC) mechanism implies that Ca(2+) is more efficient in balancing the charge of the filter because it provides twice the charge as Na(+) while occupying the same space. The CSC mechanism further implies that the main determinant of Ca(2+) versus Na(+) selectivity is the density of charged particles in the selectivity filter, i.e., the volume of the filter (after fixing the number of charged groups in the filter). In this paper we test this hypothesis by changing filter length and/or radius (shape) of the cylindrical selectivity filter of our reduced model. We show that varying volume and shape together has substantially stronger effects than varying shape alone with volume fixed. Our simulations show the importance of depletion zones of ions in determining channel conductance calculated with the integrated Nernst-Planck equation. We show that confining the protein side chains with soft or hard walls does not influence selectivity.

  18. Selective Advantage of Recombination in Evolving Protein Populations:. a Lattice Model Study

    Science.gov (United States)

    Williams, Paul D.; Pollock, David D.; Goldstein, Richard A.

    Recent research has attempted to clarify the contributions of several mutational processes, such as substitutions or homologous recombination. Simplistic, tractable protein models, which determine the compact native structure phenotype from the sequence genotype, are well-suited to such studies. In this paper, we use a lattice-protein model to examine the effects of point mutation and homologous recombination on evolving populations of proteins. We find that while the majority of mutation and recombination events are neutral or deleterious, recombination is far more likely to be beneficial. This results in a faster increase in fitness during evolution, although the final fitness level is not significantly changed. This transient advantage provides an evolutionary advantage to subpopulations that undergo recombination, allowing fixation of recombination to occur in the population.

  19. Algorithm for selection of optimized EPR distance restraints for de novo protein structure determination

    Science.gov (United States)

    Kazmier, Kelli; Alexander, Nathan S.; Meiler, Jens; Mchaourab, Hassane S.

    2010-01-01

    A hybrid protein structure determination approach combining sparse Electron Paramagnetic Resonance (EPR) distance restraints and Rosetta de novo protein folding has been previously demonstrated to yield high quality models (Alexander et al., 2008). However, widespread application of this methodology to proteins of unknown structures is hindered by the lack of a general strategy to place spin label pairs in the primary sequence. In this work, we report the development of an algorithm that optimally selects spin labeling positions for the purpose of distance measurements by EPR. For the α-helical subdomain of T4 lysozyme (T4L), simulated restraints that maximize sequence separation between the two spin labels while simultaneously ensuring pairwise connectivity of secondary structure elements yielded vastly improved models by Rosetta folding. 50% of all these models have the correct fold compared to only 21% and 8% correctly folded models when randomly placed restraints or no restraints are used, respectively. Moreover, the improvements in model quality require a limited number of optimized restraints, the number of which is determined by the pairwise connectivities of T4L α-helices. The predicted improvement in Rosetta model quality was verified by experimental determination of distances between spin labels pairs selected by the algorithm. Overall, our results reinforce the rationale for the combined use of sparse EPR distance restraints and de novo folding. By alleviating the experimental bottleneck associated with restraint selection, this algorithm sets the stage for extending computational structure determination to larger, traditionally elusive protein topologies of critical structural and biochemical importance. PMID:21074624

  20. Automated Protein Structure Modeling with SWISS-MODEL Workspace and the Protein Model Portal

    OpenAIRE

    Bordoli, Lorenza; Schwede, Torsten

    2012-01-01

    Comparative protein structure modeling is a computational approach to build three-dimensional structural models for proteins using experimental structures of related protein family members as templates. Regular blind assessments of modeling accuracy have demonstrated that comparative protein structure modeling is currently the most reliable technique to model protein structures. Homology models are often sufficiently accurate to substitute for experimental structures in a wide variety of appl...

  1. Selecting for Fast Protein-Protein Association As Demonstrated on a Random TEM1 Yeast Library Binding BLIP.

    Science.gov (United States)

    Cohen-Khait, Ruth; Schreiber, Gideon

    2018-04-27

    Protein-protein interactions mediate the vast majority of cellular processes. Though protein interactions obey basic chemical principles also within the cell, the in vivo physiological environment may not allow for equilibrium to be reached. Thus, in vitro measured thermodynamic affinity may not provide a complete picture of protein interactions in the biological context. Binding kinetics composed of the association and dissociation rate constants are relevant and important in the cell. Therefore, changes in protein-protein interaction kinetics have a significant impact on the in vivo activity of the proteins. The common protocol for the selection of tighter binders from a mutant library selects for protein complexes with slower dissociation rate constants. Here we describe a method to specifically select for variants with faster association rate constants by using pre-equilibrium selection, starting from a large random library. Toward this end, we refine the selection conditions of a TEM1-β-lactamase library against its natural nanomolar affinity binder β-lactamase inhibitor protein (BLIP). The optimal selection conditions depend on the ligand concentration and on the incubation time. In addition, we show that a second sort of the library helps to separate signal from noise, resulting in a higher percent of faster binders in the selected library. Fast associating protein variants are of particular interest for drug development and other biotechnological applications.

  2. Cotranslational protein folding reveals the selective use of ...

    Indian Academy of Sciences (India)

    to fold properly by decelerating the translation rate at these sites. Thus the cotranslational protein folding is believed to be true for many proteins and is an important selection factor for the selective codon usage to optimize proper gene expres- sion and function (Komar 2009). A web server CS and S has been created by ...

  3. Modeling complexes of modeled proteins.

    Science.gov (United States)

    Anishchenko, Ivan; Kundrotas, Petras J; Vakser, Ilya A

    2017-03-01

    Structural characterization of proteins is essential for understanding life processes at the molecular level. However, only a fraction of known proteins have experimentally determined structures. This fraction is even smaller for protein-protein complexes. Thus, structural modeling of protein-protein interactions (docking) primarily has to rely on modeled structures of the individual proteins, which typically are less accurate than the experimentally determined ones. Such "double" modeling is the Grand Challenge of structural reconstruction of the interactome. Yet it remains so far largely untested in a systematic way. We present a comprehensive validation of template-based and free docking on a set of 165 complexes, where each protein model has six levels of structural accuracy, from 1 to 6 Å C α RMSD. Many template-based docking predictions fall into acceptable quality category, according to the CAPRI criteria, even for highly inaccurate proteins (5-6 Å RMSD), although the number of such models (and, consequently, the docking success rate) drops significantly for models with RMSD > 4 Å. The results show that the existing docking methodologies can be successfully applied to protein models with a broad range of structural accuracy, and the template-based docking is much less sensitive to inaccuracies of protein models than the free docking. Proteins 2017; 85:470-478. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

  4. From Extraction of Local Structures of Protein Energy Landscapes to Improved Decoy Selection in Template-Free Protein Structure Prediction.

    Science.gov (United States)

    Akhter, Nasrin; Shehu, Amarda

    2018-01-19

    Due to the essential role that the three-dimensional conformation of a protein plays in regulating interactions with molecular partners, wet and dry laboratories seek biologically-active conformations of a protein to decode its function. Computational approaches are gaining prominence due to the labor and cost demands of wet laboratory investigations. Template-free methods can now compute thousands of conformations known as decoys, but selecting native conformations from the generated decoys remains challenging. Repeatedly, research has shown that the protein energy functions whose minima are sought in the generation of decoys are unreliable indicators of nativeness. The prevalent approach ignores energy altogether and clusters decoys by conformational similarity. Complementary recent efforts design protein-specific scoring functions or train machine learning models on labeled decoys. In this paper, we show that an informative consideration of energy can be carried out under the energy landscape view. Specifically, we leverage local structures known as basins in the energy landscape probed by a template-free method. We propose and compare various strategies of basin-based decoy selection that we demonstrate are superior to clustering-based strategies. The presented results point to further directions of research for improving decoy selection, including the ability to properly consider the multiplicity of native conformations of proteins.

  5. From Extraction of Local Structures of Protein Energy Landscapes to Improved Decoy Selection in Template-Free Protein Structure Prediction

    Directory of Open Access Journals (Sweden)

    Nasrin Akhter

    2018-01-01

    Full Text Available Due to the essential role that the three-dimensional conformation of a protein plays in regulating interactions with molecular partners, wet and dry laboratories seek biologically-active conformations of a protein to decode its function. Computational approaches are gaining prominence due to the labor and cost demands of wet laboratory investigations. Template-free methods can now compute thousands of conformations known as decoys, but selecting native conformations from the generated decoys remains challenging. Repeatedly, research has shown that the protein energy functions whose minima are sought in the generation of decoys are unreliable indicators of nativeness. The prevalent approach ignores energy altogether and clusters decoys by conformational similarity. Complementary recent efforts design protein-specific scoring functions or train machine learning models on labeled decoys. In this paper, we show that an informative consideration of energy can be carried out under the energy landscape view. Specifically, we leverage local structures known as basins in the energy landscape probed by a template-free method. We propose and compare various strategies of basin-based decoy selection that we demonstrate are superior to clustering-based strategies. The presented results point to further directions of research for improving decoy selection, including the ability to properly consider the multiplicity of native conformations of proteins.

  6. Detecting consistent patterns of directional adaptation using differential selection codon models.

    Science.gov (United States)

    Parto, Sahar; Lartillot, Nicolas

    2017-06-23

    Phylogenetic codon models are often used to characterize the selective regimes acting on protein-coding sequences. Recent methodological developments have led to models explicitly accounting for the interplay between mutation and selection, by modeling the amino acid fitness landscape along the sequence. However, thus far, most of these models have assumed that the fitness landscape is constant over time. Fluctuations of the fitness landscape may often be random or depend on complex and unknown factors. However, some organisms may be subject to systematic changes in selective pressure, resulting in reproducible molecular adaptations across independent lineages subject to similar conditions. Here, we introduce a codon-based differential selection model, which aims to detect and quantify the fine-grained consistent patterns of adaptation at the protein-coding level, as a function of external conditions experienced by the organism under investigation. The model parameterizes the global mutational pressure, as well as the site- and condition-specific amino acid selective preferences. This phylogenetic model is implemented in a Bayesian MCMC framework. After validation with simulations, we applied our method to a dataset of HIV sequences from patients with known HLA genetic background. Our differential selection model detects and characterizes differentially selected coding positions specifically associated with two different HLA alleles. Our differential selection model is able to identify consistent molecular adaptations as a function of repeated changes in the environment of the organism. These models can be applied to many other problems, ranging from viral adaptation to evolution of life-history strategies in plants or animals.

  7. Selectivity determinants of GPCR-G-protein binding

    DEFF Research Database (Denmark)

    Flock, Tilman; Hauser, Alexander S; Lund, Nadia

    2017-01-01

    of the G-protein barcode through distinct residues, like multiple keys (receptors) opening the same lock (G protein) using non-identical cuts. Considering the evolutionary history of GPCRs allows the identification of these selectivity-determining residues. These findings lay the foundation...

  8. A selection that reports on protein-protein interactions within a thermophilic bacterium.

    Science.gov (United States)

    Nguyen, Peter Q; Silberg, Jonathan J

    2010-07-01

    Many proteins can be split into fragments that exhibit enhanced function upon fusion to interacting proteins. While this strategy has been widely used to create protein-fragment complementation assays (PCAs) for discovering protein-protein interactions within mesophilic organisms, similar assays have not yet been developed for studying natural and engineered protein complexes at the temperatures where thermophilic microbes grow. We describe the development of a selection for protein-protein interactions within Thermus thermophilus that is based upon growth complementation by fragments of Thermotoga neapolitana adenylate kinase (AK(Tn)). Complementation studies with an engineered thermophile (PQN1) that is not viable above 75 degrees C because its adk gene has been replaced by a Geobacillus stearothermophilus ortholog revealed that growth could be restored at 78 degrees C by a vector that coexpresses polypeptides corresponding to residues 1-79 and 80-220 of AK(Tn). In contrast, PQN1 growth was not complemented by AK(Tn) fragments harboring a C156A mutation within the zinc-binding tetracysteine motif unless these fragments were fused to Thermotoga maritima chemotaxis proteins that heterodimerize (CheA and CheY) or homodimerize (CheX). This enhanced complementation is interpreted as arising from chemotaxis protein-protein interactions, since AK(Tn)-C156A fragments having only one polypeptide fused to a chemotaxis protein did not complement PQN1 to the same extent. This selection increases the maximum temperature where a PCA can be used to engineer thermostable protein complexes and to map protein-protein interactions.

  9. An Antibiotic Selection System For Protein Overproducing Bacteria

    DEFF Research Database (Denmark)

    Rennig, Maja; Nørholm, Morten

    2015-01-01

    Introduction: Protein overproduction is a major bottleneck for analyses of membrane proteins and for the construction of cell factories. Screening for optimized protein production can be very time consuming. In this study we show that the coupling of antibiotic resistance to poorly produced...... membrane proteins of Escherichia coli can be used as a fast and simple selection system for protein overproduction.Methods: We designed an expression plasmid encoding the gene of interest and an additional, inducible antibiotic resistance marker. Both genes were linked by a hairpin structure...... that translationally couples the genes. Consequently, high expressing gene variants also allow for higher production of the coupled antibiotic resistance marker. Therefore, high expressing gene variants in a library can be determined either by plating the expression library on selection plates or by growing...

  10. Integrative approaches to the prediction of protein functions based on the feature selection

    Directory of Open Access Journals (Sweden)

    Lee Hyunju

    2009-12-01

    Full Text Available Abstract Background Protein function prediction has been one of the most important issues in functional genomics. With the current availability of various genomic data sets, many researchers have attempted to develop integration models that combine all available genomic data for protein function prediction. These efforts have resulted in the improvement of prediction quality and the extension of prediction coverage. However, it has also been observed that integrating more data sources does not always increase the prediction quality. Therefore, selecting data sources that highly contribute to the protein function prediction has become an important issue. Results We present systematic feature selection methods that assess the contribution of genome-wide data sets to predict protein functions and then investigate the relationship between genomic data sources and protein functions. In this study, we use ten different genomic data sources in Mus musculus, including: protein-domains, protein-protein interactions, gene expressions, phenotype ontology, phylogenetic profiles and disease data sources to predict protein functions that are labelled with Gene Ontology (GO terms. We then apply two approaches to feature selection: exhaustive search feature selection using a kernel based logistic regression (KLR, and a kernel based L1-norm regularized logistic regression (KL1LR. In the first approach, we exhaustively measure the contribution of each data set for each function based on its prediction quality. In the second approach, we use the estimated coefficients of features as measures of contribution of data sources. Our results show that the proposed methods improve the prediction quality compared to the full integration of all data sources and other filter-based feature selection methods. We also show that contributing data sources can differ depending on the protein function. Furthermore, we observe that highly contributing data sets can be similar among

  11. Zeolite Nanoparticles for Selective Sorption of Plasma Proteins.

    Science.gov (United States)

    Rahimi, M; Ng, E-P; Bakhtiari, K; Vinciguerra, M; Ali Ahmad, H; Awala, H; Mintova, S; Daghighi, M; Bakhshandeh Rostami, F; de Vries, M; Motazacker, M M; Peppelenbosch, M P; Mahmoudi, M; Rezaee, F

    2015-11-30

    The affinity of zeolite nanoparticles (diameter of 8-12 nm) possessing high surface area and high pore volume towards human plasma proteins has been investigated. The protein composition (corona) of zeolite nanoparticles has been shown to be more dependent on the plasma protein concentrations and the type of zeolites than zeolite nanoparticles concentration. The number of proteins present in the corona of zeolite nanoparticles at 100% plasma (in vivo state) is less than with 10% plasma exposure. This could be due to a competition between the proteins to occupy the corona of the zeolite nanoparticles. Moreover, a high selective adsorption for apolipoprotein C-III (APOC-III) and fibrinogen on the zeolite nanoparticles at high plasma concentration (100%) was observed. While the zeolite nanoparticles exposed to low plasma concentration (10%) exhibited a high selective adsorption for immunoglobulin gamma (i.e. IGHG1, IGHG2 and IGHG4) proteins. The zeolite nanoparticles can potentially be used for selectively capture of APOC-III in order to reduce the activation of lipoprotein lipase inhibition during hypertriglyceridemia treatment. The zeolite nanoparticles can be adapted to hemophilic patients (hemophilia A (F-VIII deficient) and hemophilia B (F-IX deficient)) with a risk of bleeding, and thus might be potentially used in combination with the existing therapy.

  12. Modeling HIV-1 drug resistance as episodic directional selection.

    Science.gov (United States)

    Murrell, Ben; de Oliveira, Tulio; Seebregts, Chris; Kosakovsky Pond, Sergei L; Scheffler, Konrad

    2012-01-01

    The evolution of substitutions conferring drug resistance to HIV-1 is both episodic, occurring when patients are on antiretroviral therapy, and strongly directional, with site-specific resistant residues increasing in frequency over time. While methods exist to detect episodic diversifying selection and continuous directional selection, no evolutionary model combining these two properties has been proposed. We present two models of episodic directional selection (MEDS and EDEPS) which allow the a priori specification of lineages expected to have undergone directional selection. The models infer the sites and target residues that were likely subject to directional selection, using either codon or protein sequences. Compared to its null model of episodic diversifying selection, MEDS provides a superior fit to most sites known to be involved in drug resistance, and neither one test for episodic diversifying selection nor another for constant directional selection are able to detect as many true positives as MEDS and EDEPS while maintaining acceptable levels of false positives. This suggests that episodic directional selection is a better description of the process driving the evolution of drug resistance.

  13. Modeling HIV-1 drug resistance as episodic directional selection.

    Directory of Open Access Journals (Sweden)

    Ben Murrell

    Full Text Available The evolution of substitutions conferring drug resistance to HIV-1 is both episodic, occurring when patients are on antiretroviral therapy, and strongly directional, with site-specific resistant residues increasing in frequency over time. While methods exist to detect episodic diversifying selection and continuous directional selection, no evolutionary model combining these two properties has been proposed. We present two models of episodic directional selection (MEDS and EDEPS which allow the a priori specification of lineages expected to have undergone directional selection. The models infer the sites and target residues that were likely subject to directional selection, using either codon or protein sequences. Compared to its null model of episodic diversifying selection, MEDS provides a superior fit to most sites known to be involved in drug resistance, and neither one test for episodic diversifying selection nor another for constant directional selection are able to detect as many true positives as MEDS and EDEPS while maintaining acceptable levels of false positives. This suggests that episodic directional selection is a better description of the process driving the evolution of drug resistance.

  14. @TOME-2: a new pipeline for comparative modeling of protein-ligand complexes.

    Science.gov (United States)

    Pons, Jean-Luc; Labesse, Gilles

    2009-07-01

    @TOME 2.0 is new web pipeline dedicated to protein structure modeling and small ligand docking based on comparative analyses. @TOME 2.0 allows fold recognition, template selection, structural alignment editing, structure comparisons, 3D-model building and evaluation. These tasks are routinely used in sequence analyses for structure prediction. In our pipeline the necessary software is efficiently interconnected in an original manner to accelerate all the processes. Furthermore, we have also connected comparative docking of small ligands that is performed using protein-protein superposition. The input is a simple protein sequence in one-letter code with no comment. The resulting 3D model, protein-ligand complexes and structural alignments can be visualized through dedicated Web interfaces or can be downloaded for further studies. These original features will aid in the functional annotation of proteins and the selection of templates for molecular modeling and virtual screening. Several examples are described to highlight some of the new functionalities provided by this pipeline. The server and its documentation are freely available at http://abcis.cbs.cnrs.fr/AT2/

  15. Identification of physicochemical selective pressure on protein encoding nucleotide sequences

    Directory of Open Access Journals (Sweden)

    Sainudiin Raazesh

    2006-03-01

    Full Text Available Abstract Background Statistical methods for identifying positively selected sites in protein coding regions are one of the most commonly used tools in evolutionary bioinformatics. However, they have been limited by not taking the physiochemical properties of amino acids into account. Results We develop a new codon-based likelihood model for detecting site-specific selection pressures acting on specific physicochemical properties. Nonsynonymous substitutions are divided into substitutions that differ with respect to the physicochemical properties of interest, and those that do not. The substitution rates of these two types of changes, relative to the synonymous substitution rate, are then described by two parameters, γ and ω respectively. The new model allows us to perform likelihood ratio tests for positive selection acting on specific physicochemical properties of interest. The new method is first used to analyze simulated data and is shown to have good power and accuracy in detecting physicochemical selective pressure. We then re-analyze data from the class-I alleles of the human Major Histocompatibility Complex (MHC and from the abalone sperm lysine. Conclusion Our new method allows a more flexible framework to identify selection pressure on particular physicochemical properties.

  16. Estimates of selection parameters in protein mutants of spring barley

    International Nuclear Information System (INIS)

    Gaul, H.; Walther, H.; Seibold, K.H.; Brunner, H.; Mikaelsen, K.

    1976-01-01

    Detailed studies have been made with induced protein mutants regarding a possible genetic advance in selection including the estimation of the genetic variation and heritability coefficients. Estimates were obtained for protein content and protein yield. The variation of mutant lines in different environments was found to be many times as large as the variation of the line means. The detection of improved protein mutants seems therefore possible only in trials with more than one environment. The heritability of protein content and protein yield was estimated in different sets of environments and was found to be low. However, higher values were found with an increasing number of environments. At least four environments seem to be necessary to obtain reliable heritability estimates. The geneticall component of the variation between lines was significant for protein content in all environmental combinations. For protein yield some environmental combinations only showed significant differences. The expected genetic advance with one selection step was small for both protein traits. Genetically significant differences between protein micromutants give, however, a first indication that selection among protein mutants with small differences seems also possible. (author)

  17. Protein and energy metabolism in two lines of chickens selected for growth on high or low protein diets

    DEFF Research Database (Denmark)

    Chwalibog, André; Eggum, B O; Sørensen, Peter

    1983-01-01

    Genetic adaptation was investigated in broilers selected for seven generations on a normal (A) or a low (B) protein diet. Protein and energy metabolism were studied in males from these selected lines fed on a diet of intermediate protein content. All selected birds retained more nitrogen than those...

  18. Markov State Models Reveal a Two-Step Mechanism of miRNA Loading into the Human Argonaute Protein: Selective Binding followed by Structural Re-arrangement

    KAUST Repository

    Jiang, Hanlun

    2015-07-16

    Argonaute (Ago) proteins and microRNAs (miRNAs) are central components in RNA interference, which is a key cellular mechanism for sequence-specific gene silencing. Despite intensive studies, molecular mechanisms of how Ago recognizes miRNA remain largely elusive. In this study, we propose a two-step mechanism for this molecular recognition: selective binding followed by structural re-arrangement. Our model is based on the results of a combination of Markov State Models (MSMs), large-scale protein-RNA docking, and molecular dynamics (MD) simulations. Using MSMs, we identify an open state of apo human Ago-2 in fast equilibrium with partially open and closed states. Conformations in this open state are distinguished by their largely exposed binding grooves that can geometrically accommodate miRNA as indicated in our protein-RNA docking studies. miRNA may then selectively bind to these open conformations. Upon the initial binding, the complex may perform further structural re-arrangement as shown in our MD simulations and eventually reach the stable binary complex structure. Our results provide novel insights in Ago-miRNA recognition mechanisms and our methodology holds great potential to be widely applied in the studies of other important molecular recognition systems.

  19. Markov State Models Reveal a Two-Step Mechanism of miRNA Loading into the Human Argonaute Protein: Selective Binding followed by Structural Re-arrangement

    KAUST Repository

    Jiang, Hanlun; Sheong, Fu Kit; Zhu, Lizhe; Gao, Xin; Bernauer, Julie; Huang, Xuhui

    2015-01-01

    Argonaute (Ago) proteins and microRNAs (miRNAs) are central components in RNA interference, which is a key cellular mechanism for sequence-specific gene silencing. Despite intensive studies, molecular mechanisms of how Ago recognizes miRNA remain largely elusive. In this study, we propose a two-step mechanism for this molecular recognition: selective binding followed by structural re-arrangement. Our model is based on the results of a combination of Markov State Models (MSMs), large-scale protein-RNA docking, and molecular dynamics (MD) simulations. Using MSMs, we identify an open state of apo human Ago-2 in fast equilibrium with partially open and closed states. Conformations in this open state are distinguished by their largely exposed binding grooves that can geometrically accommodate miRNA as indicated in our protein-RNA docking studies. miRNA may then selectively bind to these open conformations. Upon the initial binding, the complex may perform further structural re-arrangement as shown in our MD simulations and eventually reach the stable binary complex structure. Our results provide novel insights in Ago-miRNA recognition mechanisms and our methodology holds great potential to be widely applied in the studies of other important molecular recognition systems.

  20. Quantum-Chemical Electron Densities of Proteins and of Selected Protein Sites from Subsystem Density Functional Theory

    NARCIS (Netherlands)

    Kiewisch, K.; Jacob, C.R.; Visscher, L.

    2013-01-01

    The ability to calculate accurate electron densities of full proteins or of selected sites in proteins is a prerequisite for a fully quantum-mechanical calculation of protein-protein and protein-ligand interaction energies. Quantum-chemical subsystem methods capable of treating proteins and other

  1. Mechanical strength of 17,134 model proteins and cysteine slipknots.

    Directory of Open Access Journals (Sweden)

    Mateusz Sikora

    2009-10-01

    Full Text Available A new theoretical survey of proteins' resistance to constant speed stretching is performed for a set of 17,134 proteins as described by a structure-based model. The proteins selected have no gaps in their structure determination and consist of no more than 250 amino acids. Our previous studies have dealt with 7510 proteins of no more than 150 amino acids. The proteins are ranked according to the strength of the resistance. Most of the predicted top-strength proteins have not yet been studied experimentally. Architectures and folds which are likely to yield large forces are identified. New types of potent force clamps are discovered. They involve disulphide bridges and, in particular, cysteine slipknots. An effective energy parameter of the model is estimated by comparing the theoretical data on characteristic forces to the corresponding experimental values combined with an extrapolation of the theoretical data to the experimental pulling speeds. These studies provide guidance for future experiments on single molecule manipulation and should lead to selection of proteins for applications. A new class of proteins, involving cysteine slipknots, is identified as one that is expected to lead to the strongest force clamps known. This class is characterized through molecular dynamics simulations.

  2. Coarse-grain modelling of protein-protein interactions

    NARCIS (Netherlands)

    Baaden, Marc; Marrink, Siewert J.

    2013-01-01

    Here, we review recent advances towards the modelling of protein-protein interactions (PPI) at the coarse-grained (CG) level, a technique that is now widely used to understand protein affinity, aggregation and self-assembly behaviour. PPI models of soluble proteins and membrane proteins are

  3. Models of protein-ligand crystal structures: trust, but verify.

    Science.gov (United States)

    Deller, Marc C; Rupp, Bernhard

    2015-09-01

    X-ray crystallography provides the most accurate models of protein-ligand structures. These models serve as the foundation of many computational methods including structure prediction, molecular modelling, and structure-based drug design. The success of these computational methods ultimately depends on the quality of the underlying protein-ligand models. X-ray crystallography offers the unparalleled advantage of a clear mathematical formalism relating the experimental data to the protein-ligand model. In the case of X-ray crystallography, the primary experimental evidence is the electron density of the molecules forming the crystal. The first step in the generation of an accurate and precise crystallographic model is the interpretation of the electron density of the crystal, typically carried out by construction of an atomic model. The atomic model must then be validated for fit to the experimental electron density and also for agreement with prior expectations of stereochemistry. Stringent validation of protein-ligand models has become possible as a result of the mandatory deposition of primary diffraction data, and many computational tools are now available to aid in the validation process. Validation of protein-ligand complexes has revealed some instances of overenthusiastic interpretation of ligand density. Fundamental concepts and metrics of protein-ligand quality validation are discussed and we highlight software tools to assist in this process. It is essential that end users select high quality protein-ligand models for their computational and biological studies, and we provide an overview of how this can be achieved.

  4. Protein structural model selection by combining consensus and single scoring methods.

    Directory of Open Access Journals (Sweden)

    Zhiquan He

    Full Text Available Quality assessment (QA for predicted protein structural models is an important and challenging research problem in protein structure prediction. Consensus Global Distance Test (CGDT methods assess each decoy (predicted structural model based on its structural similarity to all others in a decoy set and has been proved to work well when good decoys are in a majority cluster. Scoring functions evaluate each single decoy based on its structural properties. Both methods have their merits and limitations. In this paper, we present a novel method called PWCom, which consists of two neural networks sequentially to combine CGDT and single model scoring methods such as RW, DDFire and OPUS-Ca. Specifically, for every pair of decoys, the difference of the corresponding feature vectors is input to the first neural network which enables one to predict whether the decoy-pair are significantly different in terms of their GDT scores to the native. If yes, the second neural network is used to decide which one of the two is closer to the native structure. The quality score for each decoy in the pool is based on the number of winning times during the pairwise comparisons. Test results on three benchmark datasets from different model generation methods showed that PWCom significantly improves over consensus GDT and single scoring methods. The QA server (MUFOLD-Server applying this method in CASP 10 QA category was ranked the second place in terms of Pearson and Spearman correlation performance.

  5. Purification-Free, Target-Selective Immobilization of a Protein from Cell Lysates.

    Science.gov (United States)

    Cha, Jaehyun; Kwon, Inchan

    2018-02-27

    Protein immobilization has been widely used for laboratory experiments and industrial processes. Preparation of a recombinant protein for immobilization usually requires laborious and expensive purification steps. Here, a novel purification-free, target-selective immobilization technique of a protein from cell lysates is reported. Purification steps are skipped by immobilizing a target protein containing a clickable non-natural amino acid (p-azidophenylalanine) in cell lysates onto alkyne-functionalized solid supports via bioorthogonal azide-alkyne cycloaddition. In order to achieve a target protein-selective immobilization, p-azidophenylalanine was introduced into an exogenous target protein, but not into endogenous non-target proteins using host cells with amber codon-free genomic DNAs. Immobilization of superfolder fluorescent protein (sfGFP) from cell lysates is as efficient as that of the purified sfGFP. Using two fluorescent proteins (sfGFP and mCherry), the authors also demonstrated that the target proteins are immobilized with a minimal immobilization of non-target proteins (target-selective immobilization). © 2018 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  6. Binding free energy analysis of protein-protein docking model structures by evERdock.

    Science.gov (United States)

    Takemura, Kazuhiro; Matubayasi, Nobuyuki; Kitao, Akio

    2018-03-14

    To aid the evaluation of protein-protein complex model structures generated by protein docking prediction (decoys), we previously developed a method to calculate the binding free energies for complexes. The method combines a short (2 ns) all-atom molecular dynamics simulation with explicit solvent and solution theory in the energy representation (ER). We showed that this method successfully selected structures similar to the native complex structure (near-native decoys) as the lowest binding free energy structures. In our current work, we applied this method (evERdock) to 100 or 300 model structures of four protein-protein complexes. The crystal structures and the near-native decoys showed the lowest binding free energy of all the examined structures, indicating that evERdock can successfully evaluate decoys. Several decoys that show low interface root-mean-square distance but relatively high binding free energy were also identified. Analysis of the fraction of native contacts, hydrogen bonds, and salt bridges at the protein-protein interface indicated that these decoys were insufficiently optimized at the interface. After optimizing the interactions around the interface by including interfacial water molecules, the binding free energies of these decoys were improved. We also investigated the effect of solute entropy on binding free energy and found that consideration of the entropy term does not necessarily improve the evaluations of decoys using the normal model analysis for entropy calculation.

  7. A feature-based approach to modeling protein-protein interaction hot spots.

    Science.gov (United States)

    Cho, Kyu-il; Kim, Dongsup; Lee, Doheon

    2009-05-01

    Identifying features that effectively represent the energetic contribution of an individual interface residue to the interactions between proteins remains problematic. Here, we present several new features and show that they are more effective than conventional features. By combining the proposed features with conventional features, we develop a predictive model for interaction hot spots. Initially, 54 multifaceted features, composed of different levels of information including structure, sequence and molecular interaction information, are quantified. Then, to identify the best subset of features for predicting hot spots, feature selection is performed using a decision tree. Based on the selected features, a predictive model for hot spots is created using support vector machine (SVM) and tested on an independent test set. Our model shows better overall predictive accuracy than previous methods such as the alanine scanning methods Robetta and FOLDEF, and the knowledge-based method KFC. Subsequent analysis yields several findings about hot spots. As expected, hot spots have a larger relative surface area burial and are more hydrophobic than other residues. Unexpectedly, however, residue conservation displays a rather complicated tendency depending on the types of protein complexes, indicating that this feature is not good for identifying hot spots. Of the selected features, the weighted atomic packing density, relative surface area burial and weighted hydrophobicity are the top 3, with the weighted atomic packing density proving to be the most effective feature for predicting hot spots. Notably, we find that hot spots are closely related to pi-related interactions, especially pi . . . pi interactions.

  8. Developing a NIR multispectral imaging for prediction and visualization of peanut protein content using variable selection algorithms

    Science.gov (United States)

    Cheng, Jun-Hu; Jin, Huali; Liu, Zhiwei

    2018-01-01

    The feasibility of developing a multispectral imaging method using important wavelengths from hyperspectral images selected by genetic algorithm (GA), successive projection algorithm (SPA) and regression coefficient (RC) methods for modeling and predicting protein content in peanut kernel was investigated for the first time. Partial least squares regression (PLSR) calibration model was established between the spectral data from the selected optimal wavelengths and the reference measured protein content ranged from 23.46% to 28.43%. The RC-PLSR model established using eight key wavelengths (1153, 1567, 1972, 2143, 2288, 2339, 2389 and 2446 nm) showed the best predictive results with the coefficient of determination of prediction (R2P) of 0.901, and root mean square error of prediction (RMSEP) of 0.108 and residual predictive deviation (RPD) of 2.32. Based on the obtained best model and image processing algorithms, the distribution maps of protein content were generated. The overall results of this study indicated that developing a rapid and online multispectral imaging system using the feature wavelengths and PLSR analysis is potential and feasible for determination of the protein content in peanut kernels.

  9. Site-Selective Conjugation of Native Proteins with DNA

    DEFF Research Database (Denmark)

    Trads, Julie Brender; Tørring, Thomas; Gothelf, Kurt Vesterager

    2017-01-01

    Conjugation of DNA to proteins is increasingly used in academia and industry to provide proteins with tags for identification or handles for hybridization to other DNA strands. Assay technologies such as immuno-PCR and proximity ligation and the imaging technology DNA-PAINT require DNA-protein....... The introduction of a bioorthogonal handle at a specific position of a protein by recombinant techniques provides an excellent approach to site-specific conjugation, but for many laboratories and for applications where several proteins are to be labeled, the expression of recombinant proteins may be cumbersome...... conjugates. In DNA nanotechnology, the DNA handle is exploited to precisely position proteins by self-assembly. For these applications, site-selective conjugation is almost always desired because fully functional proteins are required to maintain the specificity of antibodies and the activity of enzymes...

  10. The Protein Model Portal

    OpenAIRE

    Arnold, Konstantin; Kiefer, Florian; Kopp, J?rgen; Battey, James N. D.; Podvinec, Michael; Westbrook, John D.; Berman, Helen M.; Bordoli, Lorenza; Schwede, Torsten

    2008-01-01

    Structural Genomics has been successful in determining the structures of many unique proteins in a high throughput manner. Still, the number of known protein sequences is much larger than the number of experimentally solved protein structures. Homology (or comparative) modeling methods make use of experimental protein structures to build models for evolutionary related proteins. Thereby, experimental structure determination efforts and homology modeling complement each other in the exploratio...

  11. DNABP: Identification of DNA-Binding Proteins Based on Feature Selection Using a Random Forest and Predicting Binding Residues.

    Science.gov (United States)

    Ma, Xin; Guo, Jing; Sun, Xiao

    2016-01-01

    DNA-binding proteins are fundamentally important in cellular processes. Several computational-based methods have been developed to improve the prediction of DNA-binding proteins in previous years. However, insufficient work has been done on the prediction of DNA-binding proteins from protein sequence information. In this paper, a novel predictor, DNABP (DNA-binding proteins), was designed to predict DNA-binding proteins using the random forest (RF) classifier with a hybrid feature. The hybrid feature contains two types of novel sequence features, which reflect information about the conservation of physicochemical properties of the amino acids, and the binding propensity of DNA-binding residues and non-binding propensities of non-binding residues. The comparisons with each feature demonstrated that these two novel features contributed most to the improvement in predictive ability. Furthermore, to improve the prediction performance of the DNABP model, feature selection using the minimum redundancy maximum relevance (mRMR) method combined with incremental feature selection (IFS) was carried out during the model construction. The results showed that the DNABP model could achieve 86.90% accuracy, 83.76% sensitivity, 90.03% specificity and a Matthews correlation coefficient of 0.727. High prediction accuracy and performance comparisons with previous research suggested that DNABP could be a useful approach to identify DNA-binding proteins from sequence information. The DNABP web server system is freely available at http://www.cbi.seu.edu.cn/DNABP/.

  12. A protein engineered to bind uranyl selectively and with femtomolar affinity

    Science.gov (United States)

    Zhou, Lu; Bosscher, Mike; Zhang, Changsheng; Özçubukçu, Salih; Zhang, Liang; Zhang, Wen; Li, Charles J.; Liu, Jianzhao; Jensen, Mark P.; Lai, Luhua; He, Chuan

    2014-03-01

    Uranyl (UO22+), the predominant aerobic form of uranium, is present in the ocean at a concentration of ~3.2 parts per 109 (13.7 nM) however, the successful enrichment of uranyl from this vast resource has been limited by the high concentrations of metal ions of similar size and charge, which makes it difficult to design a binding motif that is selective for uranyl. Here we report the design and rational development of a uranyl-binding protein using a computational screening process in the initial search for potential uranyl-binding sites. The engineered protein is thermally stable and offers very high affinity and selectivity for uranyl with a Kd of 7.4 femtomolar (fM) and >10,000-fold selectivity over other metal ions. We also demonstrated that the uranyl-binding protein can repeatedly sequester 30-60% of the uranyl in synthetic sea water. The chemical strategy employed here may be applied to engineer other selective metal-binding proteins for biotechnology and remediation applications.

  13. Formylbenzene diazonium hexafluorophosphate reagent for tyrosine-selective modification of proteins and the introduction of a bioorthogonal aldehyde.

    Science.gov (United States)

    Gavrilyuk, Julia; Ban, Hitoshi; Nagano, Masanobu; Hakamata, Wataru; Barbas, Carlos F

    2012-12-19

    4-Formylbenzene diazonium hexafluorophosphate (FBDP) is a novel bench-stable crystalline diazonium salt that reacts selectively with tyrosine to install a bioorthogonal aldehyde functionality. Model studies with N-acyl-tyrosine methylamide allowed us to identify conditions optimal for tyrosine ligation reactions with small peptides and proteins. FBDP-based conjugation was used for the facile introduction of small molecule tags, poly(ethylene glycol) chains (PEGylation), and functional small molecules onto model proteins and to label the surface of living cells.

  14. Scalable rule-based modelling of allosteric proteins and biochemical networks.

    Directory of Open Access Journals (Sweden)

    Julien F Ollivier

    2010-11-01

    Full Text Available Much of the complexity of biochemical networks comes from the information-processing abilities of allosteric proteins, be they receptors, ion-channels, signalling molecules or transcription factors. An allosteric protein can be uniquely regulated by each combination of input molecules that it binds. This "regulatory complexity" causes a combinatorial increase in the number of parameters required to fit experimental data as the number of protein interactions increases. It therefore challenges the creation, updating, and re-use of biochemical models. Here, we propose a rule-based modelling framework that exploits the intrinsic modularity of protein structure to address regulatory complexity. Rather than treating proteins as "black boxes", we model their hierarchical structure and, as conformational changes, internal dynamics. By modelling the regulation of allosteric proteins through these conformational changes, we often decrease the number of parameters required to fit data, and so reduce over-fitting and improve the predictive power of a model. Our method is thermodynamically grounded, imposes detailed balance, and also includes molecular cross-talk and the background activity of enzymes. We use our Allosteric Network Compiler to examine how allostery can facilitate macromolecular assembly and how competitive ligands can change the observed cooperativity of an allosteric protein. We also develop a parsimonious model of G protein-coupled receptors that explains functional selectivity and can predict the rank order of potency of agonists acting through a receptor. Our methodology should provide a basis for scalable, modular and executable modelling of biochemical networks in systems and synthetic biology.

  15. A model in which heat shock protein 90 targets protein-folding clefts: rationale for a new approach to neuroprotective treatment of protein folding diseases.

    Science.gov (United States)

    Pratt, William B; Morishima, Yoshihiro; Gestwicki, Jason E; Lieberman, Andrew P; Osawa, Yoichi

    2014-11-01

    In an EBM Minireview published in 2010, we proposed that the heat shock protein (Hsp)90/Hsp70-based chaperone machinery played a major role in determining the selection of proteins that have undergone oxidative or other toxic damage for ubiquitination and proteasomal degradation. The proposal was based on a model in which the Hsp90 chaperone machinery regulates signaling by modulating ligand-binding clefts. The model provides a framework for thinking about the development of neuroprotective therapies for protein-folding diseases like Alzheimer's disease (AD), Parkinson's disease (PD), and the polyglutamine expansion disorders, such as Huntington's disease (HD) and spinal and bulbar muscular atrophy (SBMA). Major aberrant proteins that misfold and accumulate in these diseases are "client" proteins of the abundant and ubiquitous stress chaperone Hsp90. These Hsp90 client proteins include tau (AD), α-synuclein (PD), huntingtin (HD), and the expanded glutamine androgen receptor (polyQ AR) (SBMA). In this Minireview, we update our model in which Hsp90 acts on protein-folding clefts and show how it forms a rational basis for developing drugs that promote the targeted elimination of these aberrant proteins. © 2014 by the Society for Experimental Biology and Medicine.

  16. Superoxide dismutase 1 is positively selected to minimize protein aggregation in great apes

    DEFF Research Database (Denmark)

    Dasmeh, Pouria; Kepp, Kasper Planeta

    2017-01-01

    Positive (adaptive) selection has recently been implied in human superoxide dismutase 1 (SOD1), a highly abundant antioxidant protein with energy signaling and antiaging functions, one of very few examples of direct selection on a human protein product (exon); the molecular drivers...... and SOD1 aggregates and triggered by aging. Our study thus marks an example of direct selection for a particular chemical phenotype (high net charge and stability) in a single human protein with possible implications for the evolution of aging....... of this selection are unknown. We mapped 30 extant SOD1 sequences to the recently established mammalian species tree and inferred ancestors, key substitutions, and signatures of selection during the protein's evolution. We detected elevated substitution rates leading to great apes (Hominidae) at ~1 per 2 million...

  17. LiCABEDS II. Modeling of ligand selectivity for G-protein-coupled cannabinoid receptors.

    Science.gov (United States)

    Ma, Chao; Wang, Lirong; Yang, Peng; Myint, Kyaw Z; Xie, Xiang-Qun

    2013-01-28

    The cannabinoid receptor subtype 2 (CB2) is a promising therapeutic target for blood cancer, pain relief, osteoporosis, and immune system disease. The recent withdrawal of Rimonabant, which targets another closely related cannabinoid receptor (CB1), accentuates the importance of selectivity for the development of CB2 ligands in order to minimize their effects on the CB1 receptor. In our previous study, LiCABEDS (Ligand Classifier of Adaptively Boosting Ensemble Decision Stumps) was reported as a generic ligand classification algorithm for the prediction of categorical molecular properties. Here, we report extension of the application of LiCABEDS to the modeling of cannabinoid ligand selectivity with molecular fingerprints as descriptors. The performance of LiCABEDS was systematically compared with another popular classification algorithm, support vector machine (SVM), according to prediction precision and recall rate. In addition, the examination of LiCABEDS models revealed the difference in structure diversity of CB1 and CB2 selective ligands. The structure determination from data mining could be useful for the design of novel cannabinoid lead compounds. More importantly, the potential of LiCABEDS was demonstrated through successful identification of newly synthesized CB2 selective compounds.

  18. The Protein Model Portal--a comprehensive resource for protein structure and model information.

    Science.gov (United States)

    Haas, Juergen; Roth, Steven; Arnold, Konstantin; Kiefer, Florian; Schmidt, Tobias; Bordoli, Lorenza; Schwede, Torsten

    2013-01-01

    The Protein Model Portal (PMP) has been developed to foster effective use of 3D molecular models in biomedical research by providing convenient and comprehensive access to structural information for proteins. Both experimental structures and theoretical models for a given protein can be searched simultaneously and analyzed for structural variability. By providing a comprehensive view on structural information, PMP offers the opportunity to apply consistent assessment and validation criteria to the complete set of structural models available for proteins. PMP is an open project so that new methods developed by the community can contribute to PMP, for example, new modeling servers for creating homology models and model quality estimation servers for model validation. The accuracy of participating modeling servers is continuously evaluated by the Continuous Automated Model EvaluatiOn (CAMEO) project. The PMP offers a unique interface to visualize structural coverage of a protein combining both theoretical models and experimental structures, allowing straightforward assessment of the model quality and hence their utility. The portal is updated regularly and actively developed to include latest methods in the field of computational structural biology. Database URL: http://www.proteinmodelportal.org.

  19. The Protein Model Portal—a comprehensive resource for protein structure and model information

    Science.gov (United States)

    Haas, Juergen; Roth, Steven; Arnold, Konstantin; Kiefer, Florian; Schmidt, Tobias; Bordoli, Lorenza; Schwede, Torsten

    2013-01-01

    The Protein Model Portal (PMP) has been developed to foster effective use of 3D molecular models in biomedical research by providing convenient and comprehensive access to structural information for proteins. Both experimental structures and theoretical models for a given protein can be searched simultaneously and analyzed for structural variability. By providing a comprehensive view on structural information, PMP offers the opportunity to apply consistent assessment and validation criteria to the complete set of structural models available for proteins. PMP is an open project so that new methods developed by the community can contribute to PMP, for example, new modeling servers for creating homology models and model quality estimation servers for model validation. The accuracy of participating modeling servers is continuously evaluated by the Continuous Automated Model EvaluatiOn (CAMEO) project. The PMP offers a unique interface to visualize structural coverage of a protein combining both theoretical models and experimental structures, allowing straightforward assessment of the model quality and hence their utility. The portal is updated regularly and actively developed to include latest methods in the field of computational structural biology. Database URL: http://www.proteinmodelportal.org PMID:23624946

  20. Joint Bayesian variable and graph selection for regression models with network-structured predictors

    Science.gov (United States)

    Peterson, C. B.; Stingo, F. C.; Vannucci, M.

    2015-01-01

    In this work, we develop a Bayesian approach to perform selection of predictors that are linked within a network. We achieve this by combining a sparse regression model relating the predictors to a response variable with a graphical model describing conditional dependencies among the predictors. The proposed method is well-suited for genomic applications since it allows the identification of pathways of functionally related genes or proteins which impact an outcome of interest. In contrast to previous approaches for network-guided variable selection, we infer the network among predictors using a Gaussian graphical model and do not assume that network information is available a priori. We demonstrate that our method outperforms existing methods in identifying network-structured predictors in simulation settings, and illustrate our proposed model with an application to inference of proteins relevant to glioblastoma survival. PMID:26514925

  1. Membrane proteins bind lipids selectively to modulate their structure and function.

    Science.gov (United States)

    Laganowsky, Arthur; Reading, Eamonn; Allison, Timothy M; Ulmschneider, Martin B; Degiacomi, Matteo T; Baldwin, Andrew J; Robinson, Carol V

    2014-06-05

    Previous studies have established that the folding, structure and function of membrane proteins are influenced by their lipid environments and that lipids can bind to specific sites, for example, in potassium channels. Fundamental questions remain however regarding the extent of membrane protein selectivity towards lipids. Here we report a mass spectrometry approach designed to determine the selectivity of lipid binding to membrane protein complexes. We investigate the mechanosensitive channel of large conductance (MscL) from Mycobacterium tuberculosis and aquaporin Z (AqpZ) and the ammonia channel (AmtB) from Escherichia coli, using ion mobility mass spectrometry (IM-MS), which reports gas-phase collision cross-sections. We demonstrate that folded conformations of membrane protein complexes can exist in the gas phase. By resolving lipid-bound states, we then rank bound lipids on the basis of their ability to resist gas phase unfolding and thereby stabilize membrane protein structure. Lipids bind non-selectively and with high avidity to MscL, all imparting comparable stability; however, the highest-ranking lipid is phosphatidylinositol phosphate, in line with its proposed functional role in mechanosensation. AqpZ is also stabilized by many lipids, with cardiolipin imparting the most significant resistance to unfolding. Subsequently, through functional assays we show that cardiolipin modulates AqpZ function. Similar experiments identify AmtB as being highly selective for phosphatidylglycerol, prompting us to obtain an X-ray structure in this lipid membrane-like environment. The 2.3 Å resolution structure, when compared with others obtained without lipid bound, reveals distinct conformational changes that re-position AmtB residues to interact with the lipid bilayer. Our results demonstrate that resistance to unfolding correlates with specific lipid-binding events, enabling a distinction to be made between lipids that merely bind from those that modulate membrane

  2. A modeling strategy for G-protein coupled receptors

    Directory of Open Access Journals (Sweden)

    Anna Kahler

    2016-03-01

    Full Text Available Cell responses can be triggered via G-protein coupled receptors (GPCRs that interact with small molecules, peptides or proteins and transmit the signal over the membrane via structural changes to activate intracellular pathways. GPCRs are characterized by a rather low sequence similarity and exhibit structural differences even for functionally closely related GPCRs. An accurate structure prediction for GPCRs is therefore not straightforward. We propose a computational approach that relies on the generation of several independent models based on different template structures, which are subsequently refined by molecular dynamics simulations. A comparison of their conformational stability and the agreement with GPCR-typical structural features is then used to select a favorable model. This strategy was applied to predict the structure of the herpesviral chemokine receptor US28 by generating three independent models based on the known structures of the chemokine receptors CXCR1, CXCR4, and CCR5. Model refinement and evaluation suggested that the model based on CCR5 exhibits the most favorable structural properties. In particular, the GPCR-typical structural features, such as a conserved water cluster or conserved non-covalent contacts, are present to a larger extent in the model based on CCR5 compared to the other models. A final model validation based on the recently published US28 crystal structure confirms that the CCR5-based model is the most accurate and exhibits 80.8% correctly modeled residues within the transmembrane helices. The structural agreement between the selected model and the crystal structure suggests that our modeling strategy may also be more generally applicable to other GPCRs of unknown structure.

  3. Positive selection neighboring functionally essential sites and disease-implicated regions of mammalian reproductive proteins.

    LENUS (Irish Health Repository)

    Morgan, Claire C

    2010-01-01

    ABSTRACT: BACKGROUND: Reproductive proteins are central to the continuation of all mammalian species. The evolution of these proteins has been greatly influenced by environmental pressures induced by pathogens, rival sperm, sexual selection and sexual conflict. Positive selection has been demonstrated in many of these proteins with particular focus on primate lineages. However, the mammalia are a diverse group in terms of mating habits, population sizes and germ line generation times. We have examined the selective pressures at work on a number of novel reproductive proteins across a wide variety of mammalia. RESULTS: We show that selective pressures on reproductive proteins are highly varied. Of the 10 genes analyzed in detail, all contain signatures of positive selection either across specific sites or in specific lineages or a combination of both. Our analysis of SP56 and Col1a1 are entirely novel and the results show positively selected sites present in each gene. Our findings for the Col1a1 gene are suggestive of a link between positive selection and severe disease type. We find evidence in our dataset to suggest that interacting proteins are evolving in symphony: most likely to maintain interacting functionality. CONCLUSION: Our in silico analyses show positively selected sites are occurring near catalytically important regions suggesting selective pressure to maximize efficient fertilization. In those cases where a mechanism of protein function is not fully understood, the sites presented here represent ideal candidates for mutational study. This work has highlighted the widespread rate heterogeneity in mutational rates across the mammalia and specifically has shown that the evolution of reproductive proteins is highly varied depending on the species and interacting partners. We have shown that positive selection and disease are closely linked in the Col1a1 gene.

  4. Site-selective protein-modification chemistry for basic biology and drug development.

    Science.gov (United States)

    Krall, Nikolaus; da Cruz, Filipa P; Boutureira, Omar; Bernardes, Gonçalo J L

    2016-02-01

    Nature has produced intricate machinery to covalently diversify the structure of proteins after their synthesis in the ribosome. In an attempt to mimic nature, chemists have developed a large set of reactions that enable post-expression modification of proteins at pre-determined sites. These reactions are now used to selectively install particular modifications on proteins for many biological and therapeutic applications. For example, they provide an opportunity to install post-translational modifications on proteins to determine their exact biological roles. Labelling of proteins in live cells with fluorescent dyes allows protein uptake and intracellular trafficking to be tracked and also enables physiological parameters to be measured optically. Through the conjugation of potent cytotoxicants to antibodies, novel anti-cancer drugs with improved efficacy and reduced side effects may be obtained. In this Perspective, we highlight the most exciting current and future applications of chemical site-selective protein modification and consider which hurdles still need to be overcome for more widespread use.

  5. Membrane Compartmentalization Reducing the Mobility of Lipids and Proteins within a Model Plasma Membrane.

    Science.gov (United States)

    Koldsø, Heidi; Reddy, Tyler; Fowler, Philip W; Duncan, Anna L; Sansom, Mark S P

    2016-09-01

    The cytoskeleton underlying cell membranes may influence the dynamic organization of proteins and lipids within the bilayer by immobilizing certain transmembrane (TM) proteins and forming corrals within the membrane. Here, we present coarse-grained resolution simulations of a biologically realistic membrane model of asymmetrically organized lipids and TM proteins. We determine the effects of a model of cytoskeletal immobilization of selected membrane proteins using long time scale coarse-grained molecular dynamics simulations. By introducing compartments with varying degrees of restraints within the membrane models, we are able to reveal how compartmentalization caused by cytoskeletal immobilization leads to reduced and anomalous diffusional mobility of both proteins and lipids. This in turn results in a reduced rate of protein dimerization within the membrane and of hopping of membrane proteins between compartments. These simulations provide a molecular realization of hierarchical models often invoked to explain single-molecule imaging studies of membrane proteins.

  6. Automated Hydrophobic Interaction Chromatography Column Selection for Use in Protein Purification

    Science.gov (United States)

    Murphy, Patrick J. M.; Stone, Orrin J.; Anderson, Michelle E.

    2011-01-01

    should be employed for future, more exhaustive optimization experiments and protein purification runs 4. The specific protein being purified here is recombinant green fluorescent protein (GFP); however, the approach may be adapted for purifying other proteins with one or more hydrophobic surface regions. GFP serves as a useful model protein, due to its stability, unique light absorbance peak at 397 nm, and fluorescence when exposed to UV light 5. Bacterial lysate containing wild type GFP was prepared in a high-salt buffer, loaded into a Bio-Rad DuoFlow medium pressure liquid chromatography system, and adsorbed to HiTrap HIC columns containing different HIC media. The protein was eluted from the columns and analyzed by in-line and post-run detection methods. Buffer blending, dynamic sample loop injection, sequential column selection, multi-wavelength analysis, and split fraction eluate collection increased the functionality of the system and reproducibility of the experimental approach. PMID:21968976

  7. Protein Subcellular Localization with Gaussian Kernel Discriminant Analysis and Its Kernel Parameter Selection.

    Science.gov (United States)

    Wang, Shunfang; Nie, Bing; Yue, Kun; Fei, Yu; Li, Wenjia; Xu, Dongshu

    2017-12-15

    Kernel discriminant analysis (KDA) is a dimension reduction and classification algorithm based on nonlinear kernel trick, which can be novelly used to treat high-dimensional and complex biological data before undergoing classification processes such as protein subcellular localization. Kernel parameters make a great impact on the performance of the KDA model. Specifically, for KDA with the popular Gaussian kernel, to select the scale parameter is still a challenging problem. Thus, this paper introduces the KDA method and proposes a new method for Gaussian kernel parameter selection depending on the fact that the differences between reconstruction errors of edge normal samples and those of interior normal samples should be maximized for certain suitable kernel parameters. Experiments with various standard data sets of protein subcellular localization show that the overall accuracy of protein classification prediction with KDA is much higher than that without KDA. Meanwhile, the kernel parameter of KDA has a great impact on the efficiency, and the proposed method can produce an optimum parameter, which makes the new algorithm not only perform as effectively as the traditional ones, but also reduce the computational time and thus improve efficiency.

  8. Improving Classification of Protein Interaction Articles Using Context Similarity-Based Feature Selection.

    Science.gov (United States)

    Chen, Yifei; Sun, Yuxing; Han, Bing-Qing

    2015-01-01

    Protein interaction article classification is a text classification task in the biological domain to determine which articles describe protein-protein interactions. Since the feature space in text classification is high-dimensional, feature selection is widely used for reducing the dimensionality of features to speed up computation without sacrificing classification performance. Many existing feature selection methods are based on the statistical measure of document frequency and term frequency. One potential drawback of these methods is that they treat features separately. Hence, first we design a similarity measure between the context information to take word cooccurrences and phrase chunks around the features into account. Then we introduce the similarity of context information to the importance measure of the features to substitute the document and term frequency. Hence we propose new context similarity-based feature selection methods. Their performance is evaluated on two protein interaction article collections and compared against the frequency-based methods. The experimental results reveal that the context similarity-based methods perform better in terms of the F1 measure and the dimension reduction rate. Benefiting from the context information surrounding the features, the proposed methods can select distinctive features effectively for protein interaction article classification.

  9. The PMDB Protein Model Database

    Science.gov (United States)

    Castrignanò, Tiziana; De Meo, Paolo D'Onorio; Cozzetto, Domenico; Talamo, Ivano Giuseppe; Tramontano, Anna

    2006-01-01

    The Protein Model Database (PMDB) is a public resource aimed at storing manually built 3D models of proteins. The database is designed to provide access to models published in the scientific literature, together with validating experimental data. It is a relational database and it currently contains >74 000 models for ∼240 proteins. The system is accessible at and allows predictors to submit models along with related supporting evidence and users to download them through a simple and intuitive interface. Users can navigate in the database and retrieve models referring to the same target protein or to different regions of the same protein. Each model is assigned a unique identifier that allows interested users to directly access the data. PMID:16381873

  10. Semiotic Selection of Mutated or Misfolded Receptor Proteins

    DEFF Research Database (Denmark)

    Giorgi, Franco; Bruni, Luis Emilio; Maggio, Roberto

    2013-01-01

    contention that the plasma membrane acts as the locus where several contextual cues may be integrated. As such it allows the semiotic selection of those receptor configurations that provide cells with the minimum essential requirements for agency. The occurrence of protein misfolding makes it impossible...... focused on the significance and semiotic nature of the interplay between membrane receptors and the epigenetic control of gene expression, as mediated by the control of mismatched repairing and protein folding mechanisms....

  11. Widespread Positive Selection Drives Differentiation of Centromeric Proteins in the Drosophila melanogaster subgroup.

    Science.gov (United States)

    Beck, Emily A; Llopart, Ana

    2015-11-25

    Rapid evolution of centromeric satellite repeats is thought to cause compensatory amino acid evolution in interacting centromere-associated kinetochore proteins. Cid, a protein that mediates kinetochore/centromere interactions, displays particularly high amino acid turnover. Rapid evolution of both Cid and centromeric satellite repeats led us to hypothesize that the apparent compensatory evolution may extend to interacting partners in the Condensin I complex (i.e., SMC2, SMC4, Cap-H, Cap-D2, and Cap-G) and HP1s. Missense mutations in these proteins often result in improper centromere formation and aberrant chromosome segregation, thus selection for maintained function and coevolution among proteins of the complex is likely strong. Here, we report evidence of rapid evolution and recurrent positive selection in seven centromere-associated proteins in species of the Drosophila melanogaster subgroup, and further postulate that positive selection on these proteins could be a result of centromere drive and compensatory changes, with kinetochore proteins competing for optimal spindle attachment.

  12. Introducing a Clustering Step in a Consensus Approach for the Scoring of Protein-Protein Docking Models

    KAUST Repository

    Chermak, Edrisse; De Donato, Renato; Lensink, Marc F.; Petta, Andrea; Serra, Luigi; Scarano, Vittorio; Cavallo, Luigi; Oliva, Romina

    2016-01-01

    Correctly scoring protein-protein docking models to single out native-like ones is an open challenge. It is also an object of assessment in CAPRI (Critical Assessment of PRedicted Interactions), the community-wide blind docking experiment. We introduced in the field the first pure consensus method, CONSRANK, which ranks models based on their ability to match the most conserved contacts in the ensemble they belong to. In CAPRI, scorers are asked to evaluate a set of available models and select the top ten ones, based on their own scoring approach. Scorers' performance is ranked based on the number of targets/interfaces for which they could provide at least one correct solution. In such terms, blind testing in CAPRI Round 30 (a joint prediction round with CASP11) has shown that critical cases for CONSRANK are represented by targets showing multiple interfaces or for which only a very small number of correct solutions are available. To address these challenging cases, CONSRANK has now been modified to include a contact-based clustering of the models as a preliminary step of the scoring process. We used an agglomerative hierarchical clustering based on the number of common inter-residue contacts within the models. Two criteria, with different thresholds, were explored in the cluster generation, setting either the number of common contacts or of total clusters. For each clustering approach, after selecting the top (most populated) ten clusters, CONSRANK was run on these clusters and the top-ranked model for each cluster was selected, in the limit of 10 models per target. We have applied our modified scoring approach, Clust-CONSRANK, to SCORE_SET, a set of CAPRI scoring models made recently available by CAPRI assessors, and to the subset of homodimeric targets in CAPRI Round 30 for which CONSRANK failed to include a correct solution within the ten selected models. Results show that, for the challenging cases, the clustering step typically enriches the ten top ranked

  13. Introducing a Clustering Step in a Consensus Approach for the Scoring of Protein-Protein Docking Models

    KAUST Repository

    Chermak, Edrisse

    2016-11-15

    Correctly scoring protein-protein docking models to single out native-like ones is an open challenge. It is also an object of assessment in CAPRI (Critical Assessment of PRedicted Interactions), the community-wide blind docking experiment. We introduced in the field the first pure consensus method, CONSRANK, which ranks models based on their ability to match the most conserved contacts in the ensemble they belong to. In CAPRI, scorers are asked to evaluate a set of available models and select the top ten ones, based on their own scoring approach. Scorers\\' performance is ranked based on the number of targets/interfaces for which they could provide at least one correct solution. In such terms, blind testing in CAPRI Round 30 (a joint prediction round with CASP11) has shown that critical cases for CONSRANK are represented by targets showing multiple interfaces or for which only a very small number of correct solutions are available. To address these challenging cases, CONSRANK has now been modified to include a contact-based clustering of the models as a preliminary step of the scoring process. We used an agglomerative hierarchical clustering based on the number of common inter-residue contacts within the models. Two criteria, with different thresholds, were explored in the cluster generation, setting either the number of common contacts or of total clusters. For each clustering approach, after selecting the top (most populated) ten clusters, CONSRANK was run on these clusters and the top-ranked model for each cluster was selected, in the limit of 10 models per target. We have applied our modified scoring approach, Clust-CONSRANK, to SCORE_SET, a set of CAPRI scoring models made recently available by CAPRI assessors, and to the subset of homodimeric targets in CAPRI Round 30 for which CONSRANK failed to include a correct solution within the ten selected models. Results show that, for the challenging cases, the clustering step typically enriches the ten top ranked

  14. Exploiting translational coupling for the selection of cells producing toxic recombinant proteins from expression vectors.

    Science.gov (United States)

    Tagliavia, Marcello; Cuttitta, Angela

    2016-01-01

    High rates of plasmid instability are associated with the use of some expression vectors in Escherichia coli, resulting in the loss of recombinant protein expression. This is due to sequence alterations in vector promoter elements caused by the background expression of the cloned gene, which leads to the selection of fast-growing, plasmid-containing cells that do not express the target protein. This phenomenon, which is worsened when expressing toxic proteins, results in preparations containing very little or no recombinant protein, or even in clone loss; however, no methods to prevent loss of recombinant protein expression are currently available. We have exploited the phenomenon of translational coupling, a mechanism of prokaryotic gene expression regulation, in order to select cells containing plasmids still able to express recombinant proteins. Here we designed an expression vector in which the cloned gene and selection marker are co-expressed. Our approach allowed for the selection of the recombinant protein-expressing cells and proved effective even for clones encoding toxic proteins.

  15. Selective radiolabeling of cell surface proteins to a high specific activity

    International Nuclear Information System (INIS)

    Thompson, J.A.; Lau, A.L.; Cunningham, D.D.

    1987-01-01

    A procedure was developed for selective radiolabeling of membrane proteins on cells to higher specific activities than possible with available techniques. Cell surface amino groups were derivatized with 125 I-(hydroxyphenyl)propionyl groups via 125 I-sulfosuccinimidyl (hydroxyphenyl)propionate ( 125 II-sulfo-SHPP). This reagent preferentially labeled membrane proteins exposed at the cell surface of erythrocytes as assessed by the degree of radiolabel incorporation into erythrocyte ghost proteins and hemoglobin. Comparison with the lactoperoxidase-[ 125 I]iodide labeling technique revealed that 125 I-sulfo-SHPP labeled cell surface proteins to a much higher specific activity and hemoglobin to a much lower specific activity. Additionally, this reagent was used for selective radiolabeling of membrane proteins on the cytoplasmic face of the plasma membrane by blocking exofacial amino groups with uniodinated sulfo-SHPP, lysing the cells, and then incubating them with 125 I-sulfo-SHPP. Exclusive labeling of either side of the plasma membrane was demonstrated by the labeling of some marker proteins with well-defined spacial orientations on erythroctyes. Transmembrane proteins such as the epidermal growth factor receptor on cultured cells could also be labeled differentially from either side of the plasma membrane

  16. Investigation of selection methods im mutation breeding of barley for protein quantity and quality

    International Nuclear Information System (INIS)

    Ulonska, E.; Gaul, H.; Baumer, M.; Gesellschaft fuer Strahlen- und Umweltforschung m.b.H., Gruenbach

    1975-01-01

    This mutation breeding programme is investigating the qualification of micro-mutations for the selection of improved protein quality and quantity. Normally, improvement of protein content in micro-mutations is rather small. Therefore, it is important to develop methods and conditions of selection being (a) capable of measuring these small deviations in protein content and quality, and (b) simple to use. In two experiments carried out in 1971 and 1972 nitrogen fertilization was found to be the most important factor in the improvement of selection conditions. There is a highly significant negative correlation between crude protein content and the standard deviation; i.e. the higher the content of crude protein, the lower the variation coefficient. This in turn leads to an increase of genetic variation necessary for better selection progress. Nitrogen fertilization, especially during ear emergence, covers environmental influences - e.g., planting space, sowing rate, growing in different plots (6, 3, 2, 1 rows or in half-ear hills) - to a great extent. Thus, by applying high doses of nitrogen dressings comparable results can be achieved. In an overall selection experiment (testing the entire crossing and mutation material available at Weihenstephan in a stepwise selection from 1971 to 1973) and two selection experiments conducted in 1971 to 1973 with micro-mutants - variety Nota, 4 times X-rayed and the naked barley strain 1606 treated once with EMS - significant selection results were found. (author)

  17. In vitro Selection and Interaction Studies of a DNA Aptamer Targeting Protein A.

    Directory of Open Access Journals (Sweden)

    Regina Stoltenburg

    Full Text Available A new DNA aptamer targeting Protein A is presented. The aptamer was selected by use of the FluMag-SELEX procedure. The SELEX technology (Systematic Evolution of Ligands by EXponential enrichment is widely applied as an in vitro selection and amplification method to generate target-specific aptamers and exists in various modified variants. FluMag-SELEX is one of them and is characterized by the use of magnetic beads for target immobilization and fluorescently labeled oligonucleotides for monitoring the aptamer selection progress. Structural investigations and sequence truncation experiments of the selected aptamer for Protein A led to the conclusion, that a stem-loop structure at its 5'-end including the 5'-primer binding site is essential for aptamer-target binding. Extensive interaction analyses between aptamer and Protein A were performed by methods like surface plasmon resonance, MicroScale Thermophoresis and bead-based binding assays using fluorescence measurements. The binding of the aptamer to its target was thus investigated in assays with immobilization of one of the binding partners each, and with both binding partners in solution. Affinity constants were determined in the low micromolar to submicromolar range, increasing to the nanomolar range under the assumption of avidity. Protein A provides more than one binding site for the aptamer, which may overlap with the known binding sites for immunoglobulins. The aptamer binds specifically to both native and recombinant Protein A, but not to other immunoglobulin-binding proteins like Protein G and L. Cross specificity to other proteins was not found. The application of the aptamer is directed to Protein A detection or affinity purification. Moreover, whole cells of Staphylococcus aureus, presenting Protein A on the cell surface, could also be bound by the aptamer.

  18. In vitro Selection and Interaction Studies of a DNA Aptamer Targeting Protein A.

    Science.gov (United States)

    Stoltenburg, Regina; Schubert, Thomas; Strehlitz, Beate

    2015-01-01

    A new DNA aptamer targeting Protein A is presented. The aptamer was selected by use of the FluMag-SELEX procedure. The SELEX technology (Systematic Evolution of Ligands by EXponential enrichment) is widely applied as an in vitro selection and amplification method to generate target-specific aptamers and exists in various modified variants. FluMag-SELEX is one of them and is characterized by the use of magnetic beads for target immobilization and fluorescently labeled oligonucleotides for monitoring the aptamer selection progress. Structural investigations and sequence truncation experiments of the selected aptamer for Protein A led to the conclusion, that a stem-loop structure at its 5'-end including the 5'-primer binding site is essential for aptamer-target binding. Extensive interaction analyses between aptamer and Protein A were performed by methods like surface plasmon resonance, MicroScale Thermophoresis and bead-based binding assays using fluorescence measurements. The binding of the aptamer to its target was thus investigated in assays with immobilization of one of the binding partners each, and with both binding partners in solution. Affinity constants were determined in the low micromolar to submicromolar range, increasing to the nanomolar range under the assumption of avidity. Protein A provides more than one binding site for the aptamer, which may overlap with the known binding sites for immunoglobulins. The aptamer binds specifically to both native and recombinant Protein A, but not to other immunoglobulin-binding proteins like Protein G and L. Cross specificity to other proteins was not found. The application of the aptamer is directed to Protein A detection or affinity purification. Moreover, whole cells of Staphylococcus aureus, presenting Protein A on the cell surface, could also be bound by the aptamer.

  19. Prediction of protein modification sites of pyrrolidone carboxylic acid using mRMR feature selection and analysis.

    Directory of Open Access Journals (Sweden)

    Lu-Lu Zheng

    Full Text Available Pyrrolidone carboxylic acid (PCA is formed during a common post-translational modification (PTM of extracellular and multi-pass membrane proteins. In this study, we developed a new predictor to predict the modification sites of PCA based on maximum relevance minimum redundancy (mRMR and incremental feature selection (IFS. We incorporated 727 features that belonged to 7 kinds of protein properties to predict the modification sites, including sequence conservation, residual disorder, amino acid factor, secondary structure and solvent accessibility, gain/loss of amino acid during evolution, propensity of amino acid to be conserved at protein-protein interface and protein surface, and deviation of side chain carbon atom number. Among these 727 features, 244 features were selected by mRMR and IFS as the optimized features for the prediction, with which the prediction model achieved a maximum of MCC of 0.7812. Feature analysis showed that all feature types contributed to the modification process. Further site-specific feature analysis showed that the features derived from PCA's surrounding sites contributed more to the determination of PCA sites than other sites. The detailed feature analysis in this paper might provide important clues for understanding the mechanism of the PCA formation and guide relevant experimental validations.

  20. Heme-Protein Active Site Models via Self-Assembly in Water

    NARCIS (Netherlands)

    Fiammengo, R.; Wojciechowski, Kamil; Crego Calama, Mercedes; Figoli, A.; Wessling, Matthias; Reinhoudt, David; Timmerman, P.

    2003-01-01

    Water-soluble models of heme-protein active sites are obtained via the self-assembly of cationic porphyrins 1 and tetrasulfonato calix[4]arene 2 (K1·2 = 105 M-1). Selective binding of ligands either outside or inside the cavity of assemblies 1·2 via coordination to the zinc center has been observed.

  1. Protein selectivity with immobilized metal ion-tacn sorbents: chromatographic studies with human serum proteins and several other globular proteins.

    Science.gov (United States)

    Jiang, W; Graham, B; Spiccia, L; Hearn, M T

    1998-01-01

    The chromatographic selectivity of the immobilized chelate system, 1,4,7-triazocyclononane (tacn), complexed with the borderline metal ions Cu2+, Cr3+, Mn2+, Co2+, Zn2+, and Ni2+ has been investigated with hen egg white lysozyme, horse heart cytochrome c, and horse skeletal muscle myoglobin, as well as proteins present in partially fractionated preparations of human plasma. The effects of ionic strength and pH of the loading and elution buffers on protein selectivities of these new immobilized metal ion affinity chromatographic (IMAC) systems have been examined. The results confirm that immobilized Mn;pl-tacn sorbents exhibit a novel type of IMAC behavior with proteins. In particular, the chromatographic properties of these immobilized M(n+)-tacn ligand systems were significantly different compared to the IMAC behavior observed with other types of immobilized tri- and tetradentate chelating ligands, such as iminodiacetic acid, O-phosphoserine, or nitrilotriacetic acid, when complexed with borderline metal ions. The experimental results have consequently been evaluated in terms of the additional contributions to the interactive processes mediated by effects other than solely the conventional lone pair Lewis soft acid-Lewis soft base coordination interactions, typically found for the IMAC of proteins with borderline and soft metal ions, such as Cu2+ or Ni2+.

  2. Post-model selection inference and model averaging

    Directory of Open Access Journals (Sweden)

    Georges Nguefack-Tsague

    2011-07-01

    Full Text Available Although model selection is routinely used in practice nowadays, little is known about its precise effects on any subsequent inference that is carried out. The same goes for the effects induced by the closely related technique of model averaging. This paper is concerned with the use of the same data first to select a model and then to carry out inference, in particular point estimation and point prediction. The properties of the resulting estimator, called a post-model-selection estimator (PMSE, are hard to derive. Using selection criteria such as hypothesis testing, AIC, BIC, HQ and Cp, we illustrate that, in terms of risk function, no single PMSE dominates the others. The same conclusion holds more generally for any penalised likelihood information criterion. We also compare various model averaging schemes and show that no single one dominates the others in terms of risk function. Since PMSEs can be regarded as a special case of model averaging, with 0-1 random-weights, we propose a connection between the two theories, in the frequentist approach, by taking account of the selection procedure when performing model averaging. We illustrate the point by simulating a simple linear regression model.

  3. Automatic selection of reference taxa for protein-protein interaction prediction with phylogenetic profiling

    DEFF Research Database (Denmark)

    Simonsen, Martin; Maetschke, S.R.; Ragan, M.A.

    2012-01-01

    Motivation: Phylogenetic profiling methods can achieve good accuracy in predicting protein–protein interactions, especially in prokaryotes. Recent studies have shown that the choice of reference taxa (RT) is critical for accurate prediction, but with more than 2500 fully sequenced taxa publicly......: We present three novel methods for automating the selection of RT, using machine learning based on known protein–protein interaction networks. One of these methods in particular, Tree-Based Search, yields greatly improved prediction accuracies. We further show that different methods for constituting...... phylogenetic profiles often require very different RT sets to support high prediction accuracy....

  4. Comparative vesicle proteomics reveals selective regulation of protein expression in chestnut blight fungus by a hypovirus.

    Science.gov (United States)

    Wang, Jinzi; Wang, Fangzhen; Feng, Youjun; Mi, Ke; Chen, Qi; Shang, Jinjie; Chen, Baoshan

    2013-01-14

    The chestnut blight fungus (Cryphonectria parasitica) and hypovirus constitute a model system to study fungal pathogenesis and mycovirus-host interaction. Knowledge in this field has been gained largely from investigations at gene transcription level so far. Here we report a systematic analysis of the vesicle proteins of the host fungus with/without hypovirus infection. Thirty-three differentially expressed protein spots were identified in the purified vesicle protein samples by two-dimensional electrophoresis and mass spectrometry. Down-regulated proteins were mostly cargo proteins involved in primary metabolism and energy generation and up-regulated proteins were mostly vesicle associated proteins and ABC transporter. A virus-encoded protein p48 was found to have four forms with different molecular mass in vesicles from the virus-infected strain. While a few of the randomly selected differentially expressed proteins were in accordance with their transcription profiles, majority were not in agreement with their mRNA accumulation patterns, suggesting that an extensive post-transcriptional regulation may have occurred in the host fungus upon a hypovirus infection. Copyright © 2012 Elsevier B.V. All rights reserved.

  5. Protein model discrimination using mutational sensitivity derived from deep sequencing.

    Science.gov (United States)

    Adkar, Bharat V; Tripathi, Arti; Sahoo, Anusmita; Bajaj, Kanika; Goswami, Devrishi; Chakrabarti, Purbani; Swarnkar, Mohit K; Gokhale, Rajesh S; Varadarajan, Raghavan

    2012-02-08

    A major bottleneck in protein structure prediction is the selection of correct models from a pool of decoys. Relative activities of ∼1,200 individual single-site mutants in a saturation library of the bacterial toxin CcdB were estimated by determining their relative populations using deep sequencing. This phenotypic information was used to define an empirical score for each residue (RankScore), which correlated with the residue depth, and identify active-site residues. Using these correlations, ∼98% of correct models of CcdB (RMSD ≤ 4Å) were identified from a large set of decoys. The model-discrimination methodology was further validated on eleven different monomeric proteins using simulated RankScore values. The methodology is also a rapid, accurate way to obtain relative activities of each mutant in a large pool and derive sequence-structure-function relationships without protein isolation or characterization. It can be applied to any system in which mutational effects can be monitored by a phenotypic readout. Copyright © 2012 Elsevier Ltd. All rights reserved.

  6. Application of model bread baking in the examination of arabinoxylan-protein complexes in rye bread.

    Science.gov (United States)

    Buksa, Krzysztof

    2016-09-05

    The changes in molecular mass of arabinoxylan (AX) and protein caused by bread baking process were examined using a model rye bread. Instead of the normal flour, the dough contained starch, water-extractable AX and protein which were isolated from rye wholemeal. From the crumb of selected model breads, starch was removed releasing AX-protein complexes, which were further examined by size exclusion chromatography. On the basis of the research, it was concluded that optimum model mix can be composed of 3-6% AX and 3-6% rye protein isolate at 94-88% of rye starch meaning with the most similar properties to low extraction rye flour. Application of model rye bread allowed to examine the interactions between AX and proteins. Bread baked with a share of AX, rye protein and starch, from which the complexes of the highest molar mass were isolated, was characterized by the strongest structure of the bread crumb. Copyright © 2016 Elsevier Ltd. All rights reserved.

  7. A KINETIC MODEL FOR MONO-LAYER GLOBULAR PROTEIN ADSORPTION ON SOLID/LIQUID INTERFACES

    Directory of Open Access Journals (Sweden)

    Kamal I. M. Al-Malah

    2012-12-01

    Full Text Available A kinetic model was derived for globular protein adsorption. The model takes into account the three possible scenarios of a protein molecule in solution, being exposed to an interface: adsorption step from the solution to the interface; the possible desorption back into the solution; and the surface-induced unfolding or spreading of the protein unto the substrate surface. A globular protein molecule is visualized as a sphere with radius D. In addition to the general case of protein adsorption, which portrays either the surface coverage (Theta or surface concentration (� as a function of the adsorption time, special cases, like equilibrium condition, lowsurface coverage, irreversible, and Langmuirian were also presented and treated in light of the derived model. The general model was simplified for each of the subset cases. The irreversibility versus reversibility of protein adsorption was discussed. The substrate surface energetics or effects are accounted for via the proposition of the percent relative change in D/V ratio for the adsorbing protein, called (D/VPRC parameter. (D/VPRC is calculated with respect to the monolayer surface concentration of protein, where the latter is given by D/Vratio. This can be used as a landmark to protein adsorption isotherms or even kinetics. This is visualized as an indicator for solid substrate effects on the adsorbing proteins. (D/VPRC can be zero (fresh monolayer, negative (aged monolayer, or positive (multi-layer. The reference surface concentration is reported for some selected proteins.

  8. Site-selective 13C labeling of proteins using erythrose

    International Nuclear Information System (INIS)

    Weininger, Ulrich

    2017-01-01

    NMR-spectroscopy enables unique experimental studies on protein dynamics at atomic resolution. In order to obtain a full atom view on protein dynamics, and to study specific local processes like ring-flips, proton-transfer, or tautomerization, one has to perform studies on amino-acid side chains. A key requirement for these studies is site-selective labeling with 13 C and/or 1 H, which is achieved in the most general way by using site-selectively 13 C-enriched glucose (1- and 2- 13 C) as the carbon source in bacterial expression systems. Using this strategy, multiple sites in side chains, including aromatics, become site-selectively labeled and suitable for relaxation studies. Here we systematically investigate the use of site-selectively 13 C-enriched erythrose (1-, 2-, 3- and 4- 13 C) as a suitable precursor for 13 C labeled aromatic side chains. We quantify 13 C incorporation in nearly all sites in all 20 amino acids and compare the results to glucose based labeling. In general the erythrose approach results in more selective labeling. While there is only a minor gain for phenylalanine and tyrosine side-chains, the 13 C incorporation level for tryptophan is at least doubled. Additionally, the Phe ζ and Trp η2 positions become labeled. In the aliphatic side chains, labeling using erythrose yields isolated 13 C labels for certain positions, like Ile β and His β, making these sites suitable for dynamics studies. Using erythrose instead of glucose as a source for site-selective 13 C labeling enables unique or superior labeling for certain positions and is thereby expanding the toolbox for customized isotope labeling of amino-acid side-chains.

  9. A Heckman Selection- t Model

    KAUST Repository

    Marchenko, Yulia V.

    2012-03-01

    Sample selection arises often in practice as a result of the partial observability of the outcome of interest in a study. In the presence of sample selection, the observed data do not represent a random sample from the population, even after controlling for explanatory variables. That is, data are missing not at random. Thus, standard analysis using only complete cases will lead to biased results. Heckman introduced a sample selection model to analyze such data and proposed a full maximum likelihood estimation method under the assumption of normality. The method was criticized in the literature because of its sensitivity to the normality assumption. In practice, data, such as income or expenditure data, often violate the normality assumption because of heavier tails. We first establish a new link between sample selection models and recently studied families of extended skew-elliptical distributions. Then, this allows us to introduce a selection-t (SLt) model, which models the error distribution using a Student\\'s t distribution. We study its properties and investigate the finite-sample performance of the maximum likelihood estimators for this model. We compare the performance of the SLt model to the conventional Heckman selection-normal (SLN) model and apply it to analyze ambulatory expenditures. Unlike the SLNmodel, our analysis using the SLt model provides statistical evidence for the existence of sample selection bias in these data. We also investigate the performance of the test for sample selection bias based on the SLt model and compare it with the performances of several tests used with the SLN model. Our findings indicate that the latter tests can be misleading in the presence of heavy-tailed data. © 2012 American Statistical Association.

  10. Evidence of positive selection at codon sites localized in extracellular domains of mammalian CC motif chemokine receptor proteins

    Directory of Open Access Journals (Sweden)

    Metzger Kelsey J

    2010-05-01

    Full Text Available Abstract Background CC chemokine receptor proteins (CCR1 through CCR10 are seven-transmembrane G-protein coupled receptors whose signaling pathways are known for their important roles coordinating immune system responses through targeted trafficking of white blood cells. In addition, some of these receptors have been identified as fusion proteins for viral pathogens: for example, HIV-1 strains utilize CCR5, CCR2 and CCR3 proteins to obtain cellular entry in humans. The extracellular domains of these receptor proteins are involved in ligand-binding specificity as well as pathogen recognition interactions. In mammals, the majority of chemokine receptor genes are clustered together; in humans, seven of the ten genes are clustered in the 3p21-24 chromosome region. Gene conversion events, or exchange of DNA sequence between genes, have been reported in chemokine receptor paralogs in various mammalian lineages, especially between the cytogenetically closely located pairs CCR2/5 and CCR1/3. Datasets of mammalian orthologs for each gene were analyzed separately to minimize the potential confounding impact of analyzing highly similar sequences resulting from gene conversion events. Molecular evolution approaches and the software package Phylogenetic Analyses by Maximum Likelihood (PAML were utilized to investigate the signature of selection that has acted on the mammalian CC chemokine receptor (CCR gene family. The results of neutral vs. adaptive evolution (positive selection hypothesis testing using Site Models are reported. In general, positive selection is defined by a ratio of nonsynonymous/synonymous nucleotide changes (dN/dS, or ω >1. Results Of the ten mammalian CC motif chemokine receptor sequence datasets analyzed, only CCR2 and CCR3 contain amino acid codon sites that exhibit evidence of positive selection using site based hypothesis testing in PAML. Nineteen of the twenty codon sites putatively indentified as likely to be under positive

  11. Completion of autobuilt protein models using a database of protein fragments

    International Nuclear Information System (INIS)

    Cowtan, Kevin

    2012-01-01

    Two developments in the process of automated protein model building in the Buccaneer software are described: the use of a database of protein fragments in improving the model completeness and the assembly of disconnected chain fragments into complete molecules. Two developments in the process of automated protein model building in the Buccaneer software are presented. A general-purpose library for protein fragments of arbitrary size is described, with a highly optimized search method allowing the use of a larger database than in previous work. The problem of assembling an autobuilt model into complete chains is discussed. This involves the assembly of disconnected chain fragments into complete molecules and the use of the database of protein fragments in improving the model completeness. Assembly of fragments into molecules is a standard step in existing model-building software, but the methods have not received detailed discussion in the literature

  12. Investigating energy-based pool structure selection in the structure ensemble modeling with experimental distance constraints: The example from a multidomain protein Pub1.

    Science.gov (United States)

    Zhu, Guanhua; Liu, Wei; Bao, Chenglong; Tong, Dudu; Ji, Hui; Shen, Zuowei; Yang, Daiwen; Lu, Lanyuan

    2018-05-01

    The structural variations of multidomain proteins with flexible parts mediate many biological processes, and a structure ensemble can be determined by selecting a weighted combination of representative structures from a simulated structure pool, producing the best fit to experimental constraints such as interatomic distance. In this study, a hybrid structure-based and physics-based atomistic force field with an efficient sampling strategy is adopted to simulate a model di-domain protein against experimental paramagnetic relaxation enhancement (PRE) data that correspond to distance constraints. The molecular dynamics simulations produce a wide range of conformations depicted on a protein energy landscape. Subsequently, a conformational ensemble recovered with low-energy structures and the minimum-size restraint is identified in good agreement with experimental PRE rates, and the result is also supported by chemical shift perturbations and small-angle X-ray scattering data. It is illustrated that the regularizations of energy and ensemble-size prevent an arbitrary interpretation of protein conformations. Moreover, energy is found to serve as a critical control to refine the structure pool and prevent data overfitting, because the absence of energy regularization exposes ensemble construction to the noise from high-energy structures and causes a more ambiguous representation of protein conformations. Finally, we perform structure-ensemble optimizations with a topology-based structure pool, to enhance the understanding on the ensemble results from different sources of pool candidates. © 2018 Wiley Periodicals, Inc.

  13. Insight into the intermolecular recognition mechanism between Keap1 and IKKβ combining homology modelling, protein-protein docking, molecular dynamics simulations and virtual alanine mutation.

    Directory of Open Access Journals (Sweden)

    Zheng-Yu Jiang

    Full Text Available Degradation of certain proteins through the ubiquitin-proteasome pathway is a common strategy taken by the key modulators responsible for stress responses. Kelch-like ECH-associated protein-1(Keap1, a substrate adaptor component of the Cullin3 (Cul3-based ubiquitin E3 ligase complex, mediates the ubiquitination of two key modulators, NF-E2-related factor 2 (Nrf2 and IκB kinase β (IKKβ, which are involved in the redox control of gene transcription. However, compared to the Keap1-Nrf2 protein-protein interaction (PPI, the intermolecular recognition mechanism of Keap1 and IKKβ has been poorly investigated. In order to explore the binding pattern between Keap1 and IKKβ, the PPI model of Keap1 and IKKβ was investigated. The structure of human IKKβ was constructed by means of the homology modeling method and using reported crystal structure of Xenopus laevis IKKβ as the template. A protein-protein docking method was applied to develop the Keap1-IKKβ complex model. After the refinement and visual analysis of docked proteins, the chosen pose was further optimized through molecular dynamics simulations. The resulting structure was utilized to conduct the virtual alanine mutation for the exploration of hot-spots significant for the intermolecular interaction. Overall, our results provided structural insights into the PPI model of Keap1-IKKβ and suggest that the substrate specificity of Keap1 depend on the interaction with the key tyrosines, namely Tyr525, Tyr574 and Tyr334. The study presented in the current project may be useful to design molecules that selectively modulate Keap1. The selective recognition mechanism of Keap1 with IKKβ or Nrf2 will be helpful to further know the crosstalk between NF-κB and Nrf2 signaling.

  14. Selective autophagy of non-ubiquitylated targets in plants: looking for cognate receptor/adaptor proteins

    Directory of Open Access Journals (Sweden)

    Vasko eVeljanovski

    2014-06-01

    Full Text Available Cellular homeostasis is essential for the physiology of eukaryotic cells. Eukaryotic cells, including plant cells, utilize two main pathways to adjust the level of cytoplasmic components, namely the proteasomal and the lysosomal/vacuolar pathways. Macroautophagy is a lysosomal/vacuolar pathway which, until recently, was thought to be non-specific and a bulk degradation process. However, selective autophagy which can be activated in the cell under various physiological conditions, involves the specific degradation of defined macromolecules or organelles by a conserved molecular mechanism. For this process to be efficient, the mechanisms underlying the recognition and selection of the cargo to be engulfed by the double-membrane autophagosome are critical, and not yet well understood. Ubiquitin (poly-ubiquitin conjugation to the target appears to be a conserved ligand mechanism in many types of selective autophagy, and defined receptors/adaptors recognizing and regulating the autophagosomal capture of the ubiquitylated target have been characterized. However, non-proteinaceous and non-ubiquitylated cargoes are also selectively degraded by this pathway. This ubiquitin-independent selective autophagic pathway also involves receptor and/or adaptor proteins linking the cargo to the autophagic machinery. Some of these receptor/adaptor proteins including accessory autophagy-related (Atg and non-Atg proteins have been described in yeast and animal cells but not yet in plants. In this review we discuss the ubiquitin-independent cargo selection mechanisms in selective autophagy degradation of organelles and macromolecules and speculate on potential plant receptor/adaptor proteins.

  15. Changing folding and binding stability in a viral coat protein: a comparison between substitutions accessible through mutation and those fixed by natural selection.

    Science.gov (United States)

    Miller, Craig R; Lee, Kuo Hao; Wichman, Holly A; Ytreberg, F Marty

    2014-01-01

    Previous studies have shown that most random amino acid substitutions destabilize protein folding (i.e. increase the folding free energy). No analogous studies have been carried out for protein-protein binding. Here we use a structure-based model of the major coat protein in a simple virus, bacteriophage φX174, to estimate the free energy of folding of a single coat protein and binding of five coat proteins within a pentameric unit. We confirm and extend previous work in finding that most accessible substitutions destabilize both protein folding and protein-protein binding. We compare the pool of accessible substitutions with those observed among the φX174-like wild phage and in experimental evolution with φX174. We find that observed substitutions have smaller effects on stability than expected by chance. An analysis of adaptations at high temperatures suggests that selection favors either substitutions with no effect on stability or those that simultaneously stabilize protein folding and slightly destabilize protein binding. We speculate that these mutations might involve adjusting the rate of capsid assembly. At normal laboratory temperature there is little evidence of directional selection. Finally, we show that cumulative changes in stability are highly variable; sometimes they are well beyond the bounds of single substitution changes and sometimes they are not. The variation leads us to conclude that phenotype selection acts on more than just stability. Instances of larger cumulative stability change (never via a single substitution despite their availability) lead us to conclude that selection views stability at a local, not a global, level.

  16. Effect of dataset selection on the topological interpretation of protein interaction networks

    Directory of Open Access Journals (Sweden)

    Robertson David L

    2005-09-01

    Full Text Available Abstract Background Studies of the yeast protein interaction network have revealed distinct correlations between the connectivity of individual proteins within the network and the average connectivity of their neighbours. Although a number of biological mechanisms have been proposed to account for these findings, the significance and influence of the specific datasets included in these studies has not been appreciated adequately. Results We show how the use of different interaction data sets, such as those resulting from high-throughput or small-scale studies, and different modelling methodologies for the derivation pair-wise protein interactions, can dramatically change the topology of these networks. Furthermore, we show that some of the previously reported features identified in these networks may simply be the result of experimental or methodological errors and biases. Conclusion When performing network-based studies, it is essential to define what is meant by the term "interaction" and this must be taken into account when interpreting the topologies of the networks generated. Consideration must be given to the type of data included and appropriate controls that take into account the idiosyncrasies of the data must be selected

  17. Genetic variability and natural selection at the ligand domain of the Duffy binding protein in brazilian Plasmodium vivax populations

    Directory of Open Access Journals (Sweden)

    Gil Luiz HS

    2010-11-01

    Full Text Available Abstract Background Plasmodium vivax malaria is a major public health challenge in Latin America, Asia and Oceania, with 130-435 million clinical cases per year worldwide. Invasion of host blood cells by P. vivax mainly depends on a type I membrane protein called Duffy binding protein (PvDBP. The erythrocyte-binding motif of PvDBP is a 170 amino-acid stretch located in its cysteine-rich region II (PvDBPII, which is the most variable segment of the protein. Methods To test whether diversifying natural selection has shaped the nucleotide diversity of PvDBPII in Brazilian populations, this region was sequenced in 122 isolates from six different geographic areas. A Bayesian method was applied to test for the action of natural selection under a population genetic model that incorporates recombination. The analysis was integrated with a structural model of PvDBPII, and T- and B-cell epitopes were localized on the 3-D structure. Results The results suggest that: (i recombination plays an important role in determining the haplotype structure of PvDBPII, and (ii PvDBPII appears to contain neutrally evolving codons as well as codons evolving under natural selection. Diversifying selection preferentially acts on sites identified as epitopes, particularly on amino acid residues 417, 419, and 424, which show strong linkage disequilibrium. Conclusions This study shows that some polymorphisms of PvDBPII are present near the erythrocyte-binding domain and might serve to elude antibodies that inhibit cell invasion. Therefore, these polymorphisms should be taken into account when designing vaccines aimed at eliciting antibodies to inhibit erythrocyte invasion.

  18. Selection of unadapted, pathogenic SHIVs encoding newly transmitted HIV-1 envelope proteins.

    Science.gov (United States)

    Del Prete, Gregory Q; Ailers, Braiden; Moldt, Brian; Keele, Brandon F; Estes, Jacob D; Rodriguez, Anthony; Sampias, Marissa; Oswald, Kelli; Fast, Randy; Trubey, Charles M; Chertova, Elena; Smedley, Jeremy; LaBranche, Celia C; Montefiori, David C; Burton, Dennis R; Shaw, George M; Markowitz, Marty; Piatak, Michael; KewalRamani, Vineet N; Bieniasz, Paul D; Lifson, Jeffrey D; Hatziioannou, Theodora

    2014-09-10

    Infection of macaques with chimeric viruses based on SIVMAC but expressing the HIV-1 envelope (Env) glycoproteins (SHIVs) remains the most powerful model for evaluating prevention and therapeutic strategies against AIDS. Unfortunately, only a few SHIVs are currently available. Furthermore, their generation has required extensive adaptation of the HIV-1 Env sequences in macaques so they may not accurately represent HIV-1 Env proteins circulating in humans, potentially limiting their translational utility. We developed a strategy for generating large numbers of SHIV constructs expressing Env proteins from newly transmitted HIV-1 strains. By inoculating macaques with cocktails of multiple SHIV variants, we selected SHIVs that can replicate and cause AIDS-like disease in immunologically intact rhesus macaques without requiring animal-to-animal passage. One of these SHIVs could be transmitted mucosally. We demonstrate the utility of the SHIVs generated by this method for evaluating neutralizing antibody administration as a protection against mucosal SHIV challenge. Copyright © 2014 Elsevier Inc. All rights reserved.

  19. Tuning a Protein-Labeling Reaction to Achieve Highly Site Selective Lysine Conjugation.

    Science.gov (United States)

    Pham, Grace H; Ou, Weijia; Bursulaya, Badry; DiDonato, Michael; Herath, Ananda; Jin, Yunho; Hao, Xueshi; Loren, Jon; Spraggon, Glen; Brock, Ansgar; Uno, Tetsuo; Geierstanger, Bernhard H; Cellitti, Susan E

    2018-04-16

    Activated esters are widely used to label proteins at lysine side chains and N termini. These reagents are useful for labeling virtually any protein, but robust reactivity toward primary amines generally precludes site-selective modification. In a unique case, fluorophenyl esters are shown to preferentially label human kappa antibodies at a single lysine (Lys188) within the light-chain constant domain. Neighboring residues His189 and Asp151 contribute to the accelerated rate of labeling at Lys188 relative to the ≈40 other lysine sites. Enriched Lys188 labeling can be enhanced from 50-70 % to >95 % by any of these approaches: lowering reaction temperature, applying flow chemistry, or mutagenesis of specific residues in the surrounding protein environment. Our results demonstrated that activated esters with fluoro-substituted aromatic leaving groups, including a fluoronaphthyl ester, can be generally useful reagents for site-selective lysine labeling of antibodies and other immunoglobulin-type proteins. © 2018 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.

  20. In silico modeling of the yeast protein and protein family interaction network

    Science.gov (United States)

    Goh, K.-I.; Kahng, B.; Kim, D.

    2004-03-01

    Understanding of how protein interaction networks of living organisms have evolved or are organized can be the first stepping stone in unveiling how life works on a fundamental ground. Here we introduce an in silico ``coevolutionary'' model for the protein interaction network and the protein family network. The essential ingredient of the model includes the protein family identity and its robustness under evolution, as well as the three previously proposed: gene duplication, divergence, and mutation. This model produces a prototypical feature of complex networks in a wide range of parameter space, following the generalized Pareto distribution in connectivity. Moreover, we investigate other structural properties of our model in detail with some specific values of parameters relevant to the yeast Saccharomyces cerevisiae, showing excellent agreement with the empirical data. Our model indicates that the physical constraints encoded via the domain structure of proteins play a crucial role in protein interactions.

  1. Strong Selection Significantly Increases Epistatic Interactions in the Long-Term Evolution of a Protein.

    Directory of Open Access Journals (Sweden)

    Aditi Gupta

    2016-03-01

    Full Text Available Epistatic interactions between residues determine a protein's adaptability and shape its evolutionary trajectory. When a protein experiences a changed environment, it is under strong selection to find a peak in the new fitness landscape. It has been shown that strong selection increases epistatic interactions as well as the ruggedness of the fitness landscape, but little is known about how the epistatic interactions change under selection in the long-term evolution of a protein. Here we analyze the evolution of epistasis in the protease of the human immunodeficiency virus type 1 (HIV-1 using protease sequences collected for almost a decade from both treated and untreated patients, to understand how epistasis changes and how those changes impact the long-term evolvability of a protein. We use an information-theoretic proxy for epistasis that quantifies the co-variation between sites, and show that positive information is a necessary (but not sufficient condition that detects epistasis in most cases. We analyze the "fossils" of the evolutionary trajectories of the protein contained in the sequence data, and show that epistasis continues to enrich under strong selection, but not for proteins whose environment is unchanged. The increase in epistasis compensates for the information loss due to sequence variability brought about by treatment, and facilitates adaptation in the increasingly rugged fitness landscape of treatment. While epistasis is thought to enhance evolvability via valley-crossing early-on in adaptation, it can hinder adaptation later when the landscape has turned rugged. However, we find no evidence that the HIV-1 protease has reached its potential for evolution after 9 years of adapting to a drug environment that itself is constantly changing. We suggest that the mechanism of encoding new information into pairwise interactions is central to protein evolution not just in HIV-1 protease, but for any protein adapting to a changing

  2. Rats free to select between pure protein and a fat-carbohydrate mix ingest high-protein mixed meals during the dark period and protein meals during the light period.

    Science.gov (United States)

    Makarios-Lahham, Lina; Roseau, Suzanne M; Fromentin, Gilles; Tome, Daniel; Even, Patrick C

    2004-03-01

    Rats that are allowed to select their diets [dietary self- selection (DSS)] often ingest >30% of their daily energy in the form of protein. Such an intake may seem unhealthy, but the consistency of this choice suggests that it is motivated by physiologic drives. To gain a clearer understanding of how protein selection is structured during DSS, we adapted 12 rats to a standard diet (14% Protein) and then allowed them to choose between two diets, i.e., total milk protein (P) and a mix of carbohydrates and lipids (FC). The protein intake during DSS rose above 40%; assuming an intermeal interval of 10 min, 70% of the energy intake occurred with meals that included both P and FC, with the sequence of FC followed by P preferred to the sequence of P followed by FC (70 vs. 30%, P energy intake during the light period was reduced to only 10% of the daily energy intake [vs. 30% with the control P14 diet or a with a high-protein diet (50%)], and 90% of the intake was in the form of pure protein meals. In complementary studies, we verified that the high protein intake also occurred when rats were offered casein and whey and was not due to the high palatability of the milk protein. We conclude that a specific feeding pattern accompanies high protein intake in rats allowed DSS. The mechanisms underlying this behavior and its potential beneficial/adverse consequences over the long term still must be clarified.

  3. Measurement of conformational constraints in an elastin-mimetic protein by residue-pair selected solid-state NMR

    International Nuclear Information System (INIS)

    Hong, Mei; McMillan, R. Andrew; Conticello, Vincent P.

    2002-01-01

    We introduce a solid-state NMR technique for selective detection of a residue pair in multiply labeled proteins to obtain site-specific structural constraints. The method exploits the frequency-offset dependence of cross polarization to achieve 13 CO i → 15 N i → 13 Cα i transfer between two residues. A 13 C, 15 N-labeled elastin mimetic protein (VPGVG) n is used to demonstrate the method. The technique selected the Gly3 Cα signal while suppressing the Gly5 Cα signal, and allowed the measurement of the Gly3 Cα chemical shift anisotropy to derive information on the protein conformation. This residue-pair selection technique should simplify the study of protein structure at specific residues

  4. Model selection in periodic autoregressions

    NARCIS (Netherlands)

    Ph.H.B.F. Franses (Philip Hans); R. Paap (Richard)

    1994-01-01

    textabstractThis paper focuses on the issue of period autoagressive time series models (PAR) selection in practice. One aspect of model selection is the choice for the appropriate PAR order. This can be of interest for the valuation of economic models. Further, the appropriate PAR order is important

  5. A Mesoscopic Model for Protein-Protein Interactions in Solution

    OpenAIRE

    Lund, Mikael; Jönsson, Bo

    2003-01-01

    Protein self-association may be detrimental in biological systems, but can be utilized in a controlled fashion for protein crystallization. It is hence of considerable interest to understand how factors like solution conditions prevent or promote aggregation. Here we present a computational model describing interactions between protein molecules in solution. The calculations are based on a molecular description capturing the detailed structure of the protein molecule using x-ray or nuclear ma...

  6. Sexual selection and the adaptive evolution of PKDREJ protein in primates and rodents.

    Science.gov (United States)

    Vicens, Alberto; Gómez Montoto, Laura; Couso-Ferrer, Francisco; Sutton, Keith A; Roldan, Eduardo R S

    2015-02-01

    PKDREJ is a testis-specific protein thought to be located on the sperm surface. Functional studies in the mouse revealed that loss of PKDREJ has effects on sperm transport and the ability to undergo an induced acrosome reaction. Thus, PKDREJ has been considered a potential target of post-copulatory sexual selection in the form of sperm competition. Proteins involved in reproductive processes often show accelerated evolution. In many cases, this rapid divergence is promoted by positive selection which may be driven, at least in part, by post-copulatory sexual selection. We analysed the evolution of the PKDREJ protein in primates and rodents and assessed whether PKDREJ divergence is associated with testes mass relative to body mass, which is a reliable proxy of sperm competition levels. Evidence of an association between the evolutionary rate of the PKDREJ gene and testes mass relative to body mass was not found in primates. Among rodents, evidence of positive selection was detected in the Pkdrej gene in the family Cricetidae but not in Muridae. We then assessed whether Pkdrej divergence is associated with episodes of sperm competition in these families. We detected a positive significant correlation between the evolutionary rates of Pkdrej and testes mass relative to body mass in cricetids. These findings constitute the first evidence of post-copulatory sexual selection influencing the evolution of a protein that participates in the mechanisms regulating sperm transport and the acrosome reaction, strongly suggesting that positive selection may act on these fertilization steps, leading to advantages in situations of sperm competition. © The Author 2014. Published by Oxford University Press on behalf of the European Society of Human Reproduction and Embryology. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

  7. A versatile selection system for folding competent proteins using genetic complementation in a eukaryotic host

    DEFF Research Database (Denmark)

    Lyngsø, C.; Kjaerulff, S.; Muller, S.

    2010-01-01

    in vivo selection system for folded proteins. It is based on genetic complementation of the Schizosaccharomyces pombe growth marker gene invertase fused C-terminally to a protein library. The fusion proteins are directed to the secretion system, utilizing the ability of the eukaryotic protein quality...

  8. A feature-based approach to modeling protein–protein interaction hot spots

    Science.gov (United States)

    Cho, Kyu-il; Kim, Dongsup; Lee, Doheon

    2009-01-01

    Identifying features that effectively represent the energetic contribution of an individual interface residue to the interactions between proteins remains problematic. Here, we present several new features and show that they are more effective than conventional features. By combining the proposed features with conventional features, we develop a predictive model for interaction hot spots. Initially, 54 multifaceted features, composed of different levels of information including structure, sequence and molecular interaction information, are quantified. Then, to identify the best subset of features for predicting hot spots, feature selection is performed using a decision tree. Based on the selected features, a predictive model for hot spots is created using support vector machine (SVM) and tested on an independent test set. Our model shows better overall predictive accuracy than previous methods such as the alanine scanning methods Robetta and FOLDEF, and the knowledge-based method KFC. Subsequent analysis yields several findings about hot spots. As expected, hot spots have a larger relative surface area burial and are more hydrophobic than other residues. Unexpectedly, however, residue conservation displays a rather complicated tendency depending on the types of protein complexes, indicating that this feature is not good for identifying hot spots. Of the selected features, the weighted atomic packing density, relative surface area burial and weighted hydrophobicity are the top 3, with the weighted atomic packing density proving to be the most effective feature for predicting hot spots. Notably, we find that hot spots are closely related to π–related interactions, especially π · · · π interactions. PMID:19273533

  9. Prediction of protein-protein interactions between viruses and human by an SVM model

    Directory of Open Access Journals (Sweden)

    Cui Guangyu

    2012-05-01

    Full Text Available Abstract Background Several computational methods have been developed to predict protein-protein interactions from amino acid sequences, but most of those methods are intended for the interactions within a species rather than for interactions across different species. Methods for predicting interactions between homogeneous proteins are not appropriate for finding those between heterogeneous proteins since they do not distinguish the interactions between proteins of the same species from those of different species. Results We developed a new method for representing a protein sequence of variable length in a frequency vector of fixed length, which encodes the relative frequency of three consecutive amino acids of a sequence. We built a support vector machine (SVM model to predict human proteins that interact with virus proteins. In two types of viruses, human papillomaviruses (HPV and hepatitis C virus (HCV, our SVM model achieved an average accuracy above 80%, which is higher than that of another SVM model with a different representation scheme. Using the SVM model and Gene Ontology (GO annotations of proteins, we predicted new interactions between virus proteins and human proteins. Conclusions Encoding the relative frequency of amino acid triplets of a protein sequence is a simple yet powerful representation method for predicting protein-protein interactions across different species. The representation method has several advantages: (1 it enables a prediction model to achieve a better performance than other representations, (2 it generates feature vectors of fixed length regardless of the sequence length, and (3 the same representation is applicable to different types of proteins.

  10. A Primer for Model Selection: The Decisive Role of Model Complexity

    Science.gov (United States)

    Höge, Marvin; Wöhling, Thomas; Nowak, Wolfgang

    2018-03-01

    Selecting a "best" model among several competing candidate models poses an often encountered problem in water resources modeling (and other disciplines which employ models). For a modeler, the best model fulfills a certain purpose best (e.g., flood prediction), which is typically assessed by comparing model simulations to data (e.g., stream flow). Model selection methods find the "best" trade-off between good fit with data and model complexity. In this context, the interpretations of model complexity implied by different model selection methods are crucial, because they represent different underlying goals of modeling. Over the last decades, numerous model selection criteria have been proposed, but modelers who primarily want to apply a model selection criterion often face a lack of guidance for choosing the right criterion that matches their goal. We propose a classification scheme for model selection criteria that helps to find the right criterion for a specific goal, i.e., which employs the correct complexity interpretation. We identify four model selection classes which seek to achieve high predictive density, low predictive error, high model probability, or shortest compression of data. These goals can be achieved by following either nonconsistent or consistent model selection and by either incorporating a Bayesian parameter prior or not. We allocate commonly used criteria to these four classes, analyze how they represent model complexity and what this means for the model selection task. Finally, we provide guidance on choosing the right type of criteria for specific model selection tasks. (A quick guide through all key points is given at the end of the introduction.)

  11. Selective cell-surface labeling of the molecular motor protein prestin

    International Nuclear Information System (INIS)

    McGuire, Ryan M.; Silberg, Jonathan J.; Pereira, Fred A.; Raphael, Robert M.

    2011-01-01

    Highlights: → Trafficking to the plasma membrane is required for prestin function. → Biotin acceptor peptide (BAP) was fused to prestin through a transmembrane domain. → BAP-prestin can be metabolically labeled with biotin in HEK293 cells. → Biotin-BAP-prestin allows for selective imaging of fully trafficked prestin. → The biotin-BAP-prestin displays voltage-sensitive activity. -- Abstract: Prestin, a multipass transmembrane protein whose N- and C-termini are localized to the cytoplasm, must be trafficked to the plasma membrane to fulfill its cellular function as a molecular motor. One challenge in studying prestin sequence-function relationships within living cells is separating the effects of amino acid substitutions on prestin trafficking, plasma membrane localization and function. To develop an approach for directly assessing prestin levels at the plasma membrane, we have investigated whether fusion of prestin to a single pass transmembrane protein results in a functional fusion protein with a surface-exposed N-terminal tag that can be detected in living cells. We find that fusion of the biotin-acceptor peptide (BAP) and transmembrane domain of the platelet-derived growth factor receptor (PDGFR) to the N-terminus of prestin-GFP yields a membrane protein that can be metabolically-labeled with biotin, trafficked to the plasma membrane, and selectively detected at the plasma membrane using fluorescently-tagged streptavidin. Furthermore, we show that the addition of a surface detectable tag and a single-pass transmembrane domain to prestin does not disrupt its voltage-sensitive activity.

  12. Fast and Selective Modification of Thiol Proteins/Peptides by N-(Phenylseleno)phthalimide

    Science.gov (United States)

    Wang, Zhengfang; Zhang, Yun; Zhang, Hao; Harrington, Peter B.; Chen, Hao

    2012-03-01

    We previously reported that selenamide reagents such as ebselen and N-(phenylseleno)phthalimide (NPSP) can be used to selectively derivatize thiols for mass spectrometric analysis, and the introduced selenium tags are useful as they could survive or removed with collision-induced dissociation (CID). Described herein is the further study of the reactivity of various protein/peptide thiols toward NPSP and its application to derivatize thiol peptides in protein digests. With a modified protocol (i.e., dissolving NPSP in acetonitrile instead of aqueous solvent), we found that quantitative conversion of thiols can be obtained in seconds, using NPSP in a slight excess amount (NPSP:thiol of 1.1-2:1). Further investigation shows that the thiol reactivity toward NPSP reflects its chemical environment and accessibility in proteins/peptides. For instance, adjacent basic amino acid residues increase the thiol reactivity, probably because they could stabilize the thiolate form to facilitate the nucleophilic attack of thiol on NPSP. In the case of creatine phosphokinase, the native protein predominately has one thiol reacted with NPSP while all of four thiol groups of the denatured protein can be derivatized, in accordance with the corresponding protein conformation. In addition, thiol peptides in protein/peptide enzymatic digests can be quickly and effectively tagged by NPSP following tri- n-butylphosphine (TBP) reduction. Notably, all three thiols of the peptide QCCASVCSL in the insulin peptic digest can be modified simultaneously by NPSP. These results suggest a novel and selective method for protecting thiols in the bottom-up approach for protein structure analysis.

  13. Positive selection and propeptide repeats promote rapid interspecific divergence of a gastropod sperm protein.

    Science.gov (United States)

    Hellberg, M E; Moy, G W; Vacquier, V D

    2000-03-01

    Male-specific proteins have increasingly been reported as targets of positive selection and are of special interest because of the role they may play in the evolution of reproductive isolation. We report the rapid interspecific divergence of cDNA encoding a major acrosomal protein of unknown function (TMAP) of sperm from five species of teguline gastropods. A mitochondrial DNA clock (calibrated by congeneric species divided by the Isthmus of Panama) estimates that these five species diverged 2-10 MYA. Inferred amino acid sequences reveal a propeptide that has diverged rapidly between species. The mature protein has diverged faster still due to high nonsynonymous substitution rates (> 25 nonsynonymous substitutions per site per 10(9) years). cDNA encoding the mature protein (89-100 residues) shows evidence of positive selection (Dn/Ds > 1) for 4 of 10 pairwise species comparisons. cDNA and predicted secondary-structure comparisons suggest that TMAP is neither orthologous nor paralogous to abalone lysin, and thus marks a second, phylogenetically independent, protein subject to strong positive selection in free-spawning marine gastropods. In addition, an internal repeat in one species (Tegula aureotincta) produces a duplicated cleavage site which results in two alternatively processed mature proteins differing by nine amino acid residues. Such alternative processing may provide a mechanism for introducing novel amino acid sequence variation at the amino-termini of proteins. Highly divergent TMAP N-termini from two other tegulines (Tegula regina and Norrisia norrisii) may have originated by such a mechanism.

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

    Directory of Open Access Journals (Sweden)

    Lei Jia

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

  15. Molecular Mechanism of Selectivity among G Protein-Coupled Receptor Kinase 2 Inhibitors

    Energy Technology Data Exchange (ETDEWEB)

    Thal, David M.; Yeow, Raymond Y.; Schoenau, Christian; Huber, Jochen; Tesmer, John J.G. (Sanofi); (Michigan)

    2012-07-11

    G protein-coupled receptors (GPCRs) are key regulators of cell physiology and control processes ranging from glucose homeostasis to contractility of the heart. A major mechanism for the desensitization of activated GPCRs is their phosphorylation by GPCR kinases (GRKs). Overexpression of GRK2 is strongly linked to heart failure, and GRK2 has long been considered a pharmaceutical target for the treatment of cardiovascular disease. Several lead compounds developed by Takeda Pharmaceuticals show high selectivity for GRK2 and therapeutic potential for the treatment of heart failure. To understand how these drugs achieve their selectivity, we determined crystal structures of the bovine GRK2-G{beta}{gamma} complex in the presence of two of these inhibitors. Comparison with the apoGRK2-G{beta}{gamma} structure demonstrates that the compounds bind in the kinase active site in a manner similar to that of the AGC kinase inhibitor balanol. Both balanol and the Takeda compounds induce a slight closure of the kinase domain, the degree of which correlates with the potencies of the inhibitors. Based on our crystal structures and homology modeling, we identified five amino acids surrounding the inhibitor binding site that we hypothesized could contribute to inhibitor selectivity. However, our results indicate that these residues are not major determinants of selectivity among GRK subfamilies. Rather, selectivity is achieved by the stabilization of a unique inactive conformation of the GRK2 kinase domain.

  16. Selecting highly structure-specific antibodies using structured synthetic mimics of the cystine knot protein sclerostin

    NARCIS (Netherlands)

    Back, J.W.; Frisch, C.; Van Pee, K.; Boschert, V.; van Vught, R.; Puijk, W.; Mueller, T. D.; Knappik, A.; Timmerman, P.

    2012-01-01

    Antibodies directed against specific regions of a protein have traditionally been raised against full proteins, protein domains or simple unstructured peptides, containing contiguous stretches of primary sequence. We have used a new approach of selecting antibodies against restrained peptides

  17. The different roles of selective autophagic protein degradation in mammalian cells.

    Science.gov (United States)

    Wang, Da-wei; Peng, Zhen-ju; Ren, Guang-fang; Wang, Guang-xin

    2015-11-10

    Autophagy is an intracellular pathway for bulk protein degradation and the removal of damaged organelles by lysosomes. Autophagy was previously thought to be unselective; however, studies have increasingly confirmed that autophagy-mediated protein degradation is highly regulated. Abnormal autophagic protein degradation has been associated with multiple human diseases such as cancer, neurological disability and cardiovascular disease; therefore, further elucidation of protein degradation by autophagy may be beneficial for protein-based clinical therapies. Macroautophagy and chaperone-mediated autophagy (CMA) can both participate in selective protein degradation in mammalian cells, but the process is quite different in each case. Here, we summarize the various types of macroautophagy and CMA involved in determining protein degradation. For this summary, we divide the autophagic protein degradation pathways into four categories: the post-translational modification dependent and independent CMA pathways and the ubiquitin dependent and independent macroautophagy pathways, and describe how some non-canonical pathways and modifications such as phosphorylation, acetylation and arginylation can influence protein degradation by the autophagy lysosome system (ALS). Finally, we comment on why autophagy can serve as either diagnostics or therapeutic targets in different human diseases.

  18. Site-selective {sup 13}C labeling of proteins using erythrose

    Energy Technology Data Exchange (ETDEWEB)

    Weininger, Ulrich, E-mail: ulrich.weininger@physik.uni-halle.de [Lund University, Department of Biophysical Chemistry, Center for Molecular Protein Science (Sweden)

    2017-03-15

    NMR-spectroscopy enables unique experimental studies on protein dynamics at atomic resolution. In order to obtain a full atom view on protein dynamics, and to study specific local processes like ring-flips, proton-transfer, or tautomerization, one has to perform studies on amino-acid side chains. A key requirement for these studies is site-selective labeling with {sup 13}C and/or {sup 1}H, which is achieved in the most general way by using site-selectively {sup 13}C-enriched glucose (1- and 2-{sup 13}C) as the carbon source in bacterial expression systems. Using this strategy, multiple sites in side chains, including aromatics, become site-selectively labeled and suitable for relaxation studies. Here we systematically investigate the use of site-selectively {sup 13}C-enriched erythrose (1-, 2-, 3- and 4-{sup 13}C) as a suitable precursor for {sup 13}C labeled aromatic side chains. We quantify {sup 13}C incorporation in nearly all sites in all 20 amino acids and compare the results to glucose based labeling. In general the erythrose approach results in more selective labeling. While there is only a minor gain for phenylalanine and tyrosine side-chains, the {sup 13}C incorporation level for tryptophan is at least doubled. Additionally, the Phe ζ and Trp η2 positions become labeled. In the aliphatic side chains, labeling using erythrose yields isolated {sup 13}C labels for certain positions, like Ile β and His β, making these sites suitable for dynamics studies. Using erythrose instead of glucose as a source for site-selective {sup 13}C labeling enables unique or superior labeling for certain positions and is thereby expanding the toolbox for customized isotope labeling of amino-acid side-chains.

  19. Measurement of conformational constraints in an elastin-mimetic protein by residue-pair selected solid-state NMR

    Energy Technology Data Exchange (ETDEWEB)

    Hong, Mei [Iowa State University, Department of Chemistry (United States)], E-mail: mhong@iastate.edu; McMillan, R. Andrew; Conticello, Vincent P. [Emory University, Department of Chemistry (United States)

    2002-02-15

    We introduce a solid-state NMR technique for selective detection of a residue pair in multiply labeled proteins to obtain site-specific structural constraints. The method exploits the frequency-offset dependence of cross polarization to achieve {sup 13}CO{sub i} {sup {yields}} {sup 15}N{sub i} {sup {yields}} {sup 13}C{alpha}{sub i} transfer between two residues. A {sup 13}C, {sup 15}N-labeled elastin mimetic protein (VPGVG){sub n} is used to demonstrate the method. The technique selected the Gly3 C{alpha} signal while suppressing the Gly5 C{alpha} signal, and allowed the measurement of the Gly3 C{alpha} chemical shift anisotropy to derive information on the protein conformation. This residue-pair selection technique should simplify the study of protein structure at specific residues.

  20. Protein folding simulations: from coarse-grained model to all-atom model.

    Science.gov (United States)

    Zhang, Jian; Li, Wenfei; Wang, Jun; Qin, Meng; Wu, Lei; Yan, Zhiqiang; Xu, Weixin; Zuo, Guanghong; Wang, Wei

    2009-06-01

    Protein folding is an important and challenging problem in molecular biology. During the last two decades, molecular dynamics (MD) simulation has proved to be a paramount tool and was widely used to study protein structures, folding kinetics and thermodynamics, and structure-stability-function relationship. It was also used to help engineering and designing new proteins, and to answer even more general questions such as the minimal number of amino acid or the evolution principle of protein families. Nowadays, the MD simulation is still undergoing rapid developments. The first trend is to toward developing new coarse-grained models and studying larger and more complex molecular systems such as protein-protein complex and their assembling process, amyloid related aggregations, and structure and motion of chaperons, motors, channels and virus capsides; the second trend is toward building high resolution models and explore more detailed and accurate pictures of protein folding and the associated processes, such as the coordination bond or disulfide bond involved folding, the polarization, charge transfer and protonate/deprotonate process involved in metal coupled folding, and the ion permeation and its coupling with the kinetics of channels. On these new territories, MD simulations have given many promising results and will continue to offer exciting views. Here, we review several new subjects investigated by using MD simulations as well as the corresponding developments of appropriate protein models. These include but are not limited to the attempt to go beyond the topology based Gō-like model and characterize the energetic factors in protein structures and dynamics, the study of the thermodynamics and kinetics of disulfide bond involved protein folding, the modeling of the interactions between chaperonin and the encapsulated protein and the protein folding under this circumstance, the effort to clarify the important yet still elusive folding mechanism of protein BBL

  1. DockQ: A Quality Measure for Protein-Protein Docking Models.

    Directory of Open Access Journals (Sweden)

    Sankar Basu

    Full Text Available The state-of-the-art to assess the structural quality of docking models is currently based on three related yet independent quality measures: Fnat, LRMS, and iRMS as proposed and standardized by CAPRI. These quality measures quantify different aspects of the quality of a particular docking model and need to be viewed together to reveal the true quality, e.g. a model with relatively poor LRMS (>10Å might still qualify as 'acceptable' with a descent Fnat (>0.50 and iRMS (<3.0Å. This is also the reason why the so called CAPRI criteria for assessing the quality of docking models is defined by applying various ad-hoc cutoffs on these measures to classify a docking model into the four classes: Incorrect, Acceptable, Medium, or High quality. This classification has been useful in CAPRI, but since models are grouped in only four bins it is also rather limiting, making it difficult to rank models, correlate with scoring functions or use it as target function in machine learning algorithms. Here, we present DockQ, a continuous protein-protein docking model quality measure derived by combining Fnat, LRMS, and iRMS to a single score in the range [0, 1] that can be used to assess the quality of protein docking models. By using DockQ on CAPRI models it is possible to almost completely reproduce the original CAPRI classification into Incorrect, Acceptable, Medium and High quality. An average PPV of 94% at 90% Recall demonstrating that there is no need to apply predefined ad-hoc cutoffs to classify docking models. Since DockQ recapitulates the CAPRI classification almost perfectly, it can be viewed as a higher resolution version of the CAPRI classification, making it possible to estimate model quality in a more quantitative way using Z-scores or sum of top ranked models, which has been so valuable for the CASP community. The possibility to directly correlate a quality measure to a scoring function has been crucial for the development of scoring functions for

  2. Transgenic Parasites Stably Expressing Full-Length Plasmodium falciparum Circumsporozoite Protein as a Model for Vaccine Down-Selection in Mice Using Sterile Protection as an Endpoint

    Science.gov (United States)

    Porter, Michael D.; Nicki, Jennifer; Pool, Christopher D.; DeBot, Margot; Illam, Ratish M.; Brando, Clara; Bozick, Brooke; De La Vega, Patricia; Angra, Divya; Spaccapelo, Roberta; Crisanti, Andrea; Murphy, Jittawadee R.; Bennett, Jason W.; Schwenk, Robert J.; Ockenhouse, Christian F.

    2013-01-01

    Circumsporozoite protein (CSP) of Plasmodium falciparum is a protective human malaria vaccine candidate. There is an urgent need for models that can rapidly down-select novel CSP-based vaccine candidates. In the present study, the mouse-mosquito transmission cycle of a transgenic Plasmodium berghei malaria parasite stably expressing a functional full-length P. falciparum CSP was optimized to consistently produce infective sporozoites for protection studies. A minimal sporozoite challenge dose was established, and protection was defined as the absence of blood-stage parasites 14 days after intravenous challenge. The specificity of protection was confirmed by vaccinating mice with multiple CSP constructs of differing lengths and compositions. Constructs that induced high NANP repeat-specific antibody titers in enzyme-linked immunosorbent assays were protective, and the degree of protection was dependent on the antigen dose. There was a positive correlation between antibody avidity and protection. The antibodies in the protected mice recognized the native CSP on the parasites and showed sporozoite invasion inhibitory activity. Passive transfer of anti-CSP antibodies into naive mice also induced protection. Thus, we have demonstrated the utility of a mouse efficacy model to down-select human CSP-based vaccine formulations. PMID:23536694

  3. Formylbenzene diazonium hexafluorophosphate reagent for tyrosine-selective modification of proteins and the introduction of a bioorthogonal aldehyde

    OpenAIRE

    Gavrilyuk, Julia; Ban, Hitoshi; Nagano, Masanobu; Hakamata, Wataru; Barbas, Carlos F.

    2012-01-01

    4-Formylbenzene diazonium hexafluorophosphate (FBDP) is a novel bench-stable crystalline diazonium salt that reacts selectively with tyrosine to install a bioorthogonal aldehyde functionality. Model studies with N-acyl-tyrosine methylamide allowed us to identify conditions optimal for tyrosine ligation reactions with small peptides and proteins. FBDP-based conjugation was used for the facile introduction of small molecule tags, poly(ethylene) glycol chains (PEGylation), and functional small m...

  4. Positively selected sites in cetacean myoglobins contribute to protein stability

    DEFF Research Database (Denmark)

    Dasmeh, Pouria; Serohijos, Adrian W R; Kepp, Kasper P

    2013-01-01

    Since divergence ∼50 Ma ago from their terrestrial ancestors, cetaceans underwent a series of adaptations such as a ∼10-20 fold increase in myoglobin (Mb) concentration in skeletal muscle, critical for increasing oxygen storage capacity and prolonging dive time. Whereas the O2-binding affinity...... between Mb folding stability and protein abundance, suggesting that a selection pressure for stability acts proportionally to higher expression. We also identify a major divergence event leading to the common ancestor of whales, during which major stabilization occurred. Most of the positively selected...

  5. Modelling Transcapillary Transport of Fluid and Proteins in Hemodialysis Patients.

    Directory of Open Access Journals (Sweden)

    Mauro Pietribiasi

    Full Text Available The kinetics of protein transport to and from the vascular compartment play a major role in the determination of fluid balance and plasma refilling during hemodialysis (HD sessions. In this study we propose a whole-body mathematical model describing water and protein shifts across the capillary membrane during HD and compare its output to clinical data while evaluating the impact of choosing specific values for selected parameters.The model follows a two-compartment structure (vascular and interstitial space and is based on balance equations of protein mass and water volume in each compartment. The capillary membrane was described according to the three-pore theory. Two transport parameters, the fractional contribution of large pores (αLP and the total hydraulic conductivity (LpS of the capillary membrane, were estimated from patient data. Changes in the intensity and direction of individual fluid and solute flows through each part of the transport system were analyzed in relation to the choice of different values of small pores radius and fractional conductivity, lymphatic sensitivity to hydraulic pressure, and steady-state interstitial-to-plasma protein concentration ratio.The estimated values of LpS and αLP were respectively 10.0 ± 8.4 mL/min/mmHg (mean ± standard deviation and 0.062 ± 0.041. The model was able to predict with good accuracy the profiles of plasma volume and serum total protein concentration in most of the patients (average root-mean-square deviation < 2% of the measured value.The applied model provides a mechanistic interpretation of fluid transport processes induced by ultrafiltration during HD, using a minimum of tuned parameters and assumptions. The simulated values of individual flows through each kind of pore and lymphatic absorption rate yielded by the model may suggest answers to unsolved questions on the relative impact of these not-measurable quantities on total vascular refilling and fluid balance.

  6. Simultaneous improvement of grain yield and protein content in durum wheat by different phenotypic indices and genomic selection.

    Science.gov (United States)

    Rapp, M; Lein, V; Lacoudre, F; Lafferty, J; Müller, E; Vida, G; Bozhanova, V; Ibraliu, A; Thorwarth, P; Piepho, H P; Leiser, W L; Würschum, T; Longin, C F H

    2018-06-01

    Simultaneous improvement of protein content and grain yield by index selection is possible but its efficiency largely depends on the weighting of the single traits. The genetic architecture of these indices is similar to that of the primary traits. Grain yield and protein content are of major importance in durum wheat breeding, but their negative correlation has hampered their simultaneous improvement. To account for this in wheat breeding, the grain protein deviation (GPD) and the protein yield were proposed as targets for selection. The aim of this work was to investigate the potential of different indices to simultaneously improve grain yield and protein content in durum wheat and to evaluate their genetic architecture towards genomics-assisted breeding. To this end, we investigated two different durum wheat panels comprising 159 and 189 genotypes, which were tested in multiple field locations across Europe and genotyped by a genotyping-by-sequencing approach. The phenotypic analyses revealed significant genetic variances for all traits and heritabilities of the phenotypic indices that were in a similar range as those of grain yield and protein content. The GPD showed a high and positive correlation with protein content, whereas protein yield was highly and positively correlated with grain yield. Thus, selecting for a high GPD would mainly increase the protein content whereas a selection based on protein yield would mainly improve grain yield, but a combination of both indices allows to balance this selection. The genome-wide association mapping revealed a complex genetic architecture for all traits with most QTL having small effects and being detected only in one germplasm set, thus limiting the potential of marker-assisted selection for trait improvement. By contrast, genome-wide prediction appeared promising but its performance strongly depends on the relatedness between training and prediction sets.

  7. Selective Labeling of Proteins on Living Cell Membranes Using Fluorescent Nanodiamond Probes

    Directory of Open Access Journals (Sweden)

    Shingo Sotoma

    2016-03-01

    Full Text Available The impeccable photostability of fluorescent nanodiamonds (FNDs is an ideal property for use in fluorescence imaging of proteins in living cells. However, such an application requires highly specific labeling of the target proteins with FNDs. Furthermore, the surface of unmodified FNDs tends to adsorb biomolecules nonspecifically, which hinders the reliable targeting of proteins with FNDs. Here, we combined hyperbranched polyglycerol modification of FNDs with the β-lactamase-tag system to develop a strategy for selective imaging of the protein of interest in cells. The combination of these techniques enabled site-specific labeling of Interleukin-18 receptor alpha chain, a membrane receptor, with FNDs, which eventually enabled tracking of the diffusion trajectory of FND-labeled proteins on the membrane surface.

  8. Decarboxylative alkylation for site-selective bioconjugation of native proteins via oxidation potentials

    Science.gov (United States)

    Bloom, Steven; Liu, Chun; Kölmel, Dominik K.; Qiao, Jennifer X.; Zhang, Yong; Poss, Michael A.; Ewing, William R.; MacMillan, David W. C.

    2018-02-01

    The advent of antibody-drug conjugates as pharmaceuticals has fuelled a need for reliable methods of site-selective protein modification that furnish homogeneous adducts. Although bioorthogonal methods that use engineered amino acids often provide an elegant solution to the question of selective functionalization, achieving homogeneity using native amino acids remains a challenge. Here, we explore visible-light-mediated single-electron transfer as a mechanism towards enabling site- and chemoselective bioconjugation. Specifically, we demonstrate the use of photoredox catalysis as a platform to selectivity wherein the discrepancy in oxidation potentials between internal versus C-terminal carboxylates can be exploited towards obtaining C-terminal functionalization exclusively. This oxidation potential-gated technology is amenable to endogenous peptides and has been successfully demonstrated on the protein insulin. As a fundamentally new approach to bioconjugation this methodology provides a blueprint toward the development of photoredox catalysis as a generic platform to target other redox-active side chains for native conjugation.

  9. Decarboxylative alkylation for site-selective bioconjugation of native proteins via oxidation potentials.

    Science.gov (United States)

    Bloom, Steven; Liu, Chun; Kölmel, Dominik K; Qiao, Jennifer X; Zhang, Yong; Poss, Michael A; Ewing, William R; MacMillan, David W C

    2018-02-01

    The advent of antibody-drug conjugates as pharmaceuticals has fuelled a need for reliable methods of site-selective protein modification that furnish homogeneous adducts. Although bioorthogonal methods that use engineered amino acids often provide an elegant solution to the question of selective functionalization, achieving homogeneity using native amino acids remains a challenge. Here, we explore visible-light-mediated single-electron transfer as a mechanism towards enabling site- and chemoselective bioconjugation. Specifically, we demonstrate the use of photoredox catalysis as a platform to selectivity wherein the discrepancy in oxidation potentials between internal versus C-terminal carboxylates can be exploited towards obtaining C-terminal functionalization exclusively. This oxidation potential-gated technology is amenable to endogenous peptides and has been successfully demonstrated on the protein insulin. As a fundamentally new approach to bioconjugation this methodology provides a blueprint toward the development of photoredox catalysis as a generic platform to target other redox-active side chains for native conjugation.

  10. The interface of protein structure, protein biophysics, and molecular evolution

    Science.gov (United States)

    Liberles, David A; Teichmann, Sarah A; Bahar, Ivet; Bastolla, Ugo; Bloom, Jesse; Bornberg-Bauer, Erich; Colwell, Lucy J; de Koning, A P Jason; Dokholyan, Nikolay V; Echave, Julian; Elofsson, Arne; Gerloff, Dietlind L; Goldstein, Richard A; Grahnen, Johan A; Holder, Mark T; Lakner, Clemens; Lartillot, Nicholas; Lovell, Simon C; Naylor, Gavin; Perica, Tina; Pollock, David D; Pupko, Tal; Regan, Lynne; Roger, Andrew; Rubinstein, Nimrod; Shakhnovich, Eugene; Sjölander, Kimmen; Sunyaev, Shamil; Teufel, Ashley I; Thorne, Jeffrey L; Thornton, Joseph W; Weinreich, Daniel M; Whelan, Simon

    2012-01-01

    Abstract The interface of protein structural biology, protein biophysics, molecular evolution, and molecular population genetics forms the foundations for a mechanistic understanding of many aspects of protein biochemistry. Current efforts in interdisciplinary protein modeling are in their infancy and the state-of-the art of such models is described. Beyond the relationship between amino acid substitution and static protein structure, protein function, and corresponding organismal fitness, other considerations are also discussed. More complex mutational processes such as insertion and deletion and domain rearrangements and even circular permutations should be evaluated. The role of intrinsically disordered proteins is still controversial, but may be increasingly important to consider. Protein geometry and protein dynamics as a deviation from static considerations of protein structure are also important. Protein expression level is known to be a major determinant of evolutionary rate and several considerations including selection at the mRNA level and the role of interaction specificity are discussed. Lastly, the relationship between modeling and needed high-throughput experimental data as well as experimental examination of protein evolution using ancestral sequence resurrection and in vitro biochemistry are presented, towards an aim of ultimately generating better models for biological inference and prediction. PMID:22528593

  11. Toward the description of electrostatic interactions between globular proteins: potential of mean force in the primitive model.

    Science.gov (United States)

    Dahirel, Vincent; Jardat, Marie; Dufrêche, Jean-François; Turq, Pierre

    2007-09-07

    Monte Carlo simulations are used to calculate the exact potential of mean force between charged globular proteins in aqueous solution. The aim of the present paper is to study the influence of the ions of the added salt on the effective interaction between these nanoparticles. The charges of the model proteins, either identical or opposite, are either central or distributed on a discrete pattern. Contrarily to Poisson-Boltzmann predictions, attractive, and repulsive direct forces between proteins are not screened similarly. Moreover, it has been shown that the relative orientations of the charge patterns strongly influence salt-mediated interactions. More precisely, for short distances between the proteins, ions enhance the difference of the effective forces between (i) like-charged and oppositely charged proteins, (ii) attractive and repulsive relative orientations of the proteins, which may affect the selectivity of protein/protein recognition. Finally, such results observed with the simplest models are applied to a more elaborate one to demonstrate their generality.

  12. Controlling the rejection of protein during membrane filtration by adding selected polyelectrolytes

    DEFF Research Database (Denmark)

    Pinelo, Manuel; Ferrer Roca, Carme; Meyer, Anne S.

    2012-01-01

    Electrostatic interactions among the charged groups on proteins and/or between proteins and other solutes significantly affect the aggregation/deposition phenomena that induce fouling and decrease permeate flux during membrane purification of proteins. Such interactions can be turned...... help enhance the performance of membrane filtration for fractionation/purification of a target protein by significantly reducing fouling and modifying rejection/selectivity.......) changing the pH, on the permeate flux and membrane transmission of bovin serum albumina (BSA) through a PVDF membrane. The addition of PS-co-AA to the feed solution resulted in significant increases of the BSA transmission at pH 7.4 as compared to the transmission of a pure BSA solution (1g...

  13. Deconvolution of Complex 1D NMR Spectra Using Objective Model Selection.

    Directory of Open Access Journals (Sweden)

    Travis S Hughes

    Full Text Available Fluorine (19F NMR has emerged as a useful tool for characterization of slow dynamics in 19F-labeled proteins. One-dimensional (1D 19F NMR spectra of proteins can be broad, irregular and complex, due to exchange of probe nuclei between distinct electrostatic environments; and therefore cannot be deconvoluted and analyzed in an objective way using currently available software. We have developed a Python-based deconvolution program, decon1d, which uses Bayesian information criteria (BIC to objectively determine which model (number of peaks would most likely produce the experimentally obtained data. The method also allows for fitting of intermediate exchange spectra, which is not supported by current software in the absence of a specific kinetic model. In current methods, determination of the deconvolution model best supported by the data is done manually through comparison of residual error values, which can be time consuming and requires model selection by the user. In contrast, the BIC method used by decond1d provides a quantitative method for model comparison that penalizes for model complexity helping to prevent over-fitting of the data and allows identification of the most parsimonious model. The decon1d program is freely available as a downloadable Python script at the project website (https://github.com/hughests/decon1d/.

  14. Integration of carboxyl modified magnetic particles and aqueous two-phase extraction for selective separation of proteins.

    Science.gov (United States)

    Gai, Qingqing; Qu, Feng; Zhang, Tao; Zhang, Yukui

    2011-07-15

    Both of the magnetic particle adsorption and aqueous two-phase extraction (ATPE) were simple, fast and low-cost method for protein separation. Selective proteins adsorption by carboxyl modified magnetic particles was investigated according to protein isoelectric point, solution pH and ionic strength. Aqueous two-phase system of PEG/sulphate exhibited selective separation and extraction for proteins before and after magnetic adsorption. The two combination ways, magnetic adsorption followed by ATPE and ATPE followed by magnetic adsorption, for the separation of proteins mixture of lysozyme, bovine serum albumin, trypsin, cytochrome C and myloglobin were discussed and compared. The way of magnetic adsorption followed by ATPE was also applied to human serum separation. Copyright © 2011 Elsevier B.V. All rights reserved.

  15. Variable selection and model choice in geoadditive regression models.

    Science.gov (United States)

    Kneib, Thomas; Hothorn, Torsten; Tutz, Gerhard

    2009-06-01

    Model choice and variable selection are issues of major concern in practical regression analyses, arising in many biometric applications such as habitat suitability analyses, where the aim is to identify the influence of potentially many environmental conditions on certain species. We describe regression models for breeding bird communities that facilitate both model choice and variable selection, by a boosting algorithm that works within a class of geoadditive regression models comprising spatial effects, nonparametric effects of continuous covariates, interaction surfaces, and varying coefficients. The major modeling components are penalized splines and their bivariate tensor product extensions. All smooth model terms are represented as the sum of a parametric component and a smooth component with one degree of freedom to obtain a fair comparison between the model terms. A generic representation of the geoadditive model allows us to devise a general boosting algorithm that automatically performs model choice and variable selection.

  16. Modelling of proteins in membranes

    DEFF Research Database (Denmark)

    Sperotto, Maria Maddalena; May, S.; Baumgaertner, A.

    2006-01-01

    This review describes some recent theories and simulations of mesoscopic and microscopic models of lipid membranes with embedded or attached proteins. We summarize results supporting our understanding of phenomena for which the activities of proteins in membranes are expected to be significantly ...

  17. Sequence Based Prediction of Antioxidant Proteins Using a Classifier Selection Strategy.

    Directory of Open Access Journals (Sweden)

    Lina Zhang

    Full Text Available Antioxidant proteins perform significant functions in maintaining oxidation/antioxidation balance and have potential therapies for some diseases. Accurate identification of antioxidant proteins could contribute to revealing physiological processes of oxidation/antioxidation balance and developing novel antioxidation-based drugs. In this study, an ensemble method is presented to predict antioxidant proteins with hybrid features, incorporating SSI (Secondary Structure Information, PSSM (Position Specific Scoring Matrix, RSA (Relative Solvent Accessibility, and CTD (Composition, Transition, Distribution. The prediction results of the ensemble predictor are determined by an average of prediction results of multiple base classifiers. Based on a classifier selection strategy, we obtain an optimal ensemble classifier composed of RF (Random Forest, SMO (Sequential Minimal Optimization, NNA (Nearest Neighbor Algorithm, and J48 with an accuracy of 0.925. A Relief combined with IFS (Incremental Feature Selection method is adopted to obtain optimal features from hybrid features. With the optimal features, the ensemble method achieves improved performance with a sensitivity of 0.95, a specificity of 0.93, an accuracy of 0.94, and an MCC (Matthew's Correlation Coefficient of 0.880, far better than the existing method. To evaluate the prediction performance objectively, the proposed method is compared with existing methods on the same independent testing dataset. Encouragingly, our method performs better than previous studies. In addition, our method achieves more balanced performance with a sensitivity of 0.878 and a specificity of 0.860. These results suggest that the proposed ensemble method can be a potential candidate for antioxidant protein prediction. For public access, we develop a user-friendly web server for antioxidant protein identification that is freely accessible at http://antioxidant.weka.cc.

  18. Automation of specimen selection and data acquisition for protein electron crystallography

    NARCIS (Netherlands)

    Oostergetel, G.T.; Keegstra, W.; Brisson, A.D R

    A system is presented for semi-automatic specimen selection and data acquisition for protein electron crystallography, based on a slow-scan CCD camera connected to a transmission electron microscope and control from an external computer. Areas of interest on the specimen are localised at low

  19. A Bayesian random effects discrete-choice model for resource selection: Population-level selection inference

    Science.gov (United States)

    Thomas, D.L.; Johnson, D.; Griffith, B.

    2006-01-01

    Modeling the probability of use of land units characterized by discrete and continuous measures, we present a Bayesian random-effects model to assess resource selection. This model provides simultaneous estimation of both individual- and population-level selection. Deviance information criterion (DIC), a Bayesian alternative to AIC that is sample-size specific, is used for model selection. Aerial radiolocation data from 76 adult female caribou (Rangifer tarandus) and calf pairs during 1 year on an Arctic coastal plain calving ground were used to illustrate models and assess population-level selection of landscape attributes, as well as individual heterogeneity of selection. Landscape attributes included elevation, NDVI (a measure of forage greenness), and land cover-type classification. Results from the first of a 2-stage model-selection procedure indicated that there is substantial heterogeneity among cow-calf pairs with respect to selection of the landscape attributes. In the second stage, selection of models with heterogeneity included indicated that at the population-level, NDVI and land cover class were significant attributes for selection of different landscapes by pairs on the calving ground. Population-level selection coefficients indicate that the pairs generally select landscapes with higher levels of NDVI, but the relationship is quadratic. The highest rate of selection occurs at values of NDVI less than the maximum observed. Results for land cover-class selections coefficients indicate that wet sedge, moist sedge, herbaceous tussock tundra, and shrub tussock tundra are selected at approximately the same rate, while alpine and sparsely vegetated landscapes are selected at a lower rate. Furthermore, the variability in selection by individual caribou for moist sedge and sparsely vegetated landscapes is large relative to the variability in selection of other land cover types. The example analysis illustrates that, while sometimes computationally intense, a

  20. BLProt: Prediction of bioluminescent proteins based on support vector machine and relieff feature selection

    KAUST Repository

    Kandaswamy, Krishna Kumar

    2011-08-17

    Background: Bioluminescence is a process in which light is emitted by a living organism. Most creatures that emit light are sea creatures, but some insects, plants, fungi etc, also emit light. The biotechnological application of bioluminescence has become routine and is considered essential for many medical and general technological advances. Identification of bioluminescent proteins is more challenging due to their poor similarity in sequence. So far, no specific method has been reported to identify bioluminescent proteins from primary sequence.Results: In this paper, we propose a novel predictive method that uses a Support Vector Machine (SVM) and physicochemical properties to predict bioluminescent proteins. BLProt was trained using a dataset consisting of 300 bioluminescent proteins and 300 non-bioluminescent proteins, and evaluated by an independent set of 141 bioluminescent proteins and 18202 non-bioluminescent proteins. To identify the most prominent features, we carried out feature selection with three different filter approaches, ReliefF, infogain, and mRMR. We selected five different feature subsets by decreasing the number of features, and the performance of each feature subset was evaluated.Conclusion: BLProt achieves 80% accuracy from training (5 fold cross-validations) and 80.06% accuracy from testing. The performance of BLProt was compared with BLAST and HMM. High prediction accuracy and successful prediction of hypothetical proteins suggests that BLProt can be a useful approach to identify bioluminescent proteins from sequence information, irrespective of their sequence similarity. 2011 Kandaswamy et al; licensee BioMed Central Ltd.

  1. BLProt: Prediction of bioluminescent proteins based on support vector machine and relieff feature selection

    KAUST Repository

    Kandaswamy, Krishna Kumar; Pugalenthi, Ganesan; Hazrati, Mehrnaz Khodam; Kalies, Kai-Uwe; Martinetz, Thomas

    2011-01-01

    Background: Bioluminescence is a process in which light is emitted by a living organism. Most creatures that emit light are sea creatures, but some insects, plants, fungi etc, also emit light. The biotechnological application of bioluminescence has become routine and is considered essential for many medical and general technological advances. Identification of bioluminescent proteins is more challenging due to their poor similarity in sequence. So far, no specific method has been reported to identify bioluminescent proteins from primary sequence.Results: In this paper, we propose a novel predictive method that uses a Support Vector Machine (SVM) and physicochemical properties to predict bioluminescent proteins. BLProt was trained using a dataset consisting of 300 bioluminescent proteins and 300 non-bioluminescent proteins, and evaluated by an independent set of 141 bioluminescent proteins and 18202 non-bioluminescent proteins. To identify the most prominent features, we carried out feature selection with three different filter approaches, ReliefF, infogain, and mRMR. We selected five different feature subsets by decreasing the number of features, and the performance of each feature subset was evaluated.Conclusion: BLProt achieves 80% accuracy from training (5 fold cross-validations) and 80.06% accuracy from testing. The performance of BLProt was compared with BLAST and HMM. High prediction accuracy and successful prediction of hypothetical proteins suggests that BLProt can be a useful approach to identify bioluminescent proteins from sequence information, irrespective of their sequence similarity. 2011 Kandaswamy et al; licensee BioMed Central Ltd.

  2. Molecular interaction of selected phytochemicals under the charged environment of Plasmodium falciparum chloroquine resistance transporter (PfCRT) model.

    Science.gov (United States)

    Patel, Saumya K; Khedkar, Vijay M; Jha, Prakash C; Jasrai, Yogesh T; Pandya, Himanshu A; George, Linz-Buoy; Highland, Hyacinth N; Skelton, Adam A

    2016-01-01

    Phytochemicals of Catharanthus roseus Linn. and Tylophora indica have been known for their inhibition of malarial parasite, Plasmodium falciparum in cell culture. Resistance to chloroquine (CQ), a widely used antimalarial drug, is due to the CQ resistance transporter (CRT) system. The present study deals with computational modeling of Plasmodium falciparum chloroquine resistance transporter (PfCRT) protein and development of charged environment to mimic a condition of resistance. The model of PfCRT was developed using Protein homology/analogy engine (PHYRE ver 0.2) and was validated based on the results obtained using PSI-PRED. Subsequently, molecular interactions of selected phytochemicals extracted from C. roseus Linn. and T. indica were studied using multiple-iterated genetic algorithm-based docking protocol in order to investigate the translocation of these legends across the PfCRT protein. Further, molecular dynamics studies exhibiting interaction energy estimates of these compounds within the active site of the protein showed that compounds are more selective toward PfCRT. Clusters of conformations with the free energy of binding were estimated which clearly demonstrated the potential channel and by this means the translocation across the PfCRT is anticipated.

  3. Investigation of protein selectivity in multimodal chromatography using in silico designed Fab fragment variants.

    Science.gov (United States)

    Karkov, Hanne Sophie; Krogh, Berit Olsen; Woo, James; Parimal, Siddharth; Ahmadian, Haleh; Cramer, Steven M

    2015-11-01

    In this study, a unique set of antibody Fab fragments was designed in silico and produced to examine the relationship between protein surface properties and selectivity in multimodal chromatographic systems. We hypothesized that multimodal ligands containing both hydrophobic and charged moieties would interact strongly with protein surface regions where charged groups and hydrophobic patches were in close spatial proximity. Protein surface property characterization tools were employed to identify the potential multimodal ligand binding regions on the Fab fragment of a humanized antibody and to evaluate the impact of mutations on surface charge and hydrophobicity. Twenty Fab variants were generated by site-directed mutagenesis, recombinant expression, and affinity purification. Column gradient experiments were carried out with the Fab variants in multimodal, cation-exchange, and hydrophobic interaction chromatographic systems. The results clearly indicated that selectivity in the multimodal system was different from the other chromatographic modes examined. Column retention data for the reduced charge Fab variants identified a binding site comprising light chain CDR1 as the main electrostatic interaction site for the multimodal and cation-exchange ligands. Furthermore, the multimodal ligand binding was enhanced by additional hydrophobic contributions as evident from the results obtained with hydrophobic Fab variants. The use of in silico protein surface property analyses combined with molecular biology techniques, protein expression, and chromatographic evaluations represents a previously undescribed and powerful approach for investigating multimodal selectivity with complex biomolecules. © 2015 Wiley Periodicals, Inc.

  4. Intrinsically disordered proteins--relation to general model expressing the active role of the water environment.

    Science.gov (United States)

    Kalinowska, Barbara; Banach, Mateusz; Konieczny, Leszek; Marchewka, Damian; Roterman, Irena

    2014-01-01

    This work discusses the role of unstructured polypeptide chain fragments in shaping the protein's hydrophobic core. Based on the "fuzzy oil drop" model, which assumes an idealized distribution of hydrophobicity density described by the 3D Gaussian, we can determine which fragments make up the core and pinpoint residues whose location conflicts with theoretical predictions. We show that the structural influence of the water environment determines the positions of disordered fragments, leading to the formation of a hydrophobic core overlaid by a hydrophilic mantle. This phenomenon is further described by studying selected proteins which are known to be unstable and contain intrinsically disordered fragments. Their properties are established quantitatively, explaining the causative relation between the protein's structure and function and facilitating further comparative analyses of various structural models. © 2014 Elsevier Inc. All rights reserved.

  5. Protein adsorption on nanoparticles: model development using computer simulation

    International Nuclear Information System (INIS)

    Shao, Qing; Hall, Carol K

    2016-01-01

    The adsorption of proteins on nanoparticles results in the formation of the protein corona, the composition of which determines how nanoparticles influence their biological surroundings. We seek to better understand corona formation by developing models that describe protein adsorption on nanoparticles using computer simulation results as data. Using a coarse-grained protein model, discontinuous molecular dynamics simulations are conducted to investigate the adsorption of two small proteins (Trp-cage and WW domain) on a model nanoparticle of diameter 10.0 nm at protein concentrations ranging from 0.5 to 5 mM. The resulting adsorption isotherms are well described by the Langmuir, Freundlich, Temkin and Kiselev models, but not by the Elovich, Fowler–Guggenheim and Hill–de Boer models. We also try to develop a generalized model that can describe protein adsorption equilibrium on nanoparticles of different diameters in terms of dimensionless size parameters. The simulation results for three proteins (Trp-cage, WW domain, and GB3) on four nanoparticles (diameter  =  5.0, 10.0, 15.0, and 20.0 nm) illustrate both the promise and the challenge associated with developing generalized models of protein adsorption on nanoparticles. (paper)

  6. Mathematical model of the binding of allosteric effectors to the Escherichia coli PII signal transduction protein GlnB.

    Science.gov (United States)

    da Rocha, Ricardo Alves; Weschenfelder, Thiago André; de Castilhos, Fernanda; de Souza, Emanuel Maltempi; Huergo, Luciano Fernandes; Mitchell, David Alexander

    2013-04-16

    PII proteins are important regulators of nitrogen metabolism in a wide variety of organisms: the binding of the allosteric effectors ATP, ADP, and 2-oxoglutarate (2-OG) to PII proteins affects their ability to interact with target proteins. We modeled the simultaneous binding of ATP, ADP, and 2-OG to one PII protein, namely GlnB of Escherichia coli, using a modeling approach that allows the prediction of the proportions of individual binding states. Four models with different binding rules were compared. We selected one of these models (that assumes that the binding of the first nucleotide to GlnB makes it harder for subsequent nucleotides to bind) and used it to explore how physiological concentrations of ATP, ADP, and 2-OG would affect the proportions of those states of GlnB that interact with the target proteins ATase and NtrB. Our simulations indicate that GlnB can, as suggested by previous researchers, act as a sensor of both 2-OG and the ATP:ADP ratio. We conclude that our modeling approach will be an important tool in future studies concerning the PII binding states and their interactions with target proteins.

  7. ZP Domain Proteins in the Abalone Egg Coat Include a Paralog of VERL under Positive Selection That Binds Lysin and 18-kDa Sperm Proteins

    Science.gov (United States)

    Aagaard, Jan E.; Vacquier, Victor D.; MacCoss, Michael J.; Swanson, Willie J.

    2010-01-01

    Identifying fertilization molecules is key to our understanding of reproductive biology, yet only a few examples of interacting sperm and egg proteins are known. One of the best characterized comes from the invertebrate archeogastropod abalone (Haliotis spp.), where sperm lysin mediates passage through the protective egg vitelline envelope (VE) by binding to the VE protein vitelline envelope receptor for lysin (VERL). Rapid adaptive divergence of abalone lysin and VERL are an example of positive selection on interacting fertilization proteins contributing to reproductive isolation. Previously, we characterized a subset of the abalone VE proteins that share a structural feature, the zona pellucida (ZP) domain, which is common to VERL and the egg envelopes of vertebrates. Here, we use additional expressed sequence tag sequencing and shotgun proteomics to characterize this family of proteins in the abalone egg VE. We expand 3-fold the number of known ZP domain proteins present within the VE (now 30 in total) and identify a paralog of VERL (vitelline envelope zona pellucida domain protein [VEZP] 14) that contains a putative lysin-binding motif. We find that, like VERL, the divergence of VEZP14 among abalone species is driven by positive selection on the lysin-binding motif alone and that these paralogous egg VE proteins bind a similar set of sperm proteins including a rapidly evolving 18-kDa paralog of lysin, which may mediate sperm–egg fusion. This work identifies an egg coat paralog of VERL under positive selection and the candidate sperm proteins with which it may interact during abalone fertilization. PMID:19767347

  8. Identification and Structure-Function Analysis of Subfamily Selective G Protein-Coupled Receptor Kinase Inhibitors

    Energy Technology Data Exchange (ETDEWEB)

    Homan, Kristoff T.; Larimore, Kelly M.; Elkins, Jonathan M.; Szklarz, Marta; Knapp, Stefan; Tesmer, John J.G. [Michigan; (Oxford)

    2015-02-13

    Selective inhibitors of individual subfamilies of G protein-coupled receptor kinases (GRKs) would serve as useful chemical probes as well as leads for therapeutic applications ranging from heart failure to Parkinson’s disease. To identify such inhibitors, differential scanning fluorimetry was used to screen a collection of known protein kinase inhibitors that could increase the melting points of the two most ubiquitously expressed GRKs: GRK2 and GRK5. Enzymatic assays on 14 of the most stabilizing hits revealed that three exhibit nanomolar potency of inhibition for individual GRKs, some of which exhibiting orders of magnitude selectivity. Most of the identified compounds can be clustered into two chemical classes: indazole/dihydropyrimidine-containing compounds that are selective for GRK2 and pyrrolopyrimidine-containing compounds that potently inhibit GRK1 and GRK5 but with more modest selectivity. The two most potent inhibitors representing each class, GSK180736A and GSK2163632A, were cocrystallized with GRK2 and GRK1, and their atomic structures were determined to 2.6 and 1.85 Å spacings, respectively. GSK180736A, developed as a Rho-associated, coiled-coil-containing protein kinase inhibitor, binds to GRK2 in a manner analogous to that of paroxetine, whereas GSK2163632A, developed as an insulin-like growth factor 1 receptor inhibitor, occupies a novel region of the GRK active site cleft that could likely be exploited to achieve more selectivity. However, neither compound inhibits GRKs more potently than their initial targets. This data provides the foundation for future efforts to rationally design even more potent and selective GRK inhibitors.

  9. Selective labeling of a single organelle by using two-photon conversion of a photoconvertible fluorescent protein

    Science.gov (United States)

    Watanabe, Wataru; Shimada, Tomoko; Matsunaga, Sachihiro; Kurihara, Daisuke; Arimura, Shin-ichi; Tsutsumi, Nobuhiro; Fukui, Kiichi; Itoh, Kazuyoshi

    2008-02-01

    We present space-selective labeling of organelles by using two-photon conversion of a photoconvertible fluorescent protein with near-infrared femtosecond laser pulses. Two-photon excitation of photoconvertible fluorescent-protein, Kaede, enables space-selective labeling of organelles. We alter the fluorescence of target mitochondria in a tobacco BY-2 cell from green to red by focusing femtosecond laser pulses with a wavelength of 750 nm.

  10. Highly Selective Fluorescent Sensing of Proteins Based on a Fluorescent Molecularly Imprinted Nanosensor

    Directory of Open Access Journals (Sweden)

    Shuo Wang

    2013-09-01

    Full Text Available A fluorescent molecularly imprinted nanosensor was obtained by grafting imprinted polymer onto the surface of multi-wall carbon nanotubes and post-imprinting treatment with fluorescein isothiocyanate (FITC. The fluorescence of lysozyme-imprinted polymer (Lys-MIP was quenched more strongly by Lys than that of nonimprinted polymer (NIP, which indicated that the Lys-MIP could recognize Lys. The resulted imprinted material has the ability to selectively sense a target protein, and an imprinting factor of 3.34 was achieved. The Lys-MIP also showed selective detection for Lys among other proteins such as cytochrome C (Cyt C, hemoglobin (HB and bovine serum albumin (BSA due to the imprinted sites in the Lys-MIP. This approach combines the high selectivity of surface molecular imprinting technology and fluorescence, and converts binding events into detectable signals by monitoring fluorescence spectra. Therefore, it will have further applications for Lys sensing.

  11. Efficient Feature Selection and Classification of Protein Sequence Data in Bioinformatics

    Science.gov (United States)

    Faye, Ibrahima; Samir, Brahim Belhaouari; Md Said, Abas

    2014-01-01

    Bioinformatics has been an emerging area of research for the last three decades. The ultimate aims of bioinformatics were to store and manage the biological data, and develop and analyze computational tools to enhance their understanding. The size of data accumulated under various sequencing projects is increasing exponentially, which presents difficulties for the experimental methods. To reduce the gap between newly sequenced protein and proteins with known functions, many computational techniques involving classification and clustering algorithms were proposed in the past. The classification of protein sequences into existing superfamilies is helpful in predicting the structure and function of large amount of newly discovered proteins. The existing classification results are unsatisfactory due to a huge size of features obtained through various feature encoding methods. In this work, a statistical metric-based feature selection technique has been proposed in order to reduce the size of the extracted feature vector. The proposed method of protein classification shows significant improvement in terms of performance measure metrics: accuracy, sensitivity, specificity, recall, F-measure, and so forth. PMID:25045727

  12. A study of quality measures for protein threading models

    Directory of Open Access Journals (Sweden)

    Rychlewski Leszek

    2001-08-01

    Full Text Available Abstract Background Prediction of protein structures is one of the fundamental challenges in biology today. To fully understand how well different prediction methods perform, it is necessary to use measures that evaluate their performance. Every two years, starting in 1994, the CASP (Critical Assessment of protein Structure Prediction process has been organized to evaluate the ability of different predictors to blindly predict the structure of proteins. To capture different features of the models, several measures have been developed during the CASP processes. However, these measures have not been examined in detail before. In an attempt to develop fully automatic measures that can be used in CASP, as well as in other type of benchmarking experiments, we have compared twenty-one measures. These measures include the measures used in CASP3 and CASP2 as well as have measures introduced later. We have studied their ability to distinguish between the better and worse models submitted to CASP3 and the correlation between them. Results Using a small set of 1340 models for 23 different targets we show that most methods correlate with each other. Most pairs of measures show a correlation coefficient of about 0.5. The correlation is slightly higher for measures of similar types. We found that a significant problem when developing automatic measures is how to deal with proteins of different length. Also the comparisons between different measures is complicated as many measures are dependent on the size of the target. We show that the manual assessment can be reproduced to about 70% using automatic measures. Alignment independent measures, detects slightly more of the models with the correct fold, while alignment dependent measures agree better when selecting the best models for each target. Finally we show that using automatic measures would, to a large extent, reproduce the assessors ranking of the predictors at CASP3. Conclusions We show that given a

  13. A genetic replacement system for selection-based engineering of essential proteins

    Science.gov (United States)

    2012-01-01

    Background Essential genes represent the core of biological functions required for viability. Molecular understanding of essentiality as well as design of synthetic cellular systems includes the engineering of essential proteins. An impediment to this effort is the lack of growth-based selection systems suitable for directed evolution approaches. Results We established a simple strategy for genetic replacement of an essential gene by a (library of) variant(s) during a transformation. The system was validated using three different essential genes and plasmid combinations and it reproducibly shows transformation efficiencies on the order of 107 transformants per microgram of DNA without any identifiable false positives. This allowed for reliable recovery of functional variants out of at least a 105-fold excess of non-functional variants. This outperformed selection in conventional bleach-out strains by at least two orders of magnitude, where recombination between functional and non-functional variants interfered with reliable recovery even in recA negative strains. Conclusions We propose that this selection system is extremely suitable for evaluating large libraries of engineered essential proteins resulting in the reliable isolation of functional variants in a clean strain background which can readily be used for in vivo applications as well as expression and purification for use in in vitro studies. PMID:22898007

  14. An evolutionary algorithm for model selection

    Energy Technology Data Exchange (ETDEWEB)

    Bicker, Karl [CERN, Geneva (Switzerland); Chung, Suh-Urk; Friedrich, Jan; Grube, Boris; Haas, Florian; Ketzer, Bernhard; Neubert, Sebastian; Paul, Stephan; Ryabchikov, Dimitry [Technische Univ. Muenchen (Germany)

    2013-07-01

    When performing partial-wave analyses of multi-body final states, the choice of the fit model, i.e. the set of waves to be used in the fit, can significantly alter the results of the partial wave fit. Traditionally, the models were chosen based on physical arguments and by observing the changes in log-likelihood of the fits. To reduce possible bias in the model selection process, an evolutionary algorithm was developed based on a Bayesian goodness-of-fit criterion which takes into account the model complexity. Starting from systematically constructed pools of waves which contain significantly more waves than the typical fit model, the algorithm yields a model with an optimal log-likelihood and with a number of partial waves which is appropriate for the number of events in the data. Partial waves with small contributions to the total intensity are penalized and likely to be dropped during the selection process, as are models were excessive correlations between single waves occur. Due to the automated nature of the model selection, a much larger part of the model space can be explored than would be possible in a manual selection. In addition the method allows to assess the dependence of the fit result on the fit model which is an important contribution to the systematic uncertainty.

  15. Quantitative chemogenomics: machine-learning models of protein-ligand interaction.

    Science.gov (United States)

    Andersson, Claes R; Gustafsson, Mats G; Strömbergsson, Helena

    2011-01-01

    Chemogenomics is an emerging interdisciplinary field that lies in the interface of biology, chemistry, and informatics. Most of the currently used drugs are small molecules that interact with proteins. Understanding protein-ligand interaction is therefore central to drug discovery and design. In the subfield of chemogenomics known as proteochemometrics, protein-ligand-interaction models are induced from data matrices that consist of both protein and ligand information along with some experimentally measured variable. The two general aims of this quantitative multi-structure-property-relationship modeling (QMSPR) approach are to exploit sparse/incomplete information sources and to obtain more general models covering larger parts of the protein-ligand space, than traditional approaches that focuses mainly on specific targets or ligands. The data matrices, usually obtained from multiple sparse/incomplete sources, typically contain series of proteins and ligands together with quantitative information about their interactions. A useful model should ideally be easy to interpret and generalize well to new unseen protein-ligand combinations. Resolving this requires sophisticated machine-learning methods for model induction, combined with adequate validation. This review is intended to provide a guide to methods and data sources suitable for this kind of protein-ligand-interaction modeling. An overview of the modeling process is presented including data collection, protein and ligand descriptor computation, data preprocessing, machine-learning-model induction and validation. Concerns and issues specific for each step in this kind of data-driven modeling will be discussed. © 2011 Bentham Science Publishers

  16. [Assessment of the effect of selected mixture of food additives on the protein metabolism--model studies].

    Science.gov (United States)

    Friedrich, Mariola; Kuchlewska, Magdalena

    2012-01-01

    Contemporarily, food production without food additives is very rare. Increasingly often, however, scientific works report on adverse effects of specified, single food additives on the body. Data is, in turn, lacking on the synergistic effect of a mixture of different food additives on body functions and its main metabolic pathways. The objective of this study, an animal model, was to evaluate if and in what way the compound of chosen and most frequently used and consumed food additives, along with the change of diet composition to processed, purified, influence the selected markers of protein metabolism. The animals were divided into four groups, which were fed with compound of feed pellets: group I and II with basic compound, group III and IV with modified compound in which part of the full grain was replaced by isocalorie wheat flour type 500 and saccharose. Animals from groups I and III received tap water, which was standing for some time, to drink. Animals from groups II and IV received solution of chosen additives to food and next they were given water to drink. The amount of given food additives was evaluated by taking into consideration their consumption by people recalculated to 1 kg of their body mass. The experiment spanned for 7 weeks. It was ascertained that the applied additives caused significant changes in total protein concentration and its fractions: albumin, alpha1-globulin, alpha2-globulin, beta-globulin and gamma-globulin in the blood serum of the animals under research, which can indicate and contribute to disclosure of creation of undesirable food reaction, especially when recommended levels of consumption of those additives are being exceeded. The organism response to the applied additives and accompanying it change of diet was essentially connected to sex of the animals. Undesirable character of changes taking place under the influence of applied additives, was observed both in animals fed with basic feed and modified feed with various

  17. Structural model for the interaction of a designed Ankyrin Repeat Protein with the human epidermal growth factor receptor 2.

    Directory of Open Access Journals (Sweden)

    V Chandana Epa

    Full Text Available Designed Ankyrin Repeat Proteins are a class of novel binding proteins that can be selected and evolved to bind to targets with high affinity and specificity. We are interested in the DARPin H10-2-G3, which has been evolved to bind with very high affinity to the human epidermal growth factor receptor 2 (HER2. HER2 is found to be over-expressed in 30% of breast cancers, and is the target for the FDA-approved therapeutic monoclonal antibodies trastuzumab and pertuzumab and small molecule tyrosine kinase inhibitors. Here, we use computational macromolecular docking, coupled with several interface metrics such as shape complementarity, interaction energy, and electrostatic complementarity, to model the structure of the complex between the DARPin H10-2-G3 and HER2. We analyzed the interface between the two proteins and then validated the structural model by showing that selected HER2 point mutations at the putative interface with H10-2-G3 reduce the affinity of binding up to 100-fold without affecting the binding of trastuzumab. Comparisons made with a subsequently solved X-ray crystal structure of the complex yielded a backbone atom root mean square deviation of 0.84-1.14 Ångstroms. The study presented here demonstrates the capability of the computational techniques of structural bioinformatics in generating useful structural models of protein-protein interactions.

  18. Bayesian Model Selection under Time Constraints

    Science.gov (United States)

    Hoege, M.; Nowak, W.; Illman, W. A.

    2017-12-01

    Bayesian model selection (BMS) provides a consistent framework for rating and comparing models in multi-model inference. In cases where models of vastly different complexity compete with each other, we also face vastly different computational runtimes of such models. For instance, time series of a quantity of interest can be simulated by an autoregressive process model that takes even less than a second for one run, or by a partial differential equations-based model with runtimes up to several hours or even days. The classical BMS is based on a quantity called Bayesian model evidence (BME). It determines the model weights in the selection process and resembles a trade-off between bias of a model and its complexity. However, in practice, the runtime of models is another weight relevant factor for model selection. Hence, we believe that it should be included, leading to an overall trade-off problem between bias, variance and computing effort. We approach this triple trade-off from the viewpoint of our ability to generate realizations of the models under a given computational budget. One way to obtain BME values is through sampling-based integration techniques. We argue with the fact that more expensive models can be sampled much less under time constraints than faster models (in straight proportion to their runtime). The computed evidence in favor of a more expensive model is statistically less significant than the evidence computed in favor of a faster model, since sampling-based strategies are always subject to statistical sampling error. We present a straightforward way to include this misbalance into the model weights that are the basis for model selection. Our approach follows directly from the idea of insufficient significance. It is based on a computationally cheap bootstrapping error estimate of model evidence and is easy to implement. The approach is illustrated in a small synthetic modeling study.

  19. Physiologically Based Pharmacokinetic Modeling of Therapeutic Proteins.

    Science.gov (United States)

    Wong, Harvey; Chow, Timothy W

    2017-09-01

    Biologics or therapeutic proteins are becoming increasingly important as treatments for disease. The most common class of biologics are monoclonal antibodies (mAbs). Recently, there has been an increase in the use of physiologically based pharmacokinetic (PBPK) modeling in the pharmaceutical industry in drug development. We review PBPK models for therapeutic proteins with an emphasis on mAbs. Due to their size and similarity to endogenous antibodies, there are distinct differences between PBPK models for small molecules and mAbs. The high-level organization of a typical mAb PBPK model consists of a whole-body PBPK model with organ compartments interconnected by both blood and lymph flows. The whole-body PBPK model is coupled with tissue-level submodels used to describe key mechanisms governing mAb disposition including tissue efflux via the lymphatic system, elimination by catabolism, protection from catabolism binding to the neonatal Fc (FcRn) receptor, and nonlinear binding to specific pharmacological targets of interest. The use of PBPK modeling in the development of therapeutic proteins is still in its infancy. Further application of PBPK modeling for therapeutic proteins will help to define its developing role in drug discovery and development. Copyright © 2017 American Pharmacists Association®. Published by Elsevier Inc. All rights reserved.

  20. A conserved mammalian protein interaction network.

    Directory of Open Access Journals (Sweden)

    Åsa Pérez-Bercoff

    Full Text Available Physical interactions between proteins mediate a variety of biological functions, including signal transduction, physical structuring of the cell and regulation. While extensive catalogs of such interactions are known from model organisms, their evolutionary histories are difficult to study given the lack of interaction data from phylogenetic outgroups. Using phylogenomic approaches, we infer a upper bound on the time of origin for a large set of human protein-protein interactions, showing that most such interactions appear relatively ancient, dating no later than the radiation of placental mammals. By analyzing paired alignments of orthologous and putatively interacting protein-coding genes from eight mammals, we find evidence for weak but significant co-evolution, as measured by relative selective constraint, between pairs of genes with interacting proteins. However, we find no strong evidence for shared instances of directional selection within an interacting pair. Finally, we use a network approach to show that the distribution of selective constraint across the protein interaction network is non-random, with a clear tendency for interacting proteins to share similar selective constraints. Collectively, the results suggest that, on the whole, protein interactions in mammals are under selective constraint, presumably due to their functional roles.

  1. Using mathematical models to understand the effect of nanoscale roughness on protein adsorption for improving medical devices

    Directory of Open Access Journals (Sweden)

    Ercan B

    2013-09-01

    Full Text Available Batur Ercan,1 Dongwoo Khang,2 Joseph Carpenter,3 Thomas J Webster1 1Department of Chemical Engineering, Northeastern University, Boston, MA, USA; 2School of Materials Science and Engineering and Center for PRC and RIGET, Gyeongsang National University, Jinju, South Korea; 3School of Medicine, Stanford University, Stanford, CA, USA Abstract: Surface roughness and energy significantly influence protein adsorption on to biomaterials, which, in turn, controls select cellular adhesion to determine the success and longevity of an implant. To understand these relationships at a fundamental level, a model was originally proposed by Khang et al to correlate nanoscale surface properties (specifically, nanoscale roughness and energy to protein adsorption, which explained the greater cellular responses on nanostructured surfaces commonly reported in the literature today. To test this model for different surfaces from what was previously used to develop that model, in this study we synthesized highly ordered poly(lactic-co-glycolic acid surfaces of identical chemistry but altered nanoscale surface roughness and energy using poly(dimethylsiloxane molds of polystyrene beads. Fibronectin and collagen type IV adsorption studies showed a linear adsorption behavior as the surface nanoroughness increased. This supported the general trends observed by Khang et al. However, when fitting such data to the mathematical model established by Khang et al, a strong correlation did not result. Thus, this study demonstrated that the equation proposed by Khang et al to predict protein adsorption should be modified to accommodate for additional nanoscale surface property contributions (ie, surface charge to make the model more accurate. In summary, results from this study provided an important step in developing future mathematical models that can correlate surface properties (such as nanoscale roughness and surface energy to initial protein adsorption events important to

  2. The genealogy of samples in models with selection.

    Science.gov (United States)

    Neuhauser, C; Krone, S M

    1997-02-01

    We introduce the genealogy of a random sample of genes taken from a large haploid population that evolves according to random reproduction with selection and mutation. Without selection, the genealogy is described by Kingman's well-known coalescent process. In the selective case, the genealogy of the sample is embedded in a graph with a coalescing and branching structure. We describe this graph, called the ancestral selection graph, and point out differences and similarities with Kingman's coalescent. We present simulations for a two-allele model with symmetric mutation in which one of the alleles has a selective advantage over the other. We find that when the allele frequencies in the population are already in equilibrium, then the genealogy does not differ much from the neutral case. This is supported by rigorous results. Furthermore, we describe the ancestral selection graph for other selective models with finitely many selection classes, such as the K-allele models, infinitely-many-alleles models. DNA sequence models, and infinitely-many-sites models, and briefly discuss the diploid case.

  3. A hidden markov model derived structural alphabet for proteins.

    Science.gov (United States)

    Camproux, A C; Gautier, R; Tufféry, P

    2004-06-04

    Understanding and predicting protein structures depends on the complexity and the accuracy of the models used to represent them. We have set up a hidden Markov model that discretizes protein backbone conformation as series of overlapping fragments (states) of four residues length. This approach learns simultaneously the geometry of the states and their connections. We obtain, using a statistical criterion, an optimal systematic decomposition of the conformational variability of the protein peptidic chain in 27 states with strong connection logic. This result is stable over different protein sets. Our model fits well the previous knowledge related to protein architecture organisation and seems able to grab some subtle details of protein organisation, such as helix sub-level organisation schemes. Taking into account the dependence between the states results in a description of local protein structure of low complexity. On an average, the model makes use of only 8.3 states among 27 to describe each position of a protein structure. Although we use short fragments, the learning process on entire protein conformations captures the logic of the assembly on a larger scale. Using such a model, the structure of proteins can be reconstructed with an average accuracy close to 1.1A root-mean-square deviation and for a complexity of only 3. Finally, we also observe that sequence specificity increases with the number of states of the structural alphabet. Such models can constitute a very relevant approach to the analysis of protein architecture in particular for protein structure prediction.

  4. Model Selection with the Linear Mixed Model for Longitudinal Data

    Science.gov (United States)

    Ryoo, Ji Hoon

    2011-01-01

    Model building or model selection with linear mixed models (LMMs) is complicated by the presence of both fixed effects and random effects. The fixed effects structure and random effects structure are codependent, so selection of one influences the other. Most presentations of LMM in psychology and education are based on a multilevel or…

  5. Structural optimization and structure-functional selectivity relationship studies of G protein-biased EP2 receptor agonists.

    Science.gov (United States)

    Ogawa, Seiji; Watanabe, Toshihide; Moriyuki, Kazumi; Goto, Yoshikazu; Yamane, Shinsaku; Watanabe, Akio; Tsuboi, Kazuma; Kinoshita, Atsushi; Okada, Takuya; Takeda, Hiroyuki; Tani, Kousuke; Maruyama, Toru

    2016-05-15

    The modification of the novel G protein-biased EP2 agonist 1 has been investigated to improve its G protein activity and develop a better understanding of its structure-functional selectivity relationship (SFSR). The optimization of the substituents on the phenyl ring of 1, followed by the inversion of the hydroxyl group on the cyclopentane moiety led to compound 9, which showed a 100-fold increase in its G protein activity compared with 1 without any increase in β-arrestin recruitment. Furthermore, SFSR studies revealed that the combination of meta and para substituents on the phenyl moiety was crucial to the functional selectivity. Copyright © 2016 Elsevier Ltd. All rights reserved.

  6. Methods for selecting fixed-effect models for heterogeneous codon evolution, with comments on their application to gene and genome data.

    Science.gov (United States)

    Bao, Le; Gu, Hong; Dunn, Katherine A; Bielawski, Joseph P

    2007-02-08

    Models of codon evolution have proven useful for investigating the strength and direction of natural selection. In some cases, a priori biological knowledge has been used successfully to model heterogeneous evolutionary dynamics among codon sites. These are called fixed-effect models, and they require that all codon sites are assigned to one of several partitions which are permitted to have independent parameters for selection pressure, evolutionary rate, transition to transversion ratio or codon frequencies. For single gene analysis, partitions might be defined according to protein tertiary structure, and for multiple gene analysis partitions might be defined according to a gene's functional category. Given a set of related fixed-effect models, the task of selecting the model that best fits the data is not trivial. In this study, we implement a set of fixed-effect codon models which allow for different levels of heterogeneity among partitions in the substitution process. We describe strategies for selecting among these models by a backward elimination procedure, Akaike information criterion (AIC) or a corrected Akaike information criterion (AICc). We evaluate the performance of these model selection methods via a simulation study, and make several recommendations for real data analysis. Our simulation study indicates that the backward elimination procedure can provide a reliable method for model selection in this setting. We also demonstrate the utility of these models by application to a single-gene dataset partitioned according to tertiary structure (abalone sperm lysin), and a multi-gene dataset partitioned according to the functional category of the gene (flagellar-related proteins of Listeria). Fixed-effect models have advantages and disadvantages. Fixed-effect models are desirable when data partitions are known to exhibit significant heterogeneity or when a statistical test of such heterogeneity is desired. They have the disadvantage of requiring a priori

  7. Modeling compositional dynamics based on GC and purine contents of protein-coding sequences

    KAUST Repository

    Zhang, Zhang

    2010-11-08

    Background: Understanding the compositional dynamics of genomes and their coding sequences is of great significance in gaining clues into molecular evolution and a large number of publically-available genome sequences have allowed us to quantitatively predict deviations of empirical data from their theoretical counterparts. However, the quantification of theoretical compositional variations for a wide diversity of genomes remains a major challenge.Results: To model the compositional dynamics of protein-coding sequences, we propose two simple models that take into account both mutation and selection effects, which act differently at the three codon positions, and use both GC and purine contents as compositional parameters. The two models concern the theoretical composition of nucleotides, codons, and amino acids, with no prerequisite of homologous sequences or their alignments. We evaluated the two models by quantifying theoretical compositions of a large collection of protein-coding sequences (including 46 of Archaea, 686 of Bacteria, and 826 of Eukarya), yielding consistent theoretical compositions across all the collected sequences.Conclusions: We show that the compositions of nucleotides, codons, and amino acids are largely determined by both GC and purine contents and suggest that deviations of the observed from the expected compositions may reflect compositional signatures that arise from a complex interplay between mutation and selection via DNA replication and repair mechanisms.Reviewers: This article was reviewed by Zhaolei Zhang (nominated by Mark Gerstein), Guruprasad Ananda (nominated by Kateryna Makova), and Daniel Haft. 2010 Zhang and Yu; licensee BioMed Central Ltd.

  8. Modeling compositional dynamics based on GC and purine contents of protein-coding sequences

    KAUST Repository

    Zhang, Zhang; Yu, Jun

    2010-01-01

    Background: Understanding the compositional dynamics of genomes and their coding sequences is of great significance in gaining clues into molecular evolution and a large number of publically-available genome sequences have allowed us to quantitatively predict deviations of empirical data from their theoretical counterparts. However, the quantification of theoretical compositional variations for a wide diversity of genomes remains a major challenge.Results: To model the compositional dynamics of protein-coding sequences, we propose two simple models that take into account both mutation and selection effects, which act differently at the three codon positions, and use both GC and purine contents as compositional parameters. The two models concern the theoretical composition of nucleotides, codons, and amino acids, with no prerequisite of homologous sequences or their alignments. We evaluated the two models by quantifying theoretical compositions of a large collection of protein-coding sequences (including 46 of Archaea, 686 of Bacteria, and 826 of Eukarya), yielding consistent theoretical compositions across all the collected sequences.Conclusions: We show that the compositions of nucleotides, codons, and amino acids are largely determined by both GC and purine contents and suggest that deviations of the observed from the expected compositions may reflect compositional signatures that arise from a complex interplay between mutation and selection via DNA replication and repair mechanisms.Reviewers: This article was reviewed by Zhaolei Zhang (nominated by Mark Gerstein), Guruprasad Ananda (nominated by Kateryna Makova), and Daniel Haft. 2010 Zhang and Yu; licensee BioMed Central Ltd.

  9. DockQ: A Quality Measure for Protein-Protein Docking Models

    Science.gov (United States)

    Basu, Sankar

    2016-01-01

    The state-of-the-art to assess the structural quality of docking models is currently based on three related yet independent quality measures: Fnat, LRMS, and iRMS as proposed and standardized by CAPRI. These quality measures quantify different aspects of the quality of a particular docking model and need to be viewed together to reveal the true quality, e.g. a model with relatively poor LRMS (>10Å) might still qualify as 'acceptable' with a descent Fnat (>0.50) and iRMS (iRMS to a single score in the range [0, 1] that can be used to assess the quality of protein docking models. By using DockQ on CAPRI models it is possible to almost completely reproduce the original CAPRI classification into Incorrect, Acceptable, Medium and High quality. An average PPV of 94% at 90% Recall demonstrating that there is no need to apply predefined ad-hoc cutoffs to classify docking models. Since DockQ recapitulates the CAPRI classification almost perfectly, it can be viewed as a higher resolution version of the CAPRI classification, making it possible to estimate model quality in a more quantitative way using Z-scores or sum of top ranked models, which has been so valuable for the CASP community. The possibility to directly correlate a quality measure to a scoring function has been crucial for the development of scoring functions for protein structure prediction, and DockQ should be useful in a similar development in the protein docking field. DockQ is available at http://github.com/bjornwallner/DockQ/ PMID:27560519

  10. IT vendor selection model by using structural equation model & analytical hierarchy process

    Science.gov (United States)

    Maitra, Sarit; Dominic, P. D. D.

    2012-11-01

    Selecting and evaluating the right vendors is imperative for an organization's global marketplace competitiveness. Improper selection and evaluation of potential vendors can dwarf an organization's supply chain performance. Numerous studies have demonstrated that firms consider multiple criteria when selecting key vendors. This research intends to develop a new hybrid model for vendor selection process with better decision making. The new proposed model provides a suitable tool for assisting decision makers and managers to make the right decisions and select the most suitable vendor. This paper proposes a Hybrid model based on Structural Equation Model (SEM) and Analytical Hierarchy Process (AHP) for long-term strategic vendor selection problems. The five steps framework of the model has been designed after the thorough literature study. The proposed hybrid model will be applied using a real life case study to assess its effectiveness. In addition, What-if analysis technique will be used for model validation purpose.

  11. Dealing with selection bias in educational transition models

    DEFF Research Database (Denmark)

    Holm, Anders; Jæger, Mads Meier

    2011-01-01

    This paper proposes the bivariate probit selection model (BPSM) as an alternative to the traditional Mare model for analyzing educational transitions. The BPSM accounts for selection on unobserved variables by allowing for unobserved variables which affect the probability of making educational tr...... account for selection on unobserved variables and high-quality data are both required in order to estimate credible educational transition models.......This paper proposes the bivariate probit selection model (BPSM) as an alternative to the traditional Mare model for analyzing educational transitions. The BPSM accounts for selection on unobserved variables by allowing for unobserved variables which affect the probability of making educational...... transitions to be correlated across transitions. We use simulated and real data to illustrate how the BPSM improves on the traditional Mare model in terms of correcting for selection bias and providing credible estimates of the effect of family background on educational success. We conclude that models which...

  12. Estimation of a multivariate mean under model selection uncertainty

    Directory of Open Access Journals (Sweden)

    Georges Nguefack-Tsague

    2014-05-01

    Full Text Available Model selection uncertainty would occur if we selected a model based on one data set and subsequently applied it for statistical inferences, because the "correct" model would not be selected with certainty.  When the selection and inference are based on the same dataset, some additional problems arise due to the correlation of the two stages (selection and inference. In this paper model selection uncertainty is considered and model averaging is proposed. The proposal is related to the theory of James and Stein of estimating more than three parameters from independent normal observations. We suggest that a model averaging scheme taking into account the selection procedure could be more appropriate than model selection alone. Some properties of this model averaging estimator are investigated; in particular we show using Stein's results that it is a minimax estimator and can outperform Stein-type estimators.

  13. Identifying biological concepts from a protein-related corpus with a probabilistic topic model

    Directory of Open Access Journals (Sweden)

    Lu Xinghua

    2006-02-01

    Full Text Available Abstract Background Biomedical literature, e.g., MEDLINE, contains a wealth of knowledge regarding functions of proteins. Major recurring biological concepts within such text corpora represent the domains of this body of knowledge. The goal of this research is to identify the major biological topics/concepts from a corpus of protein-related MEDLINE© titles and abstracts by applying a probabilistic topic model. Results The latent Dirichlet allocation (LDA model was applied to the corpus. Based on the Bayesian model selection, 300 major topics were extracted from the corpus. The majority of identified topics/concepts was found to be semantically coherent and most represented biological objects or concepts. The identified topics/concepts were further mapped to the controlled vocabulary of the Gene Ontology (GO terms based on mutual information. Conclusion The major and recurring biological concepts within a collection of MEDLINE documents can be extracted by the LDA model. The identified topics/concepts provide parsimonious and semantically-enriched representation of the texts in a semantic space with reduced dimensionality and can be used to index text.

  14. Markov dynamic models for long-timescale protein motion.

    KAUST Repository

    Chiang, Tsung-Han

    2010-06-01

    Molecular dynamics (MD) simulation is a well-established method for studying protein motion at the atomic scale. However, it is computationally intensive and generates massive amounts of data. One way of addressing the dual challenges of computation efficiency and data analysis is to construct simplified models of long-timescale protein motion from MD simulation data. In this direction, we propose to use Markov models with hidden states, in which the Markovian states represent potentially overlapping probabilistic distributions over protein conformations. We also propose a principled criterion for evaluating the quality of a model by its ability to predict long-timescale protein motions. Our method was tested on 2D synthetic energy landscapes and two extensively studied peptides, alanine dipeptide and the villin headpiece subdomain (HP-35 NleNle). One interesting finding is that although a widely accepted model of alanine dipeptide contains six states, a simpler model with only three states is equally good for predicting long-timescale motions. We also used the constructed Markov models to estimate important kinetic and dynamic quantities for protein folding, in particular, mean first-passage time. The results are consistent with available experimental measurements.

  15. Markov dynamic models for long-timescale protein motion.

    KAUST Repository

    Chiang, Tsung-Han; Hsu, David; Latombe, Jean-Claude

    2010-01-01

    Molecular dynamics (MD) simulation is a well-established method for studying protein motion at the atomic scale. However, it is computationally intensive and generates massive amounts of data. One way of addressing the dual challenges of computation efficiency and data analysis is to construct simplified models of long-timescale protein motion from MD simulation data. In this direction, we propose to use Markov models with hidden states, in which the Markovian states represent potentially overlapping probabilistic distributions over protein conformations. We also propose a principled criterion for evaluating the quality of a model by its ability to predict long-timescale protein motions. Our method was tested on 2D synthetic energy landscapes and two extensively studied peptides, alanine dipeptide and the villin headpiece subdomain (HP-35 NleNle). One interesting finding is that although a widely accepted model of alanine dipeptide contains six states, a simpler model with only three states is equally good for predicting long-timescale motions. We also used the constructed Markov models to estimate important kinetic and dynamic quantities for protein folding, in particular, mean first-passage time. The results are consistent with available experimental measurements.

  16. Review and selection of unsaturated flow models

    Energy Technology Data Exchange (ETDEWEB)

    Reeves, M.; Baker, N.A.; Duguid, J.O. [INTERA, Inc., Las Vegas, NV (United States)

    1994-04-04

    Since the 1960`s, ground-water flow models have been used for analysis of water resources problems. In the 1970`s, emphasis began to shift to analysis of waste management problems. This shift in emphasis was largely brought about by site selection activities for geologic repositories for disposal of high-level radioactive wastes. Model development during the 1970`s and well into the 1980`s focused primarily on saturated ground-water flow because geologic repositories in salt, basalt, granite, shale, and tuff were envisioned to be below the water table. Selection of the unsaturated zone at Yucca Mountain, Nevada, for potential disposal of waste began to shift model development toward unsaturated flow models. Under the US Department of Energy (DOE), the Civilian Radioactive Waste Management System Management and Operating Contractor (CRWMS M&O) has the responsibility to review, evaluate, and document existing computer models; to conduct performance assessments; and to develop performance assessment models, where necessary. This document describes the CRWMS M&O approach to model review and evaluation (Chapter 2), and the requirements for unsaturated flow models which are the bases for selection from among the current models (Chapter 3). Chapter 4 identifies existing models, and their characteristics. Through a detailed examination of characteristics, Chapter 5 presents the selection of models for testing. Chapter 6 discusses the testing and verification of selected models. Chapters 7 and 8 give conclusions and make recommendations, respectively. Chapter 9 records the major references for each of the models reviewed. Appendix A, a collection of technical reviews for each model, contains a more complete list of references. Finally, Appendix B characterizes the problems used for model testing.

  17. Review and selection of unsaturated flow models

    International Nuclear Information System (INIS)

    Reeves, M.; Baker, N.A.; Duguid, J.O.

    1994-01-01

    Since the 1960's, ground-water flow models have been used for analysis of water resources problems. In the 1970's, emphasis began to shift to analysis of waste management problems. This shift in emphasis was largely brought about by site selection activities for geologic repositories for disposal of high-level radioactive wastes. Model development during the 1970's and well into the 1980's focused primarily on saturated ground-water flow because geologic repositories in salt, basalt, granite, shale, and tuff were envisioned to be below the water table. Selection of the unsaturated zone at Yucca Mountain, Nevada, for potential disposal of waste began to shift model development toward unsaturated flow models. Under the US Department of Energy (DOE), the Civilian Radioactive Waste Management System Management and Operating Contractor (CRWMS M ampersand O) has the responsibility to review, evaluate, and document existing computer models; to conduct performance assessments; and to develop performance assessment models, where necessary. This document describes the CRWMS M ampersand O approach to model review and evaluation (Chapter 2), and the requirements for unsaturated flow models which are the bases for selection from among the current models (Chapter 3). Chapter 4 identifies existing models, and their characteristics. Through a detailed examination of characteristics, Chapter 5 presents the selection of models for testing. Chapter 6 discusses the testing and verification of selected models. Chapters 7 and 8 give conclusions and make recommendations, respectively. Chapter 9 records the major references for each of the models reviewed. Appendix A, a collection of technical reviews for each model, contains a more complete list of references. Finally, Appendix B characterizes the problems used for model testing

  18. Major membrane surface proteins of Mycoplasma hyopneumoniae selectively modified by covalently bound lipid

    International Nuclear Information System (INIS)

    Wise, K.S.; Kim, M.F.

    1987-01-01

    Surface protein antigens of Mycoplasma hyopneumoniae were identified by direct antibody-surface binding or by radioimmunoprecipitation of surface 125 I-labeled proteins with a series of monoclonal antibodies (MAbs). Radioimmunoprecipitation of TX-114-phase proteins from cells labeled with [ 35 S] methionine, 14 C-amino acids, or [ 3 H] palmitic acid showed that proteins p65, p50, and p44 were abundant and (with one other hydrophobic protein, p60) were selectively labeled with lipid. Alkaline hydroxylamine treatment of labeled proteins indicated linkage of lipids by amide or stable O-linked ester bonds. Proteins p65, p50, and p44 were highly immunogenic in the natural host as measured by immunoblots of TX-114-phase proteins with antisera from swine inoculated with whole organisms. These proteins were antigenically and structurally unrelated, since hyperimmune mouse antibodies to individual gel-purified proteins were monospecific and gave distinct proteolytic epitope maps. Intraspecies size variants of one surface antigen of M. hyopneumoniae were revealed by a MAb to p70 (defined in strain J, ATCC 25934), which recognized a large p73 component on strain VPP11 (ATCC 25617). In addition, MAb to internal, aqueous-phase protein p82 of strain J failed to bind an analogous antigen in strain VPP11

  19. Major membrane surface proteins of Mycoplasma hyopneumoniae selectively modified by covalently bound lipid

    Energy Technology Data Exchange (ETDEWEB)

    Wise K.S.; Kim, M.F.

    1987-12-01

    Surface protein antigens of Mycoplasma hyopneumoniae were identified by direct antibody-surface binding or by radioimmunoprecipitation of surface /sup 125/I-labeled proteins with a series of monoclonal antibodies (MAbs). Radioimmunoprecipitation of TX-114-phase proteins from cells labeled with (/sup 35/S) methionine, /sup 14/C-amino acids, or (/sup 3/H) palmitic acid showed that proteins p65, p50, and p44 were abundant and (with one other hydrophobic protein, p60) were selectively labeled with lipid. Alkaline hydroxylamine treatment of labeled proteins indicated linkage of lipids by amide or stable O-linked ester bonds. Proteins p65, p50, and p44 were highly immunogenic in the natural host as measured by immunoblots of TX-114-phase proteins with antisera from swine inoculated with whole organisms. These proteins were antigenically and structurally unrelated, since hyperimmune mouse antibodies to individual gel-purified proteins were monospecific and gave distinct proteolytic epitope maps. Intraspecies size variants of one surface antigen of M. hyopneumoniae were revealed by a MAb to p70 (defined in strain J, ATCC 25934), which recognized a large p73 component on strain VPP11 (ATCC 25617). In addition, MAb to internal, aqueous-phase protein p82 of strain J failed to bind an analogous antigen in strain VPP11.

  20. Selective association of a fragment of the knob protein with spectrin, actin and the red cell membrane.

    Science.gov (United States)

    Kilejian, A; Rashid, M A; Aikawa, M; Aji, T; Yang, Y F

    1991-02-01

    The knob protein of Plasmodium falciparum is essential for the formation of knob-like protrusions on the host erythrocyte membrane. A functional domain of the knob protein was identified. This peptide formed stable complexes with the two major red cell skeletal proteins, spectrin and actin. When introduced into resealed normal erythrocytes, the peptide associated selectively with the cytoplasmic surface of the membrane and formed knob-like electron dense deposits. Knobs are thought to play an important role in the immunopathology of P. falciparum infections. Our findings provide a first step towards understanding the molecular basis for selective membrane changes at knobs.

  1. Model Selection in Continuous Test Norming With GAMLSS.

    Science.gov (United States)

    Voncken, Lieke; Albers, Casper J; Timmerman, Marieke E

    2017-06-01

    To compute norms from reference group test scores, continuous norming is preferred over traditional norming. A suitable continuous norming approach for continuous data is the use of the Box-Cox Power Exponential model, which is found in the generalized additive models for location, scale, and shape. Applying the Box-Cox Power Exponential model for test norming requires model selection, but it is unknown how well this can be done with an automatic selection procedure. In a simulation study, we compared the performance of two stepwise model selection procedures combined with four model-fit criteria (Akaike information criterion, Bayesian information criterion, generalized Akaike information criterion (3), cross-validation), varying data complexity, sampling design, and sample size in a fully crossed design. The new procedure combined with one of the generalized Akaike information criterion was the most efficient model selection procedure (i.e., required the smallest sample size). The advocated model selection procedure is illustrated with norming data of an intelligence test.

  2. Selection for Protein Kinetic Stability Connects Denaturation Temperatures to Organismal Temperatures and Provides Clues to Archaean Life.

    Directory of Open Access Journals (Sweden)

    M Luisa Romero-Romero

    Full Text Available The relationship between the denaturation temperatures of proteins (Tm values and the living temperatures of their host organisms (environmental temperatures: TENV values is poorly understood. Since different proteins in the same organism may show widely different Tm's, no simple universal relationship between Tm and TENV should hold, other than Tm≥TENV. Yet, when analyzing a set of homologous proteins from different hosts, Tm's are oftentimes found to correlate with TENV's but this correlation is shifted upward on the Tm axis. Supporting this trend, we recently reported Tm's for resurrected Precambrian thioredoxins that mirror a proposed environmental cooling over long geological time, while remaining a shocking ~50°C above the proposed ancestral ocean temperatures. Here, we show that natural selection for protein kinetic stability (denaturation rate can produce a Tm↔TENV correlation with a large upward shift in Tm. A model for protein stability evolution suggests a link between the Tm shift and the in vivo lifetime of a protein and, more specifically, allows us to estimate ancestral environmental temperatures from experimental denaturation rates for resurrected Precambrian thioredoxins. The TENV values thus obtained match the proposed ancestral ocean cooling, support comparatively high Archaean temperatures, and are consistent with a recent proposal for the environmental temperature (above 75°C that hosted the last universal common ancestor. More generally, this work provides a framework for understanding how features of protein stability reflect the environmental temperatures of the host organisms.

  3. ESI-MS studies of the reactions of novel platinum(II) complexes containing O,O'-chelated acetylacetonate and sulfur ligands with selected model proteins.

    Science.gov (United States)

    Marzo, Tiziano; De Pascali, Sandra A; Gabbiani, Chiara; Fanizzi, Francesco P; Messori, Luigi; Pratesi, Alessandro

    2017-08-01

    A group of mixed-ligand Pt(II) complexes bearing acetylacetonate and sulphur ligands were recently developed in the University of Lecce as a new class of prospective anticancer agents that manifested promising pharma-cological properties in preliminary in vitro and in vivo tests. Though modelled on the basis of cisplatin, these Pt(II) complexes turned out to exhibit a profoundly distinct mode of action as they were found to act mainly on non-genomic targets rather than on DNA. Accordingly, we have explored here their reactions with two representative model proteins through an established ESI-MS procedure with the aim to describe their general interaction mechanism with protein targets. A pronounced reactivity with the tested proteins was indeed documented; the nature of the resulting metallodrug-protein interactions could be characterised in depth in the various cases. Preferential binding to protein targets compared to DNA is supported by independent ICP-OES measurements. The implications of these findings are discussed.

  4. Electrostatics promotes molecular crowding and selects the aggregation pathway in fibril-forming protein solutions

    International Nuclear Information System (INIS)

    Raccosta, S.; Martorana, V.; Manno, M.; Blanco, M.; Roberts, C.J.

    2016-01-01

    The role of intermolecular interaction in fibril-forming protein solutions and its relation with molecular conformation are crucial aspects for the control and inhibition of amyloid structures. Here, we study the fibril formation and the protein-protein interactions for two proteins at acidic ph, lysozyme and α-chymotrypsinogen. By using light scattering experiments and the Kirkwood-Buff integral approach, we show how concentration fluctuations are damped even at moderate protein concentrations by the dominant long-ranged electrostatic repulsion, which determines an effective crowded environment. In denaturing conditions, electrostatic repulsion keeps the monomeric solution in a thermodynamically metastable state, which is escaped through kinetically populated conformational sub-states. This explains how electrostatics acts as a gatekeeper in selecting a specific aggregation pathway.

  5. An α-Helix-Mimicking 12,13-Helix: Designed α/β/γ-Foldamers as Selective Inhibitors of Protein-Protein Interactions.

    Science.gov (United States)

    Grison, Claire M; Miles, Jennifer A; Robin, Sylvie; Wilson, Andrew J; Aitken, David J

    2016-09-05

    A major current challenge in bioorganic chemistry is the identification of effective mimics of protein secondary structures that act as inhibitors of protein-protein interactions (PPIs). In this work, trans-2-aminocyclobutanecarboxylic acid (tACBC) was used as the key β-amino acid component in the design of α/β/γ-peptides to structurally mimic a native α-helix. Suitably functionalized α/β/γ-peptides assume an α-helix-mimicking 12,13-helix conformation in solution, exhibit enhanced proteolytic stability in comparison to the wild-type α-peptide parent sequence from which they are derived, and act as selective inhibitors of the p53/hDM2 interaction. © 2016 The Authors. Published by Wiley-VCH Verlag GmbH & Co. KGaA.

  6. An evolutionary model for protein-coding regions with conserved RNA structure

    DEFF Research Database (Denmark)

    Pedersen, Jakob Skou; Forsberg, Roald; Meyer, Irmtraud Margret

    2004-01-01

    in the RNA structure. The overlap of these fundamental dependencies is sufficient to cause "contagious" context dependencies which cascade across many nucleotide sites. Such large-scale dependencies challenge the use of traditional phylogenetic models in evolutionary inference because they explicitly assume...... components of traditional phylogenetic models. We applied this to a data set of full-genome sequences from the hepatitis C virus where five RNA structures are mapped within the coding region. This allowed us to partition the effects of selection on different structural elements and to test various hypotheses......Here we present a model of nucleotide substitution in protein-coding regions that also encode the formation of conserved RNA structures. In such regions, apparent evolutionary context dependencies exist, both between nucleotides occupying the same codon and between nucleotides forming a base pair...

  7. Selective inhibition of Biotin Protein Ligase from Staphylococcus aureus*

    Science.gov (United States)

    Soares da Costa, Tatiana P.; Tieu, William; Yap, Min Y.; Pendini, Nicole R.; Polyak, Steven W.; Sejer Pedersen, Daniel; Morona, Renato; Turnidge, John D.; Wallace, John C.; Wilce, Matthew C. J.; Booker, Grant W.; Abell, Andrew D.

    2012-01-01

    There is a well documented need to replenish the antibiotic pipeline with new agents to combat the rise of drug resistant bacteria. One strategy to combat resistance is to discover new chemical classes immune to current resistance mechanisms that inhibit essential metabolic enzymes. Many of the obvious drug targets that have no homologous isozyme in the human host have now been investigated. Bacterial drug targets that have a closely related human homologue represent a new frontier in antibiotic discovery. However, to avoid potential toxicity to the host, these inhibitors must have very high selectivity for the bacterial enzyme over the human homolog. We have demonstrated that the essential enzyme biotin protein ligase (BPL) from the clinically important pathogen Staphylococcus aureus could be selectively inhibited. Linking biotin to adenosine via a 1,2,3 triazole yielded the first BPL inhibitor selective for S. aureus BPL over the human equivalent. The synthesis of new biotin 1,2,3-triazole analogues using click chemistry yielded our most potent structure (Ki 90 nm) with a >1100-fold selectivity for the S. aureus BPL over the human homologue. X-ray crystallography confirmed the mechanism of inhibitor binding. Importantly, the inhibitor showed cytotoxicity against S. aureus but not cultured mammalian cells. The biotin 1,2,3-triazole provides a novel pharmacophore for future medicinal chemistry programs to develop this new antibiotic class. PMID:22437830

  8. Selective inhibition of biotin protein ligase from Staphylococcus aureus.

    Science.gov (United States)

    Soares da Costa, Tatiana P; Tieu, William; Yap, Min Y; Pendini, Nicole R; Polyak, Steven W; Sejer Pedersen, Daniel; Morona, Renato; Turnidge, John D; Wallace, John C; Wilce, Matthew C J; Booker, Grant W; Abell, Andrew D

    2012-05-18

    There is a well documented need to replenish the antibiotic pipeline with new agents to combat the rise of drug resistant bacteria. One strategy to combat resistance is to discover new chemical classes immune to current resistance mechanisms that inhibit essential metabolic enzymes. Many of the obvious drug targets that have no homologous isozyme in the human host have now been investigated. Bacterial drug targets that have a closely related human homologue represent a new frontier in antibiotic discovery. However, to avoid potential toxicity to the host, these inhibitors must have very high selectivity for the bacterial enzyme over the human homolog. We have demonstrated that the essential enzyme biotin protein ligase (BPL) from the clinically important pathogen Staphylococcus aureus could be selectively inhibited. Linking biotin to adenosine via a 1,2,3 triazole yielded the first BPL inhibitor selective for S. aureus BPL over the human equivalent. The synthesis of new biotin 1,2,3-triazole analogues using click chemistry yielded our most potent structure (K(i) 90 nM) with a >1100-fold selectivity for the S. aureus BPL over the human homologue. X-ray crystallography confirmed the mechanism of inhibitor binding. Importantly, the inhibitor showed cytotoxicity against S. aureus but not cultured mammalian cells. The biotin 1,2,3-triazole provides a novel pharmacophore for future medicinal chemistry programs to develop this new antibiotic class.

  9. Biophysics of protein evolution and evolutionary protein biophysics

    Science.gov (United States)

    Sikosek, Tobias; Chan, Hue Sun

    2014-01-01

    The study of molecular evolution at the level of protein-coding genes often entails comparing large datasets of sequences to infer their evolutionary relationships. Despite the importance of a protein's structure and conformational dynamics to its function and thus its fitness, common phylogenetic methods embody minimal biophysical knowledge of proteins. To underscore the biophysical constraints on natural selection, we survey effects of protein mutations, highlighting the physical basis for marginal stability of natural globular proteins and how requirement for kinetic stability and avoidance of misfolding and misinteractions might have affected protein evolution. The biophysical underpinnings of these effects have been addressed by models with an explicit coarse-grained spatial representation of the polypeptide chain. Sequence–structure mappings based on such models are powerful conceptual tools that rationalize mutational robustness, evolvability, epistasis, promiscuous function performed by ‘hidden’ conformational states, resolution of adaptive conflicts and conformational switches in the evolution from one protein fold to another. Recently, protein biophysics has been applied to derive more accurate evolutionary accounts of sequence data. Methods have also been developed to exploit sequence-based evolutionary information to predict biophysical behaviours of proteins. The success of these approaches demonstrates a deep synergy between the fields of protein biophysics and protein evolution. PMID:25165599

  10. A quick method based on SIMPLISMA-KPLS for simultaneously selecting outlier samples and informative samples for model standardization in near infrared spectroscopy

    Science.gov (United States)

    Li, Li-Na; Ma, Chang-Ming; Chang, Ming; Zhang, Ren-Cheng

    2017-12-01

    A novel method based on SIMPLe-to-use Interactive Self-modeling Mixture Analysis (SIMPLISMA) and Kernel Partial Least Square (KPLS), named as SIMPLISMA-KPLS, is proposed in this paper for selection of outlier samples and informative samples simultaneously. It is a quick algorithm used to model standardization (or named as model transfer) in near infrared (NIR) spectroscopy. The NIR experiment data of the corn for analysis of the protein content is introduced to evaluate the proposed method. Piecewise direct standardization (PDS) is employed in model transfer. And the comparison of SIMPLISMA-PDS-KPLS and KS-PDS-KPLS is given in this research by discussion of the prediction accuracy of protein content and calculation speed of each algorithm. The conclusions include that SIMPLISMA-KPLS can be utilized as an alternative sample selection method for model transfer. Although it has similar accuracy to Kennard-Stone (KS), it is different from KS as it employs concentration information in selection program. This means that it ensures analyte information is involved in analysis, and the spectra (X) of the selected samples is interrelated with concentration (y). And it can be used for outlier sample elimination simultaneously by validation of calibration. According to the statistical data results of running time, it is clear that the sample selection process is more rapid when using KPLS. The quick algorithm of SIMPLISMA-KPLS is beneficial to improve the speed of online measurement using NIR spectroscopy.

  11. Prediction of recombinant protein overexpression in Escherichia coli using a machine learning based model (RPOLP).

    Science.gov (United States)

    Habibi, Narjeskhatoon; Norouzi, Alireza; Mohd Hashim, Siti Z; Shamsir, Mohd Shahir; Samian, Razip

    2015-11-01

    Recombinant protein overexpression, an important biotechnological process, is ruled by complex biological rules which are mostly unknown, is in need of an intelligent algorithm so as to avoid resource-intensive lab-based trial and error experiments in order to determine the expression level of the recombinant protein. The purpose of this study is to propose a predictive model to estimate the level of recombinant protein overexpression for the first time in the literature using a machine learning approach based on the sequence, expression vector, and expression host. The expression host was confined to Escherichia coli which is the most popular bacterial host to overexpress recombinant proteins. To provide a handle to the problem, the overexpression level was categorized as low, medium and high. A set of features which were likely to affect the overexpression level was generated based on the known facts (e.g. gene length) and knowledge gathered from related literature. Then, a representative sub-set of features generated in the previous objective was determined using feature selection techniques. Finally a predictive model was developed using random forest classifier which was able to adequately classify the multi-class imbalanced small dataset constructed. The result showed that the predictive model provided a promising accuracy of 80% on average, in estimating the overexpression level of a recombinant protein. Copyright © 2015 Elsevier Ltd. All rights reserved.

  12. Quality Quandaries- Time Series Model Selection and Parsimony

    DEFF Research Database (Denmark)

    Bisgaard, Søren; Kulahci, Murat

    2009-01-01

    Some of the issues involved in selecting adequate models for time series data are discussed using an example concerning the number of users of an Internet server. The process of selecting an appropriate model is subjective and requires experience and judgment. The authors believe an important...... consideration in model selection should be parameter parsimony. They favor the use of parsimonious mixed ARMA models, noting that research has shown that a model building strategy that considers only autoregressive representations will lead to non-parsimonious models and to loss of forecasting accuracy....

  13. Application of Bayesian Model Selection for Metal Yield Models using ALEGRA and Dakota.

    Energy Technology Data Exchange (ETDEWEB)

    Portone, Teresa; Niederhaus, John Henry; Sanchez, Jason James; Swiler, Laura Painton

    2018-02-01

    This report introduces the concepts of Bayesian model selection, which provides a systematic means of calibrating and selecting an optimal model to represent a phenomenon. This has many potential applications, including for comparing constitutive models. The ideas described herein are applied to a model selection problem between different yield models for hardened steel under extreme loading conditions.

  14. Model for calculation of electrostatic contribution into protein stability

    Science.gov (United States)

    Kundrotas, Petras; Karshikoff, Andrey

    2003-03-01

    Existing models of the denatured state of proteins consider only one possible spatial distribution of protein charges and therefore are applicable to a limited number of cases. In this presentation a more general framework for the modeling of the denatured state is proposed. It is based on the assumption that the titratable groups of an unfolded protein can adopt a quasi-random distribution, restricted by the protein sequence. The model was tested on two proteins, barnase and N-terminal domain of the ribosomal protein L9. The calculated free energy of denaturation, Δ G( pH), reproduces the experimental data essentially better than the commonly used null approximation (NA). It was demonstrated that the seemingly good agreement with experimental data obtained by NA originates from the compensatory effect between the pair-wise electrostatic interactions and the desolvation energy of the individual sites. It was also found that the ionization properties of denatured proteins are influenced by the protein sequence.

  15. Methods for model selection in applied science and engineering.

    Energy Technology Data Exchange (ETDEWEB)

    Field, Richard V., Jr.

    2004-10-01

    Mathematical models are developed and used to study the properties of complex systems and/or modify these systems to satisfy some performance requirements in just about every area of applied science and engineering. A particular reason for developing a model, e.g., performance assessment or design, is referred to as the model use. Our objective is the development of a methodology for selecting a model that is sufficiently accurate for an intended use. Information on the system being modeled is, in general, incomplete, so that there may be two or more models consistent with the available information. The collection of these models is called the class of candidate models. Methods are developed for selecting the optimal member from a class of candidate models for the system. The optimal model depends on the available information, the selected class of candidate models, and the model use. Classical methods for model selection, including the method of maximum likelihood and Bayesian methods, as well as a method employing a decision-theoretic approach, are formulated to select the optimal model for numerous applications. There is no requirement that the candidate models be random. Classical methods for model selection ignore model use and require data to be available. Examples are used to show that these methods can be unreliable when data is limited. The decision-theoretic approach to model selection does not have these limitations, and model use is included through an appropriate utility function. This is especially important when modeling high risk systems, where the consequences of using an inappropriate model for the system can be disastrous. The decision-theoretic method for model selection is developed and applied for a series of complex and diverse applications. These include the selection of the: (1) optimal order of the polynomial chaos approximation for non-Gaussian random variables and stationary stochastic processes, (2) optimal pressure load model to be

  16. A single extracellular amino acid in Free Fatty Acid Receptor 2 defines antagonist species selectivity and G protein selection bias

    DEFF Research Database (Denmark)

    Sergeev, Eugenia; Hansen, Anders Højgaard; Bolognini, Daniele

    2017-01-01

    selectivity and mutational swap studies confirmed this hypothesis. Extending these studies to agonist function indicated that although the lysine - arginine variation between human and mouse orthologs had limited effect on G protein-mediated signal transduction, removal of positive charge from this residue...... produced a signalling-biased variant of Free Fatty Acid Receptor 2 in which Gi-mediated signalling by both short chain fatty acids and synthetic agonists was maintained whilst there was marked loss of agonist potency for signalling via Gq/11 and G12/13 G proteins. A single residue at the extracellular face...

  17. Protein structure modeling and refinement by global optimization in CASP12.

    Science.gov (United States)

    Hong, Seung Hwan; Joung, InSuk; Flores-Canales, Jose C; Manavalan, Balachandran; Cheng, Qianyi; Heo, Seungryong; Kim, Jong Yun; Lee, Sun Young; Nam, Mikyung; Joo, Keehyoung; Lee, In-Ho; Lee, Sung Jong; Lee, Jooyoung

    2018-03-01

    For protein structure modeling in the CASP12 experiment, we have developed a new protocol based on our previous CASP11 approach. The global optimization method of conformational space annealing (CSA) was applied to 3 stages of modeling: multiple sequence-structure alignment, three-dimensional (3D) chain building, and side-chain re-modeling. For better template selection and model selection, we updated our model quality assessment (QA) method with the newly developed SVMQA (support vector machine for quality assessment). For 3D chain building, we updated our energy function by including restraints generated from predicted residue-residue contacts. New energy terms for the predicted secondary structure and predicted solvent accessible surface area were also introduced. For difficult targets, we proposed a new method, LEEab, where the template term played a less significant role than it did in LEE, complemented by increased contributions from other terms such as the predicted contact term. For TBM (template-based modeling) targets, LEE performed better than LEEab, but for FM targets, LEEab was better. For model refinement, we modified our CASP11 molecular dynamics (MD) based protocol by using explicit solvents and tuning down restraint weights. Refinement results from MD simulations that used a new augmented statistical energy term in the force field were quite promising. Finally, when using inaccurate information (such as the predicted contacts), it was important to use the Lorentzian function for which the maximal penalty arising from wrong information is always bounded. © 2017 Wiley Periodicals, Inc.

  18. In vitro Selection and Interaction Studies of a DNA Aptamer Targeting Protein A

    OpenAIRE

    Stoltenburg, Regina; Schubert, Thomas; Strehlitz, Beate

    2015-01-01

    A new DNA aptamer targeting Protein A is presented. The aptamer was selected by use of the FluMag-SELEX procedure. The SELEX technology (Systematic Evolution of Ligands by EXponential enrichment) is widely applied as an in vitro selection and amplification method to generate target-specific aptamers and exists in various modified variants. FluMag-SELEX is one of them and is characterized by the use of magnetic beads for target immobilization and fluorescently labeled oligonucleotides for moni...

  19. Use of hydrostatic pressure for modulation of protein chemical modification and enzymatic selectivity.

    Science.gov (United States)

    Makarov, Alexey A; Helmy, Roy; Joyce, Leo; Reibarkh, Mikhail; Maust, Mathew; Ren, Sumei; Mergelsberg, Ingrid; Welch, Christopher J

    2016-05-11

    Using hydrostatic pressure to induce protein conformational changes can be a powerful tool for altering the availability of protein reactive sites and for changing the selectivity of enzymatic reactions. Using a pressure apparatus, it has been demonstrated that hydrostatic pressure can be used to modulate the reactivity of lysine residues of the protein ubiquitin with a water-soluble amine-specific homobifunctional coupling agent. Fewer reactive lysine residues were observed when the reaction was carried out under elevated pressure of 3 kbar, consistent with a pressure-induced conformational change of ubiquitin that results in fewer exposed lysine residues. Additionally, modulation of the stereoselectivity of an enzymatic transamination reaction was observed at elevated hydrostatic pressure. In one case, the minor diasteromeric product formed at atmospheric pressure became the major product at elevated pressure. Such pressure-induced alterations of protein reactivity may provide an important new tool for enzymatic reactions and the chemical modification of proteins.

  20. Discrete persistent-chain model for protein binding on DNA.

    Science.gov (United States)

    Lam, Pui-Man; Zhen, Yi

    2011-04-01

    We describe and solve a discrete persistent-chain model of protein binding on DNA, involving an extra σ(i) at a site i of the DNA. This variable takes the value 1 or 0, depending on whether or not the site is occupied by a protein. In addition, if the site is occupied by a protein, there is an extra energy cost ɛ. For a small force, we obtain analytic expressions for the force-extension curve and the fraction of bound protein on the DNA. For higher forces, the model can be solved numerically to obtain force-extension curves and the average fraction of bound proteins as a function of applied force. Our model can be used to analyze experimental force-extension curves of protein binding on DNA, and hence deduce the number of bound proteins in the case of nonspecific binding. ©2011 American Physical Society

  1. Modeling Mercury in Proteins

    Energy Technology Data Exchange (ETDEWEB)

    Smith, Jeremy C [ORNL; Parks, Jerry M [ORNL

    2016-01-01

    Mercury (Hg) is a naturally occurring element that is released into the biosphere both by natural processes and anthropogenic activities. Although its reduced, elemental form Hg(0) is relatively non-toxic, other forms such as Hg2+ and, in particular, its methylated form, methylmercury, are toxic, with deleterious effects on both ecosystems and humans. Microorganisms play important roles in the transformation of mercury in the environment. Inorganic Hg2+ can be methylated by certain bacteria and archaea to form methylmercury. Conversely, bacteria also demethylate methylmercury and reduce Hg2+ to relatively inert Hg(0). Transformations and toxicity occur as a result of mercury interacting with various proteins. Clearly, then, understanding the toxic effects of mercury and its cycling in the environment requires characterization of these interactions. Computational approaches are ideally suited to studies of mercury in proteins because they can provide a detailed picture and circumvent issues associated with toxicity. Here we describe computational methods for investigating and characterizing how mercury binds to proteins, how inter- and intra-protein transfer of mercury is orchestrated in biological systems, and how chemical reactions in proteins transform the metal. We describe quantum chemical analyses of aqueous Hg(II), which reveal critical factors that determine ligand binding propensities. We then provide a perspective on how we used chemical reasoning to discover how microorganisms methylate mercury. We also highlight our combined computational and experimental studies of the proteins and enzymes of the mer operon, a suite of genes that confers mercury resistance in many bacteria. Lastly, we place work on mercury in proteins in the context of what is needed for a comprehensive multi-scale model of environmental mercury cycling.

  2. Magnetic deep eutectic solvents molecularly imprinted polymers for the selective recognition and separation of protein

    International Nuclear Information System (INIS)

    Liu, Yanjin; Wang, Yuzhi; Dai, Qingzhou; Zhou, Yigang

    2016-01-01

    A novel and facile magnetic deep eutectic solvents (DES) molecularly imprinted polymers (MIPs) for the selective recognition and separation of Bovine hemoglobin (BHb) was prepared. The new-type DES was adopted as the functional monomer which would bring molecular imprinted technology to a new direction. The amounts of DES were optimized. The obtained magnetic DES-MIPs were characterized with fourier transform infrared spectrometry (FT-IR), thermogravimetric analysis (TGA), field emission scanning electron microscope (FESEM), dynamic light scattering (DLS), elemental analysis and vibrating sample magnetometer (VSM). The results suggested that the imprinted polymers were successfully formed and possessed a charming magnetism. The maximum adsorption capability (Q_m_a_x) and dissociation constant (K_L) were analyzed by Langmuir isotherms (R"2 = 0.9983) and the value were estimated to be 175.44 mg/g and 0.035 mg/mL for the imprinted particles. And the imprinted particles showed a high imprinting factor of 4.77. In addition, the magnetic DES-MIPs presented outstanding recognition specificity and selectivity so that it can be utilized to separate template protein from the mixture of proteins and real samples. Last but not least, the combination of deep eutectic solvents and molecular imprinted technology in this paper provides a new perspective for the recognition and separation of proteins. - Highlights: • Combined green deep eutectic solvents (DES) and molecular imprinted technology in recognition and separation of proteins. • DES was adopted as a new-type functional monomer. • The obtained magnetic DES-MIPs can separate proteins rapidly by an external magnetic field. • Adsorption and selectivity properties were discussed.

  3. Magnetic deep eutectic solvents molecularly imprinted polymers for the selective recognition and separation of protein

    Energy Technology Data Exchange (ETDEWEB)

    Liu, Yanjin [State Key Laboratory of Chemo/Biosensing and Chemometrics, College of Chemistry and Chemical Engineering, Hunan University, Changsha, 410082 (China); Wang, Yuzhi, E-mail: wyzss@hnu.edu.cn [State Key Laboratory of Chemo/Biosensing and Chemometrics, College of Chemistry and Chemical Engineering, Hunan University, Changsha, 410082 (China); Dai, Qingzhou [State Key Laboratory of Chemo/Biosensing and Chemometrics, College of Chemistry and Chemical Engineering, Hunan University, Changsha, 410082 (China); Zhou, Yigang [Department of Microbiology, College of Basic Medicine, Central South University, Changsha, 410083 (China)

    2016-09-14

    A novel and facile magnetic deep eutectic solvents (DES) molecularly imprinted polymers (MIPs) for the selective recognition and separation of Bovine hemoglobin (BHb) was prepared. The new-type DES was adopted as the functional monomer which would bring molecular imprinted technology to a new direction. The amounts of DES were optimized. The obtained magnetic DES-MIPs were characterized with fourier transform infrared spectrometry (FT-IR), thermogravimetric analysis (TGA), field emission scanning electron microscope (FESEM), dynamic light scattering (DLS), elemental analysis and vibrating sample magnetometer (VSM). The results suggested that the imprinted polymers were successfully formed and possessed a charming magnetism. The maximum adsorption capability (Q{sub max}) and dissociation constant (K{sub L}) were analyzed by Langmuir isotherms (R{sup 2} = 0.9983) and the value were estimated to be 175.44 mg/g and 0.035 mg/mL for the imprinted particles. And the imprinted particles showed a high imprinting factor of 4.77. In addition, the magnetic DES-MIPs presented outstanding recognition specificity and selectivity so that it can be utilized to separate template protein from the mixture of proteins and real samples. Last but not least, the combination of deep eutectic solvents and molecular imprinted technology in this paper provides a new perspective for the recognition and separation of proteins. - Highlights: • Combined green deep eutectic solvents (DES) and molecular imprinted technology in recognition and separation of proteins. • DES was adopted as a new-type functional monomer. • The obtained magnetic DES-MIPs can separate proteins rapidly by an external magnetic field. • Adsorption and selectivity properties were discussed.

  4. Dynamic Proteomics Emphasizes the Importance of Selective mRNA Translation and Protein Turnover during Arabidopsis Seed Germination*

    Science.gov (United States)

    Galland, Marc; Huguet, Romain; Arc, Erwann; Cueff, Gwendal; Job, Dominique; Rajjou, Loïc

    2014-01-01

    During seed germination, the transition from a quiescent metabolic state in a dry mature seed to a proliferative metabolic state in a vigorous seedling is crucial for plant propagation as well as for optimizing crop yield. This work provides a detailed description of the dynamics of protein synthesis during the time course of germination, demonstrating that mRNA translation is both sequential and selective during this process. The complete inhibition of the germination process in the presence of the translation inhibitor cycloheximide established that mRNA translation is critical for Arabidopsis seed germination. However, the dynamics of protein turnover and the selectivity of protein synthesis (mRNA translation) during Arabidopsis seed germination have not been addressed yet. Based on our detailed knowledge of the Arabidopsis seed proteome, we have deepened our understanding of seed mRNA translation during germination by combining two-dimensional gel-based proteomics with dynamic radiolabeled proteomics using a radiolabeled amino acid precursor, namely [35S]-methionine, in order to highlight de novo protein synthesis, stability, and turnover. Our data confirm that during early imbibition, the Arabidopsis translatome keeps reflecting an embryonic maturation program until a certain developmental checkpoint. Furthermore, by dividing the seed germination time lapse into discrete time windows, we highlight precise and specific patterns of protein synthesis. These data refine and deepen our knowledge of the three classical phases of seed germination based on seed water uptake during imbibition and reveal that selective mRNA translation is a key feature of seed germination. Beyond the quantitative control of translational activity, both the selectivity of mRNA translation and protein turnover appear as specific regulatory systems, critical for timing the molecular events leading to successful germination and seedling establishment. PMID:24198433

  5. A Heckman Selection- t Model

    KAUST Repository

    Marchenko, Yulia V.; Genton, Marc G.

    2012-01-01

    for sample selection bias based on the SLt model and compare it with the performances of several tests used with the SLN model. Our findings indicate that the latter tests can be misleading in the presence of heavy-tailed data. © 2012 American Statistical

  6. Phenotype selection reveals coevolution of muscle glycogen and protein and PTEN as a gate keeper for the accretion of muscle mass in adult female mice.

    Directory of Open Access Journals (Sweden)

    Mandy Sawitzky

    Full Text Available We have investigated molecular mechanisms for muscle mass accretion in a non-inbred mouse model (DU6P mice characterized by extreme muscle mass. This extreme muscle mass was developed during 138 generations of phenotype selection for high protein content. Due to the repeated trait selection a complex setting of different mechanisms was expected to be enriched during the selection experiment. In muscle from 29-week female DU6P mice we have identified robust increases of protein kinase B activation (AKT, Ser-473, up to 2-fold if compared to 11- and 54-week DU6P mice or controls. While a number of accepted effectors of AKT activation, including IGF-I, IGF-II, insulin/IGF-receptor, myostatin or integrin-linked kinase (ILK, were not correlated with this increase, phosphatase and tensin homologue deleted on chromosome 10 (PTEN was down-regulated in 29-week female DU6P mice. In addition, higher levels of PTEN phosphorylation were found identifying a second mechanism of PTEN inhibition. Inhibition of PTEN and activation of AKT correlated with specific activation of p70S6 kinase and ribosomal protein S6, reduced phosphorylation of eukaryotic initiation factor 2α (eIF2α and higher rates of protein synthesis in 29-week female DU6P mice. On the other hand, AKT activation also translated into specific inactivation of glycogen synthase kinase 3ß (GSK3ß and an increase of muscular glycogen. In muscles from 29-week female DU6P mice a significant increase of protein/DNA was identified, which was not due to a reduction of protein breakdown or to specific increases of translation initiation. Instead our data support the conclusion that a higher rate of protein translation is contributing to the higher muscle mass in mid-aged female DU6P mice. Our results further reveal coevolution of high protein and high glycogen content during the selection experiment and identify PTEN as gate keeper for muscle mass in mid-aged female DU6P mice.

  7. Mutation-selection models of codon substitution and their use to estimate selective strengths on codon usage

    DEFF Research Database (Denmark)

    Yang, Ziheng; Nielsen, Rasmus

    2008-01-01

    Current models of codon substitution are formulated at the levels of nucleotide substitution and do not explicitly consider the separate effects of mutation and selection. They are thus incapable of inferring whether mutation or selection is responsible for evolution at silent sites. Here we impl...... codon usage in mammals. Estimates of selection coefficients nevertheless suggest that selection on codon usage is weak and most mutations are nearly neutral. The sensitivity of the analysis on the assumed mutation model is discussed.......Current models of codon substitution are formulated at the levels of nucleotide substitution and do not explicitly consider the separate effects of mutation and selection. They are thus incapable of inferring whether mutation or selection is responsible for evolution at silent sites. Here we...... implement a few population genetics models of codon substitution that explicitly consider mutation bias and natural selection at the DNA level. Selection on codon usage is modeled by introducing codon-fitness parameters, which together with mutation-bias parameters, predict optimal codon frequencies...

  8. Protein Folding: Search for Basic Physical Models

    Directory of Open Access Journals (Sweden)

    Ivan Y. Torshin

    2003-01-01

    Full Text Available How a unique three-dimensional structure is rapidly formed from the linear sequence of a polypeptide is one of the important questions in contemporary science. Apart from biological context of in vivo protein folding (which has been studied only for a few proteins, the roles of the fundamental physical forces in the in vitro folding remain largely unstudied. Despite a degree of success in using descriptions based on statistical and/or thermodynamic approaches, few of the current models explicitly include more basic physical forces (such as electrostatics and Van Der Waals forces. Moreover, the present-day models rarely take into account that the protein folding is, essentially, a rapid process that produces a highly specific architecture. This review considers several physical models that may provide more direct links between sequence and tertiary structure in terms of the physical forces. In particular, elaboration of such simple models is likely to produce extremely effective computational techniques with value for modern genomics.

  9. Models of crk adaptor proteins in cancer.

    Science.gov (United States)

    Bell, Emily S; Park, Morag

    2012-05-01

    The Crk family of adaptor proteins (CrkI, CrkII, and CrkL), originally discovered as the oncogene fusion product, v-Crk, of the CT10 chicken retrovirus, lacks catalytic activity but engages with multiple signaling pathways through their SH2 and SH3 domains. Crk proteins link upstream tyrosine kinase and integrin-dependent signals to downstream effectors, acting as adaptors in diverse signaling pathways and cellular processes. Crk proteins are now recognized to play a role in the malignancy of many human cancers, stimulating renewed interest in their mechanism of action in cancer progression. The contribution of Crk signaling to malignancy has been predominantly studied in fibroblasts and in hematopoietic models and more recently in epithelial models. A mechanistic understanding of Crk proteins in cancer progression in vivo is still poorly understood in part due to the highly pleiotropic nature of Crk signaling. Recent advances in the structural organization of Crk domains, new roles in kinase regulation, and increased knowledge of the mechanisms and frequency of Crk overexpression in human cancers have provided an incentive for further study in in vivo models. An understanding of the mechanisms through which Crk proteins act as oncogenic drivers could have important implications in therapeutic targeting.

  10. Modelling Protein Dynamics on the Microsecond Time Scale

    DEFF Research Database (Denmark)

    Siuda, Iwona Anna

    Recent years have shown an increase in coarse-grained (CG) molecular dynamics simulations, providing structural and dynamic details of large proteins and enabling studies of self-assembly of biological materials. It is not easy to acquire such data experimentally, and access is also still limited...... in atomistic simulations. During her PhD studies, Iwona Siuda used MARTINI CG models to study the dynamics of different globular and membrane proteins. In several cases, the MARTINI model was sufficient to study conformational changes of small, purely alpha-helical proteins. However, in studies of larger......ELNEDIN was therefore proposed as part of the work. Iwona Siuda’s results from the CG simulations had biological implications that provide insights into possible mechanisms of the periplasmic leucine-binding protein, the sarco(endo)plasmic reticulum calcium pump, and several proteins from the saposin-like proteins...

  11. Generation of a selectively cytotoxic fusion protein against p53 mutated cancers

    International Nuclear Information System (INIS)

    Kousparou, Christina A; Yiacoumi, Efthymia; Deonarain, Mahendra P; Epenetos, Agamemnon A

    2012-01-01

    A significant number of cancers are caused by defects in p21 causing functional defects in p21 or p53 tumour-suppressor proteins. This has led to many therapeutic approaches including restoration by gene therapy with wild-type p53 or p21 using viral or liposomal vectors, which have toxicity or side-effect limitations. We set out to develop a safer, novel fusion protein which has the ability to reconstitute cancer cell lines with active p21 by protein transduction. The fusion protein was produced from the cell-translocating peptide Antennapedia (Antp) and wild-type, full-length p21 (Antp-p21). This was expressed and refolded from E. coli and tested on a variety of cell lines and tumours (in a BALB/c nude xenograft model) with differing p21 or p53 status. Antp-p21 penetrated and killed cancer cells that do not express wild type p53 or p21. This included cells that were matched to cogenic parental cell lines. Antp-p21 killed cancer cells selectively that were malignant as a result of mutations or nuclear exclusion of the p53 and p21 genes and over-expression of MDM2. Non-specific toxicity was excluded by showing that Antp-p21 penetrated but did not kill p53- or p21- wild-type cells. Antp-p21 was not immunogenic in normal New Zealand White rabbits. Recombinant Antp peptide alone was not cytotoxic, showing that killing was due to the transduction of the p21 component of Antp-p21. Antp-p21 was shown to penetrate cancer cells engrafted in vivo and resulted in tumour eradication when administered with conventionally-used chemotherapeutic agents, which alone were unable to produce such an effect. Antp-p21 may represent a new and promising targeted therapy for patients with p53-associated cancers supporting the concept that rational design of therapies directed against specific cancer mutations will play a part in the future of medical oncology

  12. Generation of a selectively cytotoxic fusion protein against p53 mutated cancers

    Directory of Open Access Journals (Sweden)

    Kousparou Christina A

    2012-08-01

    Full Text Available Abstract Background A significant number of cancers are caused by defects in p21 causing functional defects in p21 or p53 tumour-suppressor proteins. This has led to many therapeutic approaches including restoration by gene therapy with wild-type p53 or p21 using viral or liposomal vectors, which have toxicity or side-effect limitations. We set out to develop a safer, novel fusion protein which has the ability to reconstitute cancer cell lines with active p21 by protein transduction. Methods The fusion protein was produced from the cell-translocating peptide Antennapedia (Antp and wild-type, full-length p21 (Antp-p21. This was expressed and refolded from E. coli and tested on a variety of cell lines and tumours (in a BALB/c nude xenograft model with differing p21 or p53 status. Results Antp-p21 penetrated and killed cancer cells that do not express wild type p53 or p21. This included cells that were matched to cogenic parental cell lines. Antp-p21 killed cancer cells selectively that were malignant as a result of mutations or nuclear exclusion of the p53 and p21 genes and over-expression of MDM2. Non-specific toxicity was excluded by showing that Antp-p21 penetrated but did not kill p53- or p21- wild-type cells. Antp-p21 was not immunogenic in normal New Zealand White rabbits. Recombinant Antp peptide alone was not cytotoxic, showing that killing was due to the transduction of the p21 component of Antp-p21. Antp-p21 was shown to penetrate cancer cells engrafted in vivo and resulted in tumour eradication when administered with conventionally-used chemotherapeutic agents, which alone were unable to produce such an effect. Conclusions Antp-p21 may represent a new and promising targeted therapy for patients with p53-associated cancers supporting the concept that rational design of therapies directed against specific cancer mutations will play a part in the future of medical oncology.

  13. A Computational Model of Selection by Consequences

    Science.gov (United States)

    McDowell, J. J.

    2004-01-01

    Darwinian selection by consequences was instantiated in a computational model that consisted of a repertoire of behaviors undergoing selection, reproduction, and mutation over many generations. The model in effect created a digital organism that emitted behavior continuously. The behavior of this digital organism was studied in three series of…

  14. Multi-Label Learning via Random Label Selection for Protein Subcellular Multi-Locations Prediction.

    Science.gov (United States)

    Wang, Xiao; Li, Guo-Zheng

    2013-03-12

    Prediction of protein subcellular localization is an important but challenging problem, particularly when proteins may simultaneously exist at, or move between, two or more different subcellular location sites. Most of the existing protein subcellular localization methods are only used to deal with the single-location proteins. In the past few years, only a few methods have been proposed to tackle proteins with multiple locations. However, they only adopt a simple strategy, that is, transforming the multi-location proteins to multiple proteins with single location, which doesn't take correlations among different subcellular locations into account. In this paper, a novel method named RALS (multi-label learning via RAndom Label Selection), is proposed to learn from multi-location proteins in an effective and efficient way. Through five-fold cross validation test on a benchmark dataset, we demonstrate our proposed method with consideration of label correlations obviously outperforms the baseline BR method without consideration of label correlations, indicating correlations among different subcellular locations really exist and contribute to improvement of prediction performance. Experimental results on two benchmark datasets also show that our proposed methods achieve significantly higher performance than some other state-of-the-art methods in predicting subcellular multi-locations of proteins. The prediction web server is available at http://levis.tongji.edu.cn:8080/bioinfo/MLPred-Euk/ for the public usage.

  15. Immunogenicity of therapeutic proteins: the use of animal models.

    Science.gov (United States)

    Brinks, Vera; Jiskoot, Wim; Schellekens, Huub

    2011-10-01

    Immunogenicity of therapeutic proteins lowers patient well-being and drastically increases therapeutic costs. Preventing immunogenicity is an important issue to consider when developing novel therapeutic proteins and applying them in the clinic. Animal models are increasingly used to study immunogenicity of therapeutic proteins. They are employed as predictive tools to assess different aspects of immunogenicity during drug development and have become vital in studying the mechanisms underlying immunogenicity of therapeutic proteins. However, the use of animal models needs critical evaluation. Because of species differences, predictive value of such models is limited, and mechanistic studies can be restricted. This review addresses the suitability of animal models for immunogenicity prediction and summarizes the insights in immunogenicity that they have given so far.

  16. A computational model of selection by consequences.

    OpenAIRE

    McDowell, J J

    2004-01-01

    Darwinian selection by consequences was instantiated in a computational model that consisted of a repertoire of behaviors undergoing selection, reproduction, and mutation over many generations. The model in effect created a digital organism that emitted behavior continuously. The behavior of this digital organism was studied in three series of computational experiments that arranged reinforcement according to random-interval (RI) schedules. The quantitative features of the model were varied o...

  17. Construction of multiple linear regression models using blood biomarkers for selecting against abdominal fat traits in broilers.

    Science.gov (United States)

    Dong, J Q; Zhang, X Y; Wang, S Z; Jiang, X F; Zhang, K; Ma, G W; Wu, M Q; Li, H; Zhang, H

    2018-01-01

    Plasma very low-density lipoprotein (VLDL) can be used to select for low body fat or abdominal fat (AF) in broilers, but its correlation with AF is limited. We investigated whether any other biochemical indicator can be used in combination with VLDL for a better selective effect. Nineteen plasma biochemical indicators were measured in male chickens from the Northeast Agricultural University broiler lines divergently selected for AF content (NEAUHLF) in the fed state at 46 and 48 d of age. The average concentration of every parameter for the 2 d was used for statistical analysis. Levels of these 19 plasma biochemical parameters were compared between the lean and fat lines. The phenotypic correlations between these plasma biochemical indicators and AF traits were analyzed. Then, multiple linear regression models were constructed to select the best model used for selecting against AF content. and the heritabilities of plasma indicators contained in the best models were estimated. The results showed that 11 plasma biochemical indicators (triglycerides, total bile acid, total protein, globulin, albumin/globulin, aspartate transaminase, alanine transaminase, gamma-glutamyl transpeptidase, uric acid, creatinine, and VLDL) differed significantly between the lean and fat lines (P linear regression models based on albumin/globulin, VLDL, triglycerides, globulin, total bile acid, and uric acid, had higher R2 (0.73) than the model based only on VLDL (0.21). The plasma parameters included in the best models had moderate heritability estimates (0.21 ≤ h2 ≤ 0.43). These results indicate that these multiple linear regression models can be used to select for lean broiler chickens. © 2017 Poultry Science Association Inc.

  18. Statistical model selection with “Big Data”

    Directory of Open Access Journals (Sweden)

    Jurgen A. Doornik

    2015-12-01

    Full Text Available Big Data offer potential benefits for statistical modelling, but confront problems including an excess of false positives, mistaking correlations for causes, ignoring sampling biases and selecting by inappropriate methods. We consider the many important requirements when searching for a data-based relationship using Big Data, and the possible role of Autometrics in that context. Paramount considerations include embedding relationships in general initial models, possibly restricting the number of variables to be selected over by non-statistical criteria (the formulation problem, using good quality data on all variables, analyzed with tight significance levels by a powerful selection procedure, retaining available theory insights (the selection problem while testing for relationships being well specified and invariant to shifts in explanatory variables (the evaluation problem, using a viable approach that resolves the computational problem of immense numbers of possible models.

  19. Scoring predictive models using a reduced representation of proteins: model and energy definition.

    Science.gov (United States)

    Fogolari, Federico; Pieri, Lidia; Dovier, Agostino; Bortolussi, Luca; Giugliarelli, Gilberto; Corazza, Alessandra; Esposito, Gennaro; Viglino, Paolo

    2007-03-23

    Reduced representations of proteins have been playing a keyrole in the study of protein folding. Many such models are available, with different representation detail. Although the usefulness of many such models for structural bioinformatics applications has been demonstrated in recent years, there are few intermediate resolution models endowed with an energy model capable, for instance, of detecting native or native-like structures among decoy sets. The aim of the present work is to provide a discrete empirical potential for a reduced protein model termed here PC2CA, because it employs a PseudoCovalent structure with only 2 Centers of interactions per Amino acid, suitable for protein model quality assessment. All protein structures in the set top500H have been converted in reduced form. The distribution of pseudobonds, pseudoangle, pseudodihedrals and distances between centers of interactions have been converted into potentials of mean force. A suitable reference distribution has been defined for non-bonded interactions which takes into account excluded volume effects and protein finite size. The correlation between adjacent main chain pseudodihedrals has been converted in an additional energetic term which is able to account for cooperative effects in secondary structure elements. Local energy surface exploration is performed in order to increase the robustness of the energy function. The model and the energy definition proposed have been tested on all the multiple decoys' sets in the Decoys'R'us database. The energetic model is able to recognize, for almost all sets, native-like structures (RMSD less than 2.0 A). These results and those obtained in the blind CASP7 quality assessment experiment suggest that the model compares well with scoring potentials with finer granularity and could be useful for fast exploration of conformational space. Parameters are available at the url: http://www.dstb.uniud.it/~ffogolari/download/.

  20. PredPPCrys: accurate prediction of sequence cloning, protein production, purification and crystallization propensity from protein sequences using multi-step heterogeneous feature fusion and selection.

    Directory of Open Access Journals (Sweden)

    Huilin Wang

    Full Text Available X-ray crystallography is the primary approach to solve the three-dimensional structure of a protein. However, a major bottleneck of this method is the failure of multi-step experimental procedures to yield diffraction-quality crystals, including sequence cloning, protein material production, purification, crystallization and ultimately, structural determination. Accordingly, prediction of the propensity of a protein to successfully undergo these experimental procedures based on the protein sequence may help narrow down laborious experimental efforts and facilitate target selection. A number of bioinformatics methods based on protein sequence information have been developed for this purpose. However, our knowledge on the important determinants of propensity for a protein sequence to produce high diffraction-quality crystals remains largely incomplete. In practice, most of the existing methods display poorer performance when evaluated on larger and updated datasets. To address this problem, we constructed an up-to-date dataset as the benchmark, and subsequently developed a new approach termed 'PredPPCrys' using the support vector machine (SVM. Using a comprehensive set of multifaceted sequence-derived features in combination with a novel multi-step feature selection strategy, we identified and characterized the relative importance and contribution of each feature type to the prediction performance of five individual experimental steps required for successful crystallization. The resulting optimal candidate features were used as inputs to build the first-level SVM predictor (PredPPCrys I. Next, prediction outputs of PredPPCrys I were used as the input to build second-level SVM classifiers (PredPPCrys II, which led to significantly enhanced prediction performance. Benchmarking experiments indicated that our PredPPCrys method outperforms most existing procedures on both up-to-date and previous datasets. In addition, the predicted crystallization

  1. Selectivity analysis of protein kinase CK2 inhibitors DMAT, TBB and resorufin in cisplatin-induced stress responses

    DEFF Research Database (Denmark)

    Fritz, Gerhard; Issinger, Olaf-Georg; Olsen, Birgitte Brinkmann

    2009-01-01

    Targeting protein kinases as a therapeutic approach to treat various diseases, especially cancer is currently a fast growing business. Although many inhibitors are available, exhibiting remarkable potency, the major challenge is their selectivity. Here we show that the protein kinase CK2 inhibito...

  2. A Peptidomimetic Antibiotic Targets Outer Membrane Proteins and Disrupts Selectively the Outer Membrane in Escherichia coli.

    Science.gov (United States)

    Urfer, Matthias; Bogdanovic, Jasmina; Lo Monte, Fabio; Moehle, Kerstin; Zerbe, Katja; Omasits, Ulrich; Ahrens, Christian H; Pessi, Gabriella; Eberl, Leo; Robinson, John A

    2016-01-22

    Increasing antibacterial resistance presents a major challenge in antibiotic discovery. One attractive target in Gram-negative bacteria is the unique asymmetric outer membrane (OM), which acts as a permeability barrier that protects the cell from external stresses, such as the presence of antibiotics. We describe a novel β-hairpin macrocyclic peptide JB-95 with potent antimicrobial activity against Escherichia coli. This peptide exhibits no cellular lytic activity, but electron microscopy and fluorescence studies reveal an ability to selectively disrupt the OM but not the inner membrane of E. coli. The selective targeting of the OM probably occurs through interactions of JB-95 with selected β-barrel OM proteins, including BamA and LptD as shown by photolabeling experiments. Membrane proteomic studies reveal rapid depletion of many β-barrel OM proteins from JB-95-treated E. coli, consistent with induction of a membrane stress response and/or direct inhibition of the Bam folding machine. The results suggest that lethal disruption of the OM by JB-95 occurs through a novel mechanism of action at key interaction sites within clusters of β-barrel proteins in the OM. These findings open new avenues for developing antibiotics that specifically target β-barrel proteins and the integrity of the Gram-negative OM. © 2016 by The American Society for Biochemistry and Molecular Biology, Inc.

  3. Protein structure and ionic selectivity in calcium channels: Selectivity filter size, not shape, matters

    OpenAIRE

    Malasics, Attila; Gillespie, Dirk; Nonner, Wolfgang; Henderson, Douglas; Eisenberg, Bob; Boda, Dezső

    2009-01-01

    Calcium channels have highly charged selectivity filters (4 COO− groups) that attract cations in to balance this charge and minimize free energy, forcing the cations (Na+ and Ca2+) to compete for space in the filter. A reduced model was developed to better understand the mechanism of ion selectivity in calcium channels. The charge/space competition (CSC) mechanism implies that Ca2+ is more efficient in balancing the charge of the filter because it provides twice the charge as Na+ while occupy...

  4. Predicting nucleic acid binding interfaces from structural models of proteins.

    Science.gov (United States)

    Dror, Iris; Shazman, Shula; Mukherjee, Srayanta; Zhang, Yang; Glaser, Fabian; Mandel-Gutfreund, Yael

    2012-02-01

    The function of DNA- and RNA-binding proteins can be inferred from the characterization and accurate prediction of their binding interfaces. However, the main pitfall of various structure-based methods for predicting nucleic acid binding function is that they are all limited to a relatively small number of proteins for which high-resolution three-dimensional structures are available. In this study, we developed a pipeline for extracting functional electrostatic patches from surfaces of protein structural models, obtained using the I-TASSER protein structure predictor. The largest positive patches are extracted from the protein surface using the patchfinder algorithm. We show that functional electrostatic patches extracted from an ensemble of structural models highly overlap the patches extracted from high-resolution structures. Furthermore, by testing our pipeline on a set of 55 known nucleic acid binding proteins for which I-TASSER produces high-quality models, we show that the method accurately identifies the nucleic acids binding interface on structural models of proteins. Employing a combined patch approach we show that patches extracted from an ensemble of models better predicts the real nucleic acid binding interfaces compared with patches extracted from independent models. Overall, these results suggest that combining information from a collection of low-resolution structural models could be a valuable approach for functional annotation. We suggest that our method will be further applicable for predicting other functional surfaces of proteins with unknown structure. Copyright © 2011 Wiley Periodicals, Inc.

  5. Chemical biology based on target-selective degradation of proteins and carbohydrates using light-activatable organic molecules.

    Science.gov (United States)

    Toshima, Kazunobu

    2013-05-01

    Proteins and carbohydrates play crucial roles in a wide range of biological processes, including serious diseases. The development of novel and innovative methods for selective control of specific proteins and carbohydrates functions has attracted much attention in the field of chemical biology. In this account article, the development of novel chemical tools, which can degrade target proteins and carbohydrates by irradiation with a specific wavelength of light under mild conditions without any additives, is introduced. This novel class of photochemical agents promise bright prospects for finding not only molecular-targeted bioprobes for understanding of the structure-activity relationships of proteins and carbohydrates but also novel therapeutic drugs targeting proteins and carbohydrates.

  6. Effect of Gamma Radiation and Electron Beam on Microbiological Quality and Protein Patterns of 4 Selected Beans

    International Nuclear Information System (INIS)

    Chookaew, S.; Eamsir, J.; Pewlong, W.; Sajjabut, S.

    2014-01-01

    The aim of the present study was to evaluate the effect of gamma ray and electron beam on microbiological quality and protein pattern of four selected beans: mung beans, soy beans, peanuts and black beans. All beans samples were exposed to irradiation at doses of 0, 0.5, 1, and 2 kGy before evaluated for their microbiological quality using AOAC method and protein analysis by gel electrophoresis. Results showed that the amount of bacteria, yeast and mold of irradiated mung beans and peanuts were reduced, whereas these microbiological quality values remained relatively the same for irradiated soy beans and black beans compared to non-irradiated samples. In terms of protein analysis, the protein patterns of the irradiated beans were of the same quality as the non-irradiated samples. To further tested the effect of irradiation on the bean's protein at higher doses, all four selected beans were exposed to gamma ray at 10, 50, 100, 150 and 200 kGy. We found that the protein patterns of mung beans, peanuts and black beans were altered at doses above 50 kGy.

  7. A Dual-Stage Two-Phase Model of Selective Attention

    Science.gov (United States)

    Hubner, Ronald; Steinhauser, Marco; Lehle, Carola

    2010-01-01

    The dual-stage two-phase (DSTP) model is introduced as a formal and general model of selective attention that includes both an early and a late stage of stimulus selection. Whereas at the early stage information is selected by perceptual filters whose selectivity is relatively limited, at the late stage stimuli are selected more efficiently on a…

  8. Molecular descriptor subset selection in theoretical peptide quantitative structure-retention relationship model development using nature-inspired optimization algorithms.

    Science.gov (United States)

    Žuvela, Petar; Liu, J Jay; Macur, Katarzyna; Bączek, Tomasz

    2015-10-06

    In this work, performance of five nature-inspired optimization algorithms, genetic algorithm (GA), particle swarm optimization (PSO), artificial bee colony (ABC), firefly algorithm (FA), and flower pollination algorithm (FPA), was compared in molecular descriptor selection for development of quantitative structure-retention relationship (QSRR) models for 83 peptides that originate from eight model proteins. The matrix with 423 descriptors was used as input, and QSRR models based on selected descriptors were built using partial least squares (PLS), whereas root mean square error of prediction (RMSEP) was used as a fitness function for their selection. Three performance criteria, prediction accuracy, computational cost, and the number of selected descriptors, were used to evaluate the developed QSRR models. The results show that all five variable selection methods outperform interval PLS (iPLS), sparse PLS (sPLS), and the full PLS model, whereas GA is superior because of its lowest computational cost and higher accuracy (RMSEP of 5.534%) with a smaller number of variables (nine descriptors). The GA-QSRR model was validated initially through Y-randomization. In addition, it was successfully validated with an external testing set out of 102 peptides originating from Bacillus subtilis proteomes (RMSEP of 22.030%). Its applicability domain was defined, from which it was evident that the developed GA-QSRR exhibited strong robustness. All the sources of the model's error were identified, thus allowing for further application of the developed methodology in proteomics.

  9. Plant protein and secondary metabolites influence diet selection in a mammalian specialist herbivore

    Science.gov (United States)

    Ulappa, Amy C.; Kelsey, Rick G.; Frye, Graham G.; Rachlow, Janet L.; Shipley, Lisa A.; Bond, Laura; Pu, Xinzhu; Forbey, Jennifer Sorensen

    2015-01-01

    For herbivores, nutrient intake is limited by the relatively low nutritional quality of plants and high concentrations of potentially toxic defensive compounds (plant secondary metabolites, PSMs) produced by many plants. In response to phytochemical challenges, some herbivores selectively forage on plants with higher nutrient and lower PSM concentrations relative to other plants. Pygmy rabbits (Brachylagus idahoensis) are dietary specialists that feed on sagebrush (Artemisia spp.) and forage on specific plants more than others within a foraging patch. We predicted that the plants with evidence of heavy foraging (browsed plants) would be of higher dietary quality than plants that were not browsed (unbrowsed). We used model selection to determine which phytochemical variables best explained the difference between browsed and unbrowsed plants. Higher crude protein increased the odds that plants would be browsed by pygmy rabbits and the opposite was the case for certain PSMs. Additionally, because pygmy rabbits can occupy foraging patches (burrows) for consecutive years, their browsing may influence the nutritional and PSM constituents of plants at the burrows. In a post hoc analysis, we did not find a significant relationship between phytochemical concentrations, browse status and burrow occupancy length. We concluded that pygmy rabbits use nutritional and chemical cues while making foraging decisions. PMID:26366011

  10. Comparison of climate envelope models developed using expert-selected variables versus statistical selection

    Science.gov (United States)

    Brandt, Laura A.; Benscoter, Allison; Harvey, Rebecca G.; Speroterra, Carolina; Bucklin, David N.; Romañach, Stephanie; Watling, James I.; Mazzotti, Frank J.

    2017-01-01

    Climate envelope models are widely used to describe potential future distribution of species under different climate change scenarios. It is broadly recognized that there are both strengths and limitations to using climate envelope models and that outcomes are sensitive to initial assumptions, inputs, and modeling methods Selection of predictor variables, a central step in modeling, is one of the areas where different techniques can yield varying results. Selection of climate variables to use as predictors is often done using statistical approaches that develop correlations between occurrences and climate data. These approaches have received criticism in that they rely on the statistical properties of the data rather than directly incorporating biological information about species responses to temperature and precipitation. We evaluated and compared models and prediction maps for 15 threatened or endangered species in Florida based on two variable selection techniques: expert opinion and a statistical method. We compared model performance between these two approaches for contemporary predictions, and the spatial correlation, spatial overlap and area predicted for contemporary and future climate predictions. In general, experts identified more variables as being important than the statistical method and there was low overlap in the variable sets (0.9 for area under the curve (AUC) and >0.7 for true skill statistic (TSS). Spatial overlap, which compares the spatial configuration between maps constructed using the different variable selection techniques, was only moderate overall (about 60%), with a great deal of variability across species. Difference in spatial overlap was even greater under future climate projections, indicating additional divergence of model outputs from different variable selection techniques. Our work is in agreement with other studies which have found that for broad-scale species distribution modeling, using statistical methods of variable

  11. Fast loop modeling for protein structures

    Science.gov (United States)

    Zhang, Jiong; Nguyen, Son; Shang, Yi; Xu, Dong; Kosztin, Ioan

    2015-03-01

    X-ray crystallography is the main method for determining 3D protein structures. In many cases, however, flexible loop regions of proteins cannot be resolved by this approach. This leads to incomplete structures in the protein data bank, preventing further computational study and analysis of these proteins. For instance, all-atom molecular dynamics (MD) simulation studies of structure-function relationship require complete protein structures. To address this shortcoming, we have developed and implemented an efficient computational method for building missing protein loops. The method is database driven and uses deep learning and multi-dimensional scaling algorithms. We have implemented the method as a simple stand-alone program, which can also be used as a plugin in existing molecular modeling software, e.g., VMD. The quality and stability of the generated structures are assessed and tested via energy scoring functions and by equilibrium MD simulations. The proposed method can also be used in template-based protein structure prediction. Work supported by the National Institutes of Health [R01 GM100701]. Computer time was provided by the University of Missouri Bioinformatics Consortium.

  12. Selective and extensive 13C labeling of a membrane protein for solid-state NMR investigations

    International Nuclear Information System (INIS)

    Hong, M.; Jakes, K.

    1999-01-01

    The selective and extensive 13C labeling of mostly hydrophobic amino acid residues in a 25 kDa membrane protein, the colicin Ia channel domain, is reported. The novel 13C labeling approach takes advantage of the amino acid biosynthetic pathways in bacteria and suppresses the synthesis of the amino acid products of the citric acid cycle. The selectivity and extensiveness of labeling significantly simplify the solid-state NMR spectra, reduce line broadening, and should permit the simultaneous measurement of multiple structural constraints. We show the assignment of most 13C resonances to specific amino acid types based on the characteristic chemical shifts, the 13C labeling pattern, and the amino acid composition of the protein. The assignment is partly confirmed by a 2D homonuclear double-quantum-filter experiment under magic-angle spinning. The high sensitivity and spectral resolution attained with this 13C-labeling protocol, which is termed TEASE for ten-amino acid selective and extensive labeling, are demonstrated

  13. Ellipsometric studies of synthetic albumin-binding chitosan-derivatives and selected blood plasma proteins

    Science.gov (United States)

    Sarkar, Sabyasachi

    This dissertation summarizes work on the synthesis of chitosan-derivatives and the development of ellipsometric methods to characterize materials of biological origin. Albumin-binding chitosan-derivatives were synthesized via addition reactions that involve amine groups naturally present in chitosan. These surfaces were shown to have an affinity towards human serum albumin via ELISA, UV spectroscopy and SDS PAGE. Modified surfaces were characterized with IR ellipsometry at various stages of their synthesis using appropriate optical models. It was found that spin cast chitosan films were anisotropic in nature. All optical models used for characterizing chitosan-derivatives were thus anisotropic. Chemical signal dependence on molecular structure and composition was illustrated via IR spectroscopic ellipsometry (IRSE). An anisotropic optical model of an ensemble of Lorentz oscillators were used to approximate material behavior. The presence of acetic acid in spin-cast non-neutralized chitosan samples was thus shown. IRSE application to biomaterials was also demonstrated by performing a step-wise chemical characterizations during synthesis stages. Protein adsorbed from single protein solutions on these modified surfaces was monitored by visible in-situ variable wavelength ellipsometry. Based on adsorption profiles obtained from single protein adsorption onto silicon surfaces, lumped parameter kinetic models were developed. These models were used to fit experimental data of immunoglobulin-G of different concentrations and approximate conformational changes in fibrinogen adsorption. Biomaterial characterization by ellipsometry was further extended to include characterization of individual protein solutions in the IR range. Proteins in an aqueous environment were characterized by attenuated total internal reflection (ATR) IR ellipsometry using a ZnSe prism. Parameterized dielectric functions were created for individual proteins using Lorentz oscillators. These

  14. Differential Nanos 2 protein stability results in selective germ cell accumulation in the sea urchin.

    Science.gov (United States)

    Oulhen, Nathalie; Wessel, Gary M

    2016-10-01

    Nanos is a translational regulator required for the survival and maintenance of primordial germ cells. In the sea urchin, Strongylocentrotus purpuratus (Sp), Nanos 2 mRNA is broadly transcribed but accumulates specifically in the small micromere (sMic) lineage, in part because of the 3'UTR element GNARLE leads to turnover in somatic cells but retention in the sMics. Here we found that the Nanos 2 protein is also selectively stabilized; it is initially translated throughout the embryo but turned over in the future somatic cells and retained only in the sMics, the future germ line in this animal. This differential stability of Nanos protein is dependent on the open reading frame (ORF), and is independent of the sumoylation and ubiquitylation pathways. Manipulation of the ORF indicates that 68 amino acids in the N terminus of the Nanos protein are essential for its stability in the sMics whereas a 45 amino acid element adjacent to the zinc fingers targets its degradation. Further, this regulation of Nanos protein is cell autonomous, following formation of the germ line. These results are paradigmatic for the unique presence of Nanos in the germ line by a combination of selective RNA retention, distinctive translational control mechanisms (Oulhen et al., 2013), and now also by defined Nanos protein stability. Copyright © 2016 Elsevier Inc. All rights reserved.

  15. Elementary Teachers' Selection and Use of Visual Models

    Science.gov (United States)

    Lee, Tammy D.; Gail Jones, M.

    2018-02-01

    As science grows in complexity, science teachers face an increasing challenge of helping students interpret models that represent complex science systems. Little is known about how teachers select and use models when planning lessons. This mixed methods study investigated the pedagogical approaches and visual models used by elementary in-service and preservice teachers in the development of a science lesson about a complex system (e.g., water cycle). Sixty-seven elementary in-service and 69 elementary preservice teachers completed a card sort task designed to document the types of visual models (e.g., images) that teachers choose when planning science instruction. Quantitative and qualitative analyses were conducted to analyze the card sort task. Semistructured interviews were conducted with a subsample of teachers to elicit the rationale for image selection. Results from this study showed that both experienced in-service teachers and novice preservice teachers tended to select similar models and use similar rationales for images to be used in lessons. Teachers tended to select models that were aesthetically pleasing and simple in design and illustrated specific elements of the water cycle. The results also showed that teachers were not likely to select images that represented the less obvious dimensions of the water cycle. Furthermore, teachers selected visual models more as a pedagogical tool to illustrate specific elements of the water cycle and less often as a tool to promote student learning related to complex systems.

  16. Genetic search feature selection for affective modeling

    DEFF Research Database (Denmark)

    Martínez, Héctor P.; Yannakakis, Georgios N.

    2010-01-01

    Automatic feature selection is a critical step towards the generation of successful computational models of affect. This paper presents a genetic search-based feature selection method which is developed as a global-search algorithm for improving the accuracy of the affective models built....... The method is tested and compared against sequential forward feature selection and random search in a dataset derived from a game survey experiment which contains bimodal input features (physiological and gameplay) and expressed pairwise preferences of affect. Results suggest that the proposed method...

  17. Modeling protein structures: construction and their applications.

    Science.gov (United States)

    Ring, C S; Cohen, F E

    1993-06-01

    Although no general solution to the protein folding problem exists, the three-dimensional structures of proteins are being successfully predicted when experimentally derived constraints are used in conjunction with heuristic methods. In the case of interleukin-4, mutagenesis data and CD spectroscopy were instrumental in the accurate assignment of secondary structure. In addition, the tertiary structure was highly constrained by six cysteines separated by many residues that formed three disulfide bridges. Although the correct structure was a member of a short list of plausible structures, the "best" structure was the topological enantiomer of the experimentally determined conformation. For many proteases, other experimentally derived structures can be used as templates to identify the secondary structure elements. In a procedure called modeling by homology, the structure of a known protein is used as a scaffold to predict the structure of another related protein. This method has been used to model a serine and a cysteine protease that are important in the schistosome and malarial life cycles, respectively. The model structures were then used to identify putative small molecule enzyme inhibitors computationally. Experiments confirm that some of these nonpeptidic compounds are active at concentrations of less than 10 microM.

  18. A computational model of the LGI1 protein suggests a common binding site for ADAM proteins.

    Directory of Open Access Journals (Sweden)

    Emanuela Leonardi

    Full Text Available Mutations of human leucine-rich glioma inactivated (LGI1 gene encoding the epitempin protein cause autosomal dominant temporal lateral epilepsy (ADTLE, a rare familial partial epileptic syndrome. The LGI1 gene seems to have a role on the transmission of neuronal messages but the exact molecular mechanism remains unclear. In contrast to other genes involved in epileptic disorders, epitempin shows no homology with known ion channel genes but contains two domains, composed of repeated structural units, known to mediate protein-protein interactions.A three dimensional in silico model of the two epitempin domains was built to predict the structure-function relationship and propose a functional model integrating previous experimental findings. Conserved and electrostatic charged regions of the model surface suggest a possible arrangement between the two domains and identifies a possible ADAM protein binding site in the β-propeller domain and another protein binding site in the leucine-rich repeat domain. The functional model indicates that epitempin could mediate the interaction between proteins localized to different synaptic sides in a static way, by forming a dimer, or in a dynamic way, by binding proteins at different times.The model was also used to predict effects of known disease-causing missense mutations. Most of the variants are predicted to alter protein folding while several other map to functional surface regions. In agreement with experimental evidence, this suggests that non-secreted LGI1 mutants could be retained within the cell by quality control mechanisms or by altering interactions required for the secretion process.

  19. Structural model of the hUbA1-UbcH10 quaternary complex: in silico and experimental analysis of the protein-protein interactions between E1, E2 and ubiquitin.

    Directory of Open Access Journals (Sweden)

    Stefania Correale

    Full Text Available UbcH10 is a component of the Ubiquitin Conjugation Enzymes (Ubc; E2 involved in the ubiquitination cascade controlling the cell cycle progression, whereby ubiquitin, activated by E1, is transferred through E2 to the target protein with the involvement of E3 enzymes. In this work we propose the first three dimensional model of the tetrameric complex formed by the human UbA1 (E1, two ubiquitin molecules and UbcH10 (E2, leading to the transthiolation reaction. The 3D model was built up by using an experimentally guided incremental docking strategy that combined homology modeling, protein-protein docking and refinement by means of molecular dynamics simulations. The structural features of the in silico model allowed us to identify the regions that mediate the recognition between the interacting proteins, revealing the active role of the ubiquitin crosslinked to E1 in the complex formation. Finally, the role of these regions involved in the E1-E2 binding was validated by designing short peptides that specifically interfere with the binding of UbcH10, thus supporting the reliability of the proposed model and representing valuable scaffolds for the design of peptidomimetic compounds that can bind selectively to Ubcs and inhibit the ubiquitylation process in pathological disorders.

  20. Acetylation of pregnane X receptor protein determines selective function independent of ligand activation

    International Nuclear Information System (INIS)

    Biswas, Arunima; Pasquel, Danielle; Tyagi, Rakesh Kumar; Mani, Sridhar

    2011-01-01

    Research highlights: → Pregnane X receptor (PXR), a major regulatory protein, is modified by acetylation. → PXR undergoes dynamic deacetylation upon ligand-mediated activation. → SIRT1 partially mediates PXR deacetylation. → PXR deacetylation per se induces lipogenesis mimicking ligand-mediated activation. -- Abstract: Pregnane X receptor (PXR), like other members of its class of nuclear receptors, undergoes post-translational modification [PTM] (e.g., phosphorylation). However, it is unknown if acetylation (a major and common form of protein PTM) is observed on PXR and, if it is, whether it is of functional consequence. PXR has recently emerged as an important regulatory protein with multiple ligand-dependent functions. In the present work we show that PXR is indeed acetylated in vivo. SIRT1 (Sirtuin 1), a NAD-dependent class III histone deacetylase and a member of the sirtuin family of proteins, partially mediates deacetylation of PXR. Most importantly, the acetylation status of PXR regulates its selective function independent of ligand activation.

  1. Discriminating binding mechanisms of an intrinsically disordered protein via a multi-state coarse-grained model

    Energy Technology Data Exchange (ETDEWEB)

    Knott, Michael [Department of Chemistry, Cambridge University, Lensfield Road, Cambridge CB2 1EW (United Kingdom); Best, Robert B., E-mail: robertbe@helix.nih.gov [Department of Chemistry, Cambridge University, Lensfield Road, Cambridge CB2 1EW (United Kingdom); Laboratory of Chemical Physics, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Bethesda, Maryland 20892-0520 (United States)

    2014-05-07

    Many proteins undergo a conformational transition upon binding to their cognate binding partner, with intrinsically disordered proteins (IDPs) providing an extreme example in which a folding transition occurs. However, it is often not clear whether this occurs via an “induced fit” or “conformational selection” mechanism, or via some intermediate scenario. In the first case, transient encounters with the binding partner favour transitions to the bound structure before the two proteins dissociate, while in the second the bound structure must be selected from a subset of unbound structures which are in the correct state for binding, because transient encounters of the incorrect conformation with the binding partner are most likely to result in dissociation. A particularly interesting situation involves those intrinsically disordered proteins which can bind to different binding partners in different conformations. We have devised a multi-state coarse-grained simulation model which is able to capture the binding of IDPs in alternate conformations, and by applying it to the binding of nuclear coactivator binding domain (NCBD) to either ACTR or IRF-3 we are able to determine the binding mechanism. By all measures, the binding of NCBD to either binding partner appears to occur via an induced fit mechanism. Nonetheless, we also show how a scenario closer to conformational selection could arise by choosing an alternative non-binding structure for NCBD.

  2. Discriminating binding mechanisms of an intrinsically disordered protein via a multi-state coarse-grained model

    International Nuclear Information System (INIS)

    Knott, Michael; Best, Robert B.

    2014-01-01

    Many proteins undergo a conformational transition upon binding to their cognate binding partner, with intrinsically disordered proteins (IDPs) providing an extreme example in which a folding transition occurs. However, it is often not clear whether this occurs via an “induced fit” or “conformational selection” mechanism, or via some intermediate scenario. In the first case, transient encounters with the binding partner favour transitions to the bound structure before the two proteins dissociate, while in the second the bound structure must be selected from a subset of unbound structures which are in the correct state for binding, because transient encounters of the incorrect conformation with the binding partner are most likely to result in dissociation. A particularly interesting situation involves those intrinsically disordered proteins which can bind to different binding partners in different conformations. We have devised a multi-state coarse-grained simulation model which is able to capture the binding of IDPs in alternate conformations, and by applying it to the binding of nuclear coactivator binding domain (NCBD) to either ACTR or IRF-3 we are able to determine the binding mechanism. By all measures, the binding of NCBD to either binding partner appears to occur via an induced fit mechanism. Nonetheless, we also show how a scenario closer to conformational selection could arise by choosing an alternative non-binding structure for NCBD

  3. Mature Epitope Density - A strategy for target selection based on immunoinformatics and exported prokaryotic proteins

    DEFF Research Database (Denmark)

    Santos, Anderson R; Pereira, Vanessa Bastos; Barbosa, Eudes

    2013-01-01

    . However, currently available tools do not account for the concentration of epitope products in the mature protein product and its relation to the reliability of target selection. RESULTS: We developed a computational strategy based on measuring the epitope's concentration in the mature protein, called...... Mature Epitope Density (MED). Our method, though simple, is capable of identifying promising vaccine targets. Our online software implementation provides a computationally light and reliable analysis of bacterial exoproteins and their potential for vaccines or diagnosis projects against pathogenic...... proteins were confirmed as related. There was no experimental evidence of antigenic or pathogenic contributions for three of the highest MED-scored Mtb proteins. Hence, these three proteins could represent novel putative vaccine and drug targets for Mtb. A web version of MED is publicly available online...

  4. Folding 19 proteins to their native state and stability of large proteins from a coarse-grained model.

    Science.gov (United States)

    Kapoor, Abhijeet; Travesset, Alex

    2014-03-01

    We develop an intermediate resolution model, where the backbone is modeled with atomic resolution but the side chain with a single bead, by extending our previous model (Proteins (2013) DOI: 10.1002/prot.24269) to properly include proline, preproline residues and backbone rigidity. Starting from random configurations, the model properly folds 19 proteins (including a mutant 2A3D sequence) into native states containing β sheet, α helix, and mixed α/β. As a further test, the stability of H-RAS (a 169 residue protein, critical in many signaling pathways) is investigated: The protein is stable, with excellent agreement with experimental B-factors. Despite that proteins containing only α helices fold to their native state at lower backbone rigidity, and other limitations, which we discuss thoroughly, the model provides a reliable description of the dynamics as compared with all atom simulations, but does not constrain secondary structures as it is typically the case in more coarse-grained models. Further implications are described. Copyright © 2013 Wiley Periodicals, Inc.

  5. A Dynamic Model for Limb Selection

    NARCIS (Netherlands)

    Cox, R.F.A; Smitsman, A.W.

    2008-01-01

    Two experiments and a model on limb selection are reported. In Experiment 1 left-handed and right-handed participants (N = 36) repeatedly used one hand for grasping a small cube. After a clear switch in the cube’s location, perseverative limb selection was revealed in both handedness groups. In

  6. A Permutation Approach for Selecting the Penalty Parameter in Penalized Model Selection

    Science.gov (United States)

    Sabourin, Jeremy A; Valdar, William; Nobel, Andrew B

    2015-01-01

    Summary We describe a simple, computationally effcient, permutation-based procedure for selecting the penalty parameter in LASSO penalized regression. The procedure, permutation selection, is intended for applications where variable selection is the primary focus, and can be applied in a variety of structural settings, including that of generalized linear models. We briefly discuss connections between permutation selection and existing theory for the LASSO. In addition, we present a simulation study and an analysis of real biomedical data sets in which permutation selection is compared with selection based on the following: cross-validation (CV), the Bayesian information criterion (BIC), Scaled Sparse Linear Regression, and a selection method based on recently developed testing procedures for the LASSO. PMID:26243050

  7. Trade-off between positive and negative design of protein stability: from lattice models to real proteins.

    Directory of Open Access Journals (Sweden)

    Orly Noivirt-Brik

    2009-12-01

    Full Text Available Two different strategies for stabilizing proteins are (i positive design in which the native state is stabilized and (ii negative design in which competing non-native conformations are destabilized. Here, the circumstances under which one strategy might be favored over the other are explored in the case of lattice models of proteins and then generalized and discussed with regard to real proteins. The balance between positive and negative design of proteins is found to be determined by their average "contact-frequency", a property that corresponds to the fraction of states in the conformational ensemble of the sequence in which a pair of residues is in contact. Lattice model proteins with a high average contact-frequency are found to use negative design more than model proteins with a low average contact-frequency. A mathematical derivation of this result indicates that it is general and likely to hold also for real proteins. Comparison of the results of correlated mutation analysis for real proteins with typical contact-frequencies to those of proteins likely to have high contact-frequencies (such as disordered proteins and proteins that are dependent on chaperonins for their folding indicates that the latter tend to have stronger interactions between residues that are not in contact in their native conformation. Hence, our work indicates that negative design is employed when insufficient stabilization is achieved via positive design owing to high contact-frequencies.

  8. Identification of potential nuclear reprogramming and differentiation factors by a novel selection method for cloning chromatin-binding proteins

    International Nuclear Information System (INIS)

    Wang Liu; Zheng Aihua; Yi Ling; Xu Chongren; Ding Mingxiao; Deng Hongkui

    2004-01-01

    Nuclear reprogramming is critical for animal cloning and stem cell creation through nuclear transfer, which requires extensive remodeling of chromosomal architecture involving dramatic changes in chromatin-binding proteins. To understand the mechanism of nuclear reprogramming, it is critical to identify chromatin-binding factors specify the reprogramming process. In this report, we have developed a high-throughput selection method, based on T7 phage display and chromatin immunoprecipitation, to isolate chromatin-binding factors expressed in mouse embryonic stem cells using primary mouse embryonic fibroblast chromatin. Seven chromatin-binding proteins have been isolated by this method. We have also isolated several chromatin-binding proteins involved in hepatocyte differentiation. Our method provides a powerful tool to rapidly and selectively identify chromatin-binding proteins. The method can be used to study epigenetic modification of chromatin during nuclear reprogramming, cell differentiation, and transdifferentiation

  9. Roles of beta-turns in protein folding: from peptide models to protein engineering.

    Science.gov (United States)

    Marcelino, Anna Marie C; Gierasch, Lila M

    2008-05-01

    Reverse turns are a major class of protein secondary structure; they represent sites of chain reversal and thus sites where the globular character of a protein is created. It has been speculated for many years that turns may nucleate the formation of structure in protein folding, as their propensity to occur will favor the approximation of their flanking regions and their general tendency to be hydrophilic will favor their disposition at the solvent-accessible surface. Reverse turns are local features, and it is therefore not surprising that their structural properties have been extensively studied using peptide models. In this article, we review research on peptide models of turns to test the hypothesis that the propensities of turns to form in short peptides will relate to the roles of corresponding sequences in protein folding. Turns with significant stability as isolated entities should actively promote the folding of a protein, and by contrast, turn sequences that merely allow the chain to adopt conformations required for chain reversal are predicted to be passive in the folding mechanism. We discuss results of protein engineering studies of the roles of turn residues in folding mechanisms. Factors that correlate with the importance of turns in folding indeed include their intrinsic stability, as well as their topological context and their participation in hydrophobic networks within the protein's structure.

  10. An Integrated Framework Advancing Membrane Protein Modeling and Design.

    Directory of Open Access Journals (Sweden)

    Rebecca F Alford

    2015-09-01

    Full Text Available Membrane proteins are critical functional molecules in the human body, constituting more than 30% of open reading frames in the human genome. Unfortunately, a myriad of difficulties in overexpression and reconstitution into membrane mimetics severely limit our ability to determine their structures. Computational tools are therefore instrumental to membrane protein structure prediction, consequently increasing our understanding of membrane protein function and their role in disease. Here, we describe a general framework facilitating membrane protein modeling and design that combines the scientific principles for membrane protein modeling with the flexible software architecture of Rosetta3. This new framework, called RosettaMP, provides a general membrane representation that interfaces with scoring, conformational sampling, and mutation routines that can be easily combined to create new protocols. To demonstrate the capabilities of this implementation, we developed four proof-of-concept applications for (1 prediction of free energy changes upon mutation; (2 high-resolution structural refinement; (3 protein-protein docking; and (4 assembly of symmetric protein complexes, all in the membrane environment. Preliminary data show that these algorithms can produce meaningful scores and structures. The data also suggest needed improvements to both sampling routines and score functions. Importantly, the applications collectively demonstrate the potential of combining the flexible nature of RosettaMP with the power of Rosetta algorithms to facilitate membrane protein modeling and design.

  11. Selective small-chemical inhibitors of protein arginine methyltransferase 5 with anti-lung cancer activity.

    Directory of Open Access Journals (Sweden)

    Gui-Mei Kong

    Full Text Available Protein arginine methyltransferase 5 (PRMT5 plays critical roles in a wide variety of biological processes, including tumorigenesis. By screening a library of small chemical compounds, we identified eight compounds that selectively inhibit the PRMT5 enzymatic activity, with IC50 values ranging from 0.1 to 6 μM. Molecular docking simulation and site-directed mutagenesis indicated that identified compounds target the substrate-binding site in PRMT5. Treatment of lung cancer cells with identified inhibitors led to inhibition of the symmetrical arginine methylation of SmD3 and histones and the cellular proliferation. Oral administration of the inhibitor demonstrated antitumor activity in a lung tumor xenograft model. Thus, identified PRMT5-specific small-molecule inhibitors would help elucidate the biological roles of PRMT5 and serve as lead compounds for future drug development.

  12. Baculovirus display of fusion protein of Peste des petits ruminants virus and hemagglutination protein of Rinderpest virus and immunogenicity of the displayed proteins in mouse model

    International Nuclear Information System (INIS)

    Masmudur Rahman, Md.; Shaila, M.S.; Gopinathan, Karumathil P.

    2003-01-01

    Recombinant Bombyx mori nucleopolyhedroviruses (BmNPV) displaying the immunodominant ectodomains of fusion glycoprotein (F) of Peste des petitis ruminants virus (PPRV) and the hemagglutinin protein (H) of Rinderpest virus (RPV), on the budded virions as well as the surface of the infected host cells have been constructed. The F and H protein sequences were inserted in-frame within the amino-terminal region of BmNPV envelope glycoprotein GP64 expressing under the strong viral polyhedrin (polh) promoter. We improved the recombinant virus selection in BmNPV by incorporating the green fluorescent protein gene (gfp) as selection marker under a separate promoter within the transfer cassette harboring the desired genes. Following infection of the insect larvae or the host-derived BmN cells with these recombinant BmNPVs, the expressed GP64 fusion proteins were displayed on the host cell surface and the budded virions. The antigenic epitopes of the recombinant proteins were properly displayed and the recombinant virus particles induced immune response in mice against PPRV or RPV

  13. Protein (multi-)location prediction: utilizing interdependencies via a generative model.

    Science.gov (United States)

    Simha, Ramanuja; Briesemeister, Sebastian; Kohlbacher, Oliver; Shatkay, Hagit

    2015-06-15

    Proteins are responsible for a multitude of vital tasks in all living organisms. Given that a protein's function and role are strongly related to its subcellular location, protein location prediction is an important research area. While proteins move from one location to another and can localize to multiple locations, most existing location prediction systems assign only a single location per protein. A few recent systems attempt to predict multiple locations for proteins, however, their performance leaves much room for improvement. Moreover, such systems do not capture dependencies among locations and usually consider locations as independent. We hypothesize that a multi-location predictor that captures location inter-dependencies can improve location predictions for proteins. We introduce a probabilistic generative model for protein localization, and develop a system based on it-which we call MDLoc-that utilizes inter-dependencies among locations to predict multiple locations for proteins. The model captures location inter-dependencies using Bayesian networks and represents dependency between features and locations using a mixture model. We use iterative processes for learning model parameters and for estimating protein locations. We evaluate our classifier MDLoc, on a dataset of single- and multi-localized proteins derived from the DBMLoc dataset, which is the most comprehensive protein multi-localization dataset currently available. Our results, obtained by using MDLoc, significantly improve upon results obtained by an initial simpler classifier, as well as on results reported by other top systems. MDLoc is available at: http://www.eecis.udel.edu/∼compbio/mdloc. © The Author 2015. Published by Oxford University Press.

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

    Directory of Open Access Journals (Sweden)

    Taigang Liu

    2015-12-01

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

  15. A Venom Gland Extracellular Chitin-Binding-Like Protein from Pupal Endoparasitoid Wasps, Pteromalus Puparum, Selectively Binds Chitin

    Directory of Open Access Journals (Sweden)

    Yu Zhu

    2015-11-01

    Full Text Available Chitin-binding proteins (CBPs are present in many species and they act in a variety of biological processes. We analyzed a Pteromalus puparum venom apparatus proteome and transcriptome and identified a partial gene encoding a possible CBP. Here, we report cloning a full-length cDNA of a sequence encoding a chitin-binding-like protein (PpCBP from P. puparum, a pupal endoparasitoid of Pieris rapae. The cDNA encoded a 96-amino-acid protein, including a secretory signal peptide and a chitin-binding peritrophin-A domain. Phylogenetic analysis of chitin binding domains (CBDs of cuticle proteins and peritrophic matrix proteins in selected insects revealed that the CBD of PpCBP clustered with the CBD of Nasonia vitripennis. The PpCBP is specifically expressed in the venom apparatus of P. puparum, mostly in the venom gland. PpCBP expression was highest at day one after adult eclosion and much lower for the following five days. We produced a recombinant PpCBP and binding assays showed the recombinant protein selectively binds chitin but not cellulose in vitro. We infer that PpCBP serves a structural role in the venom reservoir, or may be injected into the host to help wound healing of the host exoskeleton.

  16. Mechanical Modeling and Computer Simulation of Protein Folding

    Science.gov (United States)

    Prigozhin, Maxim B.; Scott, Gregory E.; Denos, Sharlene

    2014-01-01

    In this activity, science education and modern technology are bridged to teach students at the high school and undergraduate levels about protein folding and to strengthen their model building skills. Students are guided from a textbook picture of a protein as a rigid crystal structure to a more realistic view: proteins are highly dynamic…

  17. Robust inference in sample selection models

    KAUST Repository

    Zhelonkin, Mikhail; Genton, Marc G.; Ronchetti, Elvezio

    2015-01-01

    The problem of non-random sample selectivity often occurs in practice in many fields. The classical estimators introduced by Heckman are the backbone of the standard statistical analysis of these models. However, these estimators are very sensitive to small deviations from the distributional assumptions which are often not satisfied in practice. We develop a general framework to study the robustness properties of estimators and tests in sample selection models. We derive the influence function and the change-of-variance function of Heckman's two-stage estimator, and we demonstrate the non-robustness of this estimator and its estimated variance to small deviations from the model assumed. We propose a procedure for robustifying the estimator, prove its asymptotic normality and give its asymptotic variance. Both cases with and without an exclusion restriction are covered. This allows us to construct a simple robust alternative to the sample selection bias test. We illustrate the use of our new methodology in an analysis of ambulatory expenditures and we compare the performance of the classical and robust methods in a Monte Carlo simulation study.

  18. Robust inference in sample selection models

    KAUST Repository

    Zhelonkin, Mikhail

    2015-11-20

    The problem of non-random sample selectivity often occurs in practice in many fields. The classical estimators introduced by Heckman are the backbone of the standard statistical analysis of these models. However, these estimators are very sensitive to small deviations from the distributional assumptions which are often not satisfied in practice. We develop a general framework to study the robustness properties of estimators and tests in sample selection models. We derive the influence function and the change-of-variance function of Heckman\\'s two-stage estimator, and we demonstrate the non-robustness of this estimator and its estimated variance to small deviations from the model assumed. We propose a procedure for robustifying the estimator, prove its asymptotic normality and give its asymptotic variance. Both cases with and without an exclusion restriction are covered. This allows us to construct a simple robust alternative to the sample selection bias test. We illustrate the use of our new methodology in an analysis of ambulatory expenditures and we compare the performance of the classical and robust methods in a Monte Carlo simulation study.

  19. Model Selection in Data Analysis Competitions

    DEFF Research Database (Denmark)

    Wind, David Kofoed; Winther, Ole

    2014-01-01

    The use of data analysis competitions for selecting the most appropriate model for a problem is a recent innovation in the field of predictive machine learning. Two of the most well-known examples of this trend was the Netflix Competition and recently the competitions hosted on the online platform...... performers from Kaggle and use previous personal experiences from competing in Kaggle competitions. The stated hypotheses about feature engineering, ensembling, overfitting, model complexity and evaluation metrics give indications and guidelines on how to select a proper model for performing well...... Kaggle. In this paper, we will state and try to verify a set of qualitative hypotheses about predictive modelling, both in general and in the scope of data analysis competitions. To verify our hypotheses we will look at previous competitions and their outcomes, use qualitative interviews with top...

  20. Adverse selection model regarding tobacco consumption

    Directory of Open Access Journals (Sweden)

    Dumitru MARIN

    2006-01-01

    Full Text Available The impact of introducing a tax on tobacco consumption can be studied trough an adverse selection model. The objective of the model presented in the following is to characterize the optimal contractual relationship between the governmental authorities and the two type employees: smokers and non-smokers, taking into account that the consumers’ decision to smoke or not represents an element of risk and uncertainty. Two scenarios are run using the General Algebraic Modeling Systems software: one without taxes set on tobacco consumption and another one with taxes set on tobacco consumption, based on an adverse selection model described previously. The results of the two scenarios are compared in the end of the paper: the wage earnings levels and the social welfare in case of a smoking agent and in case of a non-smoking agent.

  1. Two-step variable selection in quantile regression models

    Directory of Open Access Journals (Sweden)

    FAN Yali

    2015-06-01

    Full Text Available We propose a two-step variable selection procedure for high dimensional quantile regressions, in which the dimension of the covariates, pn is much larger than the sample size n. In the first step, we perform ℓ1 penalty, and we demonstrate that the first step penalized estimator with the LASSO penalty can reduce the model from an ultra-high dimensional to a model whose size has the same order as that of the true model, and the selected model can cover the true model. The second step excludes the remained irrelevant covariates by applying the adaptive LASSO penalty to the reduced model obtained from the first step. Under some regularity conditions, we show that our procedure enjoys the model selection consistency. We conduct a simulation study and a real data analysis to evaluate the finite sample performance of the proposed approach.

  2. Characterizing and modeling protein-surface interactions in lab-on-chip devices

    Science.gov (United States)

    Katira, Parag

    Protein adsorption on surfaces determines the response of other biological species present in the surrounding solution. This phenomenon plays a major role in the design of biomedical and biotechnological devices. While specific protein adsorption is essential for device function, non-specific protein adsorption leads to the loss of device function. For example, non-specific protein adsorption on bioimplants triggers foreign body response, in biosensors it leads to reduced signal to noise ratios, and in hybrid bionanodevices it results in the loss of confinement and directionality of molecular shuttles. Novel surface coatings are being developed to reduce or completely prevent the non-specific adsorption of proteins to surfaces. A novel quantification technique for extremely low protein coverage on surfaces has been developed. This technique utilizes measurement of the landing rate of microtubule filaments on kinesin proteins adsorbed on a surface to determine the kinesin density. Ultra-low limits of detection, dynamic range, ease of detection and availability of a ready-made kinesin-microtubule kit makes this technique highly suitable for detecting protein adsorption below the detection limits of standard techniques. Secondly, a random sequential adsorption model is presented for protein adsorption to PEO-coated surfaces. The derived analytical expressions accurately predict the observed experimental results from various research groups, suggesting that PEO chains act as almost perfect steric barriers to protein adsorption. These expressions can be used to predict the performance of a variety of systems towards resisting protein adsorption and can help in the design of better non-fouling surface coatings. Finally, in biosensing systems, target analytes are captured and concentrated on specifically adsorbed proteins for detection. Non-specific adsorption of proteins results in the loss of signal, and an increase in the background. The use of nanoscale transducers as

  3. Automated sample plan selection for OPC modeling

    Science.gov (United States)

    Casati, Nathalie; Gabrani, Maria; Viswanathan, Ramya; Bayraktar, Zikri; Jaiswal, Om; DeMaris, David; Abdo, Amr Y.; Oberschmidt, James; Krause, Andreas

    2014-03-01

    It is desired to reduce the time required to produce metrology data for calibration of Optical Proximity Correction (OPC) models and also maintain or improve the quality of the data collected with regard to how well that data represents the types of patterns that occur in real circuit designs. Previous work based on clustering in geometry and/or image parameter space has shown some benefit over strictly manual or intuitive selection, but leads to arbitrary pattern exclusion or selection which may not be the best representation of the product. Forming the pattern selection as an optimization problem, which co-optimizes a number of objective functions reflecting modelers' insight and expertise, has shown to produce models with equivalent quality to the traditional plan of record (POR) set but in a less time.

  4. Molecular modeling of protein materials: case study of elastin

    International Nuclear Information System (INIS)

    Tarakanova, Anna; Buehler, Markus J

    2013-01-01

    Molecular modeling of protein materials is a quickly growing area of research that has produced numerous contributions in fields ranging from structural engineering to medicine and biology. We review here the history and methods commonly employed in molecular modeling of protein materials, emphasizing the advantages for using modeling as a complement to experimental work. We then consider a case study of the protein elastin, a critically important ‘mechanical protein’ to exemplify the approach in an area where molecular modeling has made a significant impact. We outline the progression of computational modeling studies that have considerably enhanced our understanding of this important protein which endows elasticity and recoil to the tissues it is found in, including the skin, lungs, arteries and the heart. A vast collection of literature has been directed at studying the structure and function of this protein for over half a century, the first molecular dynamics study of elastin being reported in the 1980s. We review the pivotal computational works that have considerably enhanced our fundamental understanding of elastin's atomistic structure and its extraordinary qualities—focusing on two in particular: elastin's superb elasticity and the inverse temperature transition—the remarkable ability of elastin to take on a more structured conformation at higher temperatures, suggesting its effectiveness as a biomolecular switch. Our hope is to showcase these methods as both complementary and enriching to experimental approaches that have thus far dominated the study of most protein-based materials. (topical review)

  5. Selective induction of cyclin B protein abrogates the G2 delay after irradiation

    International Nuclear Information System (INIS)

    Kao, G.; Muschel, R.J.; Maity, A.; Kunig, A.; McKenna, W.G.

    1996-01-01

    Purpose/Objective: Irradiation of tumor cells commonly results in G2 delay, which has been postulated to allow DNA repair and cell survival. The G2 delay after irradiation is also often marked in some cell lines by delayed expression of cyclin B protein, suggesting a role for cyclin B regulation. Investigations of these hypotheses however has been hampered by the inability to selectively perturb the G2 delay in a physiologic manner. Materials and Methods: We have devised a system, with which we are able to selectively induce cyclin B protein expression in vivo at specific points in the cell cycle, by transfecting Hela cells with an expression vector under control of a dexamethasone-inducible promoter. Experiments were subsequently performed by synchronizing, releasing, irradiating, inducing, and harvesting these cells through the cell cycle. Results: Irradiation with 5 Gy led to a pronounced G2 delay, reflected by markedly slowed progression into mitosis, concomitant with reduced expression of cyclin B protein. Induction of cyclin B after radiation in these cells abrogated the G2 delay by approximately doubling the rate at which the cells re-enter mitosis. Treatment of irradiated untransfected control cells with dexamethasone, in which cyclin B is not induced, led to minimal changes. Studies of effects of cyclin B induction on cyclin B localization (using immunofluorescence), cdc2 phosphorylation and activation will also be presented. Conclusion: This system should allow further investigations into fundamental mechanisms of cell cycle regulation after irradiation and DNA damage. This also provides direct evidence for the first time that cyclin B protein regulation may play a role in the G2 delay following irradiation in Hela cells, perhaps complementing phosphorylation events

  6. Protein (multi-)location prediction: utilizing interdependencies via a generative model

    Science.gov (United States)

    Shatkay, Hagit

    2015-01-01

    Motivation: Proteins are responsible for a multitude of vital tasks in all living organisms. Given that a protein’s function and role are strongly related to its subcellular location, protein location prediction is an important research area. While proteins move from one location to another and can localize to multiple locations, most existing location prediction systems assign only a single location per protein. A few recent systems attempt to predict multiple locations for proteins, however, their performance leaves much room for improvement. Moreover, such systems do not capture dependencies among locations and usually consider locations as independent. We hypothesize that a multi-location predictor that captures location inter-dependencies can improve location predictions for proteins. Results: We introduce a probabilistic generative model for protein localization, and develop a system based on it—which we call MDLoc—that utilizes inter-dependencies among locations to predict multiple locations for proteins. The model captures location inter-dependencies using Bayesian networks and represents dependency between features and locations using a mixture model. We use iterative processes for learning model parameters and for estimating protein locations. We evaluate our classifier MDLoc, on a dataset of single- and multi-localized proteins derived from the DBMLoc dataset, which is the most comprehensive protein multi-localization dataset currently available. Our results, obtained by using MDLoc, significantly improve upon results obtained by an initial simpler classifier, as well as on results reported by other top systems. Availability and implementation: MDLoc is available at: http://www.eecis.udel.edu/∼compbio/mdloc. Contact: shatkay@udel.edu. PMID:26072505

  7. In silico modelling and validation of differential expressed proteins in lung cancer

    Directory of Open Access Journals (Sweden)

    Bhagavathi S

    2012-05-01

    Full Text Available Objective: The present study aims predict the three dimensional structure of three major proteins responsible for causing Lung cancer. Methods: These are the differentially expressed proteins in lung cancer dataset. Initially, the structural template for these proteins is identified from structural database using homology search and perform homology modelling approach to predict its native 3D structure. Three-dimensional model obtained was validated using Ramachandran plot analysis to find the reliability of the model. Results: Four proteins were differentially expressed and were significant proteins in causing lung cancer. Among the four proteins, Matrixmetallo proteinase (P39900 had a known 3D structure and hence was not considered for modelling. The remaining proteins Polo like kinase I Q58A51, Trophinin B1AKF1, Thrombomodulin P07204 were modelled and validated. Conclusions: The three dimensional structure of proteins provides insights about the functional aspect and regulatory aspect of the protein. Thus, this study will be a breakthrough for further lung cancer related studies.

  8. Protein single-model quality assessment by feature-based probability density functions.

    Science.gov (United States)

    Cao, Renzhi; Cheng, Jianlin

    2016-04-04

    Protein quality assessment (QA) has played an important role in protein structure prediction. We developed a novel single-model quality assessment method-Qprob. Qprob calculates the absolute error for each protein feature value against the true quality scores (i.e. GDT-TS scores) of protein structural models, and uses them to estimate its probability density distribution for quality assessment. Qprob has been blindly tested on the 11th Critical Assessment of Techniques for Protein Structure Prediction (CASP11) as MULTICOM-NOVEL server. The official CASP result shows that Qprob ranks as one of the top single-model QA methods. In addition, Qprob makes contributions to our protein tertiary structure predictor MULTICOM, which is officially ranked 3rd out of 143 predictors. The good performance shows that Qprob is good at assessing the quality of models of hard targets. These results demonstrate that this new probability density distribution based method is effective for protein single-model quality assessment and is useful for protein structure prediction. The webserver of Qprob is available at: http://calla.rnet.missouri.edu/qprob/. The software is now freely available in the web server of Qprob.

  9. Quality assessment of protein model-structures based on structural and functional similarities.

    Science.gov (United States)

    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

  10. Simulation studies of protein-induced bilayer deformations, and lipid-induced protein tilting, on a mesoscopic model for lipid bilayers with embedded proteins

    DEFF Research Database (Denmark)

    Venturoli, M.; Smit, B.; Sperotto, Maria Maddalena

    2005-01-01

    membranes. Here we present a mesoscopic model for lipid bilayers with embedded proteins, which we have studied with the help of the dissipative particle dynamics simulation technique. Because hydrophobic matching is believed to be one of the main physical mechanisms regulating lipid-protein interactions......-induced protein tilt, with the hydrophobic mismatch ( positive and negative) between the protein hydrophobic length and the pure lipid bilayer hydrophobic thickness. The protein-induced bilayer perturbation was quantified in terms of a coherence length, xi(P), of the lipid bilayer hydrophobic thickness pro. le...... for positive values of mismatch; a dependence on the protein size appears as well. In the case of large model proteins experiencing extreme mismatch conditions, in the region next to the so-called lipid annulus, there appears an undershooting ( or overshooting) region where the bilayer hydrophobic thickness...

  11. High-dimensional model estimation and model selection

    CERN Multimedia

    CERN. Geneva

    2015-01-01

    I will review concepts and algorithms from high-dimensional statistics for linear model estimation and model selection. I will particularly focus on the so-called p>>n setting where the number of variables p is much larger than the number of samples n. I will focus mostly on regularized statistical estimators that produce sparse models. Important examples include the LASSO and its matrix extension, the Graphical LASSO, and more recent non-convex methods such as the TREX. I will show the applicability of these estimators in a diverse range of scientific applications, such as sparse interaction graph recovery and high-dimensional classification and regression problems in genomics.

  12. SynechoNET: integrated protein-protein interaction database of a model cyanobacterium Synechocystis sp. PCC 6803

    OpenAIRE

    Kim, Woo-Yeon; Kang, Sungsoo; Kim, Byoung-Chul; Oh, Jeehyun; Cho, Seongwoong; Bhak, Jong; Choi, Jong-Soon

    2008-01-01

    Background Cyanobacteria are model organisms for studying photosynthesis, carbon and nitrogen assimilation, evolution of plant plastids, and adaptability to environmental stresses. Despite many studies on cyanobacteria, there is no web-based database of their regulatory and signaling protein-protein interaction networks to date. Description We report a database and website SynechoNET that provides predicted protein-protein interactions. SynechoNET shows cyanobacterial domain-domain interactio...

  13. Affinity selection-mass spectrometry and its emerging application to the high throughput screening of G protein-coupled receptors.

    Science.gov (United States)

    Whitehurst, Charles E; Annis, D Allen

    2008-07-01

    Advances in combinatorial chemistry and genomics have inspired the development of novel affinity selection-based screening techniques that rely on mass spectrometry to identify compounds that preferentially bind to a protein target. Of the many affinity selection-mass spectrometry techniques so far documented, only a few solution-based implementations that separate target-ligand complexes away from unbound ligands persist today as routine high throughput screening platforms. Because affinity selection-mass spectrometry techniques do not rely on radioactive or fluorescent reporters or enzyme activities, they can complement traditional biochemical and cell-based screening assays and enable scientists to screen targets that may not be easily amenable to other methods. In addition, by employing mass spectrometry for ligand detection, these techniques enable high throughput screening of massive library collections of pooled compound mixtures, vastly increasing the chemical space that a target can encounter during screening. Of all drug targets, G protein coupled receptors yield the highest percentage of therapeutically effective drugs. In this manuscript, we present the emerging application of affinity selection-mass spectrometry to the high throughput screening of G protein coupled receptors. We also review how affinity selection-mass spectrometry can be used as an analytical tool to guide receptor purification, and further used after screening to characterize target-ligand binding interactions, enabling the classification of orthosteric and allosteric binders.

  14. Selection for a Zinc-Finger Protein Contributes to Seed Oil Increase during Soybean Domestication.

    Science.gov (United States)

    Li, Qing-Tian; Lu, Xiang; Song, Qing-Xin; Chen, Hao-Wei; Wei, Wei; Tao, Jian-Jun; Bian, Xiao-Hua; Shen, Ming; Ma, Biao; Zhang, Wan-Ke; Bi, Ying-Dong; Li, Wei; Lai, Yong-Cai; Lam, Sin-Man; Shui, Guang-Hou; Chen, Shou-Yi; Zhang, Jin-Song

    2017-04-01

    Seed oil is a momentous agronomical trait of soybean ( Glycine max ) targeted by domestication in breeding. Although multiple oil-related genes have been uncovered, knowledge of the regulatory mechanism of seed oil biosynthesis is currently limited. We demonstrate that the seed-preferred gene GmZF351 , encoding a tandem CCCH zinc finger protein, is selected during domestication. Further analysis shows that GmZF351 facilitates oil accumulation by directly activating WRINKLED1 , BIOTIN CARBOXYL CARRIER PROTEIN2 , 3-KETOACYL-ACYL CARRIER PROTEIN SYNTHASE III , DIACYLGLYCEROL O-ACYLTRANSFERASE1 , and OLEOSIN2 in transgenic Arabidopsis ( Arabidopsis thaliana ) seeds. Overexpression of GmZF351 in transgenic soybean also activates lipid biosynthesis genes, thereby accelerating seed oil accumulation. The ZF351 haplotype from the cultivated soybean group and the wild soybean ( Glycine soja ) subgroup III correlates well with high gene expression level, seed oil contents and promoter activity, suggesting that selection of GmZF351 expression leads to increased seed oil content in cultivated soybean. Our study provides novel insights into the regulatory mechanism for seed oil accumulation, and the manipulation of GmZF351 may have great potential in the improvement of oil production in soybean and other related crops. © 2017 American Society of Plant Biologists. All Rights Reserved.

  15. A rapid method for selecting suitable animal species for studying pathogen interactions with plasma protein ligands in vivo.

    Science.gov (United States)

    Naudin, Clément; Schumski, Ariane; Salo-Ahen, Outi M H; Herwald, Heiko; Smeds, Emanuel

    2017-05-01

    Species tropism constitutes a serious problem for developing relevant animal models of infection. Human pathogens can express virulence factors that show specific selectivity to human proteins, while their affinity for orthologs from other species can vary significantly. Suitable animal species must be used to analyse whether virulence factors are potential targets for drug development. We developed an assay that rapidly predicts applicable animal species for studying virulence factors binding plasma proteins. We used two well-characterized Staphylococcus aureus proteins, SSL7 and Efb, to develop an ELISA-based inhibition assay using plasma from different animal species. The interaction between SSL7 and human C5 and the binding of Efb to human fibrinogen and human C3 was studied. Affinity experiments and Western blot analyses were used to validate the assay. Human, monkey and cat plasma interfered with binding of SSL7 to human C5. Binding of Efb to human fibrinogen was blocked in human, monkey, gerbil and pig plasma, while human, monkey, gerbil, rabbit, cat and guinea pig plasma inhibited the binding of Efb to human C3. These results emphasize the importance of choosing correct animal models, and thus, our approach is a rapid and cost-effective method that can be used to prevent unnecessary animal experiments. © 2017 The Authors. Microbial Biotechnology published by John Wiley & Sons Ltd and Society for Applied Microbiology.

  16. Selective Permeation and Organic Extraction of Recombinant Green Fluorescent Protein (gfpuv from Escherichia coli

    Directory of Open Access Journals (Sweden)

    Ishii Marina

    2002-04-01

    Full Text Available Abstract Background Transformed cells of Escherichia coli DH5-α with pGFPuv, induced by IPTG (isopropyl-β-d-thiogalactopyranoside, express the green fluorescent protein (gfpuv during growth phases. E. coli subjected to the combination of selective permeation by freezing/thawing/sonication cycles followed by the three-phase partitioning extraction (TPP method were compared to the direct application of TPP to the same culture of E. coli on releasing gfpuv from the over-expressing cells. Material and Methods Cultures (37°C/100 rpm/ 24 h; μ = 0.99 h-1 - 1.10 h-1 of transformed (pGFP Escherichia coli DH5-α, expressing the green fluorescent protein (gfpuv, absorbance at 394 nm and emission at 509 nm were sonicated in successive intervals of sonication (25 vibrations/pulse to determine the maximum amount of gfpuv released from the cells. For selective permeation, the transformed previously frozen (-75°C cells were subjected to three freeze/thaw (-20°C/ 0.83°C/min cycles interlaid by sonication (3 pulses/ 6 seconds/ 25 vibrations. The intracellular permeate with gfpuv in extraction buffer (TE solution (25 mM Tris-HCl, pH 8.0, 1 mM β-mercaptoethanol β-ME, 0.1 mM PMSF was subjected to the three-phase partitioning (TPP method with t-butanol and 1.6 M ammonium sulfate. Sonication efficiency was verified on the application to the cells previously treated by the TPP method. The intra-cell releases were mixed and eluted through methyl HIC column with a buffer solution (10 mM Tris-HCl, 10 mM EDTA, pH 8.0. Results The sonication maximum released amount obtained from the cells was 327.67 μg gfpuv/mL (20.73 μg gfpuv/mg total proteins – BSA, after 9 min of treatment. Through the selective permeation by three repeated freezing/thawing/sonication cycles applied to the cells, a close content of 241.19 μg gfpuv/mL (29.74 μg gfpuv/mg BSA was obtained. The specific mass range of gfpuv released from the same cultures, by the three-phase partitioning (TPP

  17. Structure of the Regulator of G Protein Signaling 8 (RGS8)-Gαq Complex: MOLECULAR BASIS FOR Gα SELECTIVITY.

    Science.gov (United States)

    Taylor, Veronica G; Bommarito, Paige A; Tesmer, John J G

    2016-03-04

    Regulator of G protein signaling (RGS) proteins interact with activated Gα subunits via their RGS domains and accelerate the hydrolysis of GTP. Although the R4 subfamily of RGS proteins generally accepts both Gαi/o and Gαq/11 subunits as substrates, the R7 and R12 subfamilies select against Gαq/11. In contrast, only one RGS protein, RGS2, is known to be selective for Gαq/11. The molecular basis for this selectivity is not clear. Previously, the crystal structure of RGS2 in complex with Gαq revealed a non-canonical interaction that could be due to interfacial differences imposed by RGS2, the Gα subunit, or both. To resolve this ambiguity, the 2.6 Å crystal structure of RGS8, an R4 subfamily member, was determined in complex with Gαq. RGS8 adopts the same pose on Gαq as it does when bound to Gαi3, indicating that the non-canonical interaction of RGS2 with Gαq is due to unique features of RGS2. Based on the RGS8-Gαq structure, residues in RGS8 that contact a unique α-helical domain loop of Gαq were converted to those typically found in R12 subfamily members, and the reverse substitutions were introduced into RGS10, an R12 subfamily member. Although these substitutions perturbed their ability to stimulate GTP hydrolysis, they did not reverse selectivity. Instead, selectivity for Gαq seems more likely determined by whether strong contacts can be maintained between α6 of the RGS domain and Switch III of Gαq, regions of high sequence and conformational diversity in both protein families. © 2016 by The American Society for Biochemistry and Molecular Biology, Inc.

  18. Physiologically based pharmacokinetic and pharmacodynamic modeling of an antagonist (SM-406/AT-406) of multiple inhibitor of apoptosis proteins (IAPs) in a mouse xenograft model of human breast cancer.

    Science.gov (United States)

    Zhang, Tao; Li, Yanyan; Zou, Peng; Yu, Jing-yu; McEachern, Donna; Wang, Shaomeng; Sun, Duxin

    2013-09-01

    The inhibitors of apoptosis proteins (IAPs) are a class of key apoptosis regulators overexpressed or dysregulated in cancer. SM-406/AT-406 is a potent and selective small molecule mimetic of Smac that antagonizes the inhibitor of apoptosis proteins (IAPs). A physiologically based pharmacokinetic and pharmacodynamic (PBPK-PD) model was developed to predict the tissue concentration-time profiles of SM-406, the related onco-protein levels in tumor, and the tumor growth inhibition in a mouse model bearing human breast cancer xenograft. In the whole body physiologically based pharmacokinetic (PBPK) model for pharmacokinetics characterization, a well stirred (perfusion rate-limited) model was used to describe SM-406 pharmacokinetics in the lung, heart, kidney, intestine, liver and spleen, and a diffusion rate-limited (permeability limited) model was used for tumor. Pharmacodynamic (PD) models were developed to correlate the SM-406 concentration in tumor to the cIAP1 degradation, pro-caspase 8 decrease, CL-PARP accumulation and tumor growth inhibition. The PBPK-PD model well described the experimental pharmacokinetic data, the pharmacodynamic biomarker responses and tumor growth. This model may be helpful to predict tumor and plasma SM-406 concentrations in the clinic. Copyright © 2013 John Wiley & Sons, Ltd.

  19. From nonspecific DNA-protein encounter complexes to the prediction of DNA-protein interactions.

    Directory of Open Access Journals (Sweden)

    Mu Gao

    2009-03-01

    Full Text Available DNA-protein interactions are involved in many essential biological activities. Because there is no simple mapping code between DNA base pairs and protein amino acids, the prediction of DNA-protein interactions is a challenging problem. Here, we present a novel computational approach for predicting DNA-binding protein residues and DNA-protein interaction modes without knowing its specific DNA target sequence. Given the structure of a DNA-binding protein, the method first generates an ensemble of complex structures obtained by rigid-body docking with a nonspecific canonical B-DNA. Representative models are subsequently selected through clustering and ranking by their DNA-protein interfacial energy. Analysis of these encounter complex models suggests that the recognition sites for specific DNA binding are usually favorable interaction sites for the nonspecific DNA probe and that nonspecific DNA-protein interaction modes exhibit some similarity to specific DNA-protein binding modes. Although the method requires as input the knowledge that the protein binds DNA, in benchmark tests, it achieves better performance in identifying DNA-binding sites than three previously established methods, which are based on sophisticated machine-learning techniques. We further apply our method to protein structures predicted through modeling and demonstrate that our method performs satisfactorily on protein models whose root-mean-square Calpha deviation from native is up to 5 A from their native structures. This study provides valuable structural insights into how a specific DNA-binding protein interacts with a nonspecific DNA sequence. The similarity between the specific DNA-protein interaction mode and nonspecific interaction modes may reflect an important sampling step in search of its specific DNA targets by a DNA-binding protein.

  20. Hydration dynamics near a model protein surface

    International Nuclear Information System (INIS)

    Russo, Daniela; Hura, Greg; Head-Gordon, Teresa

    2003-01-01

    The evolution of water dynamics from dilute to very high concentration solutions of a prototypical hydrophobic amino acid with its polar backbone, N-acetyl-leucine-methylamide (NALMA), is studied by quasi-elastic neutron scattering and molecular dynamics simulation for both the completely deuterated and completely hydrogenated leucine monomer. We observe several unexpected features in the dynamics of these biological solutions under ambient conditions. The NALMA dynamics shows evidence of de Gennes narrowing, an indication of coherent long timescale structural relaxation dynamics. The translational water dynamics are analyzed in a first approximation with a jump diffusion model. At the highest solute concentrations, the hydration water dynamics is significantly suppressed and characterized by a long residential time and a slow diffusion coefficient. The analysis of the more dilute concentration solutions takes into account the results of the 2.0M solution as a model of the first hydration shell. Subtracting the first hydration layer based on the 2.0M spectra, the translational diffusion dynamics is still suppressed, although the rotational relaxation time and residential time are converged to bulk-water values. Molecular dynamics analysis shows spatially heterogeneous dynamics at high concentration that becomes homogeneous at more dilute concentrations. We discuss the hydration dynamics results of this model protein system in the context of glassy systems, protein function, and protein-protein interfaces

  1. Melody Track Selection Using Discriminative Language Model

    Science.gov (United States)

    Wu, Xiao; Li, Ming; Suo, Hongbin; Yan, Yonghong

    In this letter we focus on the task of selecting the melody track from a polyphonic MIDI file. Based on the intuition that music and language are similar in many aspects, we solve the selection problem by introducing an n-gram language model to learn the melody co-occurrence patterns in a statistical manner and determine the melodic degree of a given MIDI track. Furthermore, we propose the idea of using background model and posterior probability criteria to make modeling more discriminative. In the evaluation, the achieved 81.6% correct rate indicates the feasibility of our approach.

  2. Model selection for Gaussian kernel PCA denoising

    DEFF Research Database (Denmark)

    Jørgensen, Kasper Winther; Hansen, Lars Kai

    2012-01-01

    We propose kernel Parallel Analysis (kPA) for automatic kernel scale and model order selection in Gaussian kernel PCA. Parallel Analysis [1] is based on a permutation test for covariance and has previously been applied for model order selection in linear PCA, we here augment the procedure to also...... tune the Gaussian kernel scale of radial basis function based kernel PCA.We evaluate kPA for denoising of simulated data and the US Postal data set of handwritten digits. We find that kPA outperforms other heuristics to choose the model order and kernel scale in terms of signal-to-noise ratio (SNR...

  3. Determination of the Influence of Substrate Concentration on Enzyme Selectivity Using Whey Protein Isolate and Bacillus licheniformis Protease

    NARCIS (Netherlands)

    Butré, C.I.; Sforza, S.; Gruppen, H.; Wierenga, P.A.

    2014-01-01

    Increasing substrate concentration during enzymatic protein hydrolysis results in a decrease in hydrolysis rate. To test if changes in the mechanism of hydrolysis also occur, the enzyme selectivity was determined. The selectivity is defined quantitatively as the relative rate of hydrolysis of each

  4. Screening and expression of selected taxonomically conserved and unique hypothetical proteins in Burkholderia pseudomallei K96243

    Science.gov (United States)

    Akhir, Nor Azurah Mat; Nadzirin, Nurul; Mohamed, Rahmah; Firdaus-Raih, Mohd

    2015-09-01

    Hypothetical proteins of bacterial pathogens represent a large numbers of novel biological mechanisms which could belong to essential pathways in the bacteria. They lack functional characterizations mainly due to the inability of sequence homology based methods to detect functional relationships in the absence of detectable sequence similarity. The dataset derived from this study showed 550 candidates conserved in genomes that has pathogenicity information and only present in the Burkholderiales order. The dataset has been narrowed down to taxonomic clusters. Ten proteins were selected for ORF amplification, seven of them were successfully amplified, and only four proteins were successfully expressed. These proteins will be great candidates in determining the true function via structural biology.

  5. Statistical-mechanical lattice models for protein-DNA binding in chromatin

    International Nuclear Information System (INIS)

    Teif, Vladimir B; Rippe, Karsten

    2010-01-01

    Statistical-mechanical lattice models for protein-DNA binding are well established as a method to describe complex ligand binding equilibria measured in vitro with purified DNA and protein components. Recently, a new field of applications has opened up for this approach since it has become possible to experimentally quantify genome-wide protein occupancies in relation to the DNA sequence. In particular, the organization of the eukaryotic genome by histone proteins into a nucleoprotein complex termed chromatin has been recognized as a key parameter that controls the access of transcription factors to the DNA sequence. New approaches have to be developed to derive statistical-mechanical lattice descriptions of chromatin-associated protein-DNA interactions. Here, we present the theoretical framework for lattice models of histone-DNA interactions in chromatin and investigate the (competitive) DNA binding of other chromosomal proteins and transcription factors. The results have a number of applications for quantitative models for the regulation of gene expression.

  6. Sequence based prediction of DNA-binding proteins based on hybrid feature selection using random forest and Gaussian naïve Bayes.

    Directory of Open Access Journals (Sweden)

    Wangchao Lou

    Full Text Available Developing an efficient method for determination of the DNA-binding proteins, due to their vital roles in gene regulation, is becoming highly desired since it would be invaluable to advance our understanding of protein functions. In this study, we proposed a new method for the prediction of the DNA-binding proteins, by performing the feature rank using random forest and the wrapper-based feature selection using forward best-first search strategy. The features comprise information from primary sequence, predicted secondary structure, predicted relative solvent accessibility, and position specific scoring matrix. The proposed method, called DBPPred, used Gaussian naïve Bayes as the underlying classifier since it outperformed five other classifiers, including decision tree, logistic regression, k-nearest neighbor, support vector machine with polynomial kernel, and support vector machine with radial basis function. As a result, the proposed DBPPred yields the highest average accuracy of 0.791 and average MCC of 0.583 according to the five-fold cross validation with ten runs on the training benchmark dataset PDB594. Subsequently, blind tests on the independent dataset PDB186 by the proposed model trained on the entire PDB594 dataset and by other five existing methods (including iDNA-Prot, DNA-Prot, DNAbinder, DNABIND and DBD-Threader were performed, resulting in that the proposed DBPPred yielded the highest accuracy of 0.769, MCC of 0.538, and AUC of 0.790. The independent tests performed by the proposed DBPPred on completely a large non-DNA binding protein dataset and two RNA binding protein datasets also showed improved or comparable quality when compared with the relevant prediction methods. Moreover, we observed that majority of the selected features by the proposed method are statistically significantly different between the mean feature values of the DNA-binding and the non DNA-binding proteins. All of the experimental results indicate that

  7. Intracellular delivery of cell-penetrating peptide-transcriptional factor fusion protein and its role in selective osteogenesis

    Directory of Open Access Journals (Sweden)

    Suh JS

    2014-03-01

    alginate gel for the purpose of localization and controlled release. The LMWP-TAZ fusion protein-loaded alginate gel matrix significantly increased bone formation in rabbit calvarial defects compared with alginate gel matrix mixed with free TAZ protein. The protein transduction of TAZ fused with cell-penetrating LMWP peptide was able selectively to stimulate osteogenesis in vitro and in vivo. Taken together, this fusion protein-transduction technology for osteogenic protein can thus be applied in combination with biomaterials for tissue regeneration and controlled release for tissue-engineering purposes. Keywords: protein transduction, low-molecular-weight protamine (LMWP, transcriptional coactivator with PDZ-binding motif (TAZ, selective osteogenesis, bone-tissue engineering

  8. A General Model of Negative Frequency Dependent Selection Explains Global Patterns of Human ABO Polymorphism.

    Directory of Open Access Journals (Sweden)

    Fernando A Villanea

    Full Text Available The ABO locus in humans is characterized by elevated heterozygosity and very similar allele frequencies among populations scattered across the globe. Using knowledge of ABO protein function, we generated a simple model of asymmetric negative frequency dependent selection and genetic drift to explain the maintenance of ABO polymorphism and its loss in human populations. In our models, regardless of the strength of selection, models with large effective population sizes result in ABO allele frequencies that closely match those observed in most continental populations. Populations must be moderately small to fall out of equilibrium and lose either the A or B allele (N(e ≤ 50 and much smaller (N(e ≤ 25 for the complete loss of diversity, which nearly always involved the fixation of the O allele. A pattern of low heterozygosity at the ABO locus where loss of polymorphism occurs in our model is consistent with small populations, such as Native American populations. This study provides a general evolutionary model to explain the observed global patterns of polymorphism at the ABO locus and the pattern of allele loss in small populations. Moreover, these results inform the range of population sizes associated with the recent human colonization of the Americas.

  9. Exploiting conformational ensembles in modeling protein-protein interactions on the proteome scale

    Science.gov (United States)

    Kuzu, Guray; Gursoy, Attila; Nussinov, Ruth; Keskin, Ozlem

    2013-01-01

    Cellular functions are performed through protein-protein interactions; therefore, identification of these interactions is crucial for understanding biological processes. Recent studies suggest that knowledge-based approaches are more useful than ‘blind’ docking for modeling at large scales. However, a caveat of knowledge-based approaches is that they treat molecules as rigid structures. The Protein Data Bank (PDB) offers a wealth of conformations. Here, we exploited ensemble of the conformations in predictions by a knowledge-based method, PRISM. We tested ‘difficult’ cases in a docking-benchmark dataset, where the unbound and bound protein forms are structurally different. Considering alternative conformations for each protein, the percentage of successfully predicted interactions increased from ~26% to 66%, and 57% of the interactions were successfully predicted in an ‘unbiased’ scenario, in which data related to the bound forms were not utilized. If the appropriate conformation, or relevant template interface, is unavailable in the PDB, PRISM could not predict the interaction successfully. The pace of the growth of the PDB promises a rapid increase of ensemble conformations emphasizing the merit of such knowledge-based ensemble strategies for higher success rates in protein-protein interaction predictions on an interactome-scale. We constructed the structural network of ERK interacting proteins as a case study. PMID:23590674

  10. Stochastic lattice model of synaptic membrane protein domains.

    Science.gov (United States)

    Li, Yiwei; Kahraman, Osman; Haselwandter, Christoph A

    2017-05-01

    Neurotransmitter receptor molecules, concentrated in synaptic membrane domains along with scaffolds and other kinds of proteins, are crucial for signal transmission across chemical synapses. In common with other membrane protein domains, synaptic domains are characterized by low protein copy numbers and protein crowding, with rapid stochastic turnover of individual molecules. We study here in detail a stochastic lattice model of the receptor-scaffold reaction-diffusion dynamics at synaptic domains that was found previously to capture, at the mean-field level, the self-assembly, stability, and characteristic size of synaptic domains observed in experiments. We show that our stochastic lattice model yields quantitative agreement with mean-field models of nonlinear diffusion in crowded membranes. Through a combination of analytic and numerical solutions of the master equation governing the reaction dynamics at synaptic domains, together with kinetic Monte Carlo simulations, we find substantial discrepancies between mean-field and stochastic models for the reaction dynamics at synaptic domains. Based on the reaction and diffusion properties of synaptic receptors and scaffolds suggested by previous experiments and mean-field calculations, we show that the stochastic reaction-diffusion dynamics of synaptic receptors and scaffolds provide a simple physical mechanism for collective fluctuations in synaptic domains, the molecular turnover observed at synaptic domains, key features of the observed single-molecule trajectories, and spatial heterogeneity in the effective rates at which receptors and scaffolds are recycled at the cell membrane. Our work sheds light on the physical mechanisms and principles linking the collective properties of membrane protein domains to the stochastic dynamics that rule their molecular components.

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

    Science.gov (United States)

    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/ .

  12. Are animal models predictive for human postmortem muscle protein degradation?

    Science.gov (United States)

    Ehrenfellner, Bianca; Zissler, Angela; Steinbacher, Peter; Monticelli, Fabio C; Pittner, Stefan

    2017-11-01

    A most precise determination of the postmortem interval (PMI) is a crucial aspect in forensic casework. Although there are diverse approaches available to date, the high heterogeneity of cases together with the respective postmortal changes often limit the validity and sufficiency of many methods. Recently, a novel approach for time since death estimation by the analysis of postmortal changes of muscle proteins was proposed. It is however necessary to improve the reliability and accuracy, especially by analysis of possible influencing factors on protein degradation. This is ideally investigated on standardized animal models that, however, require legitimization by a comparison of human and animal tissue, and in this specific case of protein degradation profiles. Only if protein degradation events occur in comparable fashion within different species, respective findings can sufficiently be transferred from the animal model to application in humans. Therefor samples from two frequently used animal models (mouse and pig), as well as forensic cases with representative protein profiles of highly differing PMIs were analyzed. Despite physical and physiological differences between species, western blot analysis revealed similar patterns in most of the investigated proteins. Even most degradation events occurred in comparable fashion. In some other aspects, however, human and animal profiles depicted distinct differences. The results of this experimental series clearly indicate the huge importance of comparative studies, whenever animal models are considered. Although animal models could be shown to reflect the basic principles of protein degradation processes in humans, we also gained insight in the difficulties and limitations of the applicability of the developed methodology in different mammalian species regarding protein specificity and methodic functionality.

  13. Hidden Markov model-derived structural alphabet for proteins: the learning of protein local shapes captures sequence specificity.

    Science.gov (United States)

    Camproux, A C; Tufféry, P

    2005-08-05

    Understanding and predicting protein structures depend on the complexity and the accuracy of the models used to represent them. We have recently set up a Hidden Markov Model to optimally compress protein three-dimensional conformations into a one-dimensional series of letters of a structural alphabet. Such a model learns simultaneously the shape of representative structural letters describing the local conformation and the logic of their connections, i.e. the transition matrix between the letters. Here, we move one step further and report some evidence that such a model of protein local architecture also captures some accurate amino acid features. All the letters have specific and distinct amino acid distributions. Moreover, we show that words of amino acids can have significant propensities for some letters. Perspectives point towards the prediction of the series of letters describing the structure of a protein from its amino acid sequence.

  14. DENDRIMER CONJUGATES FOR SELECTIVE OF PROTEIN AGGREGATES

    DEFF Research Database (Denmark)

    2004-01-01

    Dendrimer conjugates are presented, which are formed between a dendrimer and a protein solubilising substance. Such dendrimer conjugates are effective in the treatment of protein aggregate-related diseases (e.g. prion-related diseases). The protein solubilising substance and the dendrimer together...

  15. Hidden markov model for the prediction of transmembrane proteins using MATLAB.

    Science.gov (United States)

    Chaturvedi, Navaneet; Shanker, Sudhanshu; Singh, Vinay Kumar; Sinha, Dhiraj; Pandey, Paras Nath

    2011-01-01

    Since membranous proteins play a key role in drug targeting therefore transmembrane proteins prediction is active and challenging area of biological sciences. Location based prediction of transmembrane proteins are significant for functional annotation of protein sequences. Hidden markov model based method was widely applied for transmembrane topology prediction. Here we have presented a revised and a better understanding model than an existing one for transmembrane protein prediction. Scripting on MATLAB was built and compiled for parameter estimation of model and applied this model on amino acid sequence to know the transmembrane and its adjacent locations. Estimated model of transmembrane topology was based on TMHMM model architecture. Only 7 super states are defined in the given dataset, which were converted to 96 states on the basis of their length in sequence. Accuracy of the prediction of model was observed about 74 %, is a good enough in the area of transmembrane topology prediction. Therefore we have concluded the hidden markov model plays crucial role in transmembrane helices prediction on MATLAB platform and it could also be useful for drug discovery strategy. The database is available for free at bioinfonavneet@gmail.comvinaysingh@bhu.ac.in.

  16. Selected HIV-1 Env trimeric formulations act as potent immunogens in a rabbit vaccination model.

    Directory of Open Access Journals (Sweden)

    Leo Heyndrickx

    Full Text Available BACKGROUND: Ten to 30% of HIV-1 infected subjects develop broadly neutralizing antibodies (bNAbs during chronic infection. We hypothesized that immunizing rabbits with viral envelope glycoproteins (Envs from these patients may induce bNAbs, when formulated as a trimeric protein and in the presence of an adjuvant. METHODS: Based on in vitro neutralizing activity in serum, patients with bNAbs were selected for cloning of their HIV-1 Env. Seven stable soluble trimeric gp140 proteins were generated from sequences derived from four adults and two children infected with either clade A or B HIV-1. From one of the clade A Envs both the monomeric and trimeric Env were produced for comparison. Rabbits were immunized with soluble gp120 or trimeric gp140 proteins in combination with the adjuvant dimethyl dioctadecyl ammonium/trehalose dibehenate (CAF01. Env binding in rabbit immune serum was determined using ELISAs based on gp120-IIIB protein. Neutralizing activity of IgG purified from rabbit immune sera was measured with the pseudovirus-TZMbl assay and a PBMC-based neutralization assay for selected experiments. RESULTS: It was initially established that gp140 trimers induce better antibody responses over gp120 monomers and that the adjuvant CAF01 was necessary for such strong responses. Gp140 trimers, based on HIV-1 variants from patients with bNAbs, were able to elicit both gp120IIIB specific IgG and NAbs to Tier 1 viruses of different subtypes. Potency of NAbs closely correlated with titers, and an gp120-binding IgG titer above a threshold of 100,000 was predictive of neutralization capability. Finally, peptide inhibition experiments showed that a large fraction of the neutralizing IgG was directed against the gp120 V3 region. CONCLUSIONS: Our results indicate that the strategy of reverse immunology based on selected Env sequences is promising when immunogens are delivered as stabilized trimers in CAF01 adjuvant and that the rabbit is a valuable model

  17. Selected HIV-1 Env trimeric formulations act as potent immunogens in a rabbit vaccination model.

    Science.gov (United States)

    Heyndrickx, Leo; Stewart-Jones, Guillaume; Jansson, Marianne; Schuitemaker, Hanneke; Bowles, Emma; Buonaguro, Luigi; Grevstad, Berit; Vinner, Lasse; Vereecken, Katleen; Parker, Joe; Ramaswamy, Meghna; Biswas, Priscilla; Vanham, Guido; Scarlatti, Gabriella; Fomsgaard, Anders

    2013-01-01

    Ten to 30% of HIV-1 infected subjects develop broadly neutralizing antibodies (bNAbs) during chronic infection. We hypothesized that immunizing rabbits with viral envelope glycoproteins (Envs) from these patients may induce bNAbs, when formulated as a trimeric protein and in the presence of an adjuvant. Based on in vitro neutralizing activity in serum, patients with bNAbs were selected for cloning of their HIV-1 Env. Seven stable soluble trimeric gp140 proteins were generated from sequences derived from four adults and two children infected with either clade A or B HIV-1. From one of the clade A Envs both the monomeric and trimeric Env were produced for comparison. Rabbits were immunized with soluble gp120 or trimeric gp140 proteins in combination with the adjuvant dimethyl dioctadecyl ammonium/trehalose dibehenate (CAF01). Env binding in rabbit immune serum was determined using ELISAs based on gp120-IIIB protein. Neutralizing activity of IgG purified from rabbit immune sera was measured with the pseudovirus-TZMbl assay and a PBMC-based neutralization assay for selected experiments. It was initially established that gp140 trimers induce better antibody responses over gp120 monomers and that the adjuvant CAF01 was necessary for such strong responses. Gp140 trimers, based on HIV-1 variants from patients with bNAbs, were able to elicit both gp120IIIB specific IgG and NAbs to Tier 1 viruses of different subtypes. Potency of NAbs closely correlated with titers, and an gp120-binding IgG titer above a threshold of 100,000 was predictive of neutralization capability. Finally, peptide inhibition experiments showed that a large fraction of the neutralizing IgG was directed against the gp120 V3 region. Our results indicate that the strategy of reverse immunology based on selected Env sequences is promising when immunogens are delivered as stabilized trimers in CAF01 adjuvant and that the rabbit is a valuable model for HIV vaccine studies.

  18. Stapled Voltage-Gated Calcium Channel (CaV) α-Interaction Domain (AID) Peptides Act As Selective Protein-Protein Interaction Inhibitors of CaV Function.

    Science.gov (United States)

    Findeisen, Felix; Campiglio, Marta; Jo, Hyunil; Abderemane-Ali, Fayal; Rumpf, Christine H; Pope, Lianne; Rossen, Nathan D; Flucher, Bernhard E; DeGrado, William F; Minor, Daniel L

    2017-06-21

    For many voltage-gated ion channels (VGICs), creation of a properly functioning ion channel requires the formation of specific protein-protein interactions between the transmembrane pore-forming subunits and cystoplasmic accessory subunits. Despite the importance of such protein-protein interactions in VGIC function and assembly, their potential as sites for VGIC modulator development has been largely overlooked. Here, we develop meta-xylyl (m-xylyl) stapled peptides that target a prototypic VGIC high affinity protein-protein interaction, the interaction between the voltage-gated calcium channel (Ca V ) pore-forming subunit α-interaction domain (AID) and cytoplasmic β-subunit (Ca V β). We show using circular dichroism spectroscopy, X-ray crystallography, and isothermal titration calorimetry that the m-xylyl staples enhance AID helix formation are structurally compatible with native-like AID:Ca V β interactions and reduce the entropic penalty associated with AID binding to Ca V β. Importantly, electrophysiological studies reveal that stapled AID peptides act as effective inhibitors of the Ca V α 1 :Ca V β interaction that modulate Ca V function in an Ca V β isoform-selective manner. Together, our studies provide a proof-of-concept demonstration of the use of protein-protein interaction inhibitors to control VGIC function and point to strategies for improved AID-based Ca V modulator design.

  19. Enzastaurin (LY317615), a Protein Kinase C Beta Selective Inhibitor, Enhances Antiangiogenic Effect of Radiation

    International Nuclear Information System (INIS)

    Willey, Christopher D.; Xiao Dakai; Tu Tianxiang; Kim, Kwang Woon; Moretti, Luigi; Niermann, Kenneth J.; Tawtawy, Mohammed N.; Quarles, Chad C. Ph.D.; Lu Bo

    2010-01-01

    Purpose: Angiogenesis has generated interest in oncology because of its important role in cancer growth and progression, particularly when combined with cytotoxic therapies, such as radiotherapy. Among the numerous pathways influencing vascular growth and stability, inhibition of protein kinase B(Akt) or protein kinase C(PKC) can influence tumor blood vessels within tumor microvasculature. Therefore, we wanted to determine whether PKC inhibition could sensitize lung tumors to radiation. Methods and Materials: The combination of the selective PKCβ inhibitor Enzastaurin (ENZ, LY317615) and ionizing radiation were used in cell culture and a mouse model of lung cancer. Lung cancer cell lines and human umbilical vascular endothelial cells (HUVEC) were examined using immunoblotting, cytotoxic assays including cell proliferation and clonogenic assays, and Matrigel endothelial tubule formation. In vivo, H460 lung cancer xenografts were examined for tumor vasculature and proliferation using immunohistochemistry. Results: ENZ effectively radiosensitizes HUVEC within in vitro models. Furthermore, concurrent ENZ treatment of lung cancer xenografts enhanced radiation-induced destruction of tumor vasculature and proliferation by IHC. However, tumor growth delay was not enhanced with combination treatment compared with either treatment alone. Analysis of downstream effectors revealed that HUVEC and the lung cancer cell lines differed in their response to ENZ and radiation such that only HUVEC demonstrate phosphorylated S6 suppression, which is downstream of mTOR. When ENZ was combined with the mTOR inhibitor, rapamycin, in H460 lung cancer cells, radiosensitization was observed. Conclusion: PKC appears to be crucial for angiogenesis, and its inhibition by ENZ has potential to enhance radiotherapy in vivo.

  20. Molecular phylodynamics and protein modeling of infectious salmon anemia virus (ISAV

    Directory of Open Access Journals (Sweden)

    Castro-Nallar Eduardo

    2011-12-01

    surveillance settings. In addition, we hypothesized that genetic diversity of the HPR region is governed by recombination, probably due to template switching and that novel fusion gene proteolytic sites confer a selective advantage for the isolates that carry them. Additionally, protein modeling allowed us to relate the results of phylogenetic studies with the predicted structures. This study demonstrates that phylogenetic methods are important tools to predict future outbreaks of ISAV and other salmon pathogens.

  1. Palatability of water-soluble extracts of protein sources and replacement of fishmeal by a selected mixture of protein sources for juvenile turbot ( Scophthalmus maximus)

    Science.gov (United States)

    Dong, Chun; He, Gen; Mai, Kangsen; Zhou, Huihui; Xu, Wei

    2016-06-01

    Poor palatability is a limiting factor for replacing fishmeal with other protein sources in aquaculture. The water-soluble molecules with low molecular weights are the major determinants of the palatability of diets. The present study was conducted to investigate the palatability of water-soluble extracts from single protein source (single extract pellets) and the mixture of these extracts with different proportions (blended extract pellets) in juvenile turbot ( Scophthalmus maximus). Then according to the palatability of blended extract pellets, an optimal mixture proportion was selected, and a new protein source made from raw protein materials with the selected proportion was formulated to replace fishmeal. Summarily, the palatability of single extract pellets for turbot was descendent from fishmeal to pet-food grade poultry by-product meal, wheat gluten meal, soybean meal, peanut meal, meat and bone meal, and corn gluten meal. Subsequently, according to the palatability of single extract pellets, 52 kinds of blended extract pellets were designed to test their palatability. The results showed that the pellets presented remarkably different palatability, and the optimal one was diet 52 (wheat gluten meal: pet-food grade poultry by-product meal: meat and bone meal: corn gluten meal = 1:6:1:2). The highest ingestion ratio (the number of pellets ingested/the number of pellets fed) was 0.73 ± 0.03, which was observed in Diet 52. Then five isonitrogenous (52% crude protein) and isocaloric (20 kJ g-1 gross energy) diets were formulated by replacing 0 (control), 35%, 50%, 65% and 80% of fishmeal with No.52 blending proportion. After a 10-weeks feeding trial, a consistent feed intake was found among all replacement treatments. Replacement level of fishmeal up to 35% did not significantly influence final body weight, specific growth rate, feed efficiency ratio, and protein efficiency ratio of turbot. Therefore, the water-soluble extracts of protein sources play an

  2. Modeling of axonal endoplasmic reticulum network by spastic paraplegia proteins.

    Science.gov (United States)

    Yalçın, Belgin; Zhao, Lu; Stofanko, Martin; O'Sullivan, Niamh C; Kang, Zi Han; Roost, Annika; Thomas, Matthew R; Zaessinger, Sophie; Blard, Olivier; Patto, Alex L; Sohail, Anood; Baena, Valentina; Terasaki, Mark; O'Kane, Cahir J

    2017-07-25

    Axons contain a smooth tubular endoplasmic reticulum (ER) network that is thought to be continuous with ER throughout the neuron; the mechanisms that form this axonal network are unknown. Mutations affecting reticulon or REEP proteins, with intramembrane hairpin domains that model ER membranes, cause an axon degenerative disease, hereditary spastic paraplegia (HSP). We show that Drosophila axons have a dynamic axonal ER network, which these proteins help to model. Loss of HSP hairpin proteins causes ER sheet expansion, partial loss of ER from distal motor axons, and occasional discontinuities in axonal ER. Ultrastructural analysis reveals an extensive ER network in axons, which shows larger and fewer tubules in larvae that lack reticulon and REEP proteins, consistent with loss of membrane curvature. Therefore HSP hairpin-containing proteins are required for shaping and continuity of axonal ER, thus suggesting roles for ER modeling in axon maintenance and function.

  3. Models of protein and amino acid requirements for cattle

    Directory of Open Access Journals (Sweden)

    Luis Orlindo Tedeschi

    2015-03-01

    Full Text Available Protein supply and requirements by ruminants have been studied for more than a century. These studies led to the accumulation of lots of scientific information about digestion and metabolism of protein by ruminants as well as the characterization of the dietary protein in order to maximize animal performance. During the 1980s and 1990s, when computers became more accessible and powerful, scientists began to conceptualize and develop mathematical nutrition models, and to program them into computers to assist with ration balancing and formulation for domesticated ruminants, specifically dairy and beef cattle. The most commonly known nutrition models developed during this period were the National Research Council (NRC in the United States, Agricultural Research Council (ARC in the United Kingdom, Institut National de la Recherche Agronomique (INRA in France, and the Commonwealth Scientific and Industrial Research Organization (CSIRO in Australia. Others were derivative works from these models with different degrees of modifications in the supply or requirement calculations, and the modeling nature (e.g., static or dynamic, mechanistic, or deterministic. Circa 1990s, most models adopted the metabolizable protein (MP system over the crude protein (CP and digestible CP systems to estimate supply of MP and the factorial system to calculate MP required by the animal. The MP system included two portions of protein (i.e., the rumen-undegraded dietary CP - RUP - and the contributions of microbial CP - MCP as the main sources of MP for the animal. Some models would explicitly account for the impact of dry matter intake (DMI on the MP required for maintenance (MPm; e.g., Cornell Net Carbohydrate and Protein System - CNCPS, the Dutch system - DVE/OEB, while others would simply account for scurf, urinary, metabolic fecal, and endogenous contributions independently of DMI. All models included milk yield and its components in estimating MP required for lactation

  4. NegGOA: negative GO annotations selection using ontology structure.

    Science.gov (United States)

    Fu, Guangyuan; Wang, Jun; Yang, Bo; Yu, Guoxian

    2016-10-01

    Predicting the biological functions of proteins is one of the key challenges in the post-genomic era. Computational models have demonstrated the utility of applying machine learning methods to predict protein function. Most prediction methods explicitly require a set of negative examples-proteins that are known not carrying out a particular function. However, Gene Ontology (GO) almost always only provides the knowledge that proteins carry out a particular function, and functional annotations of proteins are incomplete. GO structurally organizes more than tens of thousands GO terms and a protein is annotated with several (or dozens) of these terms. For these reasons, the negative examples of a protein can greatly help distinguishing true positive examples of the protein from such a large candidate GO space. In this paper, we present a novel approach (called NegGOA) to select negative examples. Specifically, NegGOA takes advantage of the ontology structure, available annotations and potentiality of additional annotations of a protein to choose negative examples of the protein. We compare NegGOA with other negative examples selection algorithms and find that NegGOA produces much fewer false negatives than them. We incorporate the selected negative examples into an efficient function prediction model to predict the functions of proteins in Yeast, Human, Mouse and Fly. NegGOA also demonstrates improved accuracy than these comparing algorithms across various evaluation metrics. In addition, NegGOA is less suffered from incomplete annotations of proteins than these comparing methods. The Matlab and R codes are available at https://sites.google.com/site/guoxian85/neggoa gxyu@swu.edu.cn Supplementary data are available at Bioinformatics online. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

  5. A resource for benchmarking the usefulness of protein structure models.

    Science.gov (United States)

    Carbajo, Daniel; Tramontano, Anna

    2012-08-02

    Increasingly, biologists and biochemists use computational tools to design experiments to probe the function of proteins and/or to engineer them for a variety of different purposes. The most effective strategies rely on the knowledge of the three-dimensional structure of the protein of interest. However it is often the case that an experimental structure is not available and that models of different quality are used instead. On the other hand, the relationship between the quality of a model and its appropriate use is not easy to derive in general, and so far it has been analyzed in detail only for specific application. This paper describes a database and related software tools that allow testing of a given structure based method on models of a protein representing different levels of accuracy. The comparison of the results of a computational experiment on the experimental structure and on a set of its decoy models will allow developers and users to assess which is the specific threshold of accuracy required to perform the task effectively. The ModelDB server automatically builds decoy models of different accuracy for a given protein of known structure and provides a set of useful tools for their analysis. Pre-computed data for a non-redundant set of deposited protein structures are available for analysis and download in the ModelDB database. IMPLEMENTATION, AVAILABILITY AND REQUIREMENTS: Project name: A resource for benchmarking the usefulness of protein structure models. Project home page: http://bl210.caspur.it/MODEL-DB/MODEL-DB_web/MODindex.php.Operating system(s): Platform independent. Programming language: Perl-BioPerl (program); mySQL, Perl DBI and DBD modules (database); php, JavaScript, Jmol scripting (web server). Other requirements: Java Runtime Environment v1.4 or later, Perl, BioPerl, CPAN modules, HHsearch, Modeller, LGA, NCBI Blast package, DSSP, Speedfill (Surfnet) and PSAIA. License: Free. Any restrictions to use by non-academics: No.

  6. A resource for benchmarking the usefulness of protein structure models

    Directory of Open Access Journals (Sweden)

    Carbajo Daniel

    2012-08-01

    Full Text Available Abstract Background Increasingly, biologists and biochemists use computational tools to design experiments to probe the function of proteins and/or to engineer them for a variety of different purposes. The most effective strategies rely on the knowledge of the three-dimensional structure of the protein of interest. However it is often the case that an experimental structure is not available and that models of different quality are used instead. On the other hand, the relationship between the quality of a model and its appropriate use is not easy to derive in general, and so far it has been analyzed in detail only for specific application. Results This paper describes a database and related software tools that allow testing of a given structure based method on models of a protein representing different levels of accuracy. The comparison of the results of a computational experiment on the experimental structure and on a set of its decoy models will allow developers and users to assess which is the specific threshold of accuracy required to perform the task effectively. Conclusions The ModelDB server automatically builds decoy models of different accuracy for a given protein of known structure and provides a set of useful tools for their analysis. Pre-computed data for a non-redundant set of deposited protein structures are available for analysis and download in the ModelDB database. Implementation, availability and requirements Project name: A resource for benchmarking the usefulness of protein structure models. Project home page: http://bl210.caspur.it/MODEL-DB/MODEL-DB_web/MODindex.php. Operating system(s: Platform independent. Programming language: Perl-BioPerl (program; mySQL, Perl DBI and DBD modules (database; php, JavaScript, Jmol scripting (web server. Other requirements: Java Runtime Environment v1.4 or later, Perl, BioPerl, CPAN modules, HHsearch, Modeller, LGA, NCBI Blast package, DSSP, Speedfill (Surfnet and PSAIA. License: Free. Any

  7. A resource for benchmarking the usefulness of protein structure models.

    KAUST Repository

    Carbajo, Daniel

    2012-08-02

    BACKGROUND: Increasingly, biologists and biochemists use computational tools to design experiments to probe the function of proteins and/or to engineer them for a variety of different purposes. The most effective strategies rely on the knowledge of the three-dimensional structure of the protein of interest. However it is often the case that an experimental structure is not available and that models of different quality are used instead. On the other hand, the relationship between the quality of a model and its appropriate use is not easy to derive in general, and so far it has been analyzed in detail only for specific application. RESULTS: This paper describes a database and related software tools that allow testing of a given structure based method on models of a protein representing different levels of accuracy. The comparison of the results of a computational experiment on the experimental structure and on a set of its decoy models will allow developers and users to assess which is the specific threshold of accuracy required to perform the task effectively. CONCLUSIONS: The ModelDB server automatically builds decoy models of different accuracy for a given protein of known structure and provides a set of useful tools for their analysis. Pre-computed data for a non-redundant set of deposited protein structures are available for analysis and download in the ModelDB database. IMPLEMENTATION, AVAILABILITY AND REQUIREMENTS: Project name: A resource for benchmarking the usefulness of protein structure models. Project home page: http://bl210.caspur.it/MODEL-DB/MODEL-DB_web/MODindex.php.Operating system(s): Platform independent. Programming language: Perl-BioPerl (program); mySQL, Perl DBI and DBD modules (database); php, JavaScript, Jmol scripting (web server). Other requirements: Java Runtime Environment v1.4 or later, Perl, BioPerl, CPAN modules, HHsearch, Modeller, LGA, NCBI Blast package, DSSP, Speedfill (Surfnet) and PSAIA. License: Free. Any restrictions to use by

  8. A resource for benchmarking the usefulness of protein structure models.

    KAUST Repository

    Carbajo, Daniel; Tramontano, Anna

    2012-01-01

    BACKGROUND: Increasingly, biologists and biochemists use computational tools to design experiments to probe the function of proteins and/or to engineer them for a variety of different purposes. The most effective strategies rely on the knowledge of the three-dimensional structure of the protein of interest. However it is often the case that an experimental structure is not available and that models of different quality are used instead. On the other hand, the relationship between the quality of a model and its appropriate use is not easy to derive in general, and so far it has been analyzed in detail only for specific application. RESULTS: This paper describes a database and related software tools that allow testing of a given structure based method on models of a protein representing different levels of accuracy. The comparison of the results of a computational experiment on the experimental structure and on a set of its decoy models will allow developers and users to assess which is the specific threshold of accuracy required to perform the task effectively. CONCLUSIONS: The ModelDB server automatically builds decoy models of different accuracy for a given protein of known structure and provides a set of useful tools for their analysis. Pre-computed data for a non-redundant set of deposited protein structures are available for analysis and download in the ModelDB database. IMPLEMENTATION, AVAILABILITY AND REQUIREMENTS: Project name: A resource for benchmarking the usefulness of protein structure models. Project home page: http://bl210.caspur.it/MODEL-DB/MODEL-DB_web/MODindex.php.Operating system(s): Platform independent. Programming language: Perl-BioPerl (program); mySQL, Perl DBI and DBD modules (database); php, JavaScript, Jmol scripting (web server). Other requirements: Java Runtime Environment v1.4 or later, Perl, BioPerl, CPAN modules, HHsearch, Modeller, LGA, NCBI Blast package, DSSP, Speedfill (Surfnet) and PSAIA. License: Free. Any restrictions to use by

  9. Induced variation in protein mutants after multiple EMS and X-ray treatments

    International Nuclear Information System (INIS)

    Walther, H.; Seibold, K.H.

    1978-01-01

    Results from two experiments with spring barley in the M 3 and M 8 generations gave information on the efficiency of selected mutants, improved in protein yield by different mutagenic treatments. A selection rate of 1-2% was found to be realistic according to a 5% significance level. However, differences between mutagenic treatments with EMS and X-rays and between varieties were found to be not only incidental. To evaluate the results within a protein mutation breeding programme a bivariate selection model was applied and was found to allow clear decisions on mutagenic improvements in protein production, measured in g protein/m 2 . When substituting protein yield in g/seed for protein yield in g/m 2 in the early generations, all relations to protein and grain yield in g/m 2 were found to be low and negative. We conclude that this substituted selection character can be of only limited aid. But very high positive correlations exist between protein yield/m 2 , lysine yield/m 2 and grain yield/m 2 , which means that these selection characters would render a more reliable basis for selection in early generations. (author)

  10. Structural Elements in the Gαs and Gαq C Termini That Mediate Selective G Protein-coupled Receptor (GPCR) Signaling.

    Science.gov (United States)

    Semack, Ansley; Sandhu, Manbir; Malik, Rabia U; Vaidehi, Nagarajan; Sivaramakrishnan, Sivaraj

    2016-08-19

    Although the importance of the C terminus of the α subunit of the heterotrimeric G protein in G protein-coupled receptor (GPCR)-G protein pairing is well established, the structural basis of selective interactions remains unknown. Here, we combine live cell FRET-based measurements and molecular dynamics simulations of the interaction between the GPCR and a peptide derived from the C terminus of the Gα subunit (Gα peptide) to dissect the molecular mechanisms of G protein selectivity. We observe a direct link between Gα peptide binding and stabilization of the GPCR conformational ensemble. We find that cognate and non-cognate Gα peptides show deep and shallow binding, respectively, and in distinct orientations within the GPCR. Binding of the cognate Gα peptide stabilizes the agonist-bound GPCR conformational ensemble resulting in favorable binding energy and lower flexibility of the agonist-GPCR pair. We identify three hot spot residues (Gαs/Gαq-Gln-384/Leu-349, Gln-390/Glu-355, and Glu-392/Asn-357) that contribute to selective interactions between the β2-adrenergic receptor (β2-AR)-Gαs and V1A receptor (V1AR)-Gαq The Gαs and Gαq peptides adopt different orientations in β2-AR and V1AR, respectively. The β2-AR/Gαs peptide interface is dominated by electrostatic interactions, whereas the V1AR/Gαq peptide interactions are predominantly hydrophobic. Interestingly, our study reveals a role for both favorable and unfavorable interactions in G protein selection. Residue Glu-355 in Gαq prevents this peptide from interacting strongly with β2-AR. Mutagenesis to the Gαs counterpart (E355Q) imparts a cognate-like interaction. Overall, our study highlights the synergy in molecular dynamics and FRET-based approaches to dissect the structural basis of selective G protein interactions. © 2016 by The American Society for Biochemistry and Molecular Biology, Inc.

  11. Preclinical models used for immunogenicity prediction of therapeutic proteins.

    Science.gov (United States)

    Brinks, Vera; Weinbuch, Daniel; Baker, Matthew; Dean, Yann; Stas, Philippe; Kostense, Stefan; Rup, Bonita; Jiskoot, Wim

    2013-07-01

    All therapeutic proteins are potentially immunogenic. Antibodies formed against these drugs can decrease efficacy, leading to drastically increased therapeutic costs and in rare cases to serious and sometimes life threatening side-effects. Many efforts are therefore undertaken to develop therapeutic proteins with minimal immunogenicity. For this, immunogenicity prediction of candidate drugs during early drug development is essential. Several in silico, in vitro and in vivo models are used to predict immunogenicity of drug leads, to modify potentially immunogenic properties and to continue development of drug candidates with expected low immunogenicity. Despite the extensive use of these predictive models, their actual predictive value varies. Important reasons for this uncertainty are the limited/insufficient knowledge on the immune mechanisms underlying immunogenicity of therapeutic proteins, the fact that different predictive models explore different components of the immune system and the lack of an integrated clinical validation. In this review, we discuss the predictive models in use, summarize aspects of immunogenicity that these models predict and explore the merits and the limitations of each of the models.

  12. Model test on the relationship feed energy and protein ratio to the production and quality of milk protein

    Science.gov (United States)

    Hartanto, R.; Jantra, M. A. C.; Santosa, S. A. B.; Purnomoadi, A.

    2018-01-01

    The purpose of this research was to find an appropriate relationship model between the feed energy and protein ratio with the amount of production and quality of milk proteins. This research was conducted at Getasan Sub-district, Semarang Regency, Central Java Province, Indonesia using 40 samples (Holstein Friesian cattle, lactation period II-III and lactation month 3-4). Data were analyzed using linear and quadratic regressions, to predict the production and quality of milk protein from feed energy and protein ratio that describe the diet. The significance of model was tested using analysis of variance. Coefficient of determination (R2), residual variance (RV) and root mean square prediction error (RMSPE) were reported for the developed equations as an indicator of the goodness of model fit. The results showed no relationship in milk protein (kg), milk casein (%), milk casein (kg) and milk urea N (mg/dl) as function of CP/TDN. The significant relationship was observed in milk production (L or kg) and milk protein (%) as function of CP/TDN, both in linear and quadratic models. In addition, a quadratic change in milk production (L) (P = 0.003), milk production (kg) (P = 0.003) and milk protein concentration (%) (P = 0.026) were observed with increase of CP/TDN. It can be concluded that quadratic equation was the good fitting model for this research, because quadratic equation has larger R2, smaller RV and smaller RMSPE than those of linear equation.

  13. ASYMMETRIC PRICE TRANSMISSION MODELING: THE IMPORTANCE OF MODEL COMPLEXITY AND THE PERFORMANCE OF THE SELECTION CRITERIA

    Directory of Open Access Journals (Sweden)

    Henry de-Graft Acquah

    2013-01-01

    Full Text Available Information Criteria provides an attractive basis for selecting the best model from a set of competing asymmetric price transmission models or theories. However, little is understood about the sensitivity of the model selection methods to model complexity. This study therefore fits competing asymmetric price transmission models that differ in complexity to simulated data and evaluates the ability of the model selection methods to recover the true model. The results of Monte Carlo experimentation suggest that in general BIC, CAIC and DIC were superior to AIC when the true data generating process was the standard error correction model, whereas AIC was more successful when the true model was the complex error correction model. It is also shown that the model selection methods performed better in large samples for a complex asymmetric data generating process than with a standard asymmetric data generating process. Except for complex models, AIC's performance did not make substantial gains in recovery rates as sample size increased. The research findings demonstrate the influence of model complexity in asymmetric price transmission model comparison and selection.

  14. Protein structure analysis using the resonant recognition model and wavelet transforms

    International Nuclear Information System (INIS)

    Fang, Q.; Cosic, I.

    1998-01-01

    An approach based on the resonant recognition model and the discrete wavelet transform is introduced here for characterising proteins' biological function. The protein sequence is converted into a numerical series by assigning the electron-ion interaction potential to each amino acid from N-terminal to C-terminal. A set of peaks is found after performing a wavelet transform onto a numerical series representing a group of homologous proteins. These peaks are related to protein structural and functional properties and named characteristic vector of that protein group. Further more, the amino acids contributing mostly to a protein's biological functions, the so-called 'hot spots' amino acids, are predicted by the continuous wavelet transform. It is found that the hot spots are clustered around the protein's cleft structure. The wavelets approach provides a novel methods for amino acid sequence analysis as well as an expansion for the newly established macromolecular interaction model: the resonant recognition model. Copyright (1998) Australasian Physical and Engineering Sciences in Medicine

  15. Multi-Population Selective Genotyping to Identify Soybean [Glycine max (L.) Merr.] Seed Protein and Oil QTLs.

    Science.gov (United States)

    Phansak, Piyaporn; Soonsuwon, Watcharin; Hyten, David L; Song, Qijian; Cregan, Perry B; Graef, George L; Specht, James E

    2016-06-01

    Plant breeders continually generate ever-higher yielding cultivars, but also want to improve seed constituent value, which is mainly protein and oil, in soybean [Glycine max (L.) Merr.]. Identification of genetic loci governing those two traits would facilitate that effort. Though genome-wide association offers one such approach, selective genotyping of multiple biparental populations offers a complementary alternative, and was evaluated here, using 48 F2:3 populations (n = ∼224 plants) created by mating 48 high protein germplasm accessions to cultivars of similar maturity, but with normal seed protein content. All F2:3 progeny were phenotyped for seed protein and oil, but only 22 high and 22 low extreme progeny in each F2:3 phenotypic distribution were genotyped with a 1536-SNP chip (ca 450 bimorphic SNPs detected per mating). A significant quantitative trait locus (QTL) on one or more chromosomes was detected for protein in 35 (73%), and for oil in 25 (52%), of the 48 matings, and these QTL exhibited additive effects of ≥ 4 g kg(-1) and R(2) values of 0.07 or more. These results demonstrated that a multiple-population selective genotyping strategy, when focused on matings between parental phenotype extremes, can be used successfully to identify germplasm accessions possessing large-effect QTL alleles. Such accessions would be of interest to breeders to serve as parental donors of those alleles in cultivar development programs, though 17 of the 48 accessions were not unique in terms of SNP genotype, indicating that diversity among high protein accessions in the germplasm collection is less than what might ordinarily be assumed. Copyright © 2016 Phansak et al.

  16. Betulinic acid selectively increases protein degradation and enhances prostate cancer-specific apoptosis: possible role for inhibition of deubiquitinase activity.

    Directory of Open Access Journals (Sweden)

    Teresita Reiner

    Full Text Available Inhibition of the ubiquitin-proteasome system (UPS of protein degradation is a valid anti-cancer strategy and has led to the approval of bortezomib for the treatment of multiple myeloma. However, the alternative approach of enhancing the degradation of oncoproteins that are frequently overexpressed in cancers is less developed. Betulinic acid (BA is a plant-derived small molecule that can increase apoptosis specifically in cancer but not in normal cells, making it an attractive anti-cancer agent. Our results in prostate cancer suggested that BA inhibited multiple deubiquitinases (DUBs, which resulted in the accumulation of poly-ubiquitinated proteins, decreased levels of oncoproteins, and increased apoptotic cell death. In normal fibroblasts, however, BA did not inhibit DUB activity nor increased total poly-ubiquitinated proteins, which was associated with a lack of effect on cell death. In the TRAMP transgenic mouse model of prostate cancer, treatment with BA (10 mg/kg inhibited primary tumors, increased apoptosis, decreased angiogenesis and proliferation, and lowered androgen receptor and cyclin D1 protein. BA treatment also inhibited DUB activity and increased ubiquitinated proteins in TRAMP prostate cancer but had no effect on apoptosis or ubiquitination in normal mouse tissues. Overall, our data suggests that BA-mediated inhibition of DUBs and induction of apoptotic cell death specifically in prostate cancer but not in normal cells and tissues may provide an effective non-toxic and clinically selective agent for chemotherapy.

  17. Positive Selection Drives the Evolution of rhino, a Member of the Heterochromatin Protein 1 Family in Drosophila.

    Directory of Open Access Journals (Sweden)

    2005-07-01

    Full Text Available Heterochromatin comprises a significant component of many eukaryotic genomes. In comparison to euchromatin, heterochromatin is gene poor, transposon rich, and late replicating. It serves many important biological roles, from gene silencing to accurate chromosome segregation, yet little is known about the evolutionary constraints that shape heterochromatin. A complementary approach to the traditional one of directly studying heterochromatic DNA sequence is to study the evolution of proteins that bind and define heterochromatin. One of the best markers for heterochromatin is the heterochromatin protein 1 (HP1, which is an essential, nonhistone chromosomal protein. Here we investigate the molecular evolution of five HP1 paralogs present in Drosophila melanogaster. Three of these paralogs have ubiquitous expression patterns in adult Drosophila tissues, whereas HP1D/rhino and HP1E are expressed predominantly in ovaries and testes respectively. The HP1 paralogs also have distinct localization preferences in Drosophila cells. Thus, Rhino localizes to the heterochromatic compartment in Drosophila tissue culture cells, but in a pattern distinct from HP1A and lysine-9 dimethylated H3. Using molecular evolution and population genetic analyses, we find that rhino has been subject to positive selection in all three domains of the protein: the N-terminal chromo domain, the C-terminal chromo-shadow domain, and the hinge region that connects these two modules. Maximum likelihood analysis of rhino sequences from 20 species of Drosophila reveals that a small number of residues of the chromo and shadow domains have been subject to repeated positive selection. The rapid and positive selection of rhino is highly unusual for a gene encoding a chromosomal protein and suggests that rhino is involved in a genetic conflict that affects the germline, belying the notion that heterochromatin is simply a passive recipient of "junk DNA" in eukaryotic genomes.

  18. Positive selection drives the evolution of rhino, a member of the heterochromatin protein 1 family in Drosophila.

    Directory of Open Access Journals (Sweden)

    Danielle Vermaak

    2005-07-01

    Full Text Available Heterochromatin comprises a significant component of many eukaryotic genomes. In comparison to euchromatin, heterochromatin is gene poor, transposon rich, and late replicating. It serves many important biological roles, from gene silencing to accurate chromosome segregation, yet little is known about the evolutionary constraints that shape heterochromatin. A complementary approach to the traditional one of directly studying heterochromatic DNA sequence is to study the evolution of proteins that bind and define heterochromatin. One of the best markers for heterochromatin is the heterochromatin protein 1 (HP1, which is an essential, nonhistone chromosomal protein. Here we investigate the molecular evolution of five HP1 paralogs present in Drosophila melanogaster. Three of these paralogs have ubiquitous expression patterns in adult Drosophila tissues, whereas HP1D/rhino and HP1E are expressed predominantly in ovaries and testes respectively. The HP1 paralogs also have distinct localization preferences in Drosophila cells. Thus, Rhino localizes to the heterochromatic compartment in Drosophila tissue culture cells, but in a pattern distinct from HP1A and lysine-9 dimethylated H3. Using molecular evolution and population genetic analyses, we find that rhino has been subject to positive selection in all three domains of the protein: the N-terminal chromo domain, the C-terminal chromo-shadow domain, and the hinge region that connects these two modules. Maximum likelihood analysis of rhino sequences from 20 species of Drosophila reveals that a small number of residues of the chromo and shadow domains have been subject to repeated positive selection. The rapid and positive selection of rhino is highly unusual for a gene encoding a chromosomal protein and suggests that rhino is involved in a genetic conflict that affects the germline, belying the notion that heterochromatin is simply a passive recipient of "junk DNA" in eukaryotic genomes.

  19. Weak cation magnetic separation technology and MALDI-TOF-MS in screening serum protein markers in primary type I osteoporosis.

    Science.gov (United States)

    Shi, X L; Li, C W; Liang, B C; He, K H; Li, X Y

    2015-11-30

    We investigated weak cation magnetic separation technology and matrix-assisted laser desorption ionization-time of flight-mass spectrometry (MALDI-TOF-MS) in screening serum protein markers of primary type I osteoporosis. We selected 16 postmenopausal women with osteoporosis and nine postmenopausal women as controls to find a new method for screening biomarkers and establishing a diagnostic model for primary type I osteoporosis. Serum samples were obtained from controls and patients. Serum protein was extracted with the WCX protein chip system; protein fingerprints were examined using MALDI-TOF-MS. The preprocessed and model construction data were handled by the ProteinChip system. The diagnostic models were established using a genetic arithmetic model combined with a support vector machine (SVM). The SVM model with the highest Youden index was selected. Combinations with the highest accuracy in distinguishing different groups of data were selected as potential biomarkers. From the two groups of serum proteins, 123 cumulative MS protein peaks were selected. Significant intensity differences in the protein peaks of 16 postmenopausal women with osteoporosis were screened. The difference in Youden index between the four groups of protein peaks showed that the highest peaks had mass-to-charge ratios of 8909.047, 8690.658, 13745.48, and 15114.52. A diagnosis model was established with these four markers as the candidates, and the model specificity and sensitivity were found to be 100%. Two groups of specimens in the SVM results on the scatterplot were distinguishable. We established a diagnosis model, and provided a new serological method for screening and diagnosis of osteoporosis with high sensitivity and specificity.

  20. A generative, probabilistic model of local protein structure

    DEFF Research Database (Denmark)

    Boomsma, Wouter; Mardia, Kanti V.; Taylor, Charles C.

    2008-01-01

    Despite significant progress in recent years, protein structure prediction maintains its status as one of the prime unsolved problems in computational biology. One of the key remaining challenges is an efficient probabilistic exploration of the structural space that correctly reflects the relative...... conformational stabilities. Here, we present a fully probabilistic, continuous model of local protein structure in atomic detail. The generative model makes efficient conformational sampling possible and provides a framework for the rigorous analysis of local sequence-structure correlations in the native state...

  1. Selective functional activity measurement of a PEGylated protein with a modification-dependent activity assay.

    Science.gov (United States)

    Weber, Alfred; Engelmaier, Andrea; Mohr, Gabriele; Haindl, Sonja; Schwarz, Hans Peter; Turecek, Peter L

    2017-01-05

    BAX 855 (ADYNOVATE) is a PEGylated recombinant factor VIII (rFVIII) that showed prolonged circulatory half-life compared to unmodified rFVIII in hemophilic patients. Here, the development and validation of a novel assay is described that selectively measures the activity of BAX 855 as cofactor for the serine protease factor IX, which actives factor X. This method type, termed modification-dependent activity assay, is based on PEG-specific capture of BAX 855 by an anti-PEG IgG preparation, followed by a chromogenic FVIII activity assay. The assay principle enabled sensitive measurement of the FVIII cofactor activity of BAX 855 down to the pM-range without interference by non-PEGylated FVIII. The selectivity of the capture step, shown by competition studies to primarily target the terminal methoxy group of PEG, also allowed assessment of the intactness of the attached PEG chains. Altogether, the modification-dependent activity not only enriches, but complements the group of methods to selectively, accurately, and precisely measure a PEGylated drug in complex biological matrices. In contrast to all other methods described so far, it allows measurement of the biological activity of the PEGylated protein. Data obtained demonstrate that this new method principle can be extended to protein modifications other than PEGylation and to a variety of functional activity assays. Copyright © 2016 Elsevier B.V. All rights reserved.

  2. Plasmalemmal Vesicle Associated Protein-1 (PV-1 is a marker of blood-brain barrier disruption in rodent models

    Directory of Open Access Journals (Sweden)

    Ali Zarina S

    2008-02-01

    Full Text Available Abstract Background Plasmalemmal vesicle associated protein-1 (PV-1 is selectively expressed in human brain microvascular endothelial cells derived from clinical specimens of primary and secondary malignant brain tumors, cerebral ischemia, and other central nervous system (CNS diseases associated with blood-brain barrier breakdown. In this study, we characterize the murine CNS expression pattern of PV-1 to determine whether localized PV-1 induction is conserved across species and disease state. Results We demonstrate that PV-1 is selectively upregulated in mouse blood vessels recruited by brain tumor xenografts at the RNA and protein levels, but is not detected in non-neoplastic brain. Additionally, PV-1 is induced in a mouse model of acute ischemia. Expression is confined to the cerebovasculature within the region of infarct and is temporally regulated. Conclusion Our results confirm that PV-1 is preferentially induced in the endothelium of mouse brain tumors and acute ischemic brain tissue and corresponds to blood-brain barrier disruption in a fashion analogous to human patients. Characterization of PV-1 expression in mouse brain is the first step towards development of rodent models for testing anti-edema and anti-angiogenesis therapeutic strategies based on this molecule.

  3. A SUPPLIER SELECTION MODEL FOR SOFTWARE DEVELOPMENT OUTSOURCING

    Directory of Open Access Journals (Sweden)

    Hancu Lucian-Viorel

    2010-12-01

    Full Text Available This paper presents a multi-criteria decision making model used for supplier selection for software development outsourcing on e-marketplaces. This model can be used in auctions. The supplier selection process becomes complex and difficult on last twenty years since the Internet plays an important role in business management. Companies have to concentrate their efforts on their core activities and the others activities should be realized by outsourcing. They can achieve significant cost reduction by using e-marketplaces in their purchase process and by using decision support systems on supplier selection. In the literature were proposed many approaches for supplier evaluation and selection process. The performance of potential suppliers is evaluated using multi criteria decision making methods rather than considering a single factor cost.

  4. Quantitative and Selective Analysis of Feline Growth Related Proteins Using Parallel Reaction Monitoring High Resolution Mass Spectrometry.

    Directory of Open Access Journals (Sweden)

    Mårten Sundberg

    Full Text Available Today immunoassays are widely used in veterinary medicine, but lack of species specific assays often necessitates the use of assays developed for human applications. Mass spectrometry (MS is an attractive alternative due to high specificity and versatility, allowing for species-independent analysis. Targeted MS-based quantification methods are valuable complements to large scale shotgun analysis. A method referred to as parallel reaction monitoring (PRM, implemented on Orbitrap MS, has lately been presented as an excellent alternative to more traditional selected reaction monitoring/multiple reaction monitoring (SRM/MRM methods. The insulin-like growth factor (IGF-system is not well described in the cat but there are indications of important differences between cats and humans. In feline medicine IGF-I is mainly analyzed for diagnosis of growth hormone disorders but also for research, while the other proteins in the IGF-system are not routinely analyzed within clinical practice. Here, a PRM method for quantification of IGF-I, IGF-II, IGF binding protein (BP -3 and IGFBP-5 in feline serum is presented. Selective quantification was supported by the use of a newly launched internal standard named QPrEST™. Homology searches demonstrated the possibility to use this standard of human origin for quantification of the targeted feline proteins. Excellent quantitative sensitivity at the attomol/μL (pM level and selectivity were obtained. As the presented approach is very generic we show that high resolution mass spectrometry in combination with PRM and QPrEST™ internal standards is a versatile tool for protein quantitation across multispecies.

  5. Modeling disordered regions in proteins using Rosetta.

    Directory of Open Access Journals (Sweden)

    Ray Yu-Ruei Wang

    Full Text Available Protein structure prediction methods such as Rosetta search for the lowest energy conformation of the polypeptide chain. However, the experimentally observed native state is at a minimum of the free energy, rather than the energy. The neglect of the missing configurational entropy contribution to the free energy can be partially justified by the assumption that the entropies of alternative folded states, while very much less than unfolded states, are not too different from one another, and hence can be to a first approximation neglected when searching for the lowest free energy state. The shortcomings of current structure prediction methods may be due in part to the breakdown of this assumption. Particularly problematic are proteins with significant disordered regions which do not populate single low energy conformations even in the native state. We describe two approaches within the Rosetta structure modeling methodology for treating such regions. The first does not require advance knowledge of the regions likely to be disordered; instead these are identified by minimizing a simple free energy function used previously to model protein folding landscapes and transition states. In this model, residues can be either completely ordered or completely disordered; they are considered disordered if the gain in entropy outweighs the loss of favorable energetic interactions with the rest of the protein chain. The second approach requires identification in advance of the disordered regions either from sequence alone using for example the DISOPRED server or from experimental data such as NMR chemical shifts. During Rosetta structure prediction calculations the disordered regions make only unfavorable repulsive contributions to the total energy. We find that the second approach has greater practical utility and illustrate this with examples from de novo structure prediction, NMR structure calculation, and comparative modeling.

  6. Verification Techniques for Parameter Selection and Bayesian Model Calibration Presented for an HIV Model

    Science.gov (United States)

    Wentworth, Mami Tonoe

    Uncertainty quantification plays an important role when making predictive estimates of model responses. In this context, uncertainty quantification is defined as quantifying and reducing uncertainties, and the objective is to quantify uncertainties in parameter, model and measurements, and propagate the uncertainties through the model, so that one can make a predictive estimate with quantified uncertainties. Two of the aspects of uncertainty quantification that must be performed prior to propagating uncertainties are model calibration and parameter selection. There are several efficient techniques for these processes; however, the accuracy of these methods are often not verified. This is the motivation for our work, and in this dissertation, we present and illustrate verification frameworks for model calibration and parameter selection in the context of biological and physical models. First, HIV models, developed and improved by [2, 3, 8], describe the viral infection dynamics of an HIV disease. These are also used to make predictive estimates of viral loads and T-cell counts and to construct an optimal control for drug therapy. Estimating input parameters is an essential step prior to uncertainty quantification. However, not all the parameters are identifiable, implying that they cannot be uniquely determined by the observations. These unidentifiable parameters can be partially removed by performing parameter selection, a process in which parameters that have minimal impacts on the model response are determined. We provide verification techniques for Bayesian model calibration and parameter selection for an HIV model. As an example of a physical model, we employ a heat model with experimental measurements presented in [10]. A steady-state heat model represents a prototypical behavior for heat conduction and diffusion process involved in a thermal-hydraulic model, which is a part of nuclear reactor models. We employ this simple heat model to illustrate verification

  7. Protein-protein interface detection using the energy centrality relationship (ECR characteristic of proteins.

    Directory of Open Access Journals (Sweden)

    Sanjana Sudarshan

    Full Text Available Specific protein interactions are responsible for most biological functions. Distinguishing Functionally Linked Interfaces of Proteins (FLIPs, from Functionally uncorrelated Contacts (FunCs, is therefore important to characterizing these interactions. To achieve this goal, we have created a database of protein structures called FLIPdb, containing proteins belonging to various functional sub-categories. Here, we use geometric features coupled with Kortemme and Baker's computational alanine scanning method to calculate the energetic sensitivity of each amino acid at the interface to substitution, identify hotspots, and identify other factors that may contribute towards an interface being FLIP or FunC. Using Principal Component Analysis and K-means clustering on a training set of 160 interfaces, we could distinguish FLIPs from FunCs with an accuracy of 76%. When these methods were applied to two test sets of 18 and 170 interfaces, we achieved similar accuracies of 78% and 80%. We have identified that FLIP interfaces have a stronger central organizing tendency than FunCs, due, we suggest, to greater specificity. We also observe that certain functional sub-categories, such as enzymes, antibody-heavy-light, antibody-antigen, and enzyme-inhibitors form distinct sub-clusters. The antibody-antigen and enzyme-inhibitors interfaces have patterns of physical characteristics similar to those of FunCs, which is in agreement with the fact that the selection pressures of these interfaces is differently evolutionarily driven. As such, our ECR model also successfully describes the impact of evolution and natural selection on protein-protein interfaces. Finally, we indicate how our ECR method may be of use in reducing the false positive rate of docking calculations.

  8. Conformational Sampling in Template-Free Protein Loop Structure Modeling: An Overview

    OpenAIRE

    Li, Yaohang

    2013-01-01

    Accurately modeling protein loops is an important step to predict three-dimensional structures as well as to understand functions of many proteins. Because of their high flexibility, modeling the three-dimensional structures of loops is difficult and is usually treated as a “mini protein folding problem” under geometric constraints. In the past decade, there has been remarkable progress in template-free loop structure modeling due to advances of computational methods as well as stably increas...

  9. Protein Structure Classification and Loop Modeling Using Multiple Ramachandran Distributions

    KAUST Repository

    Najibi, Seyed Morteza; Maadooliat, Mehdi; Zhou, Lan; Huang, Jianhua Z.; Gao, Xin

    2017-01-01

    Recently, the study of protein structures using angular representations has attracted much attention among structural biologists. The main challenge is how to efficiently model the continuous conformational space of the protein structures based on the differences and similarities between different Ramachandran plots. Despite the presence of statistical methods for modeling angular data of proteins, there is still a substantial need for more sophisticated and faster statistical tools to model the large-scale circular datasets. To address this need, we have developed a nonparametric method for collective estimation of multiple bivariate density functions for a collection of populations of protein backbone angles. The proposed method takes into account the circular nature of the angular data using trigonometric spline which is more efficient compared to existing methods. This collective density estimation approach is widely applicable when there is a need to estimate multiple density functions from different populations with common features. Moreover, the coefficients of adaptive basis expansion for the fitted densities provide a low-dimensional representation that is useful for visualization, clustering, and classification of the densities. The proposed method provides a novel and unique perspective to two important and challenging problems in protein structure research: structure-based protein classification and angular-sampling-based protein loop structure prediction.

  10. Protein Structure Classification and Loop Modeling Using Multiple Ramachandran Distributions

    KAUST Repository

    Najibi, Seyed Morteza

    2017-02-08

    Recently, the study of protein structures using angular representations has attracted much attention among structural biologists. The main challenge is how to efficiently model the continuous conformational space of the protein structures based on the differences and similarities between different Ramachandran plots. Despite the presence of statistical methods for modeling angular data of proteins, there is still a substantial need for more sophisticated and faster statistical tools to model the large-scale circular datasets. To address this need, we have developed a nonparametric method for collective estimation of multiple bivariate density functions for a collection of populations of protein backbone angles. The proposed method takes into account the circular nature of the angular data using trigonometric spline which is more efficient compared to existing methods. This collective density estimation approach is widely applicable when there is a need to estimate multiple density functions from different populations with common features. Moreover, the coefficients of adaptive basis expansion for the fitted densities provide a low-dimensional representation that is useful for visualization, clustering, and classification of the densities. The proposed method provides a novel and unique perspective to two important and challenging problems in protein structure research: structure-based protein classification and angular-sampling-based protein loop structure prediction.

  11. Pareto-Optimal Model Selection via SPRINT-Race.

    Science.gov (United States)

    Zhang, Tiantian; Georgiopoulos, Michael; Anagnostopoulos, Georgios C

    2018-02-01

    In machine learning, the notion of multi-objective model selection (MOMS) refers to the problem of identifying the set of Pareto-optimal models that optimize by compromising more than one predefined objectives simultaneously. This paper introduces SPRINT-Race, the first multi-objective racing algorithm in a fixed-confidence setting, which is based on the sequential probability ratio with indifference zone test. SPRINT-Race addresses the problem of MOMS with multiple stochastic optimization objectives in the proper Pareto-optimality sense. In SPRINT-Race, a pairwise dominance or non-dominance relationship is statistically inferred via a non-parametric, ternary-decision, dual-sequential probability ratio test. The overall probability of falsely eliminating any Pareto-optimal models or mistakenly returning any clearly dominated models is strictly controlled by a sequential Holm's step-down family-wise error rate control method. As a fixed-confidence model selection algorithm, the objective of SPRINT-Race is to minimize the computational effort required to achieve a prescribed confidence level about the quality of the returned models. The performance of SPRINT-Race is first examined via an artificially constructed MOMS problem with known ground truth. Subsequently, SPRINT-Race is applied on two real-world applications: 1) hybrid recommender system design and 2) multi-criteria stock selection. The experimental results verify that SPRINT-Race is an effective and efficient tool for such MOMS problems. code of SPRINT-Race is available at https://github.com/watera427/SPRINT-Race.

  12. Model of a DNA-protein complex of the architectural monomeric protein MC1 from Euryarchaea.

    Directory of Open Access Journals (Sweden)

    Françoise Paquet

    Full Text Available In Archaea the two major modes of DNA packaging are wrapping by histone proteins or bending by architectural non-histone proteins. To supplement our knowledge about the binding mode of the different DNA-bending proteins observed across the three domains of life, we present here the first model of a complex in which the monomeric Methanogen Chromosomal protein 1 (MC1 from Euryarchaea binds to the concave side of a strongly bent DNA. In laboratory growth conditions MC1 is the most abundant architectural protein present in Methanosarcina thermophila CHTI55. Like most proteins that strongly bend DNA, MC1 is known to bind in the minor groove. Interaction areas for MC1 and DNA were mapped by Nuclear Magnetic Resonance (NMR data. The polarity of protein binding was determined using paramagnetic probes attached to the DNA. The first structural model of the DNA-MC1 complex we propose here was obtained by two complementary docking approaches and is in good agreement with the experimental data previously provided by electron microscopy and biochemistry. Residues essential to DNA-binding and -bending were highlighted and confirmed by site-directed mutagenesis. It was found that the Arg25 side-chain was essential to neutralize the negative charge of two phosphates that come very close in response to a dramatic curvature of the DNA.

  13. Protein homology model refinement by large-scale energy optimization.

    Science.gov (United States)

    Park, Hahnbeom; Ovchinnikov, Sergey; Kim, David E; DiMaio, Frank; Baker, David

    2018-03-20

    Proteins fold to their lowest free-energy structures, and hence the most straightforward way to increase the accuracy of a partially incorrect protein structure model is to search for the lowest-energy nearby structure. This direct approach has met with little success for two reasons: first, energy function inaccuracies can lead to false energy minima, resulting in model degradation rather than improvement; and second, even with an accurate energy function, the search problem is formidable because the energy only drops considerably in the immediate vicinity of the global minimum, and there are a very large number of degrees of freedom. Here we describe a large-scale energy optimization-based refinement method that incorporates advances in both search and energy function accuracy that can substantially improve the accuracy of low-resolution homology models. The method refined low-resolution homology models into correct folds for 50 of 84 diverse protein families and generated improved models in recent blind structure prediction experiments. Analyses of the basis for these improvements reveal contributions from both the improvements in conformational sampling techniques and the energy function.

  14. On Optimal Input Design and Model Selection for Communication Channels

    Energy Technology Data Exchange (ETDEWEB)

    Li, Yanyan [ORNL; Djouadi, Seddik M [ORNL; Olama, Mohammed M [ORNL

    2013-01-01

    In this paper, the optimal model (structure) selection and input design which minimize the worst case identification error for communication systems are provided. The problem is formulated using metric complexity theory in a Hilbert space setting. It is pointed out that model selection and input design can be handled independently. Kolmogorov n-width is used to characterize the representation error introduced by model selection, while Gel fand and Time n-widths are used to represent the inherent error introduced by input design. After the model is selected, an optimal input which minimizes the worst case identification error is shown to exist. In particular, it is proven that the optimal model for reducing the representation error is a Finite Impulse Response (FIR) model, and the optimal input is an impulse at the start of the observation interval. FIR models are widely popular in communication systems, such as, in Orthogonal Frequency Division Multiplexing (OFDM) systems.

  15. Selection Criteria in Regime Switching Conditional Volatility Models

    Directory of Open Access Journals (Sweden)

    Thomas Chuffart

    2015-05-01

    Full Text Available A large number of nonlinear conditional heteroskedastic models have been proposed in the literature. Model selection is crucial to any statistical data analysis. In this article, we investigate whether the most commonly used selection criteria lead to choice of the right specification in a regime switching framework. We focus on two types of models: the Logistic Smooth Transition GARCH and the Markov-Switching GARCH models. Simulation experiments reveal that information criteria and loss functions can lead to misspecification ; BIC sometimes indicates the wrong regime switching framework. Depending on the Data Generating Process used in the experiments, great care is needed when choosing a criterion.

  16. Working covariance model selection for generalized estimating equations.

    Science.gov (United States)

    Carey, Vincent J; Wang, You-Gan

    2011-11-20

    We investigate methods for data-based selection of working covariance models in the analysis of correlated data with generalized estimating equations. We study two selection criteria: Gaussian pseudolikelihood and a geodesic distance based on discrepancy between model-sensitive and model-robust regression parameter covariance estimators. The Gaussian pseudolikelihood is found in simulation to be reasonably sensitive for several response distributions and noncanonical mean-variance relations for longitudinal data. Application is also made to a clinical dataset. Assessment of adequacy of both correlation and variance models for longitudinal data should be routine in applications, and we describe open-source software supporting this practice. Copyright © 2011 John Wiley & Sons, Ltd.

  17. Selection on Coding and Regulatory Variation Maintains Individuality in Major Urinary Protein Scent Marks in Wild Mice.

    Directory of Open Access Journals (Sweden)

    Michael J Sheehan

    2016-03-01

    Full Text Available Recognition of individuals by scent is widespread across animal taxa. Though animals can often discriminate chemical blends based on many compounds, recent work shows that specific protein pheromones are necessary and sufficient for individual recognition via scent marks in mice. The genetic nature of individuality in scent marks (e.g. coding versus regulatory variation and the evolutionary processes that maintain diversity are poorly understood. The individual signatures in scent marks of house mice are the protein products of a group of highly similar paralogs in the major urinary protein (Mup gene family. Using the offspring of wild-caught mice, we examine individuality in the major urinary protein (MUP scent marks at the DNA, RNA and protein levels. We show that individuality arises through a combination of variation at amino acid coding sites and differential transcription of central Mup genes across individuals, and we identify eSNPs in promoters. There is no evidence of post-transcriptional processes influencing phenotypic diversity as transcripts accurately predict the relative abundance of proteins in urine samples. The match between transcripts and urine samples taken six months earlier also emphasizes that the proportional relationships across central MUP isoforms in urine is stable. Balancing selection maintains coding variants at moderate frequencies, though pheromone diversity appears limited by interactions with vomeronasal receptors. We find that differential transcription of the central Mup paralogs within and between individuals significantly increases the individuality of pheromone blends. Balancing selection on gene regulation allows for increased individuality via combinatorial diversity in a limited number of pheromones.

  18. Applying Four Different Risk Models in Local Ore Selection

    International Nuclear Information System (INIS)

    Richmond, Andrew

    2002-01-01

    Given the uncertainty in grade at a mine location, a financially risk-averse decision-maker may prefer to incorporate this uncertainty into the ore selection process. A FORTRAN program risksel is presented to calculate local risk-adjusted optimal ore selections using a negative exponential utility function and three dominance models: mean-variance, mean-downside risk, and stochastic dominance. All four methods are demonstrated in a grade control environment. In the case study, optimal selections range with the magnitude of financial risk that a decision-maker is prepared to accept. Except for the stochastic dominance method, the risk models reassign material from higher cost to lower cost processing options as the aversion to financial risk increases. The stochastic dominance model usually was unable to determine the optimal local selection

  19. On the selection of ordinary differential equation models with application to predator-prey dynamical models.

    Science.gov (United States)

    Zhang, Xinyu; Cao, Jiguo; Carroll, Raymond J

    2015-03-01

    We consider model selection and estimation in a context where there are competing ordinary differential equation (ODE) models, and all the models are special cases of a "full" model. We propose a computationally inexpensive approach that employs statistical estimation of the full model, followed by a combination of a least squares approximation (LSA) and the adaptive Lasso. We show the resulting method, here called the LSA method, to be an (asymptotically) oracle model selection method. The finite sample performance of the proposed LSA method is investigated with Monte Carlo simulations, in which we examine the percentage of selecting true ODE models, the efficiency of the parameter estimation compared to simply using the full and true models, and coverage probabilities of the estimated confidence intervals for ODE parameters, all of which have satisfactory performances. Our method is also demonstrated by selecting the best predator-prey ODE to model a lynx and hare population dynamical system among some well-known and biologically interpretable ODE models. © 2014, The International Biometric Society.

  20. Plasticity of the Binding Site of Renin: Optimized Selection of Protein Structures for Ensemble Docking.

    Science.gov (United States)

    Strecker, Claas; Meyer, Bernd

    2018-05-02

    Protein flexibility poses a major challenge to docking of potential ligands in that the binding site can adopt different shapes. Docking algorithms usually keep the protein rigid and only allow the ligand to be treated as flexible. However, a wrong assessment of the shape of the binding pocket can prevent a ligand from adapting a correct pose. Ensemble docking is a simple yet promising method to solve this problem: Ligands are docked into multiple structures, and the results are subsequently merged. Selection of protein structures is a significant factor for this approach. In this work we perform a comprehensive and comparative study evaluating the impact of structure selection on ensemble docking. We perform ensemble docking with several crystal structures and with structures derived from molecular dynamics simulations of renin, an attractive target for antihypertensive drugs. Here, 500 ns of MD simulations revealed binding site shapes not found in any available crystal structure. We evaluate the importance of structure selection for ensemble docking by comparing binding pose prediction, ability to rank actives above nonactives (screening utility), and scoring accuracy. As a result, for ensemble definition k-means clustering appears to be better suited than hierarchical clustering with average linkage. The best performing ensemble consists of four crystal structures and is able to reproduce the native ligand poses better than any individual crystal structure. Moreover this ensemble outperforms 88% of all individual crystal structures in terms of screening utility as well as scoring accuracy. Similarly, ensembles of MD-derived structures perform on average better than 75% of any individual crystal structure in terms of scoring accuracy at all inspected ensembles sizes.

  1. The research methods and model of protein turnover in animal

    International Nuclear Information System (INIS)

    Wu Xilin; Yang Feng

    2002-01-01

    The author discussed the concept and research methods of protein turnover in animal body. The existing problems and the research results of animal protein turnover in recent years were presented. Meanwhile, the measures to improve the models of animal protein turnover were analyzed

  2. Variable selection in Logistic regression model with genetic algorithm.

    Science.gov (United States)

    Zhang, Zhongheng; Trevino, Victor; Hoseini, Sayed Shahabuddin; Belciug, Smaranda; Boopathi, Arumugam Manivanna; Zhang, Ping; Gorunescu, Florin; Subha, Velappan; Dai, Songshi

    2018-02-01

    Variable or feature selection is one of the most important steps in model specification. Especially in the case of medical-decision making, the direct use of a medical database, without a previous analysis and preprocessing step, is often counterproductive. In this way, the variable selection represents the method of choosing the most relevant attributes from the database in order to build a robust learning models and, thus, to improve the performance of the models used in the decision process. In biomedical research, the purpose of variable selection is to select clinically important and statistically significant variables, while excluding unrelated or noise variables. A variety of methods exist for variable selection, but none of them is without limitations. For example, the stepwise approach, which is highly used, adds the best variable in each cycle generally producing an acceptable set of variables. Nevertheless, it is limited by the fact that it commonly trapped in local optima. The best subset approach can systematically search the entire covariate pattern space, but the solution pool can be extremely large with tens to hundreds of variables, which is the case in nowadays clinical data. Genetic algorithms (GA) are heuristic optimization approaches and can be used for variable selection in multivariable regression models. This tutorial paper aims to provide a step-by-step approach to the use of GA in variable selection. The R code provided in the text can be extended and adapted to other data analysis needs.

  3. Signal sequence and keyword trap in silico for selection of full-length human cDNAs encoding secretion or membrane proteins from oligo-capped cDNA libraries.

    Science.gov (United States)

    Otsuki, Tetsuji; Ota, Toshio; Nishikawa, Tetsuo; Hayashi, Koji; Suzuki, Yutaka; Yamamoto, Jun-ichi; Wakamatsu, Ai; Kimura, Kouichi; Sakamoto, Katsuhiko; Hatano, Naoto; Kawai, Yuri; Ishii, Shizuko; Saito, Kaoru; Kojima, Shin-ichi; Sugiyama, Tomoyasu; Ono, Tetsuyoshi; Okano, Kazunori; Yoshikawa, Yoko; Aotsuka, Satoshi; Sasaki, Naokazu; Hattori, Atsushi; Okumura, Koji; Nagai, Keiichi; Sugano, Sumio; Isogai, Takao

    2005-01-01

    We have developed an in silico method of selection of human full-length cDNAs encoding secretion or membrane proteins from oligo-capped cDNA libraries. Fullness rates were increased to about 80% by combination of the oligo-capping method and ATGpr, software for prediction of translation start point and the coding potential. Then, using 5'-end single-pass sequences, cDNAs having the signal sequence were selected by PSORT ('signal sequence trap'). We also applied 'secretion or membrane protein-related keyword trap' based on the result of BLAST search against the SWISS-PROT database for the cDNAs which could not be selected by PSORT. Using the above procedures, 789 cDNAs were primarily selected and subjected to full-length sequencing, and 334 of these cDNAs were finally selected as novel. Most of the cDNAs (295 cDNAs: 88.3%) were predicted to encode secretion or membrane proteins. In particular, 165(80.5%) of the 205 cDNAs selected by PSORT were predicted to have signal sequences, while 70 (54.2%) of the 129 cDNAs selected by 'keyword trap' preserved the secretion or membrane protein-related keywords. Many important cDNAs were obtained, including transporters, receptors, and ligands, involved in significant cellular functions. Thus, an efficient method of selecting secretion or membrane protein-encoding cDNAs was developed by combining the above four procedures.

  4. A simple quantitative model of macromolecular crowding effects on protein folding: Application to the murine prion protein(121-231)

    Science.gov (United States)

    Bergasa-Caceres, Fernando; Rabitz, Herschel A.

    2013-06-01

    A model of protein folding kinetics is applied to study the effects of macromolecular crowding on protein folding rate and stability. Macromolecular crowding is found to promote a decrease of the entropic cost of folding of proteins that produces an increase of both the stability and the folding rate. The acceleration of the folding rate due to macromolecular crowding is shown to be a topology-dependent effect. The model is applied to the folding dynamics of the murine prion protein (121-231). The differential effect of macromolecular crowding as a function of protein topology suffices to make non-native configurations relatively more accessible.

  5. On a Robust MaxEnt Process Regression Model with Sample-Selection

    Directory of Open Access Journals (Sweden)

    Hea-Jung Kim

    2018-04-01

    Full Text Available In a regression analysis, a sample-selection bias arises when a dependent variable is partially observed as a result of the sample selection. This study introduces a Maximum Entropy (MaxEnt process regression model that assumes a MaxEnt prior distribution for its nonparametric regression function and finds that the MaxEnt process regression model includes the well-known Gaussian process regression (GPR model as a special case. Then, this special MaxEnt process regression model, i.e., the GPR model, is generalized to obtain a robust sample-selection Gaussian process regression (RSGPR model that deals with non-normal data in the sample selection. Various properties of the RSGPR model are established, including the stochastic representation, distributional hierarchy, and magnitude of the sample-selection bias. These properties are used in the paper to develop a hierarchical Bayesian methodology to estimate the model. This involves a simple and computationally feasible Markov chain Monte Carlo algorithm that avoids analytical or numerical derivatives of the log-likelihood function of the model. The performance of the RSGPR model in terms of the sample-selection bias correction, robustness to non-normality, and prediction, is demonstrated through results in simulations that attest to its good finite-sample performance.

  6. Clustering structures of large proteins using multifractal analyses based on a 6-letter model and hydrophobicity scale of amino acids

    International Nuclear Information System (INIS)

    Yang Jianyi; Yu Zuguo; Anh, Vo

    2009-01-01

    The Schneider and Wrede hydrophobicity scale of amino acids and the 6-letter model of protein are proposed to study the relationship between the primary structure and the secondary structural classification of proteins. Two kinds of multifractal analyses are performed on the two measures obtained from these two kinds of data on large proteins. Nine parameters from the multifractal analyses are considered to construct the parameter spaces. Each protein is represented by one point in these spaces. A procedure is proposed to separate large proteins in the α, β, α + β and α/β structural classes in these parameter spaces. Fisher's linear discriminant algorithm is used to assess our clustering accuracy on the 49 selected large proteins. Numerical results indicate that the discriminant accuracies are satisfactory. In particular, they reach 100.00% and 84.21% in separating the α proteins from the {β, α + β, α/β} proteins in a parameter space; 92.86% and 86.96% in separating the β proteins from the {α + β, α/β} proteins in another parameter space; 91.67% and 83.33% in separating the α/β proteins from the α + β proteins in the last parameter space.

  7. Equilibrium and nonequilibrium attractors for a discrete, selection-migration model

    Science.gov (United States)

    James F. Selgrade; James H. Roberds

    2003-01-01

    This study presents a discrete-time model for the effects of selection and immigration on the demographic and genetic compositions of a population. Under biologically reasonable conditions, it is shown that the model always has an equilibrium. Although equilibria for similar models without migration must have real eigenvalues, for this selection-migration model we...

  8. Multi-component adsorption model for pellicle formation: the influence of salivary proteins and non-salivary phospho proteins on the binding of histatin 5 onto hydroxyapatite.

    Science.gov (United States)

    Yin, A; Margolis, H C; Yao, Y; Grogan, J; Oppenheim, F G

    2006-02-01

    The acquired enamel pellicle formed by selective adsorption of proteins in whole saliva is a protective integument on the tooth surface. The purpose of the present study was to investigate the formation of human acquired enamel pellicle using an in vitro hydroxyapatite (HA) model and 3H-histatin 5 to allow accurate measurement of histatin 5 binding in a multi-component experimental system. A binary system was employed by mixing 3H-histatin 5 with one unlabeled protein prior to incubation with HA or by first incubating 3H-histatin 5 with the HA which had been pre-coated with one of a panel of unlabeled proteins (human albumin, salivary amylase, lysozyme, acidic PIFs, statherin, the N-terminal fragment of statherin, and egg yolk phosvitin). A ternary system was employed by mixing 3H-histatin 5 with HA sequentially pre-coated with two different unlabeled proteins, including recombinant histatin 1. The results showed that only salivary statherin and egg yolk phosvitin promote histatin 5 adsorption significantly. The amount of histatin 5 adsorbed was also found to increase as a function of the amount of phosvitin and statherin used to pre-coat HA up to a maximum level that was two- to four-fold greater than that observed on untreated HA. These data suggest that specific protein-protein interactions may play important roles in pellicle formation in vivo.

  9. Possibilities of microscopic detection of isolated porcine proteins in model meat products

    Directory of Open Access Journals (Sweden)

    Michaela Petrášová

    2016-05-01

    Full Text Available In recent years, various protein additives intended for manufacture of meat products have increasing importance in the food industry. These ingredients include both, plant-origin as well as animal-origin proteins. Among animal proteins, blood plasma, milk protein or collagen are used most commonly. Collagen is obtained from pork, beef, and poultry or fish skin. Collagen does not contain all the essential amino acids, thus it is not a full protein in terms of essential amino acids supply for one's organism. However, it is rather rich in amino acids of glycine, hydroxyproline and proline which are almost absent in other proteins and their synthesis is very energy intensive. Collagen, which is added to the soft and small meat products in the form of isolated porcine protein, significantly affects the organoleptic properties of these products. This work focused on detection of isolated porcine protein in model meat products where detection of isolated porcine protein was verified by histological staining and light microscopy. Seven model meat products from poultry meat and 7 model meat products from beef and pork in the ratio of 1:1, which contained 2.5% concentration of various commercially produced isolated porcine proteins, were examined. These model meat products were histologically processed by means of cryosections and stained with hematoxylin-eosin staining, toluidine blue staining and Calleja. For the validation phase, Calleja was utilized. To determine the sensitivity and specificity, five model meat products containing the addition of isolated porcine protein and five model meat products free of it were used. The sensitivity was determined for isolated porcine protein at 1.00 and specificity was determined at 1.00. The detection limit of the method was at the level of 0.001% addition. Repeatability of the method was carried out using products with addition as well as without addition of isolated porcine protein and detection was repeated

  10. A linear programming model for protein inference problem in shotgun proteomics.

    Science.gov (United States)

    Huang, Ting; He, Zengyou

    2012-11-15

    Assembling peptides identified from tandem mass spectra into a list of proteins, referred to as protein inference, is an important issue in shotgun proteomics. The objective of protein inference is to find a subset of proteins that are truly present in the sample. Although many methods have been proposed for protein inference, several issues such as peptide degeneracy still remain unsolved. In this article, we present a linear programming model for protein inference. In this model, we use a transformation of the joint probability that each peptide/protein pair is present in the sample as the variable. Then, both the peptide probability and protein probability can be expressed as a formula in terms of the linear combination of these variables. Based on this simple fact, the protein inference problem is formulated as an optimization problem: minimize the number of proteins with non-zero probabilities under the constraint that the difference between the calculated peptide probability and the peptide probability generated from peptide identification algorithms should be less than some threshold. This model addresses the peptide degeneracy issue by forcing some joint probability variables involving degenerate peptides to be zero in a rigorous manner. The corresponding inference algorithm is named as ProteinLP. We test the performance of ProteinLP on six datasets. Experimental results show that our method is competitive with the state-of-the-art protein inference algorithms. The source code of our algorithm is available at: https://sourceforge.net/projects/prolp/. zyhe@dlut.edu.cn. Supplementary data are available at Bioinformatics Online.

  11. Development of a Model Protein Interaction Pair as a Benchmarking Tool for the Quantitative Analysis of 2-Site Protein-Protein Interactions.

    Science.gov (United States)

    Yamniuk, Aaron P; Newitt, John A; Doyle, Michael L; Arisaka, Fumio; Giannetti, Anthony M; Hensley, Preston; Myszka, David G; Schwarz, Fred P; Thomson, James A; Eisenstein, Edward

    2015-12-01

    A significant challenge in the molecular interaction field is to accurately determine the stoichiometry and stepwise binding affinity constants for macromolecules having >1 binding site. The mission of the Molecular Interactions Research Group (MIRG) of the Association of Biomolecular Resource Facilities (ABRF) is to show how biophysical technologies are used to quantitatively characterize molecular interactions, and to educate the ABRF members and scientific community on the utility and limitations of core technologies [such as biosensor, microcalorimetry, or analytic ultracentrifugation (AUC)]. In the present work, the MIRG has developed a robust model protein interaction pair consisting of a bivalent variant of the Bacillus amyloliquefaciens extracellular RNase barnase and a variant of its natural monovalent intracellular inhibitor protein barstar. It is demonstrated that this system can serve as a benchmarking tool for the quantitative analysis of 2-site protein-protein interactions. The protein interaction pair enables determination of precise binding constants for the barstar protein binding to 2 distinct sites on the bivalent barnase binding partner (termed binase), where the 2 binding sites were engineered to possess affinities that differed by 2 orders of magnitude. Multiple MIRG laboratories characterized the interaction using isothermal titration calorimetry (ITC), AUC, and surface plasmon resonance (SPR) methods to evaluate the feasibility of the system as a benchmarking model. Although general agreement was seen for the binding constants measured using solution-based ITC and AUC approaches, weaker affinity was seen for surface-based method SPR, with protein immobilization likely affecting affinity. An analysis of the results from multiple MIRG laboratories suggests that the bivalent barnase-barstar system is a suitable model for benchmarking new approaches for the quantitative characterization of complex biomolecular interactions.

  12. Identify High-Quality Protein Structural Models by Enhanced K-Means.

    Science.gov (United States)

    Wu, Hongjie; Li, Haiou; Jiang, Min; Chen, Cheng; Lv, Qiang; Wu, Chuang

    2017-01-01

    Background. One critical issue in protein three-dimensional structure prediction using either ab initio or comparative modeling involves identification of high-quality protein structural models from generated decoys. Currently, clustering algorithms are widely used to identify near-native models; however, their performance is dependent upon different conformational decoys, and, for some algorithms, the accuracy declines when the decoy population increases. Results. Here, we proposed two enhanced K -means clustering algorithms capable of robustly identifying high-quality protein structural models. The first one employs the clustering algorithm SPICKER to determine the initial centroids for basic K -means clustering ( SK -means), whereas the other employs squared distance to optimize the initial centroids ( K -means++). Our results showed that SK -means and K -means++ were more robust as compared with SPICKER alone, detecting 33 (59%) and 42 (75%) of 56 targets, respectively, with template modeling scores better than or equal to those of SPICKER. Conclusions. We observed that the classic K -means algorithm showed a similar performance to that of SPICKER, which is a widely used algorithm for protein-structure identification. Both SK -means and K -means++ demonstrated substantial improvements relative to results from SPICKER and classical K -means.

  13. Modeling heat stress effect on Holstein cows under hot and dry conditions: selection tools.

    Science.gov (United States)

    Carabaño, M J; Bachagha, K; Ramón, M; Díaz, C

    2014-12-01

    Data from milk recording of Holstein-Friesian cows together with weather information from 2 regions in Southern Spain were used to define the models that can better describe heat stress response for production traits and somatic cell score (SCS). Two sets of analyses were performed, one aimed at defining the population phenotypic response and the other at studying the genetic components. The first involved 2,514,762 test-day records from up to 5 lactations of 128,112 cows. Two models, one fitting a comfort threshold for temperature and a slope of decay after the threshold, and the other a cubic Legendre polynomial (LP) model were tested. Average (TAVE) and maximum daily temperatures were alternatively considered as covariates. The LP model using TAVE as covariate showed the best goodness of fit for all traits. Estimated rates of decay from this model for production at 25 and 34°C were 36 and 170, 3.8 and 3.0, and 3.9 and 8.2g/d per degree Celsius for milk, fat, and protein yield, respectively. In the second set of analyses, a sample of 280,958 test-day records from first lactations of 29,114 cows was used. Random regression models including quadratic or cubic LP regressions (TEM_) on TAVE or a fixed threshold and an unknown slope (DUMMY), including or not cubic regressions on days in milk (DIM3_), were tested. For milk and SCS, the best models were the DIM3_ models. In contrast, for fat and protein yield, the best model was TEM3. The DIM3DUMMY models showed similar performance to DIM3TEM3. The estimated genetic correlations between the same trait under cold and hot temperatures (ρ) indicated the existence of a large genotype by environment interaction for fat (ρ=0.53 for model TEM3) and protein yield (ρ around 0.6 for DIM3TEM3) and for SCS (ρ=0.64 for model DIM3TEM3), and a small genotype by environment interaction for milk (ρ over 0.8). The eigendecomposition of the additive genetic covariance matrix from model TEM3 showed the existence of a dominant

  14. A Gambler's Model of Natural Selection.

    Science.gov (United States)

    Nolan, Michael J.; Ostrovsky, David S.

    1996-01-01

    Presents an activity that highlights the mechanism and power of natural selection. Allows students to think in terms of modeling a biological process and instills an appreciation for a mathematical approach to biological problems. (JRH)

  15. Characteristics of selective fluoride adsorption by biocarbon-Mg/Al layered double hydroxides composites from protein solutions: kinetics and equilibrium isotherms study.

    Science.gov (United States)

    Ma, Wei; Lv, Tengfei; Song, Xiaoyan; Cheng, Zihong; Duan, Shibo; Xin, Gang; Liu, Fujun; Pan, Decong

    2014-03-15

    In the study, two novel applied biocarbon-Mg/Al layered double hydroxides composites (CPLDH and CPLDH-Ca) were successfully prepared and characterized by TEM, ICP-AES, XFS, EDS, FTIR, XRD, BET and pHpzc. The fluoride removal efficiency (RF) and protein recovery ratio (RP) of the adsorbents were studied in protein systems of lysozyme (LSZ) and bovine serum albumin (BSA). The results showed that the CPLDH-Ca presented remarkable performance for selective fluoride removal from protein solution. It reached the maximum RF of 92.1% and 94.8% at the CPLDH-Ca dose of 2.0g/L in LSZ and BSA system, respectively. The RP in both systems of LSZ and BSA were more than 90%. Additionally, the RP of CPLDH-Ca increased with the increase of ionic strengths, and it almost can be 100% with more than 93% RF. Fluoride adsorption by the CPLDH-Ca with different initial fluoride concentrations was found to obey the mixed surface reaction and diffusion controlled adsorption kinetic model, and the overall reaction rate is probably controlled by intra-particle diffusion, boundary layer diffusion and reaction process. The adsorption isotherms of fluoride in BSA system fit the Langmuir-Freundlich model well. The BSA has synergistic effect on fluoride adsorption and the degree increased with the increase of the initial BSA concentration. Copyright © 2014 Elsevier B.V. All rights reserved.

  16. NASCENT: an automatic protein interaction network generation tool for non-model organisms.

    Science.gov (United States)

    Banky, Daniel; Ordog, Rafael; Grolmusz, Vince

    2009-04-24

    Large quantity of reliable protein interaction data are available for model organisms in public depositories (e.g., MINT, DIP, HPRD, INTERACT). Most data correspond to experiments with the proteins of Saccharomyces cerevisiae, Drosophila melanogaster, Homo sapiens, Caenorhabditis elegans, Escherichia coli and Mus musculus. For other important organisms the data availability is poor or non-existent. Here we present NASCENT, a completely automatic web-based tool and also a downloadable Java program, capable of modeling and generating protein interaction networks even for non-model organisms. The tool performs protein interaction network modeling through gene-name mapping, and outputs the resulting network in graphical form and also in computer-readable graph-forms, directly applicable by popular network modeling software. http://nascent.pitgroup.org.

  17. Evaluation and comparison of alternative fleet-level selective maintenance models

    International Nuclear Information System (INIS)

    Schneider, Kellie; Richard Cassady, C.

    2015-01-01

    Fleet-level selective maintenance refers to the process of identifying the subset of maintenance actions to perform on a fleet of repairable systems when the maintenance resources allocated to the fleet are insufficient for performing all desirable maintenance actions. The original fleet-level selective maintenance model is designed to maximize the probability that all missions in a future set are completed successfully. We extend this model in several ways. First, we consider a cost-based optimization model and show that a special case of this model maximizes the expected value of the number of successful missions in the future set. We also consider the situation in which one or more of the future missions may be canceled. These models and the original fleet-level selective maintenance optimization models are nonlinear. Therefore, we also consider an alternative model in which the objective function can be linearized. We show that the alternative model is a good approximation to the other models. - Highlights: • Investigate nonlinear fleet-level selective maintenance optimization models. • A cost based model is used to maximize the expected number of successful missions. • Another model is allowed to cancel missions if reliability is sufficiently low. • An alternative model has an objective function that can be linearized. • We show that the alternative model is a good approximation to the other models

  18. Virtual Screening of M3 Protein Antagonists for Finding a Model to Study the Gammaherpesvirus Damaged Immune System and Chemokine Related Diseases

    Directory of Open Access Journals (Sweden)

    Ibrahim Torktaz

    2013-12-01

    Full Text Available Introduction: M3 protein is a chemokine decoy receptor involved in pathogenesis of persistent infection with gammaherpesvirus and complications related to the latency of this pathogen. We proposed that antagonists of the M3 would provide a unique opportunity for studying new therapeutic strategies in disordered immune system, immune-deficient states and role of chemokines in pathogenesis development. Methods: Comparative modeling and fold recognition algorithms have been used for prediction of M3 protein 3-D model. Evaluation of the models using Q-mean and ProSA-web score, has led to choosing predicted model by fold recognition algorithm as the best model which was minimized regarding energy level using Molegro Virtual Docker 2011.4.3.0 (MVD software. Pockets and active sites of model were recognized using MVD cavity detection, and MetaPocket algorithms. Ten thousand compounds accessible on KEGG database were screened; MVD was used for computer simulated docking study; MolDock SE was selected as docking scoring function and final results were evaluated based on MolDock and Re-rank score. Results: Docking data suggested that prilocaine, which is generally applied as a topical anesthetic, binds strongly to 3-D model of M3 protein. Conclusion: This study proposes that prilocaine is a potential inhibitor of M3 protein and possibly has immune enhancing properties.

  19. Fast Proton Titration Scheme for Multiscale Modeling of Protein Solutions.

    Science.gov (United States)

    Teixeira, Andre Azevedo Reis; Lund, Mikael; da Silva, Fernando Luís Barroso

    2010-10-12

    Proton exchange between titratable amino acid residues and the surrounding solution gives rise to exciting electric processes in proteins. We present a proton titration scheme for studying acid-base equilibria in Metropolis Monte Carlo simulations where salt is treated at the Debye-Hückel level. The method, rooted in the Kirkwood model of impenetrable spheres, is applied on the three milk proteins α-lactalbumin, β-lactoglobulin, and lactoferrin, for which we investigate the net-charge, molecular dipole moment, and charge capacitance. Over a wide range of pH and salt conditions, excellent agreement is found with more elaborate simulations where salt is explicitly included. The implicit salt scheme is orders of magnitude faster than the explicit analog and allows for transparent interpretation of physical mechanisms. It is shown how the method can be expanded to multiscale modeling of aqueous salt solutions of many biomolecules with nonstatic charge distributions. Important examples are protein-protein aggregation, protein-polyelectrolyte complexation, and protein-membrane association.

  20. Stock Selection for Portfolios Using Expected Utility-Entropy Decision Model

    Directory of Open Access Journals (Sweden)

    Jiping Yang

    2017-09-01

    Full Text Available Yang and Qiu proposed and then recently improved an expected utility-entropy (EU-E measure of risk and decision model. When segregation holds, Luce et al. derived an expected utility term, plus a constant multiplies the Shannon entropy as the representation of risky choices, further demonstrating the reasonability of the EU-E decision model. In this paper, we apply the EU-E decision model to selecting the set of stocks to be included in the portfolios. We first select 7 and 10 stocks from the 30 component stocks of Dow Jones Industrial Average index, and then derive and compare the efficient portfolios in the mean-variance framework. The conclusions imply that efficient portfolios composed of 7(10 stocks selected using the EU-E model with intermediate intervals of the tradeoff coefficients are more efficient than that composed of the sets of stocks selected using the expected utility model. Furthermore, the efficient portfolio of 7(10 stocks selected by the EU-E decision model have almost the same efficient frontier as that of the sample of all stocks. This suggests the necessity of incorporating both the expected utility and Shannon entropy together when taking risky decisions, further demonstrating the importance of Shannon entropy as the measure of uncertainty, as well as the applicability of the EU-E model as a decision-making model.

  1. Genome-scale modeling of the protein secretory machinery in yeast

    DEFF Research Database (Denmark)

    Feizi, Amir; Österlund, Tobias; Petranovic, Dina

    2013-01-01

    The protein secretory machinery in Eukarya is involved in post-translational modification (PTMs) and sorting of the secretory and many transmembrane proteins. While the secretory machinery has been well-studied using classic reductionist approaches, a holistic view of its complex nature is lacking....... Here, we present the first genome-scale model for the yeast secretory machinery which captures the knowledge generated through more than 50 years of research. The model is based on the concept of a Protein Specific Information Matrix (PSIM: characterized by seven PTMs features). An algorithm...

  2. Models of microbiome evolution incorporating host and microbial selection.

    Science.gov (United States)

    Zeng, Qinglong; Wu, Steven; Sukumaran, Jeet; Rodrigo, Allen

    2017-09-25

    Numerous empirical studies suggest that hosts and microbes exert reciprocal selective effects on their ecological partners. Nonetheless, we still lack an explicit framework to model the dynamics of both hosts and microbes under selection. In a previous study, we developed an agent-based forward-time computational framework to simulate the neutral evolution of host-associated microbial communities in a constant-sized, unstructured population of hosts. These neutral models allowed offspring to sample microbes randomly from parents and/or from the environment. Additionally, the environmental pool of available microbes was constituted by fixed and persistent microbial OTUs and by contributions from host individuals in the preceding generation. In this paper, we extend our neutral models to allow selection to operate on both hosts and microbes. We do this by constructing a phenome for each microbial OTU consisting of a sample of traits that influence host and microbial fitnesses independently. Microbial traits can influence the fitness of hosts ("host selection") and the fitness of microbes ("trait-mediated microbial selection"). Additionally, the fitness effects of traits on microbes can be modified by their hosts ("host-mediated microbial selection"). We simulate the effects of these three types of selection, individually or in combination, on microbiome diversities and the fitnesses of hosts and microbes over several thousand generations of hosts. We show that microbiome diversity is strongly influenced by selection acting on microbes. Selection acting on hosts only influences microbiome diversity when there is near-complete direct or indirect parental contribution to the microbiomes of offspring. Unsurprisingly, microbial fitness increases under microbial selection. Interestingly, when host selection operates, host fitness only increases under two conditions: (1) when there is a strong parental contribution to microbial communities or (2) in the absence of a strong

  3. A nonadaptive origin of a beneficial trait: in silico selection for free energy of folding leads to the neutral emergence of mutational robustness in single domain proteins.

    Science.gov (United States)

    Pagan, Rafael F; Massey, Steven E

    2014-02-01

    Proteins are regarded as being robust to the deleterious effects of mutations. Here, the neutral emergence of mutational robustness in a population of single domain proteins is explored using computer simulations. A pairwise contact model was used to calculate the ΔG of folding (ΔG folding) using the three dimensional protein structure of leech eglin C. A random amino acid sequence with low mutational robustness, defined as the average ΔΔG resulting from a point mutation (ΔΔG average), was threaded onto the structure. A population of 1,000 threaded sequences was evolved under selection for stability, using an upper and lower energy threshold. Under these conditions, mutational robustness increased over time in the most common sequence in the population. In contrast, when the wild type sequence was used it did not show an increase in robustness. This implies that the emergence of mutational robustness is sequence specific and that wild type sequences may be close to maximal robustness. In addition, an inverse relationship between ∆∆G average and protein stability is shown, resulting partly from a larger average effect of point mutations in more stable proteins. The emergence of mutational robustness was also observed in the Escherichia coli colE1 Rop and human CD59 proteins, implying that the property may be common in single domain proteins under certain simulation conditions. The results indicate that at least a portion of mutational robustness in small globular proteins might have arisen by a process of neutral emergence, and could be an example of a beneficial trait that has not been directly selected for, termed a "pseudaptation."

  4. Short-Run Asset Selection using a Logistic Model

    Directory of Open Access Journals (Sweden)

    Walter Gonçalves Junior

    2011-06-01

    Full Text Available Investors constantly look for significant predictors and accurate models to forecast future results, whose occasional efficacy end up being neutralized by market efficiency. Regardless, such predictors are widely used for seeking better (and more unique perceptions. This paper aims to investigate to what extent some of the most notorious indicators have discriminatory power to select stocks, and if it is feasible with such variables to build models that could anticipate those with good performance. In order to do that, logistical regressions were conducted with stocks traded at Bovespa using the selected indicators as explanatory variables. Investigated in this study were the outputs of Bovespa Index, liquidity, the Sharpe Ratio, ROE, MB, size and age evidenced to be significant predictors. Also examined were half-year, logistical models, which were adjusted in order to check the potential acceptable discriminatory power for the asset selection.

  5. Improved negative selection protocol for Plasmodium berghei in the rodent malarial model

    Directory of Open Access Journals (Sweden)

    Orr Rachael Y

    2012-03-01

    Full Text Available Abstract An improved methodology is presented here for transgenic Plasmodium berghei lines that express the negative selectable marker yFCU (a bifunctional protein that combines yeast cytosine deaminase and uridyl phosphoribosyl transferase (UPRT and substitutes delivery of selection drug 5-fluorocytosine (5FC by intraperitoneal injection for administration via the drinking water of the mice. The improved methodology is shown to be as effective, less labour-intensive, reduces animal handling and animal numbers required for successful selection thereby contributing to two of the "three Rs" of animal experimentation, namely refinement and reduction.

  6. Integrated model for supplier selection and performance evaluation

    Directory of Open Access Journals (Sweden)

    Borges de Araújo, Maria Creuza

    2015-08-01

    Full Text Available This paper puts forward a model for selecting suppliers and evaluating the performance of those already working with a company. A simulation was conducted in a food industry. This sector has high significance in the economy of Brazil. The model enables the phases of selecting and evaluating suppliers to be integrated. This is important so that a company can have partnerships with suppliers who are able to meet their needs. Additionally, a group method is used to enable managers who will be affected by this decision to take part in the selection stage. Finally, the classes resulting from the performance evaluation are shown to support the contractor in choosing the most appropriate relationship with its suppliers.

  7. Nickel nanoparticle decorated graphene for highly selective isolation of polyhistidine-tagged proteins

    Science.gov (United States)

    Liu, Jia-Wei; Yang, Ting; Ma, Lin-Yu; Chen, Xu-Wei; Wang, Jian-Hua

    2013-12-01

    Nickel nanoparticle decorated graphene (GP-Ni) is prepared by one-pot hydrothermal reduction of graphene oxide and nickel cations by hydrazine hydrate in the presence of poly(sodium-p-styrenesulfonate) (PSS). The GP-Ni hybrid is characterized by XRD, TEM, SEM, XPS, Raman and FT-IR spectra, demonstrating the formation of poly-dispersed nickel nanoparticles with an average size of 83 nm attached on the surface of graphene sheets. The GP-Ni hybrid exhibits ferromagnetic behavior with a magnetization saturation of 31.1 emu g-1 at 10 000 Oersted (Oe). The GP-Ni also possesses favorable stability in aqueous medium and rapid magnetic response to an external magnetic field. These make it a novel magnetic adsorbent for the separation/isolation of His6-tagged recombinant proteins from a complex sample matrix (cell lysate). The targeted protein species is captured onto the surface of the GP-Ni hybrid via specific metal affinity force between polyhistidine groups and nickel nanoparticles. The SDS-PAGE assay indicates highly selective separation of His6-tagged Smt A from cell lysate. The GP-Ni hybrid displays favorable performance on the separation/isolation of His6-tagged recombinant proteins with respect to the commercial NTA-Ni2+ column.

  8. A discriminative method for family-based protein remote homology detection that combines inductive logic programming and propositional models.

    Science.gov (United States)

    Bernardes, Juliana S; Carbone, Alessandra; Zaverucha, Gerson

    2011-03-23

    Remote homology detection is a hard computational problem. Most approaches have trained computational models by using either full protein sequences or multiple sequence alignments (MSA), including all positions. However, when we deal with proteins in the "twilight zone" we can observe that only some segments of sequences (motifs) are conserved. We introduce a novel logical representation that allows us to represent physico-chemical properties of sequences, conserved amino acid positions and conserved physico-chemical positions in the MSA. From this, Inductive Logic Programming (ILP) finds the most frequent patterns (motifs) and uses them to train propositional models, such as decision trees and support vector machines (SVM). We use the SCOP database to perform our experiments by evaluating protein recognition within the same superfamily. Our results show that our methodology when using SVM performs significantly better than some of the state of the art methods, and comparable to other. However, our method provides a comprehensible set of logical rules that can help to understand what determines a protein function. The strategy of selecting only the most frequent patterns is effective for the remote homology detection. This is possible through a suitable first-order logical representation of homologous properties, and through a set of frequent patterns, found by an ILP system, that summarizes essential features of protein functions.

  9. In-cell intrabody selection from a diverse human library identifies C12orf4 protein as a new player in rodent mast cell degranulation.

    Directory of Open Access Journals (Sweden)

    Elsa Mazuc

    Full Text Available The high specificity of antibodies for their antigen allows a fine discrimination of target conformations and post-translational modifications, making antibodies the first choice tool to interrogate the proteome. We describe here an approach based on a large-scale intracellular expression and selection of antibody fragments in eukaryotic cells, so-called intrabodies, and the subsequent identification of their natural target within living cell. Starting from a phenotypic trait, this integrated system allows the identification of new therapeutic targets together with their companion inhibitory intrabody. We applied this system in a model of allergy and inflammation. We first cloned a large and highly diverse intrabody library both in a plasmid and a retroviral eukaryotic expression vector. After transfection in the RBL-2H3 rat basophilic leukemia cell line, we performed seven rounds of selection to isolate cells displaying a defect in FcεRI-induced degranulation. We used high throughput sequencing to identify intrabody sequences enriched during the course of selection. Only one intrabody was common to both plasmid and retroviral selections, and was used to capture and identify its target from cell extracts. Mass spectrometry analysis identified protein RGD1311164 (C12orf4, with no previously described function. Our data demonstrate that RGD1311164 is a cytoplasmic protein implicated in the early signaling events following FcεRI-induced cell activation. This work illustrates the strength of the intrabody-based in-cell selection, which allowed the identification of a new player in mast cell activation together with its specific inhibitor intrabody.

  10. Model Selection in Historical Research Using Approximate Bayesian Computation

    Science.gov (United States)

    Rubio-Campillo, Xavier

    2016-01-01

    Formal Models and History Computational models are increasingly being used to study historical dynamics. This new trend, which could be named Model-Based History, makes use of recently published datasets and innovative quantitative methods to improve our understanding of past societies based on their written sources. The extensive use of formal models allows historians to re-evaluate hypotheses formulated decades ago and still subject to debate due to the lack of an adequate quantitative framework. The initiative has the potential to transform the discipline if it solves the challenges posed by the study of historical dynamics. These difficulties are based on the complexities of modelling social interaction, and the methodological issues raised by the evaluation of formal models against data with low sample size, high variance and strong fragmentation. Case Study This work examines an alternate approach to this evaluation based on a Bayesian-inspired model selection method. The validity of the classical Lanchester’s laws of combat is examined against a dataset comprising over a thousand battles spanning 300 years. Four variations of the basic equations are discussed, including the three most common formulations (linear, squared, and logarithmic) and a new variant introducing fatigue. Approximate Bayesian Computation is then used to infer both parameter values and model selection via Bayes Factors. Impact Results indicate decisive evidence favouring the new fatigue model. The interpretation of both parameter estimations and model selection provides new insights into the factors guiding the evolution of warfare. At a methodological level, the case study shows how model selection methods can be used to guide historical research through the comparison between existing hypotheses and empirical evidence. PMID:26730953

  11. Measures and limits of models of fixation selection.

    Directory of Open Access Journals (Sweden)

    Niklas Wilming

    Full Text Available Models of fixation selection are a central tool in the quest to understand how the human mind selects relevant information. Using this tool in the evaluation of competing claims often requires comparing different models' relative performance in predicting eye movements. However, studies use a wide variety of performance measures with markedly different properties, which makes a comparison difficult. We make three main contributions to this line of research: First we argue for a set of desirable properties, review commonly used measures, and conclude that no single measure unites all desirable properties. However the area under the ROC curve (a classification measure and the KL-divergence (a distance measure of probability distributions combine many desirable properties and allow a meaningful comparison of critical model performance. We give an analytical proof of the linearity of the ROC measure with respect to averaging over subjects and demonstrate an appropriate correction of entropy-based measures like KL-divergence for small sample sizes in the context of eye-tracking data. Second, we provide a lower bound and an upper bound of these measures, based on image-independent properties of fixation data and between subject consistency respectively. Based on these bounds it is possible to give a reference frame to judge the predictive power of a model of fixation selection. We provide open-source python code to compute the reference frame. Third, we show that the upper, between subject consistency bound holds only for models that predict averages of subject populations. Departing from this we show that incorporating subject-specific viewing behavior can generate predictions which surpass that upper bound. Taken together, these findings lay out the required information that allow a well-founded judgment of the quality of any model of fixation selection and should therefore be reported when a new model is introduced.

  12. The Phyre2 web portal for protein modeling, prediction and analysis.

    Science.gov (United States)

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

    2015-06-01

    Phyre2 is a suite of tools available on the web to predict and analyze 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 paper in Nature Protocols. In this updated protocol, we describe Phyre2, which uses advanced remote homology detection methods to build 3D models, predict ligand binding sites and analyze the effect of amino acid variants (e.g., nonsynonymous SNPs (nsSNPs)) for a user's protein sequence. Users are guided through results by a simple interface at a level of detail they determine. This protocol will guide users 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 that are difficult to model. The server is available at http://www.sbg.bio.ic.ac.uk/phyre2. A typical structure prediction will be returned between 30 min and 2 h after submission.

  13. Automated selected reaction monitoring software for accurate label-free protein quantification.

    Science.gov (United States)

    Teleman, Johan; Karlsson, Christofer; Waldemarson, Sofia; Hansson, Karin; James, Peter; Malmström, Johan; Levander, Fredrik

    2012-07-06

    Selected reaction monitoring (SRM) is a mass spectrometry method with documented ability to quantify proteins accurately and reproducibly using labeled reference peptides. However, the use of labeled reference peptides becomes impractical if large numbers of peptides are targeted and when high flexibility is desired when selecting peptides. We have developed a label-free quantitative SRM workflow that relies on a new automated algorithm, Anubis, for accurate peak detection. Anubis efficiently removes interfering signals from contaminating peptides to estimate the true signal of the targeted peptides. We evaluated the algorithm on a published multisite data set and achieved results in line with manual data analysis. In complex peptide mixtures from whole proteome digests of Streptococcus pyogenes we achieved a technical variability across the entire proteome abundance range of 6.5-19.2%, which was considerably below the total variation across biological samples. Our results show that the label-free SRM workflow with automated data analysis is feasible for large-scale biological studies, opening up new possibilities for quantitative proteomics and systems biology.

  14. On the characterization and software implementation of general protein lattice models.

    Directory of Open Access Journals (Sweden)

    Alessio Bechini

    Full Text Available models of proteins have been widely used as a practical means to computationally investigate general properties of the system. In lattice models any sterically feasible conformation is represented as a self-avoiding walk on a lattice, and residue types are limited in number. So far, only two- or three-dimensional lattices have been used. The inspection of the neighborhood of alpha carbons in the core of real proteins reveals that also lattices with higher coordination numbers, possibly in higher dimensional spaces, can be adopted. In this paper, a new general parametric lattice model for simplified protein conformations is proposed and investigated. It is shown how the supporting software can be consistently designed to let algorithms that operate on protein structures be implemented in a lattice-agnostic way. The necessary theoretical foundations are developed and organically presented, pinpointing the role of the concept of main directions in lattice-agnostic model handling. Subsequently, the model features across dimensions and lattice types are explored in tests performed on benchmark protein sequences, using a Python implementation. Simulations give insights on the use of square and triangular lattices in a range of dimensions. The trend of potential minimum for sequences of different lengths, varying the lattice dimension, is uncovered. Moreover, an extensive quantitative characterization of the usage of the so-called "move types" is reported for the first time. The proposed general framework for the development of lattice models is simple yet complete, and an object-oriented architecture can be proficiently employed for the supporting software, by designing ad-hoc classes. The proposed framework represents a new general viewpoint that potentially subsumes a number of solutions previously studied. The adoption of the described model pushes to look at protein structure issues from a more general and essential perspective, making

  15. Maximizing Selective Cleavages at Aspartic Acid and Proline Residues for the Identification of Intact Proteins

    Science.gov (United States)

    Foreman, David J.; Dziekonski, Eric T.; McLuckey, Scott A.

    2018-04-01

    A new approach for the identification of intact proteins has been developed that relies on the generation of relatively few abundant products from specific cleavage sites. This strategy is intended to complement standard approaches that seek to generate many fragments relatively non-selectively. Specifically, this strategy seeks to maximize selective cleavage at aspartic acid and proline residues via collisional activation of precursor ions formed via electrospray ionization (ESI) under denaturing conditions. A statistical analysis of the SWISS-PROT database was used to predict the number of arginine residues for a given intact protein mass and predict a m/z range where the protein carries a similar charge to the number of arginine residues thereby enhancing cleavage at aspartic acid residues by limiting proton mobility. Cleavage at aspartic acid residues is predicted to be most favorable in the m/z range of 1500-2500, a range higher than that normally generated by ESI at low pH. Gas-phase proton transfer ion/ion reactions are therefore used for precursor ion concentration from relatively high charge states followed by ion isolation and subsequent generation of precursor ions within the optimal m/z range via a second proton transfer reaction step. It is shown that the majority of product ion abundance is concentrated into cleavages C-terminal to aspartic acid residues and N-terminal to proline residues for ions generated by this process. Implementation of a scoring system that weights both ion fragment type and ion fragment area demonstrated identification of standard proteins, ranging in mass from 8.5 to 29.0 kDa. [Figure not available: see fulltext.

  16. Selection of antifungal protein-producing molds from dry-cured meat products.

    Science.gov (United States)

    Acosta, Raquel; Rodríguez-Martín, Andrea; Martín, Alberto; Núñez, Félix; Asensio, Miguel A

    2009-09-30

    To control unwanted molds in dry-cured meats it is necessary to allow the fungal development essential for the desired characteristics of the final product. Molds producing antifungal proteins could be useful to prevent hazards due to the growth of mycotoxigenic molds. The objective has been to select Penicillium spp. that produce antifungal proteins against toxigenic molds. To obtain strains adapted to these products, molds were isolated from dry-cured ham. A first screening with 281 isolates by the radial inhibition assay revealed that 166 were active against some of the toxigenic P. echinulatum, P. commune, and Aspergillusniger used as reference molds. The activity of different extracts from cultured medium was evaluated by a microspectroscopic assay. Molds producing active chloroform extracts were eliminated from further consideration. A total of 16 Penicillium isolates were screened for antifungal activity from both cell-free media and the aqueous residues obtained after chloroform extraction. The cell-free media of 10 isolates that produced a strong inhibition of the three reference molds were fractionated by FPLC on a cationic column. For protein purification, the fractions of the three molds that showed high inhibitory activity were further chromatographed on a gel filtration column, and the subfractions containing the highest absorbance peaks were assayed against the most sensitive reference molds. One subfraction each from strains AS51D and RP42C from Penicilliumchrysogenum confirmed the inhibitory activity against the reference molds. SDS-PAGE revealed a single band from each subfraction, with estimated molecular masses of 37kDa for AS51D and 9kDa for RP42C. Although further characterisation is required, both these proteins and the producing strains can be of interest to control unwanted molds on foods.

  17. Automated de novo phasing and model building of coiled-coil proteins.

    Science.gov (United States)

    Rämisch, Sebastian; Lizatović, Robert; André, Ingemar

    2015-03-01

    Models generated by de novo structure prediction can be very useful starting points for molecular replacement for systems where suitable structural homologues cannot be readily identified. Protein-protein complexes and de novo-designed proteins are examples of systems that can be challenging to phase. In this study, the potential of de novo models of protein complexes for use as starting points for molecular replacement is investigated. The approach is demonstrated using homomeric coiled-coil proteins, which are excellent model systems for oligomeric systems. Despite the stereotypical fold of coiled coils, initial phase estimation can be difficult and many structures have to be solved with experimental phasing. A method was developed for automatic structure determination of homomeric coiled coils from X-ray diffraction data. In a benchmark set of 24 coiled coils, ranging from dimers to pentamers with resolutions down to 2.5 Å, 22 systems were automatically solved, 11 of which had previously been solved by experimental phasing. The generated models contained 71-103% of the residues present in the deposited structures, had the correct sequence and had free R values that deviated on average by 0.01 from those of the respective reference structures. The electron-density maps were of sufficient quality that only minor manual editing was necessary to produce final structures. The method, named CCsolve, combines methods for de novo structure prediction, initial phase estimation and automated model building into one pipeline. CCsolve is robust against errors in the initial models and can readily be modified to make use of alternative crystallographic software. The results demonstrate the feasibility of de novo phasing of protein-protein complexes, an approach that could also be employed for other small systems beyond coiled coils.

  18. Validation of protein models by a neural network approach

    Directory of Open Access Journals (Sweden)

    Fantucci Piercarlo

    2008-01-01

    Full Text Available Abstract Background The development and improvement of reliable computational methods designed to evaluate the quality of protein models is relevant in the context of protein structure refinement, which has been recently identified as one of the bottlenecks limiting the quality and usefulness of protein structure prediction. Results In this contribution, we present a computational method (Artificial Intelligence Decoys Evaluator: AIDE which is able to consistently discriminate between correct and incorrect protein models. In particular, the method is based on neural networks that use as input 15 structural parameters, which include energy, solvent accessible surface, hydrophobic contacts and secondary structure content. The results obtained with AIDE on a set of decoy structures were evaluated using statistical indicators such as Pearson correlation coefficients, Znat, fraction enrichment, as well as ROC plots. It turned out that AIDE performances are comparable and often complementary to available state-of-the-art learning-based methods. Conclusion In light of the results obtained with AIDE, as well as its comparison with available learning-based methods, it can be concluded that AIDE can be successfully used to evaluate the quality of protein structures. The use of AIDE in combination with other evaluation tools is expected to further enhance protein refinement efforts.

  19. Generic framework for mining cellular automata models on protein-folding simulations.

    Science.gov (United States)

    Diaz, N; Tischer, I

    2016-05-13

    Cellular automata model identification is an important way of building simplified simulation models. In this study, we describe a generic architectural framework to ease the development process of new metaheuristic-based algorithms for cellular automata model identification in protein-folding trajectories. Our framework was developed by a methodology based on design patterns that allow an improved experience for new algorithms development. The usefulness of the proposed framework is demonstrated by the implementation of four algorithms, able to obtain extremely precise cellular automata models of the protein-folding process with a protein contact map representation. Dynamic rules obtained by the proposed approach are discussed, and future use for the new tool is outlined.

  20. Selected Tether Applications Cost Model

    Science.gov (United States)

    Keeley, Michael G.

    1988-01-01

    Diverse cost-estimating techniques and data combined into single program. Selected Tether Applications Cost Model (STACOM 1.0) is interactive accounting software tool providing means for combining several independent cost-estimating programs into fully-integrated mathematical model capable of assessing costs, analyzing benefits, providing file-handling utilities, and putting out information in text and graphical forms to screen, printer, or plotter. Program based on Lotus 1-2-3, version 2.0. Developed to provide clear, concise traceability and visibility into methodology and rationale for estimating costs and benefits of operations of Space Station tether deployer system.

  1. Modeling the effect of selection history on pop-out visual search.

    Directory of Open Access Journals (Sweden)

    Yuan-Chi Tseng

    Full Text Available While attentional effects in visual selection tasks have traditionally been assigned "top-down" or "bottom-up" origins, more recently it has been proposed that there are three major factors affecting visual selection: (1 physical salience, (2 current goals and (3 selection history. Here, we look further into selection history by investigating Priming of Pop-out (POP and the Distractor Preview Effect (DPE, two inter-trial effects that demonstrate the influence of recent history on visual search performance. Using the Ratcliff diffusion model, we model observed saccadic selections from an oddball search experiment that included a mix of both POP and DPE conditions. We find that the Ratcliff diffusion model can effectively model the manner in which selection history affects current attentional control in visual inter-trial effects. The model evidence shows that bias regarding the current trial's most likely target color is the most critical parameter underlying the effect of selection history. Our results are consistent with the view that the 3-item color-oddball task used for POP and DPE experiments is best understood as an attentional decision making task.

  2. Forecasting house prices in the 50 states using Dynamic Model Averaging and Dynamic Model Selection

    DEFF Research Database (Denmark)

    Bork, Lasse; Møller, Stig Vinther

    2015-01-01

    We examine house price forecastability across the 50 states using Dynamic Model Averaging and Dynamic Model Selection, which allow for model change and parameter shifts. By allowing the entire forecasting model to change over time and across locations, the forecasting accuracy improves substantia......We examine house price forecastability across the 50 states using Dynamic Model Averaging and Dynamic Model Selection, which allow for model change and parameter shifts. By allowing the entire forecasting model to change over time and across locations, the forecasting accuracy improves...

  3. Selection for a Zinc-Finger Protein Contributes to Seed Oil Increase during Soybean Domestication1[OPEN

    Science.gov (United States)

    Li, Qing-Tian; Lu, Xiang; Song, Qing-Xin; Chen, Hao-Wei; Wei, Wei; Tao, Jian-Jun; Ma, Biao; Bi, Ying-Dong; Li, Wei; Lai, Yong-Cai; Shui, Guang-Hou; Chen, Shou-Yi

    2017-01-01

    Seed oil is a momentous agronomical trait of soybean (Glycine max) targeted by domestication in breeding. Although multiple oil-related genes have been uncovered, knowledge of the regulatory mechanism of seed oil biosynthesis is currently limited. We demonstrate that the seed-preferred gene GmZF351, encoding a tandem CCCH zinc finger protein, is selected during domestication. Further analysis shows that GmZF351 facilitates oil accumulation by directly activating WRINKLED1, BIOTIN CARBOXYL CARRIER PROTEIN2, 3-KETOACYL-ACYL CARRIER PROTEIN SYNTHASE III, DIACYLGLYCEROL O-ACYLTRANSFERASE1, and OLEOSIN2 in transgenic Arabidopsis (Arabidopsis thaliana) seeds. Overexpression of GmZF351 in transgenic soybean also activates lipid biosynthesis genes, thereby accelerating seed oil accumulation. The ZF351 haplotype from the cultivated soybean group and the wild soybean (Glycine soja) subgroup III correlates well with high gene expression level, seed oil contents and promoter activity, suggesting that selection of GmZF351 expression leads to increased seed oil content in cultivated soybean. Our study provides novel insights into the regulatory mechanism for seed oil accumulation, and the manipulation of GmZF351 may have great potential in the improvement of oil production in soybean and other related crops. PMID:28184009

  4. Ensembling Variable Selectors by Stability Selection for the Cox Model

    Directory of Open Access Journals (Sweden)

    Qing-Yan Yin

    2017-01-01

    Full Text Available As a pivotal tool to build interpretive models, variable selection plays an increasingly important role in high-dimensional data analysis. In recent years, variable selection ensembles (VSEs have gained much interest due to their many advantages. Stability selection (Meinshausen and Bühlmann, 2010, a VSE technique based on subsampling in combination with a base algorithm like lasso, is an effective method to control false discovery rate (FDR and to improve selection accuracy in linear regression models. By adopting lasso as a base learner, we attempt to extend stability selection to handle variable selection problems in a Cox model. According to our experience, it is crucial to set the regularization region Λ in lasso and the parameter λmin properly so that stability selection can work well. To the best of our knowledge, however, there is no literature addressing this problem in an explicit way. Therefore, we first provide a detailed procedure to specify Λ and λmin. Then, some simulated and real-world data with various censoring rates are used to examine how well stability selection performs. It is also compared with several other variable selection approaches. Experimental results demonstrate that it achieves better or competitive performance in comparison with several other popular techniques.

  5. Skewed factor models using selection mechanisms

    KAUST Repository

    Kim, Hyoung-Moon

    2015-12-21

    Traditional factor models explicitly or implicitly assume that the factors follow a multivariate normal distribution; that is, only moments up to order two are involved. However, it may happen in real data problems that the first two moments cannot explain the factors. Based on this motivation, here we devise three new skewed factor models, the skew-normal, the skew-tt, and the generalized skew-normal factor models depending on a selection mechanism on the factors. The ECME algorithms are adopted to estimate related parameters for statistical inference. Monte Carlo simulations validate our new models and we demonstrate the need for skewed factor models using the classic open/closed book exam scores dataset.

  6. Skewed factor models using selection mechanisms

    KAUST Repository

    Kim, Hyoung-Moon; Maadooliat, Mehdi; Arellano-Valle, Reinaldo B.; Genton, Marc G.

    2015-01-01

    Traditional factor models explicitly or implicitly assume that the factors follow a multivariate normal distribution; that is, only moments up to order two are involved. However, it may happen in real data problems that the first two moments cannot explain the factors. Based on this motivation, here we devise three new skewed factor models, the skew-normal, the skew-tt, and the generalized skew-normal factor models depending on a selection mechanism on the factors. The ECME algorithms are adopted to estimate related parameters for statistical inference. Monte Carlo simulations validate our new models and we demonstrate the need for skewed factor models using the classic open/closed book exam scores dataset.

  7. Selective {sup 2}H and {sup 13}C labeling in NMR analysis of solution protein structure and dynamics

    Energy Technology Data Exchange (ETDEWEB)

    LeMaster, D.M. [Northwestern Univ., Evanston, IL (United States)

    1994-12-01

    Preparation of samples bearing combined isotope enrichment patterns has played a central role in the recent advances in NMR analysis of proteins in solution. In particular, uniform {sup 13}C, {sup 15}N enrichment has made it possible to apply heteronuclear multidimensional correlation experiments for the mainchain assignments of proteins larger than 30 KDa. In contrast, selective labeling approaches can offer advantages in terms of the directedness of the information provided, such as chirality and residue type assignments, as well as through enhancements in resolution and sensitivity that result from editing the spectral complexity, the relaxation pathways and the scalar coupling networks. In addition, the combination of selective {sup 13}C and {sup 2}H enrichment can greatly facilitate the determination of heteronuclear relaxation behavior.

  8. A lock-and-key model for protein–protein interactions

    OpenAIRE

    Morrison, Julie L.; Breitling, Rainer; Higham, Desmond J.; Gilbert, David R.

    2006-01-01

    Motivation: Protein–protein interaction networks are one of the major post-genomic data sources available to molecular biologists. They provide a comprehensive view of the global interaction structure of an organism’s proteome, as well as detailed information on specific interactions. Here we suggest a physical model of protein interactions that can be used to extract additional information at an intermediate level: It enables us to identify proteins which share biological interaction motifs,...

  9. Antisense oligonucleotides targeting translation inhibitory elements in 5' UTRs can selectively increase protein levels.

    Science.gov (United States)

    Liang, Xue-Hai; Sun, Hong; Shen, Wen; Wang, Shiyu; Yao, Joyee; Migawa, Michael T; Bui, Huynh-Hoa; Damle, Sagar S; Riney, Stan; Graham, Mark J; Crooke, Rosanne M; Crooke, Stanley T

    2017-09-19

    A variety of diseases are caused by deficiencies in amounts or activity of key proteins. An approach that increases the amount of a specific protein might be of therapeutic benefit. We reasoned that translation could be specifically enhanced using trans-acting agents that counter the function of negative regulatory elements present in the 5' UTRs of some mRNAs. We recently showed that translation can be enhanced by antisense oligonucleotides (ASOs) that target upstream open reading frames. Here we report the amount of a protein can also be selectively increased using ASOs designed to hybridize to other translation inhibitory elements in 5' UTRs. Levels of human RNASEH1, LDLR, and ACP1 and of mouse ACP1 and ARF1 were increased up to 2.7-fold in different cell types and species upon treatment with chemically modified ASOs targeting 5' UTR inhibitory regions in the mRNAs encoding these proteins. The activities of ASOs in enhancing translation were sequence and position dependent and required helicase activity. The ASOs appear to improve the recruitment of translation initiation factors to the target mRNA. Importantly, ASOs targeting ACP1 mRNA significantly increased the level of ACP1 protein in mice, suggesting that this approach has therapeutic and research potentials. © The Author(s) 2017. Published by Oxford University Press on behalf of Nucleic Acids Research.

  10. Protein buffering in model systems and in whole human saliva.

    Directory of Open Access Journals (Sweden)

    Andreas Lamanda

    Full Text Available The aim of this study was to quantify the buffer attributes (value, power, range and optimum of two model systems for whole human resting saliva, the purified proteins from whole human resting saliva and single proteins. Two model systems, the first containing amyloglucosidase and lysozyme, and the second containing amyloglucosidase and alpha-amylase, were shown to provide, in combination with hydrogencarbonate and di-hydrogenphosphate, almost identical buffer attributes as whole human resting saliva. It was further demonstrated that changes in the protein concentration as small as 0.1% may change the buffer value of a buffer solution up to 15 times. Additionally, it was shown that there was a protein concentration change in the same range (0.16% between saliva samples collected at the time periods of 13:00 and others collected at 9:00 am and 17:00. The mode of the protein expression changed between these samples corresponded to the change in basic buffer power and the change of the buffer value at pH 6.7. Finally, SDS Page and Ruthenium II tris (bathophenantroline disulfonate staining unveiled a constant protein expression in all samples except for one 50 kDa protein band. As the change in the expression pattern of that 50 kDa protein band corresponded to the change in basic buffer power and the buffer value at pH 6.7, it was reasonable to conclude that this 50 kDa protein band may contain the protein(s belonging to the protein buffer system of human saliva.

  11. Analysis and Ranking of Protein-Protein Docking Models Using Inter-Residue Contacts and Inter-Molecular Contact Maps

    KAUST Repository

    Oliva, Romina; Chermak, Edrisse; Cavallo, Luigi

    2015-01-01

    In view of the increasing interest both in inhibitors of protein-protein interactions and in protein drugs themselves, analysis of the three-dimensional structure of protein-protein complexes is assuming greater relevance in drug design. In the many cases where an experimental structure is not available, protein-protein docking becomes the method of choice for predicting the arrangement of the complex. However, reliably scoring protein-protein docking poses is still an unsolved problem. As a consequence, the screening of many docking models is usually required in the analysis step, to possibly single out the correct ones. Here, making use of exemplary cases, we review our recently introduced methods for the analysis of protein complex structures and for the scoring of protein docking poses, based on the use of inter-residue contacts and their visualization in inter-molecular contact maps. We also show that the ensemble of tools we developed can be used in the context of rational drug design targeting protein-protein interactions.

  12. Analysis and Ranking of Protein-Protein Docking Models Using Inter-Residue Contacts and Inter-Molecular Contact Maps

    KAUST Repository

    Oliva, Romina

    2015-07-01

    In view of the increasing interest both in inhibitors of protein-protein interactions and in protein drugs themselves, analysis of the three-dimensional structure of protein-protein complexes is assuming greater relevance in drug design. In the many cases where an experimental structure is not available, protein-protein docking becomes the method of choice for predicting the arrangement of the complex. However, reliably scoring protein-protein docking poses is still an unsolved problem. As a consequence, the screening of many docking models is usually required in the analysis step, to possibly single out the correct ones. Here, making use of exemplary cases, we review our recently introduced methods for the analysis of protein complex structures and for the scoring of protein docking poses, based on the use of inter-residue contacts and their visualization in inter-molecular contact maps. We also show that the ensemble of tools we developed can be used in the context of rational drug design targeting protein-protein interactions.

  13. National HIV prevalence estimates for sub-Saharan Africa: controlling selection bias with Heckman-type selection models

    Science.gov (United States)

    Hogan, Daniel R; Salomon, Joshua A; Canning, David; Hammitt, James K; Zaslavsky, Alan M; Bärnighausen, Till

    2012-01-01

    Objectives Population-based HIV testing surveys have become central to deriving estimates of national HIV prevalence in sub-Saharan Africa. However, limited participation in these surveys can lead to selection bias. We control for selection bias in national HIV prevalence estimates using a novel approach, which unlike conventional imputation can account for selection on unobserved factors. Methods For 12 Demographic and Health Surveys conducted from 2001 to 2009 (N=138 300), we predict HIV status among those missing a valid HIV test with Heckman-type selection models, which allow for correlation between infection status and participation in survey HIV testing. We compare these estimates with conventional ones and introduce a simulation procedure that incorporates regression model parameter uncertainty into confidence intervals. Results Selection model point estimates of national HIV prevalence were greater than unadjusted estimates for 10 of 12 surveys for men and 11 of 12 surveys for women, and were also greater than the majority of estimates obtained from conventional imputation, with significantly higher HIV prevalence estimates for men in Cote d'Ivoire 2005, Mali 2006 and Zambia 2007. Accounting for selective non-participation yielded 95% confidence intervals around HIV prevalence estimates that are wider than those obtained with conventional imputation by an average factor of 4.5. Conclusions Our analysis indicates that national HIV prevalence estimates for many countries in sub-Saharan African are more uncertain than previously thought, and may be underestimated in several cases, underscoring the need for increasing participation in HIV surveys. Heckman-type selection models should be included in the set of tools used for routine estimation of HIV prevalence. PMID:23172342

  14. Development of Pharmacophore Model for Indeno[1,2-b]indoles as Human Protein Kinase CK2 Inhibitors and Database Mining

    Directory of Open Access Journals (Sweden)

    Samer Haidar

    2017-01-01

    Full Text Available Protein kinase CK2, initially designated as casein kinase 2, is an ubiquitously expressed serine/threonine kinase. This enzyme, implicated in many cellular processes, is highly expressed and active in many tumor cells. A large number of compounds has been developed as inhibitors comprising different backbones. Beside others, structures with an indeno[1,2-b]indole scaffold turned out to be potent new leads. With the aim of developing new inhibitors of human protein kinase CK2, we report here on the generation of common feature pharmacophore model to further explain the binding requirements for human CK2 inhibitors. Nine common chemical features of indeno[1,2-b]indole-type CK2 inhibitors were determined using MOE software (Chemical Computing Group, Montreal, Canada. This pharmacophore model was used for database mining with the aim to identify novel scaffolds for developing new potent and selective CK2 inhibitors. Using this strategy several structures were selected by searching inside the ZINC compound database. One of the selected compounds was bikaverin (6,11-dihydroxy-3,8-dimethoxy-1-methylbenzo[b]xanthene-7,10,12-trione, a natural compound which is produced by several kinds of fungi. This compound was tested on human recombinant CK2 and turned out to be an active inhibitor with an IC50 value of 1.24 µM.

  15. Selective Cooperation in Early Childhood - How to Choose Models and Partners.

    Directory of Open Access Journals (Sweden)

    Jonas Hermes

    Full Text Available Cooperation is essential for human society, and children engage in cooperation from early on. It is unclear, however, how children select their partners for cooperation. We know that children choose selectively whom to learn from (e.g. preferring reliable over unreliable models on a rational basis. The present study investigated whether children (and adults also choose their cooperative partners selectively and what model characteristics they regard as important for cooperative partners and for informants about novel words. Three- and four-year-old children (N = 64 and adults (N = 14 saw contrasting pairs of models differing either in physical strength or in accuracy (in labeling known objects. Participants then performed different tasks (cooperative problem solving and word learning requiring the choice of a partner or informant. Both children and adults chose their cooperative partners selectively. Moreover they showed the same pattern of selective model choice, regarding a wide range of model characteristics as important for cooperation (preferring both the strong and the accurate model for a strength-requiring cooperation tasks, but only prior knowledge as important for word learning (preferring the knowledgeable but not the strong model for word learning tasks. Young children's selective model choice thus reveals an early rational competence: They infer characteristics from past behavior and flexibly consider what characteristics are relevant for certain tasks.

  16. Cardiac protein expression patterns are associated with distinct inborn exercise capacity in non-selectively bred rats

    Directory of Open Access Journals (Sweden)

    L.P. Ribeiro

    2018-01-01

    Full Text Available In the present study, we successfully demonstrated for the first time the existence of cardiac proteomic differences between non-selectively bred rats with distinct intrinsic exercise capacities. A proteomic approach based on two-dimensional gel electrophoresis coupled to mass spectrometry was used to study the left ventricle (LV tissue proteome of rats with distinct intrinsic exercise capacity. Low running performance (LRP and high running performance (HRP rats were categorized by a treadmill exercise test, according to distance run to exhaustion. The running capacity of HRPs was 3.5-fold greater than LRPs. Protein profiling revealed 29 differences between HRP and LRP rats (15 proteins were identified. We detected alterations in components involved in metabolism, antioxidant and stress response, microfibrillar and cytoskeletal proteins. Contractile proteins were upregulated in the LVs of HRP rats (α-myosin heavy chain-6, myosin light chain-1 and creatine kinase, whereas the LVs of LRP rats exhibited upregulation in proteins associated with stress response (aldehyde dehydrogenase 2, α-crystallin B chain and HSPβ-2. In addition, the cytoskeletal proteins desmin and α-actin were upregulated in LRPs. Taken together, our results suggest that the increased contractile protein levels in HRP rats partly accounted for their improved exercise capacity, and that proteins considered risk factors to the development of cardiovascular disease were expressed in higher amounts in LRP animals.

  17. Peptidomics of Peptic Digest of Selected Potato Tuber Proteins: Post-Translational Modifications and Limited Cleavage Specificity.

    Science.gov (United States)

    C K Rajendran, Subin R; Mason, Beth; Udenigwe, Chibuike C

    2016-03-23

    Bioinformatic tools are useful in predicting bioactive peptides from food proteins. This study was focused on using bioinformatics and peptidomics to evaluate the specificity of peptide release and post-translational modifications (PTMs) in a peptic digest of potato protein isolate. Peptides in the protein hydrolysate were identified by LC-MS/MS and subsequently aligned to their parent potato tuber proteins. Five major proteins were selected for further analysis, namely, lipoxygenase, α-1,4-glucan phosphorylase, annexin, patatin, and polyubiquitin, based on protein coverage, abundance, confidence levels, and function. Comparison of the in silico peptide profile generated with ExPASy PeptideCutter and experimental peptidomics data revealed several differences. The experimental peptic cleavage sites were found to vary in number and specificity from PeptideCutter predictions. Average peptide chain length was also found to be higher than predicted with hexapeptides as the smallest detected peptides. Moreover, PTMs, particularly Met oxidation and Glu/Asp deamidation, were observed in some peptides, and these were unaccounted for during in silico analysis. PTMs can be formed during aging of potato tubers, or as a result of processing conditions during protein isolation and hydrolysis. The findings provide insights on the limitations of current bioinformatics tools for predicting bioactive peptide release from proteins, and on the existence of structural modifications that can alter the peptide bioactivity and functionality.

  18. Anomalous diffusion in neutral evolution of model proteins

    Science.gov (United States)

    Nelson, Erik D.; Grishin, Nick V.

    2015-06-01

    Protein evolution is frequently explored using minimalist polymer models, however, little attention has been given to the problem of structural drift, or diffusion. Here, we study neutral evolution of small protein motifs using an off-lattice heteropolymer model in which individual monomers interact as low-resolution amino acids. In contrast to most earlier models, both the length and folded structure of the polymers are permitted to change. To describe structural change, we compute the mean-square distance (MSD) between monomers in homologous folds separated by n neutral mutations. We find that structural change is episodic, and, averaged over lineages (for example, those extending from a single sequence), exhibits a power-law dependence on n . We show that this exponent depends on the alignment method used, and we analyze the distribution of waiting times between neutral mutations. The latter are more disperse than for models required to maintain a specific fold, but exhibit a similar power-law tail.

  19. A Comparison Study for DNA Motif Modeling on Protein Binding Microarray

    KAUST Repository

    Wong, Ka-Chun; Li, Yue; Peng, Chengbin; Wong, Hau-San

    2015-01-01

    Transcription Factor Binding Sites (TFBSs) are relatively short (5-15 bp) and degenerate. Identifying them is a computationally challenging task. In particular, Protein Binding Microarray (PBM) is a high-throughput platform that can measure the DNA binding preference of a protein in a comprehensive and unbiased manner; for instance, a typical PBM experiment can measure binding signal intensities of a protein to all possible DNA k-mers (k=810). Since proteins can often bind to DNA with different binding intensities, one of the major challenges is to build motif models which can fully capture the quantitative binding affinity data. To learn DNA motif models from the non-convex objective function landscape, several optimization methods are compared and applied to the PBM motif model building problem. In particular, representative methods from different optimization paradigms have been chosen for modeling performance comparison on hundreds of PBM datasets. The results suggest that the multimodal optimization methods are very effective for capturing the binding preference information from PBM data. In particular, we observe a general performance improvement using di-nucleotide modeling over mono-nucleotide modeling. In addition, the models learned by the best-performing method are applied to two independent applications: PBM probe rotation testing and ChIP-Seq peak sequence prediction, demonstrating its biological applicability.

  20. A Comparison Study for DNA Motif Modeling on Protein Binding Microarray

    KAUST Repository

    Wong, Ka-Chun

    2015-06-11

    Transcription Factor Binding Sites (TFBSs) are relatively short (5-15 bp) and degenerate. Identifying them is a computationally challenging task. In particular, Protein Binding Microarray (PBM) is a high-throughput platform that can measure the DNA binding preference of a protein in a comprehensive and unbiased manner; for instance, a typical PBM experiment can measure binding signal intensities of a protein to all possible DNA k-mers (k=810). Since proteins can often bind to DNA with different binding intensities, one of the major challenges is to build motif models which can fully capture the quantitative binding affinity data. To learn DNA motif models from the non-convex objective function landscape, several optimization methods are compared and applied to the PBM motif model building problem. In particular, representative methods from different optimization paradigms have been chosen for modeling performance comparison on hundreds of PBM datasets. The results suggest that the multimodal optimization methods are very effective for capturing the binding preference information from PBM data. In particular, we observe a general performance improvement using di-nucleotide modeling over mono-nucleotide modeling. In addition, the models learned by the best-performing method are applied to two independent applications: PBM probe rotation testing and ChIP-Seq peak sequence prediction, demonstrating its biological applicability.

  1. Conformational sampling in template-free protein loop structure modeling: an overview.

    Science.gov (United States)

    Li, Yaohang

    2013-01-01

    Accurately modeling protein loops is an important step to predict three-dimensional structures as well as to understand functions of many proteins. Because of their high flexibility, modeling the three-dimensional structures of loops is difficult and is usually treated as a "mini protein folding problem" under geometric constraints. In the past decade, there has been remarkable progress in template-free loop structure modeling due to advances of computational methods as well as stably increasing number of known structures available in PDB. This mini review provides an overview on the recent computational approaches for loop structure modeling. In particular, we focus on the approaches of sampling loop conformation space, which is a critical step to obtain high resolution models in template-free methods. We review the potential energy functions for loop modeling, loop buildup mechanisms to satisfy geometric constraints, and loop conformation sampling algorithms. The recent loop modeling results are also summarized.

  2. CONFORMATIONAL SAMPLING IN TEMPLATE-FREE PROTEIN LOOP STRUCTURE MODELING: AN OVERVIEW

    Directory of Open Access Journals (Sweden)

    Yaohang Li

    2013-02-01

    Full Text Available Accurately modeling protein loops is an important step to predict three-dimensional structures as well as to understand functions of many proteins. Because of their high flexibility, modeling the three-dimensional structures of loops is difficult and is usually treated as a “mini protein folding problem” under geometric constraints. In the past decade, there has been remarkable progress in template-free loop structure modeling due to advances of computational methods as well as stably increasing number of known structures available in PDB. This mini review provides an overview on the recent computational approaches for loop structure modeling. In particular, we focus on the approaches of sampling loop conformation space, which is a critical step to obtain high resolution models in template-free methods. We review the potential energy functions for loop modeling, loop buildup mechanisms to satisfy geometric constraints, and loop conformation sampling algorithms. The recent loop modeling results are also summarized.

  3. Exploration of freely available web-interfaces for comparative homology modelling of microbial proteins.

    Science.gov (United States)

    Nema, Vijay; Pal, Sudhir Kumar

    2013-01-01

    This study was conducted to find the best suited freely available software for modelling of proteins by taking a few sample proteins. The proteins used were small to big in size with available crystal structures for the purpose of benchmarking. Key players like Phyre2, Swiss-Model, CPHmodels-3.0, Homer, (PS)2, (PS)(2)-V(2), Modweb were used for the comparison and model generation. Benchmarking process was done for four proteins, Icl, InhA, and KatG of Mycobacterium tuberculosis and RpoB of Thermus Thermophilus to get the most suited software. Parameters compared during analysis gave relatively better values for Phyre2 and Swiss-Model. This comparative study gave the information that Phyre2 and Swiss-Model make good models of small and large proteins as compared to other screened software. Other software was also good but is often not very efficient in providing full-length and properly folded structure.

  4. The effect of palatability of protein source on dietary selection in dairy calves.

    Science.gov (United States)

    Miller-Cushon, E K; Terré, M; DeVries, T J; Bach, A

    2014-07-01

    Evidence has shown that soybean meal is perceived as more palatable than canola meal by dairy calves in short-term preference tests. This study evaluated the effect of protein source on longer-term dietary selection of dairy calves. In experiment 1, 40 Holstein bull calves (11.4 ± 4.3 d of age) were randomly assigned to 1 of 2 choice diets for 6 wk: base starter pellet (S; 12% crude protein; CP) and high-protein pellet (40% CP) containing either (1) soybean meal (SB) or (2) canola meal (CM). In wk 7 to 8, all calves were offered a single pelleted diet containing the protein source to which they were previously exposed. In experiment 2, 22 Holstein bull calves (9.9 ± 4.6d of age) were offered, for 6 wk, a choice of 2 mixed pelleted diets: (1) 70% S and 30% SB (SB mix), or (2) 70% S and 30% CM (CM mix). In wk 7 to 8, calves were randomly assigned to 1 of 2 choice diets, as in experiment 1: (1) SB + S, or (2) CM + S. All feeds were provided ad libitum. Calves received 6 L/d of milk replacer [0.75 kg/d of dry matter (DM)] for the duration of both experiments. Feed intake was recorded daily and calves were weighed every 14 d. Feeds were sampled weekly to analyze DM and nutrient intake. Mixed diets in experiment 2 were analyzed for CP in wk 4 and 6 to assess feed sorting (calculated as actual CP intake as a percentage of predicted intake). In experiment 1, calves offered SB + S in wk 1 to 6 consumed more high-protein pellet than calves offered CM + S [73 vs. 42% of DM intake (DMI)] and, consequently, more CP (168 vs. 117 g/d). Solid feed DMI and average daily gain were similar between treatments. When offered a single diet in wk 7 to 8, calves offered starter containing soybean meal increased intake to a greater extent than calves offered the starter containing canola meal. In experiment 2, calves preferred the SB mix to CM mix (preference ratio: 0.7). Calves consumed more CP than predicted from SB mix in wk 4 and 6 (108 ± 2.0%), indicating that they were sorting in

  5. Intracellular delivery of cell-penetrating peptide-transcriptional factor fusion protein and its role in selective osteogenesis

    Science.gov (United States)

    Suh, Jin Sook; Lee, Jue Yeon; Choi, Yoon Jung; You, Hyung Keun; Hong, Seong-Doo; Chung, Chong Pyoung; Park, Yoon Jeong

    2014-01-01

    Protein-transduction technology has been attempted to deliver macromolecular materials, including protein, nucleic acids, and polymeric drugs, for either diagnosis or therapeutic purposes. Herein, fusion protein composed of an arginine-rich cell-penetrating peptide, termed low-molecular-weight protamine (LMWP), and a transcriptional coactivator with a PDZ-binding motif (TAZ) protein was prepared and applied in combination with biomaterials to increase bone-forming capacity. TAZ has been recently identified as a specific osteogenic stimulating transcriptional coactivator in human mesenchymal stem cell (hMSC) differentiation, while simultaneously blocking adipogenic differentiation. However, TAZ by itself cannot penetrate the cells, and thus needs a transfection tool for translocalization. The LMWP-TAZ fusion proteins were efficiently translocalized into the cytosol of hMSCs. The hMSCs treated with cell-penetrating LMWP-TAZ exhibited increased expression of osteoblastic genes and protein, producing significantly higher quantities of mineralized matrix compared to free TAZ. In contrast, adipogenic differentiation of the hMSCs was blocked by treatment of LMWP-TAZ fusion protein, as reflected by reduced marker-protein expression, adipocyte fatty acid-binding protein 2, and peroxisome proliferator-activated receptor-γ messenger ribonucleic acid levels. LMWP-TAZ was applied in alginate gel for the purpose of localization and controlled release. The LMWP-TAZ fusion protein-loaded alginate gel matrix significantly increased bone formation in rabbit calvarial defects compared with alginate gel matrix mixed with free TAZ protein. The protein transduction of TAZ fused with cell-penetrating LMWP peptide was able selectively to stimulate osteogenesis in vitro and in vivo. Taken together, this fusion protein-transduction technology for osteogenic protein can thus be applied in combination with biomaterials for tissue regeneration and controlled release for tissue

  6. Statin Selection in Qatar Based on Multi-indication Pharmacotherapeutic Multi-criteria Scoring Model, and Clinician Preference.

    Science.gov (United States)

    Al-Badriyeh, Daoud; Fahey, Michael; Alabbadi, Ibrahim; Al-Khal, Abdullatif; Zaidan, Manal

    2015-12-01

    Statin selection for the largest hospital formulary in Qatar is not systematic, not comparative, and does not consider the multi-indication nature of statins. There are no reports in the literature of multi-indication-based comparative scoring models of statins or of statin selection criteria weights that are based primarily on local clinicians' preferences and experiences. This study sought to comparatively evaluate statins for first-line therapy in Qatar, and to quantify the economic impact of this. An evidence-based, multi-indication, multi-criteria pharmacotherapeutic model was developed for the scoring of statins from the perspective of the main health care provider in Qatar. The literature and an expert panel informed the selection criteria of statins. Relative weighting of selection criteria was based on the input of the relevant local clinician population. Statins were comparatively scored based on literature evidence, with those exceeding a defined scoring threshold being recommended for use. With 95% CI and 5% margin of error, the scoring model was successfully developed. Selection criteria comprised 28 subcriteria under the following main criteria: clinical efficacy, best publish evidence and experience, adverse effects, drug interaction, dosing time, and fixed dose combination availability. Outcome measures for multiple indications were related to effects on LDL cholesterol, HDL cholesterol, triglyceride, total cholesterol, and C-reactive protein. Atorvastatin, pravastatin, and rosuvastatin exceeded defined pharmacotherapeutic thresholds. Atorvastatin and pravastatin were recommended as first-line use and rosuvastatin as a nonformulary alternative. It was estimated that this would produce a 17.6% cost savings in statins expenditure. Sensitivity analyses confirmed the robustness of the evaluation's outcomes against input uncertainties. Incorporating a comparative evaluation of statins in Qatari practices based on a locally developed, transparent, multi

  7. Natural selection in avian protein-coding genes expressed in brain.

    Science.gov (United States)

    Axelsson, Erik; Hultin-Rosenberg, Lina; Brandström, Mikael; Zwahlén, Martin; Clayton, David F; Ellegren, Hans

    2008-06-01

    The evolution of birds from theropod dinosaurs took place approximately 150 million years ago, and was associated with a number of specific adaptations that are still evident among extant birds, including feathers, song and extravagant secondary sexual characteristics. Knowledge about the molecular evolutionary background to such adaptations is lacking. Here, we analyse the evolution of > 5000 protein-coding gene sequences expressed in zebra finch brain by comparison to orthologous sequences in chicken. Mean d(N)/d(S) is 0.085 and genes with their maximal expression in the eye and central nervous system have the lowest mean d(N)/d(S) value, while those expressed in digestive and reproductive tissues exhibit the highest. We find that fast-evolving genes (those which have higher than expected rate of nonsynonymous substitution, indicative of adaptive evolution) are enriched for biological functions such as fertilization, muscle contraction, defence response, response to stress, wounding and endogenous stimulus, and cell death. After alignment to mammalian orthologues, we identify a catalogue of 228 genes that show a significantly higher rate of protein evolution in the two bird lineages than in mammals. These accelerated bird genes, representing candidates for avian-specific adaptations, include genes implicated in vocal learning and other cognitive processes. Moreover, colouration genes evolve faster in birds than in mammals, which may have been driven by sexual selection for extravagant plumage characteristics.

  8. Variable selection for mixture and promotion time cure rate models.

    Science.gov (United States)

    Masud, Abdullah; Tu, Wanzhu; Yu, Zhangsheng

    2016-11-16

    Failure-time data with cured patients are common in clinical studies. Data from these studies are typically analyzed with cure rate models. Variable selection methods have not been well developed for cure rate models. In this research, we propose two least absolute shrinkage and selection operators based methods, for variable selection in mixture and promotion time cure models with parametric or nonparametric baseline hazards. We conduct an extensive simulation study to assess the operating characteristics of the proposed methods. We illustrate the use of the methods using data from a study of childhood wheezing. © The Author(s) 2016.

  9. The evolution of the protein synthesis system. I - A model of a primitive protein synthesis system

    Science.gov (United States)

    Mizutani, H.; Ponnamperuma, C.

    1977-01-01

    A model is developed to describe the evolution of the protein synthesis system. The model is comprised of two independent autocatalytic systems, one including one gene (A-gene) and two activated amino acid polymerases (O and A-polymerases), and the other including the addition of another gene (N-gene) and a nucleotide polymerase. Simulation results have suggested that even a small enzymic activity and polymerase specificity could lead the system to the most accurate protein synthesis, as far as permitted by transitions to systems with higher accuracy.

  10. Interpretation of protein quantitation using the Bradford assay: comparison with two calculation models.

    Science.gov (United States)

    Ku, Hyung-Keun; Lim, Hyuk-Min; Oh, Kyong-Hwa; Yang, Hyo-Jin; Jeong, Ji-Seon; Kim, Sook-Kyung

    2013-03-01

    The Bradford assay is a simple method for protein quantitation, but variation in the results between proteins is a matter of concern. In this study, we compared and normalized quantitative values from two models for protein quantitation, where the residues in the protein that bind to anionic Coomassie Brilliant Blue G-250 comprise either Arg and Lys (Method 1, M1) or Arg, Lys, and His (Method 2, M2). Use of the M2 model yielded much more consistent quantitation values compared with use of the M1 model, which exhibited marked overestimations against protein standards. Copyright © 2012 Elsevier Inc. All rights reserved.

  11. Model structure selection in convolutive mixtures

    DEFF Research Database (Denmark)

    Dyrholm, Mads; Makeig, S.; Hansen, Lars Kai

    2006-01-01

    The CICAAR algorithm (convolutive independent component analysis with an auto-regressive inverse model) allows separation of white (i.i.d) source signals from convolutive mixtures. We introduce a source color model as a simple extension to the CICAAR which allows for a more parsimonious represent......The CICAAR algorithm (convolutive independent component analysis with an auto-regressive inverse model) allows separation of white (i.i.d) source signals from convolutive mixtures. We introduce a source color model as a simple extension to the CICAAR which allows for a more parsimonious...... representation in many practical mixtures. The new filter-CICAAR allows Bayesian model selection and can help answer questions like: ’Are we actually dealing with a convolutive mixture?’. We try to answer this question for EEG data....

  12. Adaptation of model proteins from cold to hot environments involves continuous and small adjustments of average parameters related to amino acid composition.

    Science.gov (United States)

    De Vendittis, Emmanuele; Castellano, Immacolata; Cotugno, Roberta; Ruocco, Maria Rosaria; Raimo, Gennaro; Masullo, Mariorosario

    2008-01-07

    The growth temperature adaptation of six model proteins has been studied in 42 microorganisms belonging to eubacterial and archaeal kingdoms, covering optimum growth temperatures from 7 to 103 degrees C. The selected proteins include three elongation factors involved in translation, the enzymes glyceraldehyde-3-phosphate dehydrogenase and superoxide dismutase, the cell division protein FtsZ. The common strategy of protein adaptation from cold to hot environments implies the occurrence of small changes in the amino acid composition, without altering the overall structure of the macromolecule. These continuous adjustments were investigated through parameters related to the amino acid composition of each protein. The average value per residue of mass, volume and accessible surface area allowed an evaluation of the usage of bulky residues, whereas the average hydrophobicity reflected that of hydrophobic residues. The specific proportion of bulky and hydrophobic residues in each protein almost linearly increased with the temperature of the host microorganism. This finding agrees with the structural and functional properties exhibited by proteins in differently adapted sources, thus explaining the great compactness or the high flexibility exhibited by (hyper)thermophilic or psychrophilic proteins, respectively. Indeed, heat-adapted proteins incline toward the usage of heavier-size and more hydrophobic residues with respect to mesophiles, whereas the cold-adapted macromolecules show the opposite behavior with a certain preference for smaller-size and less hydrophobic residues. An investigation on the different increase of bulky residues along with the growth temperature observed in the six model proteins suggests the relevance of the possible different role and/or structure organization played by protein domains. The significance of the linear correlations between growth temperature and parameters related to the amino acid composition improved when the analysis was

  13. Modular protein switches derived from antibody mimetic proteins.

    Science.gov (United States)

    Nicholes, N; Date, A; Beaujean, P; Hauk, P; Kanwar, M; Ostermeier, M

    2016-02-01

    Protein switches have potential applications as biosensors and selective protein therapeutics. Protein switches built by fusion of proteins with the prerequisite input and output functions are currently developed using an ad hoc process. A modular switch platform in which existing switches could be readily adapted to respond to any ligand would be advantageous. We investigated the feasibility of a modular protein switch platform based on fusions of the enzyme TEM-1 β-lactamase (BLA) with two different antibody mimetic proteins: designed ankyrin repeat proteins (DARPins) and monobodies. We created libraries of random insertions of the gene encoding BLA into genes encoding a DARPin or a monobody designed to bind maltose-binding protein (MBP). From these libraries, we used a genetic selection system for β-lactamase activity to identify genes that conferred MBP-dependent ampicillin resistance to Escherichia coli. Some of these selected genes encoded switch proteins whose enzymatic activity increased up to 14-fold in the presence of MBP. We next introduced mutations into the antibody mimetic domain of these switches that were known to cause binding to different ligands. To different degrees, introduction of the mutations resulted in switches with the desired specificity, illustrating the potential modularity of these platforms. © The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

  14. Inference of epistatic effects in a key mitochondrial protein

    Science.gov (United States)

    Nelson, Erik D.; Grishin, Nick V.

    2018-06-01

    We use Potts model inference to predict pair epistatic effects in a key mitochondrial protein—cytochrome c oxidase subunit 2—for ray-finned fishes. We examine the effect of phylogenetic correlations on our predictions using a simple exact fitness model, and we find that, although epistatic effects are underpredicted, they maintain a roughly linear relationship to their true (model) values. After accounting for this correction, epistatic effects in the protein are still relatively weak, leading to fitness valleys of depth 2 N s ≃-5 in compensatory double mutants. Interestingly, positive epistasis is more pronounced than negative epistasis, and the strongest positive effects capture nearly all sites subject to positive selection in fishes, similar to virus proteins evolving under selection pressure in the context of drug therapy.

  15. Effect of beta-hydroxy-beta-methylbutyrate (HMB) on protein metabolism in whole body and in selected tissues.

    Science.gov (United States)

    Holecek, M; Muthny, T; Kovarik, M; Sispera, L

    2009-01-01

    Beta-hydroxy-beta-methylbutyrate (HMB) is a leucine metabolite with protein anabolic effect. The aim of the study was to examine the role of exogenous HMB on leucine and protein metabolism in whole body and selected tissues. Rats were administered by HMB (0.1 g/kg b.w.) or by saline. The parameters of whole-body protein metabolism were evaluated 24 h later using L-[1-14C]leucine and L-[3,4,5-3H]phenylalanine. Changes in proteasome dependent proteolysis and protein synthesis were determined according the "chymotrypsin-like" enzyme activity and labeled leucine and phenylalanine incorporation into the protein. A decrease in leucine clearance and whole-body protein turnover (i.e., a decrease in whole-body proteolysis and protein synthesis) was observed in HMB treated rats. Proteasome-dependent proteolysis decreased significantly in skeletal muscle, changes in heart, liver, jejunum, colon, kidney, and spleen were insignificant. Decrease in protein synthesis was observed in the heart, colon, kidney, and spleen, while an increase was observed in the liver. There were no significant changes in leucine oxidation. We conclude that protein anabolic effect of HMB in skeletal muscle is related to inhibition of proteolysis in proteasome. Alterations in protein synthesis in visceral tissues may affect several important functions and the metabolic status of the whole body.

  16. A novel typing method for Streptococcus pneumoniae using selected surface proteins

    Directory of Open Access Journals (Sweden)

    Arnau eDomenech

    2016-03-01

    Full Text Available The diverse pneumococcal diseases are associated with different pneumococcal lineages, or clonal complexes. Nevertheless, intra-clonal genomic variability, which influences pathogenicity, has been reported for surface virulence factors. These factors constitute the communication interface between the pathogen and its host and their corresponding genes are subjected to strong selective pressures affecting functionality and immunogenicity. First, the presence and allelic dispersion of 97 outer protein families were screened in 19 complete pneumococcal genomes. Seventeen families were deemed variable and were then examined in 216 draft genomes. This procedure allowed the generation of binary vectors with 17 positions and the classification of strains into surfotypes. They represent the outer protein subsets with the highest inter-strain discriminative power. A total of 116 non-redundant surfotypes were identified. Those sharing a critical number of common protein features were hierarchically clustered into 18 surfogroups. Most clonal complexes with comparable epidemiological characteristics belonged to the same or similar surfogroups. However, the very large CC156 clonal complex was dispersed over several surfogroups. In order to establish a relationship between surfogroup and pathogenicity, the surfotypes of 95 clinical isolates with different serogroup/serotype combinations were analysed. We found a significant correlation between surfogroup and type of pathogenic behaviour (primary invasive, opportunistic invasive and non-invasive. We conclude that the virulent behaviour of S. pneumoniae is related to the activity of collections of, rather than individual, surface virulence factors. Since surfotypes evolve faster than MLSTs and directly reflect virulence potential, this novel typing protocol is appropriate for the identification of emerging clones.

  17. Model for Stress-induced Protein Degradation in Lemna minor1

    Science.gov (United States)

    Cooke, Robert J.; Roberts, Keith; Davies, David D.

    1980-01-01

    Transfer of Lemna minor fronds to adverse or stress conditions produces a large increase in the rate of protein degradation. Cycloheximide partially inhibits stress-induced protein degradation and also partially inhibits the protein degradation which occurs in the absence of stress. The increased protein degradation does not appear to be due to an increase in activity of soluble proteolytic enzymes. Biochemical evidence indicates that stress, perhaps acting via hormones, affects the permeability of certain membranes, particularly the tonoplast. A general model for stress-induced protein degradation is presented in which changes in membrane properties allow vacuolar proteolytic enzymes increased access to cytoplasmic proteins. PMID:16661588

  18. Expert System Model for Educational Personnel Selection

    Directory of Open Access Journals (Sweden)

    Héctor A. Tabares-Ospina

    2013-06-01

    Full Text Available The staff selection is a difficult task due to the subjectivity that the evaluation means. This process can be complemented using a system to support decision. This paper presents the implementation of an expert system to systematize the selection process of professors. The management of software development is divided into 4 parts: requirements, design, implementation and commissioning. The proposed system models a specific knowledge through relationships between variables evidence and objective.

  19. Diversified models for portfolio selection based on uncertain semivariance

    Science.gov (United States)

    Chen, Lin; Peng, Jin; Zhang, Bo; Rosyida, Isnaini

    2017-02-01

    Since the financial markets are complex, sometimes the future security returns are represented mainly based on experts' estimations due to lack of historical data. This paper proposes a semivariance method for diversified portfolio selection, in which the security returns are given subjective to experts' estimations and depicted as uncertain variables. In the paper, three properties of the semivariance of uncertain variables are verified. Based on the concept of semivariance of uncertain variables, two types of mean-semivariance diversified models for uncertain portfolio selection are proposed. Since the models are complex, a hybrid intelligent algorithm which is based on 99-method and genetic algorithm is designed to solve the models. In this hybrid intelligent algorithm, 99-method is applied to compute the expected value and semivariance of uncertain variables, and genetic algorithm is employed to seek the best allocation plan for portfolio selection. At last, several numerical examples are presented to illustrate the modelling idea and the effectiveness of the algorithm.

  20. CalFitter: a web server for analysis of protein thermal denaturation data.

    Science.gov (United States)

    Mazurenko, Stanislav; Stourac, Jan; Kunka, Antonin; Nedeljkovic, Sava; Bednar, David; Prokop, Zbynek; Damborsky, Jiri

    2018-05-14

    Despite significant advances in the understanding of protein structure-function relationships, revealing protein folding pathways still poses a challenge due to a limited number of relevant experimental tools. Widely-used experimental techniques, such as calorimetry or spectroscopy, critically depend on a proper data analysis. Currently, there are only separate data analysis tools available for each type of experiment with a limited model selection. To address this problem, we have developed the CalFitter web server to be a unified platform for comprehensive data fitting and analysis of protein thermal denaturation data. The server allows simultaneous global data fitting using any combination of input data types and offers 12 protein unfolding pathway models for selection, including irreversible transitions often missing from other tools. The data fitting produces optimal parameter values, their confidence intervals, and statistical information to define unfolding pathways. The server provides an interactive and easy-to-use interface that allows users to directly analyse input datasets and simulate modelled output based on the model parameters. CalFitter web server is available free at https://loschmidt.chemi.muni.cz/calfitter/.

  1. The visualCMAT: A web-server to select and interpret correlated mutations/co-evolving residues in protein families.

    Science.gov (United States)

    Suplatov, Dmitry; Sharapova, Yana; Timonina, Daria; Kopylov, Kirill; Švedas, Vytas

    2018-04-01

    The visualCMAT web-server was designed to assist experimental research in the fields of protein/enzyme biochemistry, protein engineering, and drug discovery by providing an intuitive and easy-to-use interface to the analysis of correlated mutations/co-evolving residues. Sequence and structural information describing homologous proteins are used to predict correlated substitutions by the Mutual information-based CMAT approach, classify them into spatially close co-evolving pairs, which either form a direct physical contact or interact with the same ligand (e.g. a substrate or a crystallographic water molecule), and long-range correlations, annotate and rank binding sites on the protein surface by the presence of statistically significant co-evolving positions. The results of the visualCMAT are organized for a convenient visual analysis and can be downloaded to a local computer as a content-rich all-in-one PyMol session file with multiple layers of annotation corresponding to bioinformatic, statistical and structural analyses of the predicted co-evolution, or further studied online using the built-in interactive analysis tools. The online interactivity is implemented in HTML5 and therefore neither plugins nor Java are required. The visualCMAT web-server is integrated with the Mustguseal web-server capable of constructing large structure-guided sequence alignments of protein families and superfamilies using all available information about their structures and sequences in public databases. The visualCMAT web-server can be used to understand the relationship between structure and function in proteins, implemented at selecting hotspots and compensatory mutations for rational design and directed evolution experiments to produce novel enzymes with improved properties, and employed at studying the mechanism of selective ligand's binding and allosteric communication between topologically independent sites in protein structures. The web-server is freely available at https

  2. Predicting Protein Secondary Structure with Markov Models

    DEFF Research Database (Denmark)

    Fischer, Paul; Larsen, Simon; Thomsen, Claus

    2004-01-01

    we are considering here, is to predict the secondary structure from the primary one. To this end we train a Markov model on training data and then use it to classify parts of unknown protein sequences as sheets, helices or coils. We show how to exploit the directional information contained...... in the Markov model for this task. Classifications that are purely based on statistical models might not always be biologically meaningful. We present combinatorial methods to incorporate biological background knowledge to enhance the prediction performance....

  3. Histopathologic characterization of the BTBR mouse model of autistic-like behavior reveals selective changes in neurodevelopmental proteins and adult hippocampal neurogenesis

    Directory of Open Access Journals (Sweden)

    Stephenson Diane T

    2011-05-01

    Full Text Available Abstract Background The inbred mouse strain BTBR T+ tf/J (BTBR exhibits behavioral deficits that mimic the core deficits of autism. Neuroanatomically, the BTBR strain is also characterized by a complete absence of the corpus callosum. The goal of this study was to identify novel molecular and cellular changes in the BTBR mouse, focusing on neuronal, synaptic, glial and plasticity markers in the limbic system as a model for identifying putative molecular and cellular substrates associated with autistic behaviors. Methods Forebrains of 8 to 10-week-old male BTBR and age-matched C57Bl/6J control mice were evaluated by immunohistochemistry using free-floating and paraffin embedded sections. Twenty antibodies directed against antigens specific to neurons, synapses and glia were used. Nissl, Timm and acetylcholinesterase (AchE stains were performed to assess cytoarchitecture, mossy fibers and cholinergic fiber density, respectively. In the hippocampus, quantitative stereological estimates for the mitotic marker bromodeoxyuridine (BrdU were performed to determine hippocampal progenitor proliferation, survival and differentiation, and brain-derived neurotrophic factor (BDNF mRNA was quantified by in situ hybridization. Quantitative image analysis was performed for NG2, doublecortin (DCX, NeuroD, GAD67 and Poly-Sialic Acid Neural Cell Adhesion Molecule (PSA-NCAM. Results In midline structures including the region of the absent corpus callosum of BTBR mice, the myelin markers 2',3'-cyclic nucleotide 3'-phosphodiesterase (CNPase and myelin basic protein (MBP were reduced, and the oligodendrocyte precursor NG2 was increased. MBP and CNPase were expressed in small ectopic white matter bundles within the cingulate cortex. Microglia and astrocytes showed no evidence of gliosis, yet orientations of glial fibers were altered in specific white-matter areas. In the hippocampus, evidence of reduced neurogenesis included significant reductions in the number of

  4. Behavioral optimization models for multicriteria portfolio selection

    Directory of Open Access Journals (Sweden)

    Mehlawat Mukesh Kumar

    2013-01-01

    Full Text Available In this paper, behavioral construct of suitability is used to develop a multicriteria decision making framework for portfolio selection. To achieve this purpose, we rely on multiple methodologies. Analytical hierarchy process technique is used to model the suitability considerations with a view to obtaining the suitability performance score in respect of each asset. A fuzzy multiple criteria decision making method is used to obtain the financial quality score of each asset based upon investor's rating on the financial criteria. Two optimization models are developed for optimal asset allocation considering simultaneously financial and suitability criteria. An empirical study is conducted on randomly selected assets from National Stock Exchange, Mumbai, India to demonstrate the effectiveness of the proposed methodology.

  5. Use of multiple-trait animal models for genetic evaluation of milk, fat and protein lactation yields of dairy cattle in Belgium

    Directory of Open Access Journals (Sweden)

    Pierre Coenraets

    1997-01-01

    Full Text Available Comparison of computation time between single-trait and multiple-trait evaluations showed that with the use of the canonicat transformation associated with multiple diagonalization of (covariance matrices, multiple-trait analysis for milk, fat and protein yields is not more expensive than three single-trait analyzes. Rank correlations between breeding values for 54,820 cows with records (for their 1,406 sires estimated with the single-trait and multiple-trait models were over .98 (.99 in fat yield and over .99 (.99 in milk and protein yields. The relative gain expressed as reduction in mean prediction error variance was 3% (1% in milk yield, 6% (3% in fat yield, and .4% (.2% in protein yield for cows (for sires. Relative genetic gains were 3% (1%, 6% (2% and .5% (.2% respectively in milk, fat and protein yields for cows (for sires. The use of multiple-trait models bas therefore the advantages of improved precision and reduced selection bics. Multiple-trait analysis could be extended for the analyzes of test-day records. Results show that this or similar multiple-trait animal model could be implemented immediately in Belgium at low computing cost, using the proposed algorithme and could be the first step to new, more advanced evaluation methods.

  6. Locating protein-coding sequences under selection for additional, overlapping functions in 29 mammalian genomes

    DEFF Research Database (Denmark)

    Lin, Michael F; Kheradpour, Pouya; Washietl, Stefan

    2011-01-01

    conservation compared to typical protein-coding genes—especially at synonymous sites. In this study, we use genome alignments of 29 placental mammals to systematically locate short regions within human ORFs that show conspicuously low estimated rates of synonymous substitution across these species. The 29......-species alignment provides statistical power to locate more than 10,000 such regions with resolution down to nine-codon windows, which are found within more than a quarter of all human protein-coding genes and contain ~2% of their synonymous sites. We collect numerous lines of evidence that the observed...... synonymous constraint in these regions reflects selection on overlapping functional elements including splicing regulatory elements, dual-coding genes, RNA secondary structures, microRNA target sites, and developmental enhancers. Our results show that overlapping functional elements are common in mammalian...

  7. An Efficient Null Model for Conformational Fluctuations in Proteins

    DEFF Research Database (Denmark)

    Harder, Tim Philipp; Borg, Mikael; Bottaro, Sandro

    2012-01-01

    Protein dynamics play a crucial role in function, catalytic activity, and pathogenesis. Consequently, there is great interest in computational methods that probe the conformational fluctuations of a protein. However, molecular dynamics simulations are computationally costly and therefore are often...... limited to comparatively short timescales. TYPHON is a probabilistic method to explore the conformational space of proteins under the guidance of a sophisticated probabilistic model of local structure and a given set of restraints that represent nonlocal interactions, such as hydrogen bonds or disulfide...... on conformational fluctuations that is in correspondence with experimental measurements. TYPHON provides a flexible, yet computationally efficient, method to explore possible conformational fluctuations in proteins....

  8. Fisher-Wright model with deterministic seed bank and selection.

    Science.gov (United States)

    Koopmann, Bendix; Müller, Johannes; Tellier, Aurélien; Živković, Daniel

    2017-04-01

    Seed banks are common characteristics to many plant species, which allow storage of genetic diversity in the soil as dormant seeds for various periods of time. We investigate an above-ground population following a Fisher-Wright model with selection coupled with a deterministic seed bank assuming the length of the seed bank is kept constant and the number of seeds is large. To assess the combined impact of seed banks and selection on genetic diversity, we derive a general diffusion model. The applied techniques outline a path of approximating a stochastic delay differential equation by an appropriately rescaled stochastic differential equation. We compute the equilibrium solution of the site-frequency spectrum and derive the times to fixation of an allele with and without selection. Finally, it is demonstrated that seed banks enhance the effect of selection onto the site-frequency spectrum while slowing down the time until the mutation-selection equilibrium is reached. Copyright © 2016 Elsevier Inc. All rights reserved.

  9. Genome-scale metabolic model of Pichia pastoris with native and humanized glycosylation of recombinant proteins.

    Science.gov (United States)

    Irani, Zahra Azimzadeh; Kerkhoven, Eduard J; Shojaosadati, Seyed Abbas; Nielsen, Jens

    2016-05-01

    Pichia pastoris is used for commercial production of human therapeutic proteins, and genome-scale models of P. pastoris metabolism have been generated in the past to study the metabolism and associated protein production by this yeast. A major challenge with clinical usage of recombinant proteins produced by P. pastoris is the difference in N-glycosylation of proteins produced by humans and this yeast. However, through metabolic engineering, a P. pastoris strain capable of producing humanized N-glycosylated proteins was constructed. The current genome-scale models of P. pastoris do not address native nor humanized N-glycosylation, and we therefore developed ihGlycopastoris, an extension to the iLC915 model with both native and humanized N-glycosylation for recombinant protein production, but also an estimation of N-glycosylation of P. pastoris native proteins. This new model gives a better prediction of protein yield, demonstrates the effect of the different types of N-glycosylation of protein yield, and can be used to predict potential targets for strain improvement. The model represents a step towards a more complete description of protein production in P. pastoris, which is required for using these models to understand and optimize protein production processes. © 2015 Wiley Periodicals, Inc.

  10. Multi-Criteria Decision Making For Determining A Simple Model of Supplier Selection

    Science.gov (United States)

    Harwati

    2017-06-01

    Supplier selection is a decision with many criteria. Supplier selection model usually involves more than five main criteria and more than 10 sub-criteria. In fact many model includes more than 20 criteria. Too many criteria involved in supplier selection models sometimes make it difficult to apply in many companies. This research focuses on designing supplier selection that easy and simple to be applied in the company. Analytical Hierarchy Process (AHP) is used to weighting criteria. The analysis results there are four criteria that are easy and simple can be used to select suppliers: Price (weight 0.4) shipment (weight 0.3), quality (weight 0.2) and services (weight 0.1). A real case simulation shows that simple model provides the same decision with a more complex model.

  11. A potential new selection criterion for breeding winter barley optimal protein and amino acid profiles for liquid pig feed

    DEFF Research Database (Denmark)

    Christensen, Jesper Bjerg; Blaabjerg, Karoline; Poulsen, Hanne Damgaard

    The hypothesis is that cereal proteases in liquid feed degrade and convert water insoluble storage protein into water soluble protein, which may improve the digestibility of protein in pigs compared with dry feeding. Protein utilization is increased by matching the amino acid (AAs) content...... of the diet as close as possible to the pigs’ requirement. By improving the availability of isoleucine, leucine, histidine and phenylalanine, which are limiting and commercial unavailable, the amount of crude protein in the pig feed can be reduced, resulting in a decreased excretion of nitrogen. The aim...... of glutamic acid revealed differences between the cultivars and the solubilised protein at all three times. These preliminary results may indicate that improvements of the nitrogen utilization in pigs fed soaked winter barley depends on the choice of cultivar and soaking time, and may serve as a new selection...

  12. A BAYESIAN NONPARAMETRIC MIXTURE MODEL FOR SELECTING GENES AND GENE SUBNETWORKS.

    Science.gov (United States)

    Zhao, Yize; Kang, Jian; Yu, Tianwei

    2014-06-01

    It is very challenging to select informative features from tens of thousands of measured features in high-throughput data analysis. Recently, several parametric/regression models have been developed utilizing the gene network information to select genes or pathways strongly associated with a clinical/biological outcome. Alternatively, in this paper, we propose a nonparametric Bayesian model for gene selection incorporating network information. In addition to identifying genes that have a strong association with a clinical outcome, our model can select genes with particular expressional behavior, in which case the regression models are not directly applicable. We show that our proposed model is equivalent to an infinity mixture model for which we develop a posterior computation algorithm based on Markov chain Monte Carlo (MCMC) methods. We also propose two fast computing algorithms that approximate the posterior simulation with good accuracy but relatively low computational cost. We illustrate our methods on simulation studies and the analysis of Spellman yeast cell cycle microarray data.

  13. Emergence, Retention and Selection: A Trilogy of Origination for Functional De Novo Proteins from Ancestral LncRNAs in Primates.

    Directory of Open Access Journals (Sweden)

    Jia-Yu Chen

    2015-07-01

    Full Text Available While some human-specific protein-coding genes have been proposed to originate from ancestral lncRNAs, the transition process remains poorly understood. Here we identified 64 hominoid-specific de novo genes and report a mechanism for the origination of functional de novo proteins from ancestral lncRNAs with precise splicing structures and specific tissue expression profiles. Whole-genome sequencing of dozens of rhesus macaque animals revealed that these lncRNAs are generally not more selectively constrained than other lncRNA loci. The existence of these newly-originated de novo proteins is also not beyond anticipation under neutral expectation, as they generally have longer theoretical lifespan than their current age, due to their GC-rich sequence property enabling stable ORFs with lower chance of non-sense mutations. Interestingly, although the emergence and retention of these de novo genes are likely driven by neutral forces, population genetics study in 67 human individuals and 82 macaque animals revealed signatures of purifying selection on these genes specifically in human population, indicating a proportion of these newly-originated proteins are already functional in human. We thus propose a mechanism for creation of functional de novo proteins from ancestral lncRNAs during the primate evolution, which may contribute to human-specific genetic novelties by taking advantage of existed genomic contexts.

  14. Selection of climate change scenario data for impact modelling

    DEFF Research Database (Denmark)

    Sloth Madsen, M; Fox Maule, C; MacKellar, N

    2012-01-01

    Impact models investigating climate change effects on food safety often need detailed climate data. The aim of this study was to select climate change projection data for selected crop phenology and mycotoxin impact models. Using the ENSEMBLES database of climate model output, this study...... illustrates how the projected climate change signal of important variables as temperature, precipitation and relative humidity depends on the choice of the climate model. Using climate change projections from at least two different climate models is recommended to account for model uncertainty. To make...... the climate projections suitable for impact analysis at the local scale a weather generator approach was adopted. As the weather generator did not treat all the necessary variables, an ad-hoc statistical method was developed to synthesise realistic values of missing variables. The method is presented...

  15. Proteotoxic stress induces phosphorylation of p62/SQSTM1 by ULK1 to regulate selective autophagic clearance of protein aggregates.

    Directory of Open Access Journals (Sweden)

    Junghyun Lim

    Full Text Available Disruption of proteostasis, or protein homeostasis, is often associated with aberrant accumulation of misfolded proteins or protein aggregates. Autophagy offers protection to cells by removing toxic protein aggregates and injured organelles in response to proteotoxic stress. However, the exact mechanism whereby autophagy recognizes and degrades misfolded or aggregated proteins has yet to be elucidated. Mounting evidence demonstrates the selectivity of autophagy, which is mediated through autophagy receptor proteins (e.g. p62/SQSTM1 linking autophagy cargos and autophagosomes. Here we report that proteotoxic stress imposed by the proteasome inhibition or expression of polyglutamine expanded huntingtin (polyQ-Htt induces p62 phosphorylation at its ubiquitin-association (UBA domain that regulates its binding to ubiquitinated proteins. We find that autophagy-related kinase ULK1 phosphorylates p62 at a novel phosphorylation site S409 in UBA domain. Interestingly, phosphorylation of p62 by ULK1 does not occur upon nutrient starvation, in spite of its role in canonical autophagy signaling. ULK1 also phosphorylates S405, while S409 phosphorylation critically regulates S405 phosphorylation. We find that S409 phosphorylation destabilizes the UBA dimer interface, and increases binding affinity of p62 to ubiquitin. Furthermore, lack of S409 phosphorylation causes accumulation of p62, aberrant localization of autophagy proteins and inhibition of the clearance of ubiquitinated proteins or polyQ-Htt. Therefore, our data provide mechanistic insights into the regulation of selective autophagy by ULK1 and p62 upon proteotoxic stress. Our study suggests a potential novel drug target in developing autophagy-based therapeutics for the treatment of proteinopathies including Huntington's disease.

  16. Selectable high-yield recombinant protein production in human cells using a GFP/YFP nanobody affinity support.

    Science.gov (United States)

    Schellenberg, Matthew J; Petrovich, Robert M; Malone, Christine C; Williams, R Scott

    2018-03-25

    Recombinant protein expression systems that produce high yields of pure proteins and multi-protein complexes are essential to meet the needs of biologists, biochemists, and structural biologists using X-ray crystallography and cryo-electron microscopy. An ideal expression system for recombinant human proteins is cultured human cells where the correct translation and chaperone machinery are present. However, compared to bacterial expression systems, human cell cultures present several technical challenges to their use as an expression system. We developed a method that utilizes a YFP fusion-tag to generate recombinant proteins using suspension-cultured HEK293F cells. YFP is a dual-function tag that enables direct visualization and fluorescence-based selection of high expressing clones for and rapid purification using a high-stringency, high-affinity anti-GFP/YFP nanobody support. We demonstrate the utility of this system by expressing two large human proteins, TOP2α (340 KDa dimer) and a TOP2β catalytic core (260 KDa dimer). This robustly and reproducibly yields >10 mg/L liter of cell culture using transient expression or 2.5 mg/L using stable expression. Published 2018. This article is a US Government work and is in the public domain in the USA.

  17. Protein corona: a new approach for nanomedicine design

    Directory of Open Access Journals (Sweden)

    Nguyen VH

    2017-04-01

    Full Text Available Van Hong Nguyen, Beom-Jin Lee Department of Pharmacy, Bioavailability Control Laboratory, College of Pharmacy, Ajou University, Suwon, Republic of Korea Abstract: After administration of nanoparticle (NP into biological fluids, an NP–protein complex is formed, which represents the “true identity” of NP in our body. Hence, protein–NP interaction should be carefully investigated to predict and control the fate of NPs or drug-loaded NPs, including systemic circulation, biodistribution, and bioavailability. In this review, we mainly focus on the formation of protein corona and its potential applications in pharmaceutical sciences such as prediction modeling based on NP-adsorbed proteins, usage of active proteins for modifying NP to achieve toxicity reduction, circulation time enhancement, and targeting effect. Validated correlative models for NP biological responses mainly based on protein corona fingerprints of NPs are more highly accurate than the models solely set up from NP properties. Based on these models, effectiveness as well as the toxicity of NPs can be predicted without in vivo tests, while novel cell receptors could be identified from prominent proteins which play important key roles in the models. The ungoverned protein adsorption onto NPs may have generally negative effects such as rapid clearance from the bloodstream, hindrance of targeting capacity, and induction of toxicity. In contrast, controlling protein adsorption by modifying NPs with diverse functional proteins or tailoring appropriate NPs which favor selective endogenous peptides and proteins will bring promising therapeutic benefits in drug delivery and targeted cancer treatment. Keywords: protein-nanoparticle interaction, protein corona, exchange of adsorbed protein, toxicity reduction, predictive modeling, targeting drug delivery

  18. A model selection support system for numerical simulations of nuclear thermal-hydraulics

    International Nuclear Information System (INIS)

    Gofuku, Akio; Shimizu, Kenji; Sugano, Keiji; Yoshikawa, Hidekazu; Wakabayashi, Jiro

    1990-01-01

    In order to execute efficiently a dynamic simulation of a large-scaled engineering system such as a nuclear power plant, it is necessary to develop intelligent simulation support system for all phases of the simulation. This study is concerned with the intelligent support for the program development phase and is engaged in the adequate model selection support method by applying AI (Artificial Intelligence) techniques to execute a simulation consistent with its purpose and conditions. A proto-type expert system to support the model selection for numerical simulations of nuclear thermal-hydraulics in the case of cold leg small break loss-of-coolant accident of PWR plant is now under development on a personal computer. The steps to support the selection of both fluid model and constitutive equations for the drift flux model have been developed. Several cases of model selection were carried out and reasonable model selection results were obtained. (author)

  19. Chloroform-assisted phenol extraction improving proteome profiling of maize embryos through selective depletion of high-abundance storage proteins.

    Directory of Open Access Journals (Sweden)

    Erhui Xiong

    Full Text Available The presence of abundant storage proteins in plant embryos greatly impedes seed proteomics analysis. Vicilin (or globulin-1 is the most abundant storage protein in maize embryo. There is a need to deplete the vicilins from maize embryo extracts for enhanced proteomics analysis. We here reported a chloroform-assisted phenol extraction (CAPE method for vicilin depletion. By CAPE, maize embryo proteins were first extracted in an aqueous buffer, denatured by chloroform and then subjected to phenol extraction. We found that CAPE can effectively deplete the vicilins from maize embryo extract, allowing the detection of low-abundance proteins that were masked by vicilins in 2-DE gel. The novelty of CAPE is that it selectively depletes abundant storage proteins from embryo extracts of both monocot (maize and dicot (soybean and pea seeds, whereas other embryo proteins were not depleted. CAPE can significantly improve proteome profiling of embryos and extends the application of chloroform and phenol extraction in plant proteomics. In addition, the rationale behind CAPE depletion of abundant storage proteins was explored.

  20. Biotin-tagged proteins: Reagents for efficient ELISA-based serodiagnosis and phage display-based affinity selection.

    Science.gov (United States)

    Verma, Vaishali; Kaur, Charanpreet; Grover, Payal; Gupta, Amita; Chaudhary, Vijay K

    2018-01-01

    The high-affinity interaction between biotin and streptavidin has opened avenues for using recombinant proteins with site-specific biotinylation to achieve efficient and directional immobilization. The site-specific biotinylation of proteins carrying a 15 amino acid long Biotin Acceptor Peptide tag (BAP; also known as AviTag) is effected on a specific lysine either by co-expressing the E. coli BirA enzyme in vivo or by using purified recombinant E. coli BirA enzyme in the presence of ATP and biotin in vitro. In this paper, we have designed a T7 promoter-lac operator-based expression vector for rapid and efficient cloning, and high-level cytosolic expression of proteins carrying a C-terminal BAP tag in E. coli with TEV protease cleavable N-terminal deca-histidine tag, useful for initial purification. Furthermore, a robust three-step purification pipeline integrated with well-optimized protocols for TEV protease-based H10 tag removal, and recombinant BirA enzyme-based site-specific in vitro biotinylation is described to obtain highly pure biotinylated proteins. Most importantly, the paper demonstrates superior sensitivities in indirect ELISA with directional and efficient immobilization of biotin-tagged proteins on streptavidin-coated surfaces in comparison to passive immobilization. The use of biotin-tagged proteins through specific immobilization also allows more efficient selection of binders from a phage-displayed naïve antibody library. In addition, for both these applications, specific immobilization requires much less amount of protein as compared to passive immobilization and can be easily multiplexed. The simplified strategy described here for the production of highly pure biotin-tagged proteins will find use in numerous applications, including those, which may require immobilization of multiple proteins simultaneously on a solid surface.

  1. Modeling selective pressures on phytoplankton in the global ocean.

    Directory of Open Access Journals (Sweden)

    Jason G Bragg

    Full Text Available Our view of marine microbes is transforming, as culture-independent methods facilitate rapid characterization of microbial diversity. It is difficult to assimilate this information into our understanding of marine microbe ecology and evolution, because their distributions, traits, and genomes are shaped by forces that are complex and dynamic. Here we incorporate diverse forces--physical, biogeochemical, ecological, and mutational--into a global ocean model to study selective pressures on a simple trait in a widely distributed lineage of picophytoplankton: the nitrogen use abilities of Synechococcus and Prochlorococcus cyanobacteria. Some Prochlorococcus ecotypes have lost the ability to use nitrate, whereas their close relatives, marine Synechococcus, typically retain it. We impose mutations for the loss of nitrogen use abilities in modeled picophytoplankton, and ask: in which parts of the ocean are mutants most disadvantaged by losing the ability to use nitrate, and in which parts are they least disadvantaged? Our model predicts that this selective disadvantage is smallest for picophytoplankton that live in tropical regions where Prochlorococcus are abundant in the real ocean. Conversely, the selective disadvantage of losing the ability to use nitrate is larger for modeled picophytoplankton that live at higher latitudes, where Synechococcus are abundant. In regions where we expect Prochlorococcus and Synechococcus populations to cycle seasonally in the real ocean, we find that model ecotypes with seasonal population dynamics similar to Prochlorococcus are less disadvantaged by losing the ability to use nitrate than model ecotypes with seasonal population dynamics similar to Synechococcus. The model predictions for the selective advantage associated with nitrate use are broadly consistent with the distribution of this ability among marine picocyanobacteria, and at finer scales, can provide insights into interactions between temporally varying

  2. Modeling selective pressures on phytoplankton in the global ocean.

    Science.gov (United States)

    Bragg, Jason G; Dutkiewicz, Stephanie; Jahn, Oliver; Follows, Michael J; Chisholm, Sallie W

    2010-03-10

    Our view of marine microbes is transforming, as culture-independent methods facilitate rapid characterization of microbial diversity. It is difficult to assimilate this information into our understanding of marine microbe ecology and evolution, because their distributions, traits, and genomes are shaped by forces that are complex and dynamic. Here we incorporate diverse forces--physical, biogeochemical, ecological, and mutational--into a global ocean model to study selective pressures on a simple trait in a widely distributed lineage of picophytoplankton: the nitrogen use abilities of Synechococcus and Prochlorococcus cyanobacteria. Some Prochlorococcus ecotypes have lost the ability to use nitrate, whereas their close relatives, marine Synechococcus, typically retain it. We impose mutations for the loss of nitrogen use abilities in modeled picophytoplankton, and ask: in which parts of the ocean are mutants most disadvantaged by losing the ability to use nitrate, and in which parts are they least disadvantaged? Our model predicts that this selective disadvantage is smallest for picophytoplankton that live in tropical regions where Prochlorococcus are abundant in the real ocean. Conversely, the selective disadvantage of losing the ability to use nitrate is larger for modeled picophytoplankton that live at higher latitudes, where Synechococcus are abundant. In regions where we expect Prochlorococcus and Synechococcus populations to cycle seasonally in the real ocean, we find that model ecotypes with seasonal population dynamics similar to Prochlorococcus are less disadvantaged by losing the ability to use nitrate than model ecotypes with seasonal population dynamics similar to Synechococcus. The model predictions for the selective advantage associated with nitrate use are broadly consistent with the distribution of this ability among marine picocyanobacteria, and at finer scales, can provide insights into interactions between temporally varying ocean processes and

  3. Modeling Directional Selectivity Using Self-Organizing Delay-Aadaptation Maps

    OpenAIRE

    Tversky, Mr. Tal; Miikkulainen, Dr. Risto

    2002-01-01

    Using a delay adaptation learning rule, we model the activity-dependent development of directionally selective cells in the primary visual cortex. Based on input stimuli, a learning rule shifts delays to create synchronous arrival of spikes at cortical cells. As a result, delays become tuned creating a smooth cortical map of direction selectivity. This result demonstrates how delay adaption can serve as a powerful abstraction for modeling temporal learning in the brain.

  4. HIV-specific probabilistic models of protein evolution.

    Directory of Open Access Journals (Sweden)

    David C Nickle

    2007-06-01

    Full Text Available Comparative sequence analyses, including such fundamental bioinformatics techniques as similarity searching, sequence alignment and phylogenetic inference, have become a mainstay for researchers studying type 1 Human Immunodeficiency Virus (HIV-1 genome structure and evolution. Implicit in comparative analyses is an underlying model of evolution, and the chosen model can significantly affect the results. In general, evolutionary models describe the probabilities of replacing one amino acid character with another over a period of time. Most widely used evolutionary models for protein sequences have been derived from curated alignments of hundreds of proteins, usually based on mammalian genomes. It is unclear to what extent these empirical models are generalizable to a very different organism, such as HIV-1-the most extensively sequenced organism in existence. We developed a maximum likelihood model fitting procedure to a collection of HIV-1 alignments sampled from different viral genes, and inferred two empirical substitution models, suitable for describing between-and within-host evolution. Our procedure pools the information from multiple sequence alignments, and provided software implementation can be run efficiently in parallel on a computer cluster. We describe how the inferred substitution models can be used to generate scoring matrices suitable for alignment and similarity searches. Our models had a consistently superior fit relative to the best existing models and to parameter-rich data-driven models when benchmarked on independent HIV-1 alignments, demonstrating evolutionary biases in amino-acid substitution that are unique to HIV, and that are not captured by the existing models. The scoring matrices derived from the models showed a marked difference from common amino-acid scoring matrices. The use of an appropriate evolutionary model recovered a known viral transmission history, whereas a poorly chosen model introduced phylogenetic

  5. Plant G-Proteins Come of Age: Breaking the Bond with Animal Models.

    Science.gov (United States)

    Trusov, Yuri; Botella, José R

    2016-01-01

    G-proteins are universal signal transducers mediating many cellular responses. Plant G-protein signaling has been modeled on the well-established animal paradigm but accumulated experimental evidence indicates that G-protein-dependent signaling in plants has taken a very different evolutionary path. Here we review the differences between plant and animal G-proteins reported over past two decades. Most importantly, while in animal systems the G-protein signaling cycle is activated by seven transmembrane-spanning G-protein coupled receptors, the existence of these type of receptors in plants is highly controversial. Instead plant G-proteins have been proven to be functionally associated with atypical receptors such as the Arabidopsis RGS1 and a number of receptor-like kinases. We propose that, instead of the GTP/GDP cycle used in animals, plant G-proteins are activated/de-activated by phosphorylation/de-phosphorylation. We discuss the need of a fresh new look at these signaling molecules and provide a hypothetical model that departs from the accepted animal paradigm.

  6. Uniform design based SVM model selection for face recognition

    Science.gov (United States)

    Li, Weihong; Liu, Lijuan; Gong, Weiguo

    2010-02-01

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