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Sample records for protein model selection

  1. Selective refinement and selection of near-native models in protein structure prediction.

    PubMed

    Zhang, Jiong; Barz, Bogdan; Zhang, Jingfen; Xu, Dong; Kosztin, Ioan

    2015-10-01

    In recent years in silico protein structure prediction reached a level where fully automated servers can generate large pools of near-native structures. However, the identification and further refinement of the best structures from the pool of models remain problematic. To address these issues, we have developed (i) a target-specific selective refinement (SR) protocol; and (ii) molecular dynamics (MD) simulation based ranking (SMDR) method. In SR the all-atom refinement of structures is accomplished via the Rosetta Relax protocol, subject to specific constraints determined by the size and complexity of the target. The best-refined models are selected with SMDR by testing their relative stability against gradual heating through all-atom MD simulations. Through extensive testing we have found that Mufold-MD, our fully automated protein structure prediction server updated with the SR and SMDR modules consistently outperformed its previous versions. © 2015 Wiley Periodicals, Inc.

  2. Selective refinement and selection of near-native models in protein structure prediction

    PubMed Central

    Zhang, Jiong; Barz, Bagdan; Zhang, Jingfen; Xu, Dong; Kosztin, Ioan

    2015-01-01

    In recent years in silico protein structure prediction reached a level where fully automated servers can generate large pools of near-native structures. However, the identification and further refinement of the best structures from the pool of models remain problematic. To address these issues, we have developed (i) a target-specific selective refinement (SR) protocol; and (ii) molecular dynamics (MD) simulation based ranking (SMDR) method. In SR the all-atom refinement of structures is accomplished via the Rosetta Relax protocol, subject to specific constraints determined by the size and complexity of the target. The best-refined models are selected with SMDR by testing their relative stability against gradual heating through all-atom MD simulations. Through extensive testing we have found that Mufold-MD, our fully automated protein structure prediction server updated with the SR and SMDR modules consistently outperformed its previous versions. PMID:26214389

  3. Alternating evolutionary pressure in a genetic algorithm facilitates protein model selection

    PubMed Central

    Offman, Marc N; Tournier, Alexander L; Bates, Paul A

    2008-01-01

    Background Automatic protein modelling pipelines are becoming ever more accurate; this has come hand in hand with an increasingly complicated interplay between all components involved. Nevertheless, there are still potential improvements to be made in template selection, refinement and protein model selection. Results In the context of an automatic modelling pipeline, we analysed each step separately, revealing several non-intuitive trends and explored a new strategy for protein conformation sampling using Genetic Algorithms (GA). We apply the concept of alternating evolutionary pressure (AEP), i.e. intermediate rounds within the GA runs where unrestrained, linear growth of the model populations is allowed. Conclusion This approach improves the overall performance of the GA by allowing models to overcome local energy barriers. AEP enabled the selection of the best models in 40% of all targets; compared to 25% for a normal GA. PMID:18673557

  4. NEW MDS AND CLUSTERING BASED ALGORITHMS FOR PROTEIN MODEL QUALITY ASSESSMENT AND SELECTION.

    PubMed

    Wang, Qingguo; Shang, Charles; Xu, Dong; Shang, Yi

    2013-10-25

    In protein tertiary structure prediction, assessing the quality of predicted models is an essential task. Over the past years, many methods have been proposed for the protein model quality assessment (QA) and selection problem. Despite significant advances, the discerning power of current methods is still unsatisfactory. In this paper, we propose two new algorithms, CC-Select and MDS-QA, based on multidimensional scaling and k-means clustering. For the model selection problem, CC-Select combines consensus with clustering techniques to select the best models from a given pool. Given a set of predicted models, CC-Select first calculates a consensus score for each structure based on its average pairwise structural similarity to other models. Then, similar structures are grouped into clusters using multidimensional scaling and clustering algorithms. In each cluster, the one with the highest consensus score is selected as a candidate model. For the QA problem, MDS-QA combines single-model scoring functions with consensus to determine more accurate assessment score for every model in a given pool. Using extensive benchmark sets of a large collection of predicted models, we compare the two algorithms with existing state-of-the-art quality assessment methods and show significant improvement.

  5. Extension of the selection of protein chromatography and the rate model to affinity chromatography.

    PubMed

    Sandoval, G; Shene, C; Andrews, B A; Asenjo, J A

    2010-01-01

    The rational selection of optimal protein purification sequences, as well as mathematical models that simulate and allow optimization of chromatographic protein purification processes have been developed for purification procedures such as ion-exchange, hydrophobic interaction and gel filtration chromatography. This paper investigates the extension of such analysis to affinity chromatography both in the selection of chromatographic processes and in the use of the rate model for mathematical modelling and simulation. Two affinity systems were used: Blue Sepharose and Protein A. The extension of the theory developed previously for ion-exchange and HIC chromatography to affinity separations is analyzed in this paper. For the selection of operations two algorithms are used. In the first, the value of η, which corresponds to the efficiency (resolution) of the actual chromatography and, Σ, which determines the amount of a particular contaminant eliminated after each separation step, which determines the purity, have to be determined. It was found that the value of both these parameters is not generic for affinity separations but will depend on the type of affinity system used and will have to be determined on a case by case basis. With Blue Sepharose a salt gradient was used and with Protein A, a pH gradient. Parameters were determined with individual proteins and simulations of the protein mixtures were done. This approach allows investigation of chromatographic protein purification in a holistic manner that includes ion-exchange, HIC, gel filtration and affinity separations for the first time.

  6. Knowledge of Native Protein-Protein Interfaces Is Sufficient To Construct Predictive Models for the Selection of Binding Candidates.

    PubMed

    Popov, Petr; Grudinin, Sergei

    2015-10-26

    Selection of putative binding poses is a challenging part of virtual screening for protein-protein interactions. Predictive models to filter out binding candidates with the highest binding affinities comprise scoring functions that assign a score to each binding pose. Existing scoring functions are typically deduced by collecting statistical information about interfaces of native conformations of protein complexes along with interfaces of a large generated set of non-native conformations. However, the obtained scoring functions become biased toward the method used to generate the non-native conformations, i.e., they may not recognize near-native interfaces generated with a different method. The present study demonstrates that knowledge of only native protein-protein interfaces is sufficient to construct well-discriminative predictive models for the selection of binding candidates. Here we introduce a new scoring method that comprises a knowledge-based potential called KSENIA deduced from structural information about the native interfaces of 844 crystallographic protein-protein complexes. We derive KSENIA using convex optimization with a training set composed of native protein complexes and their near-native conformations obtained using deformations along the low-frequency normal modes. As a result, our knowledge-based potential has only marginal bias toward a method used to generate putative binding poses. Furthermore, KSENIA is smooth by construction, which allows it to be used along with rigid-body optimization to refine the binding poses. Using several test benchmarks, we demonstrate that our method discriminates well native and near-native conformations of protein complexes from non-native ones. Our methodology can be easily adapted to the recognition of other types of molecular interactions, such as protein-ligand, protein-RNA, etc. KSENIA will be made publicly available as a part of the SAMSON software platform at https://team.inria.fr/nano-d/software .

  7. Protein purification using chromatography: selection of type, modelling and optimization of operating conditions.

    PubMed

    Asenjo, J A; Andrews, B A

    2009-01-01

    To achieve a high level of purity in the purification of recombinant proteins for therapeutic or analytical application, it is necessary to use several chromatographic steps. There is a range of techniques available including anion and cation exchange, which can be carried out at different pHs, hydrophobic interaction chromatography, gel filtration and affinity chromatography. In the case of a complex mixture of partially unknown proteins or a clarified cell extract, there are many different routes one can take in order to choose the minimum and most efficient number of purification steps to achieve a desired level of purity (e.g. 98%, 99.5% or 99.9%). This review shows how an initial 'proteomic' characterization of the complex mixture of target protein and protein contaminants can be used to select the most efficient chromatographic separation steps in order to achieve a specific level of purity with a minimum number of steps. The chosen methodology was implemented in a computer- based Expert System. Two algorithms were developed, the first algorithm was used to select the most efficient purification method to separate a protein from its contaminants based on the physicochemical properties of the protein product and the protein contaminants and the second algorithm was used to predict the number and concentration of contaminants after each separation as well as protein product purity. The application of the Expert System approach was experimentally tested and validated with a mixture of four proteins and the experimental validation was also carried out with a supernatant of Bacillus subtilis producing a recombinant beta-1,3-glucanase. Once the type of chromatography is chosen, optimization of the operating conditions is essential. Chromatographic elution curves for a three-protein mixture (alpha-lactoalbumin, ovalbumin and beta-lactoglobulin), carried out under different flow rates and ionic strength conditions, were simulated using two different mathematical

  8. Detecting selection for negative design in proteins through an improved model of the misfolded state.

    PubMed

    Minning, Jonas; Porto, Markus; Bastolla, Ugo

    2013-07-01

    Proteins that need to be structured in their native state must be stable both against the unfolded ensemble and against incorrectly folded (misfolded) conformations with low free energy. Positive design targets the first type of stability by strengthening native interactions. The second type of stability is achieved by destabilizing interactions that occur frequently in the misfolded ensemble, a strategy called negative design. Here, we investigate negative design adopting a statistical mechanical model of the misfolded ensemble, which improves the usual Gaussian approximation by taking into account the third moment of the energy distribution and contact correlations. Applying this model, we detect and quantify selection for negative design in most natural proteins, and we analytically design protein sequences that are stable both against unfolding and against misfolding. Copyright © 2013 Wiley Periodicals, Inc.

  9. Evolutionary model selection and parameter estimation for protein-protein interaction network based on differential evolution algorithm

    PubMed Central

    Huang, Lei; Liao, Li; Wu, Cathy H.

    2016-01-01

    Revealing the underlying evolutionary mechanism plays an important role in understanding protein interaction networks in the cell. While many evolutionary models have been proposed, the problem about applying these models to real network data, especially for differentiating which model can better describe evolutionary process for the observed network urgently remains as a challenge. The traditional way is to use a model with presumed parameters to generate a network, and then evaluate the fitness by summary statistics, which however cannot capture the complete network structures information and estimate parameter distribution. In this work we developed a novel method based on Approximate Bayesian Computation and modified Differential Evolution (ABC-DEP) that is capable of conducting model selection and parameter estimation simultaneously and detecting the underlying evolutionary mechanisms more accurately. We tested our method for its power in differentiating models and estimating parameters on the simulated data and found significant improvement in performance benchmark, as compared with a previous method. We further applied our method to real data of protein interaction networks in human and yeast. Our results show Duplication Attachment model as the predominant evolutionary mechanism for human PPI networks and Scale-Free model as the predominant mechanism for yeast PPI networks. PMID:26357273

  10. On morphological selection rule of noisy character applied to model (dis)orderly protein formations

    NASA Astrophysics Data System (ADS)

    Siódmiak, Jacek; Santamaría-Holek, Ivan; Gadomski, Adam

    2010-05-01

    We propose that the main mechanism controlling the selection rule of model (dis)orderly protein formations, such as non-Kossel crystal growth and aggregation of lysozyme from aqueous solution, is an ion-channeling filter having flicker-noise properties. This filter is originated at the interfaces between growing solidlike object and its external liquid-type phase, and it can be considered as a series of voltage gated ion subchannels. The dynamics of each channel is studied by using both simulation and analytic argumentation lines, and represents a novel thought on how to utilize the presence of constructive-noise sources in protein formation, a field of utmost experimental and technological interest.

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

    PubMed

    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.

  12. Detecting Selection on Protein Stability through Statistical Mechanical Models of Folding and Evolution

    PubMed Central

    Bastolla, Ugo

    2014-01-01

    The properties of biomolecules depend both on physics and on the evolutionary process that formed them. These two points of view produce a powerful synergism. Physics sets the stage and the constraints that molecular evolution has to obey, and evolutionary theory helps in rationalizing the physical properties of biomolecules, including protein folding thermodynamics. To complete the parallelism, protein thermodynamics is founded on the statistical mechanics in the space of protein structures, and molecular evolution can be viewed as statistical mechanics in the space of protein sequences. In this review, we will integrate both points of view, applying them to detecting selection on the stability of the folded state of proteins. We will start discussing positive design, which strengthens the stability of the folded against the unfolded state of proteins. Positive design justifies why statistical potentials for protein folding can be obtained from the frequencies of structural motifs. Stability against unfolding is easier to achieve for longer proteins. On the contrary, negative design, which consists in destabilizing frequently formed misfolded conformations, is more difficult to achieve for longer proteins. The folding rate can be enhanced by strengthening short-range native interactions, but this requirement contrasts with negative design, and evolution has to trade-off between them. Finally, selection can accelerate functional movements by favoring low frequency normal modes of the dynamics of the native state that strongly correlate with the functional conformation change. PMID:24970217

  13. Selective activator protein-1 inhibitor T-5224 prevents lymph node metastasis in an oral cancer model.

    PubMed

    Kamide, Daisuke; Yamashita, Taku; Araki, Koji; Tomifuji, Masayuki; Tanaka, Yuya; Tanaka, Shingo; Shiozawa, Shunichi; Shiotani, Akihiro

    2016-05-01

    Activator protein-1 (AP-1) is a transcriptional factor that regulates the expression of various genes associated with tumor invasion and migration. The purpose of our study was to assess the therapeutic effects of a novel selective AP-1 inhibitor, T-5224, in preventing lymph node metastasis in head and neck squamous cell carcinoma (HNSCC) in an orthotopic mouse model. We assessed the effect of T-5224 on HNSCC cell invasion, migration, proliferation, and MMP activity by carrying out an in vitro study using an invasion assay, scratch assay, WST-8 assay, and gelatin zymography. We also observed morphological changes in HNSCC cells by time-lapse microscopy. Furthermore, cervical lymph node metastasis was assessed using an orthotopic tumor model of human oral squamous cell carcinoma cells (HSC-3-M3) injected in the tongue of a BALB/c nude mouse. T-5224 (150 mg/kg) or vehicle was given orally every day for 4 weeks. Animals were killed and assessed for lymph node metastasis by H&E staining of resected lymph nodes. T-5224 significantly inhibited the invasion, migration, and MMP activity of HNSCC cells in a dose-dependent manner; there was no significant influence on cell proliferation. The antimetastatic effect of T-5224 was also confirmed in our animal study. The rate of cervical lymph node metastasis in the model was 40.0% in the T-5224-treated group (n = 30) versus 74.1% in the vehicle-treated group (n = 27; P < 0.05). In conclusion, T-5224 inhibited the invasion and migration of HNSCC cells in vitro, and prevented lymph node metastasis in head and neck cancer in an animal model. © 2016 The Authors. Cancer Science published by John Wiley & Sons Australia, Ltd on behalf of Japanese Cancer Association.

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

    NASA Astrophysics Data System (ADS)

    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.

  15. Optimized parameter selection reveals trends in Markov state models for protein folding

    NASA Astrophysics Data System (ADS)

    Husic, Brooke E.; McGibbon, Robert T.; Sultan, Mohammad M.; Pande, Vijay S.

    2016-11-01

    As molecular dynamics simulations access increasingly longer time scales, complementary advances in the analysis of biomolecular time-series data are necessary. Markov state models offer a powerful framework for this analysis by describing a system's states and the transitions between them. A recently established variational theorem for Markov state models now enables modelers to systematically determine the best way to describe a system's dynamics. In the context of the variational theorem, we analyze ultra-long folding simulations for a canonical set of twelve proteins [K. Lindorff-Larsen et al., Science 334, 517 (2011)] by creating and evaluating many types of Markov state models. We present a set of guidelines for constructing Markov state models of protein folding; namely, we recommend the use of cross-validation and a kinetically motivated dimensionality reduction step for improved descriptions of folding dynamics. We also warn that precise kinetics predictions rely on the features chosen to describe the system and pose the description of kinetic uncertainty across ensembles of models as an open issue.

  16. LiCABEDS II. Modeling of Ligand Selectivity for G-protein Coupled Cannabinoid Receptors

    PubMed Central

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

    2014-01-01

    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 at 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. PMID:23278450

  17. Intelligent computational model for classification of sub-Golgi protein using oversampling and fisher feature selection methods.

    PubMed

    Ahmad, Jamal; Javed, Faisal; Hayat, Maqsood

    2017-05-01

    Golgi is one of the core proteins of a cell, constitutes in both plants and animals, which is involved in protein synthesis. Golgi is responsible for receiving and processing the macromolecules and trafficking of newly processed protein to its intended destination. Dysfunction in Golgi protein is expected to cause many neurodegenerative and inherited diseases that may be cured well if they are detected effectively and timely. Golgi protein is categorized into two parts cis-Golgi and trans-Golgi. The identification of Golgi protein via direct method is very hard due to limited available recognized structures. Therefore, the researchers divert their attention toward the sequences from structures. However, owing to technological advancement, exploration of huge amount of sequences was reported in the databases. So recognition of large amount of unprocessed data using conventional methods is very difficult. Therefore, the concept of intelligence was incorporated with computational model. Intelligence based computational model obtained reasonable results, but the gap of improvement is still under consideration. In this regard, an intelligent automatic recognition model is developed in order to enhance the true classification rate of sub-Golgi proteins. In this approach, discrete and evolutionary feature extraction methods are applied on the benchmark Golgi protein datasets to excerpt salient, propound and variant numerical descriptors. After that, an oversampling technique Syntactic Minority over Sampling Technique is employed to balance the data. Hybrid spaces are also generated with combination of these feature spaces. Further, Fisher feature selection method is utilized to reduce the extra noisy and redundant features from feature vector. Finally, k-nearest neighbor algorithm is used as learning hypothesis. Three distinct cross validation tests are used to examine the stability and efficiency of the proposed model. The predicted outcomes of proposed model are better

  18. Selective Inhibitors of Protein Methyltransferases

    PubMed Central

    2015-01-01

    Mounting evidence suggests that protein methyltransferases (PMTs), which catalyze methylation of histone and nonhistone proteins, play a crucial role in diverse biological processes and human diseases. In particular, PMTs have been recognized as major players in regulating gene expression and chromatin state. PMTs are divided into two categories: protein lysine methyltransferases (PKMTs) and protein arginine methyltransferases (PRMTs). There has been a steadily growing interest in these enzymes as potential therapeutic targets and therefore discovery of PMT inhibitors has also been pursued increasingly over the past decade. Here, we present a perspective on selective, small-molecule inhibitors of PMTs with an emphasis on their discovery, characterization, and applicability as chemical tools for deciphering the target PMTs’ physiological functions and involvement in human diseases. We highlight the current state of PMT inhibitors and discuss future directions and opportunities for PMT inhibitor discovery. PMID:25406853

  19. Directional Darwinian Selection in proteins

    PubMed Central

    2013-01-01

    Background 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. Methods 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. Results 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. Conclusions 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. PMID:24267049

  20. Model Selection for Geostatistical Models

    SciTech Connect

    Hoeting, Jennifer A.; Davis, Richard A.; Merton, Andrew A.; Thompson, Sandra E.

    2006-02-01

    We consider the problem of model selection for geospatial data. Spatial correlation is typically ignored in the selection of explanatory variables and this can influence model selection results. For example, the inclusion or exclusion of particular explanatory variables may not be apparent when spatial correlation is ignored. To address this problem, we consider the Akaike Information Criterion (AIC) as applied to a geostatistical model. We offer a heuristic derivation of the AIC in this context and provide simulation results that show that using AIC for a geostatistical model is superior to the often used approach of ignoring spatial correlation in the selection of explanatory variables. These ideas are further demonstrated via a model for lizard abundance. We also employ the principle of minimum description length (MDL) to variable selection for the geostatistical model. The effect of sampling design on the selection of explanatory covariates is also explored.

  1. Understanding protein evolution: from protein physics to Darwinian selection.

    PubMed

    Zeldovich, Konstantin B; Shakhnovich, Eugene I

    2008-01-01

    Efforts in whole-genome sequencing and structural proteomics start to provide a global view of the protein universe, the set of existing protein structures and sequences. However, approaches based on the selection of individual sequences have not been entirely successful at the quantitative description of the distribution of structures and sequences in the protein universe because evolutionary pressure acts on the entire organism, rather than on a particular molecule. In parallel to this line of study, studies in population genetics and phenomenological molecular evolution established a mathematical framework to describe the changes in genome sequences in populations of organisms over time. Here, we review both microscopic (physics-based) and macroscopic (organism-level) models of protein-sequence evolution and demonstrate that bridging the two scales provides the most complete description of the protein universe starting from clearly defined, testable, and physiologically relevant assumptions.

  2. Model selection for geostatistical models.

    PubMed

    Hoeting, Jennifer A; Davis, Richard A; Merton, Andrew A; Thompson, Sandra E

    2006-02-01

    We consider the problem of model selection for geospatial data. Spatial correlation is often ignored in the selection of explanatory variables, and this can influence model selection results. For example, the importance of particular explanatory variables may not be apparent when spatial correlation is ignored. To address this problem, we consider the Akaike Information Criterion (AIC) as applied to a geostatistical model. We offer a heuristic derivation of the AIC in this context and provide simulation results that show that using AIC for a geostatistical model is superior to the often-used traditional approach of ignoring spatial correlation in the selection of explanatory variables. These ideas are further demonstrated via a model for lizard abundance. We also apply the principle of minimum description length (MDL) to variable selection for the geostatistical model. The effect of sampling design on the selection of explanatory covariates is also explored. R software to implement the geostatistical model selection methods described in this paper is available in the Supplement.

  3. Integrating Pharmacophore into Membrane Molecular Dynamics Simulations to Improve Homology Modeling of G Protein-coupled Receptors with Ligand Selectivity: A2A Adenosine Receptor as an Example.

    PubMed

    Zeng, Lingxiao; Guan, Mengxin; Jin, Hongwei; Liu, Zhenming; Zhang, Liangren

    2015-12-01

    Homology modeling has been applied to fill in the gap in experimental G protein-coupled receptors structure determination. However, achievement of G protein-coupled receptors homology models with ligand selectivity remains challenging due to structural diversity of G protein-coupled receptors. In this work, we propose a novel strategy by integrating pharmacophore and membrane molecular dynamics (MD) simulations to improve homology modeling of G protein-coupled receptors with ligand selectivity. To validate this integrated strategy, the A2A adenosine receptor (A2A AR), whose structures in both active and inactive states have been established, has been chosen as an example. We performed blind predictions of the active-state A2A AR structure based on the inactive-state structure and compared the performance of different refinement strategies. The blind prediction model combined with the integrated strategy identified ligand-receptor interactions and conformational changes of key structural elements related to the activation of A2 A AR, including (i) the movements of intracellular ends of TM3 and TM5/TM6; (ii) the opening of ionic lock; (iii) the movements of binding site residues. The integrated strategy of pharmacophore with molecular dynamics simulations can aid in the optimization in the identification of side chain conformations in receptor models. This strategy can be further investigated in homology modeling and expand its applicability to other G protein-coupled receptor modeling, which should aid in the discovery of more effective and selective G protein-coupled receptor ligands. © 2015 John Wiley & Sons A/S.

  4. Quantitative Rheological Model Selection

    NASA Astrophysics Data System (ADS)

    Freund, Jonathan; Ewoldt, Randy

    2014-11-01

    The more parameters in a rheological the better it will reproduce available data, though this does not mean that it is necessarily a better justified model. Good fits are only part of model selection. We employ a Bayesian inference approach that quantifies model suitability by balancing closeness to data against both the number of model parameters and their a priori uncertainty. The penalty depends upon prior-to-calibration expectation of the viable range of values that model parameters might take, which we discuss as an essential aspect of the selection criterion. Models that are physically grounded are usually accompanied by tighter physical constraints on their respective parameters. The analysis reflects a basic principle: models grounded in physics can be expected to enjoy greater generality and perform better away from where they are calibrated. In contrast, purely empirical models can provide comparable fits, but the model selection framework penalizes their a priori uncertainty. We demonstrate the approach by selecting the best-justified number of modes in a Multi-mode Maxwell description of PVA-Borax. We also quantify relative merits of the Maxwell model relative to powerlaw fits and purely empirical fits for PVA-Borax, a viscoelastic liquid, and gluten.

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

    PubMed

    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

  6. Succination is Increased on Select Proteins in the Brainstem of the NADH dehydrogenase (ubiquinone) Fe-S protein 4 (Ndufs4) Knockout Mouse, a Model of Leigh Syndrome*

    PubMed Central

    Piroli, Gerardo G.; Manuel, Allison M.; Clapper, Anna C.; Walla, Michael D.; Baatz, John E.; Palmiter, Richard D.; Quintana, Albert; Frizzell, Norma

    2016-01-01

    Elevated fumarate concentrations as a result of Krebs cycle inhibition lead to increases in protein succination, an irreversible post-translational modification that occurs when fumarate reacts with cysteine residues to generate S-(2-succino)cysteine (2SC). Metabolic events that reduce NADH re-oxidation can block Krebs cycle activity; therefore we hypothesized that oxidative phosphorylation deficiencies, such as those observed in some mitochondrial diseases, would also lead to increased protein succination. Using the Ndufs4 knockout (Ndufs4 KO) mouse, a model of Leigh syndrome, we demonstrate for the first time that protein succination is increased in the brainstem (BS), particularly in the vestibular nucleus. Importantly, the brainstem is the most affected region exhibiting neurodegeneration and astrocyte and microglial proliferation, and these mice typically die of respiratory failure attributed to vestibular nucleus pathology. In contrast, no increases in protein succination were observed in the skeletal muscle, corresponding with the lack of muscle pathology observed in this model. 2D SDS-PAGE followed by immunoblotting for succinated proteins and MS/MS analysis of BS proteins allowed us to identify the voltage-dependent anion channels 1 and 2 as specific targets of succination in the Ndufs4 knockout. Using targeted mass spectrometry, Cys77 and Cys48 were identified as endogenous sites of succination in voltage-dependent anion channels 2. Given the important role of voltage-dependent anion channels isoforms in the exchange of ADP/ATP between the cytosol and the mitochondria, and the already decreased capacity for ATP synthesis in the Ndufs4 KO mice, we propose that the increased protein succination observed in the BS of these animals would further decrease the already compromised mitochondrial function. These data suggest that fumarate is a novel biochemical link that may contribute to the progression of the neuropathology in this mitochondrial disease model

  7. Selection of mutations for increased protein stability.

    PubMed

    van den Burg, Bertus; Eijsink, Vincent G H

    2002-08-01

    There are many ways to select mutations that increase the stability of proteins, including rational design, functional screening of randomly generated mutant libraries, and comparison of naturally occurring homologous proteins. The protein engineer's toolbox is expanding and the number of successful examples of engineered protein stability is increasing. Still, the selection of thermostable mutations is not a standard process. Selection is complicated by lack of knowledge of the process that leads to thermal inactivation and by the fact that proteins employ a large variety of structural tricks to achieve stability.

  8. Mass Spectrometric-Based Selected Reaction Monitoring of Protein Phosphorylation during Symbiotic Signaling in the Model Legume, Medicago truncatula

    PubMed Central

    Maeda, Junko; Barrett-Wilt, Gregory A.; Sussman, Michael R.

    2016-01-01

    Unlike the major cereal crops corn, rice, and wheat, leguminous plants such as soybean and alfalfa can meet their nitrogen requirement via endosymbiotic associations with soil bacteria. The establishment of this symbiosis is a complex process playing out over several weeks and is facilitated by the exchange of chemical signals between these partners from different kingdoms. Several plant components that are involved in this signaling pathway have been identified, but there is still a great deal of uncertainty regarding the early events in symbiotic signaling, i.e., within the first minutes and hours after the rhizobial signals (Nod factors) are perceived at the plant plasma membrane. The presence of several protein kinases in this pathway suggests a mechanism of signal transduction via posttranslational modification of proteins in which phosphate is added to the hydroxyl groups of serine, threonine and tyrosine amino acid side chains. To monitor the phosphorylation dynamics and complement our previous untargeted 'discovery' approach, we report here the results of experiments using a targeted mass spectrometric technique, Selected Reaction Monitoring (SRM) that enables the quantification of phosphorylation targets with great sensitivity and precision. Using this approach, we confirm a rapid change in the level of phosphorylation in 4 phosphosites of at least 4 plant phosphoproteins that have not been previously characterized. This detailed analysis reveals aspects of the symbiotic signaling mechanism in legumes that, in the long term, will inform efforts to engineer this nitrogen-fixing symbiosis in important non-legume crops such as rice, wheat and corn. PMID:27203723

  9. On the ion selectivity in Ca-binding proteins: the cyclo(-L-Pro-Gly-)3 peptide as a model.

    PubMed Central

    Sussman, F; Weinstein, H

    1989-01-01

    Calcium plays a crucial role in many cellular processes. Its functions are directly dependent on the high specificity for Ca2+ exhibited by the proteins and ion carriers that bind divalent ions. To elucidate the basis for this specificity we have calculated the relative energies of solvation of calcium and magnesium ions in complexes with cyclo(-L-Pro-Gly-)3, a small synthetic peptide that binds Ca2+ with an affinity comparable to those of the naturally occurring proteins. The results show that the ion selectivity of the peptide resides in the difference in the solvation energies of the competing ions in water. Although the peptide is able to complex Mg2+ better than Ca2+ in the stoichiometries in which cyclo(-L-Pro-Gly-)3 binds divalent ions, it is not always able to provide as much stabilization for Mg2+ as water does. These results also explain why cyclo(-L-Pro-Gly-)3 binds Ca2+ and Mg2+ with different stoichiometries and indicate the source for expected differences in the structures of complexes of the two ions. Images PMID:2813364

  10. Modeling complexes of modeled proteins.

    PubMed

    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.

  11. Protein solubility modeling

    NASA Technical Reports Server (NTRS)

    Agena, S. M.; Pusey, M. L.; Bogle, I. D.

    1999-01-01

    A thermodynamic framework (UNIQUAC model with temperature dependent parameters) is applied to model the salt-induced protein crystallization equilibrium, i.e., protein solubility. The framework introduces a term for the solubility product describing protein transfer between the liquid and solid phase and a term for the solution behavior describing deviation from ideal solution. Protein solubility is modeled as a function of salt concentration and temperature for a four-component system consisting of a protein, pseudo solvent (water and buffer), cation, and anion (salt). Two different systems, lysozyme with sodium chloride and concanavalin A with ammonium sulfate, are investigated. Comparison of the modeled and experimental protein solubility data results in an average root mean square deviation of 5.8%, demonstrating that the model closely follows the experimental behavior. Model calculations and model parameters are reviewed to examine the model and protein crystallization process. Copyright 1999 John Wiley & Sons, Inc.

  12. Limited Phenotypic Effects of Selectively Augmenting the SMN Protein in the Neurons of a Mouse Model of Severe Spinal Muscular Atrophy

    PubMed Central

    Lee, Andrew J-H.; Monani, Umrao R.

    2012-01-01

    The selective vulnerability of motor neurons to paucity of Survival Motor Neuron (SMN) protein is a defining feature of human spinal muscular atrophy (SMA) and indicative of a unique requirement for adequate levels of the protein in these cells. However, the relative contribution of SMN-depleted motor neurons to the disease process is uncertain and it is possible that their characteristic loss and the overall SMA phenotype is a consequence of low protein in multiple cell types including neighboring spinal neurons and non-neuronal tissue. To explore the tissue-specific requirements for SMN and, especially, the salutary effects of restoring normal levels of the protein to neuronal tissue of affected individuals, we have selectively expressed the protein in neurons of mice that model severe SMA. Expressing SMN pan-neuronally in mutant mice mitigated specific aspects of the disease phenotype. Motor performance of the mice improved and the loss of spinal motor neurons that characterizes the disease was arrested. Proprioceptive synapses on the motor neurons were restored and defects of the neuromuscular junctions mitigated. The improvements at the cellular level were reflected in a four-fold increase in survival. Nevertheless, mutants expressing neuronal SMN did not live beyond three weeks of birth, a relatively poor outcome compared to the effects of ubiquitously restoring SMN. This suggests that although neurons and, in particular, spinal motor neurons constitute critical cellular sites of action of the SMN protein, a truly effective treatment of severe SMA will require restoring the protein to multiple cell types including non-neuronal tissue. PMID:23029491

  13. Modeling Protein Expression and Protein Signaling Pathways

    PubMed Central

    Telesca, Donatello; Müller, Peter; Kornblau, Steven M.; Suchard, Marc A.; Ji, Yuan

    2015-01-01

    High-throughput functional proteomic technologies provide a way to quantify the expression of proteins of interest. Statistical inference centers on identifying the activation state of proteins and their patterns of molecular interaction formalized as dependence structure. Inference on dependence structure is particularly important when proteins are selected because they are part of a common molecular pathway. In that case, inference on dependence structure reveals properties of the underlying pathway. We propose a probability model that represents molecular interactions at the level of hidden binary latent variables that can be interpreted as indicators for active versus inactive states of the proteins. The proposed approach exploits available expert knowledge about the target pathway to define an informative prior on the hidden conditional dependence structure. An important feature of this prior is that it provides an instrument to explicitly anchor the model space to a set of interactions of interest, favoring a local search approach to model determination. We apply our model to reverse-phase protein array data from a study on acute myeloid leukemia. Our inference identifies relevant subpathways in relation to the unfolding of the biological process under study. PMID:26246646

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

    PubMed Central

    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. PMID:26181723

  15. Young proteins experience more variable selection pressures than old proteins

    PubMed Central

    Vishnoi, Anchal; Kryazhimskiy, Sergey; Bazykin, Georgii A.; Hannenhalli, Sridhar; Plotkin, Joshua B.

    2010-01-01

    It is well known that young proteins tend to experience weaker purifying selection and evolve more quickly than old proteins. Here, we show that, in addition, young proteins tend to experience more variable selection pressures over time than old proteins. We demonstrate this pattern in three independent taxonomic groups: yeast, Drosophila, and mammals. The increased variability of selection pressures on young proteins is highly significant even after controlling for the fact that young proteins are typically shorter and experience weaker purifying selection than old proteins. The majority of our results are consistent with the hypothesis that the function of a young gene tends to change over time more readily than that of an old gene. At the same time, our results may be caused in part by young genes that serve constant functions over time, but nevertheless appear to evolve under changing selection pressures due to depletion of adaptive mutations. In either case, our results imply that the evolution of a protein-coding sequence is partly determined by its age and origin, and not only by the phenotypic properties of the encoded protein. We discuss, via specific examples, the consequences of these findings for understanding of the sources of evolutionary novelty. PMID:20921233

  16. Chemical site-selective prenylation of proteins.

    PubMed

    Gamblin, David P; van Kasteren, Sander; Bernardes, Gonçalo J L; Chalker, Justin M; Oldham, Neil J; Fairbanks, Antony J; Davis, Benjamin G

    2008-06-01

    A direct thionation procedure allows conversion of allylic alcohols into the corresponding thiols, the products of which are immediately compatible with one-pot site-selective selenenyl sulfide mediated protein conjugation.

  17. Computer Models of Proteins

    NASA Technical Reports Server (NTRS)

    2000-01-01

    Dr. Marc Pusey (seated) and Dr. Craig Kundrot use computers to analyze x-ray maps and generate three-dimensional models of protein structures. With this information, scientists at Marshall Space Flight Center can learn how proteins are made and how they work. The computer screen depicts a proten structure as a ball-and-stick model. Other models depict the actual volume occupied by the atoms, or the ribbon-like structures that are crucial to a protein's function.

  18. Viral cystatin evolution and three-dimensional structure modelling: A case of directional selection acting on a viral protein involved in a host-parasitoid interaction

    PubMed Central

    Serbielle, Céline; Chowdhury, Shafinaz; Pichon, Samuel; Dupas, Stéphane; Lesobre, Jérôme; Purisima, Enrico O; Drezen, Jean-Michel; Huguet, Elisabeth

    2008-01-01

    Background In pathogens, certain genes encoding proteins that directly interact with host defences coevolve with their host and are subject to positive selection. In the lepidopteran host-wasp parasitoid system, one of the most original strategies developed by the wasps to defeat host defences is the injection of a symbiotic polydnavirus at the same time as the wasp eggs. The virus is essential for wasp parasitism success since viral gene expression alters the immune system and development of the host. As a wasp mutualist symbiont, the virus is expected to exhibit a reduction in genome complexity and evolve under wasp phyletic constraints. However, as a lepidopteran host pathogenic symbiont, the virus is likely undergoing strong selective pressures for the acquisition of new functions by gene acquisition or duplication. To understand the constraints imposed by this particular system on virus evolution, we studied a polydnavirus gene family encoding cyteine protease inhibitors of the cystatin superfamily. Results We show that cystatins are the first bracovirus genes proven to be subject to strong positive selection within a host-parasitoid system. A generated three-dimensional model of Cotesia congregata bracovirus cystatin 1 provides a powerful framework to position positively selected residues and reveal that they are concentrated in the vicinity of actives sites which interact with cysteine proteases directly. In addition, phylogenetic analyses reveal two different cystatin forms which evolved under different selective constraints and are characterized by independent adaptive duplication events. Conclusion Positive selection acts to maintain cystatin gene duplications and induces directional divergence presumably to ensure the presence of efficient and adapted cystatin forms. Directional selection has acted on key cystatin active sites, suggesting that cystatins coevolve with their host target. We can strongly suggest that cystatins constitute major virulence

  19. Charged ultrafiltration membranes increase the selectivity of whey protein separations.

    PubMed

    Bhushan, S; Etzel, M R

    2009-04-01

    Ultrafiltration is widely used to concentrate proteins, but fractionation of one protein from another is much less common. This study examined the use of positively charged membranes to increase the selectivity of ultrafiltration and allow the fractionation of proteins from cheese whey. By adding a positive charge to ultrafiltration membranes, and adjusting the solution pH, it was possible to permeate proteins having little or no charge, such as glycomacropeptide, and retain proteins having a positive charge. Placing a charge on the membrane increased the selectivity by over 600% compared to using an uncharged membrane. The data were fit using the stagnant film model that relates the observed sieving coefficient to membrane parameters such as the flux, mass transfer coefficient, and membrane Peclet number. The model was a useful tool for data analysis and for the scale up of membrane separations for whey protein fractionation.

  20. Individual Influence on Model Selection

    ERIC Educational Resources Information Center

    Sterba, Sonya K.; Pek, Jolynn

    2012-01-01

    Researchers in psychology are increasingly using model selection strategies to decide among competing models, rather than evaluating the fit of a given model in isolation. However, such interest in model selection outpaces an awareness that one or a few cases can have disproportionate impact on the model ranking. Though case influence on the fit…

  1. Rhesus macaque and mouse models for down-selecting circumsporozoite protein based malaria vaccines differ significantly in immunogenicity and functional outcomes.

    PubMed

    Phares, Timothy W; May, Anthony D; Genito, Christopher J; Hoyt, Nathan A; Khan, Farhat A; Porter, Michael D; DeBot, Margot; Waters, Norman C; Saudan, Philippe; Dutta, Sheetij

    2017-03-13

    Non-human primates, such as the rhesus macaques, are the preferred model for down-selecting human malaria vaccine formulations, but the rhesus model is expensive and does not allow for direct efficacy testing of human malaria vaccines. Transgenic rodent parasites expressing genes of human Plasmodium are now routinely used for efficacy studies of human malaria vaccines. Mice have however rarely predicted success in human malaria trials and there is scepticism whether mouse studies alone are sufficient to move a vaccine candidate into the clinic. A comparison of immunogenicity, fine-specificity and functional activity of two Alum-adjuvanted Plasmodium falciparum circumsporozoite protein (CSP)-based vaccines was conducted in mouse and rhesus models. One vaccine was a soluble recombinant protein (CSP) and the other was the same CSP covalently conjugated to the Qβ phage particle (Qβ-CSP). Mice showed different kinetics of antibody responses and different sensitivity to the NANP-repeat and N-terminal epitopes as compared to rhesus. While mice failed to discern differences between the protective efficacy of CSP versus Qβ-CSP vaccine following direct challenge with transgenic Plasmodium berghei parasites, rhesus serum from the Qβ-CSP-vaccinated animals induced higher in vivo sporozoite neutralization activity. Despite some immunologic parallels between models, these data demonstrate that differences between the immune responses induced in the two models risk conflicting decisions regarding potential vaccine utility in humans. In combination with historical observations, the data presented here suggest that although murine models may be useful for some purposes, non-human primate models may be more likely to predict the human response to investigational vaccines.

  2. TSTMP: target selection for structural genomics of human transmembrane proteins.

    PubMed

    Varga, Julia; Dobson, László; Reményi, István; Tusnády, Gábor E

    2017-01-04

    The TSTMP database is designed to help the target selection of human transmembrane proteins for structural genomics projects and structure modeling studies. Currently, there are only 60 known 3D structures among the polytopic human transmembrane proteins and about a further 600 could be modeled using existing structures. Although there are a great number of human transmembrane protein structures left to be determined, surprisingly only a small fraction of these proteins have 'selected' (or above) status according to the current version the TargetDB/TargetTrack database. This figure is even worse regarding those transmembrane proteins that would contribute the most to the structural coverage of the human transmembrane proteome. The database was built by sorting out proteins from the human transmembrane proteome with known structure and searching for suitable model structures for the remaining proteins by combining the results of a state-of-the-art transmembrane specific fold recognition algorithm and a sequence similarity search algorithm. Proteins were searched for homologues among the human transmembrane proteins in order to select targets whose successful structure determination would lead to the best structural coverage of the human transmembrane proteome. The pipeline constructed for creating the TSTMP database guarantees to keep the database up-to-date. The database is available at http://tstmp.enzim.ttk.mta.hu. © The Author(s) 2016. Published by Oxford University Press on behalf of Nucleic Acids Research.

  3. Protein Model Database

    SciTech Connect

    Fidelis, K; Adzhubej, A; Kryshtafovych, A; Daniluk, P

    2005-02-23

    The phenomenal success of the genome sequencing projects reveals the power of completeness in revolutionizing biological science. Currently it is possible to sequence entire organisms at a time, allowing for a systemic rather than fractional view of their organization and the various genome-encoded functions. There is an international plan to move towards a similar goal in the area of protein structure. This will not be achieved by experiment alone, but rather by a combination of efforts in crystallography, NMR spectroscopy, and computational modeling. Only a small fraction of structures are expected to be identified experimentally, the remainder to be modeled. Presently there is no organized infrastructure to critically evaluate and present these data to the biological community. The goal of the Protein Model Database project is to create such infrastructure, including (1) public database of theoretically derived protein structures; (2) reliable annotation of protein model quality, (3) novel structure analysis tools, and (4) access to the highest quality modeling techniques available.

  4. Targeting Human Central Nervous System Protein Kinases: An Isoform Selective p38αMAPK Inhibitor That Attenuates Disease Progression in Alzheimer’s Disease Mouse Models

    PubMed Central

    2015-01-01

    The first kinase inhibitor drug approval in 2001 initiated a remarkable decade of tyrosine kinase inhibitor drugs for oncology indications, but a void exists for serine/threonine protein kinase inhibitor drugs and central nervous system indications. Stress kinases are of special interest in neurological and neuropsychiatric disorders due to their involvement in synaptic dysfunction and complex disease susceptibility. Clinical and preclinical evidence implicates the stress related kinase p38αMAPK as a potential neurotherapeutic target, but isoform selective p38αMAPK inhibitor candidates are lacking and the mixed kinase inhibitor drugs that are promising in peripheral tissue disease indications have limitations for neurologic indications. Therefore, pursuit of the neurotherapeutic hypothesis requires kinase isoform selective inhibitors with appropriate neuropharmacology features. Synaptic dysfunction disorders offer a potential for enhanced pharmacological efficacy due to stress-induced activation of p38αMAPK in both neurons and glia, the interacting cellular components of the synaptic pathophysiological axis, to be modulated. We report a novel isoform selective p38αMAPK inhibitor, MW01-18-150SRM (=MW150), that is efficacious in suppression of hippocampal-dependent associative and spatial memory deficits in two distinct synaptic dysfunction mouse models. A synthetic scheme for biocompatible product and positive outcomes from pharmacological screens are presented. The high-resolution crystallographic structure of the p38αMAPK/MW150 complex documents active site binding, reveals a potential low energy conformation of the bound inhibitor, and suggests a structural explanation for MW150’s exquisite target selectivity. As far as we are aware, MW150 is without precedent as an isoform selective p38MAPK inhibitor or as a kinase inhibitor capable of modulating in vivo stress related behavior. PMID:25676389

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

    PubMed Central

    2017-01-01

    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. PMID:28355874

  6. Cytosolic selection systems to study protein stability.

    PubMed

    Malik, Ajamaluddin; Mueller-Schickert, Antje; Bardwell, James C A

    2014-12-01

    Here we describe biosensors that provide readouts for protein stability in the cytosolic compartment of prokaryotes. These biosensors consist of tripartite sandwich fusions that link the in vitro stability or aggregation susceptibility of guest proteins to the in vivo resistance of host cells to the antibiotics kanamycin, spectinomycin, and nourseothricin. These selectable markers confer antibiotic resistance in a wide range of hosts and are easily quantifiable. We show that mutations within guest proteins that affect their stability alter the antibiotic resistances of the cells expressing the biosensors in a manner that is related to the in vitro stabilities of the mutant guest proteins. In addition, we find that polyglutamine tracts of increasing length are associated with an increased tendency to form amyloids in vivo and, in our sandwich fusion system, with decreased resistance to aminoglycoside antibiotics. We demonstrate that our approach allows the in vivo analysis of protein stability in the cytosolic compartment without the need for prior structural and functional knowledge.

  7. 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

    PubMed Central

    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

  8. TSTMP: target selection for structural genomics of human transmembrane proteins

    PubMed Central

    Varga, Julia; Dobson, László; Reményi, István; Tusnády, Gábor E.

    2017-01-01

    The TSTMP database is designed to help the target selection of human transmembrane proteins for structural genomics projects and structure modeling studies. Currently, there are only 60 known 3D structures among the polytopic human transmembrane proteins and about a further 600 could be modeled using existing structures. Although there are a great number of human transmembrane protein structures left to be determined, surprisingly only a small fraction of these proteins have ‘selected’ (or above) status according to the current version the TargetDB/TargetTrack database. This figure is even worse regarding those transmembrane proteins that would contribute the most to the structural coverage of the human transmembrane proteome. The database was built by sorting out proteins from the human transmembrane proteome with known structure and searching for suitable model structures for the remaining proteins by combining the results of a state-of-the-art transmembrane specific fold recognition algorithm and a sequence similarity search algorithm. Proteins were searched for homologues among the human transmembrane proteins in order to select targets whose successful structure determination would lead to the best structural coverage of the human transmembrane proteome. The pipeline constructed for creating the TSTMP database guarantees to keep the database up-to-date. The database is available at http://tstmp.enzim.ttk.mta.hu. PMID:27924015

  9. Protein pharmacophore selection using hydration-site analysis

    PubMed Central

    Hu, Bingjie; Lill, Markus A.

    2012-01-01

    Virtual screening using pharmacophore models is an efficient method to identify potential lead compounds for target proteins. Pharmacophore models based on protein structures are advantageous because a priori knowledge of active ligands is not required and the models are not biased by the chemical space of previously identified actives. However, in order to capture most potential interactions between all potentially binding ligands and the protein, the size of the pharmacophore model, i.e. number of pharmacophore elements, is typically quite large and therefore reduces the efficiency of pharmacophore based screening. We have developed a new method to select important pharmacophore elements using hydration-site information. The basic premise is that ligand functional groups that replace water molecules in the apo protein contribute strongly to the overall binding affinity of the ligand, due to the additional free energy gained from releasing the water molecule into the bulk solvent. We computed the free energy of water released from the binding site for each hydration site using thermodynamic analysis of molecular dynamics (MD) simulations. Pharmacophores which are co-localized with hydration sites with estimated favorable contributions to the free energy of binding are selected to generate a reduced pharmacophore model. We constructed reduced pharmacophore models for three protein systems and demonstrated good enrichment quality combined with high efficiency. The reduction in pharmacophore model size reduces the required screening time by a factor of 200–500 compared to using all protein pharmacophore elements. We also describe a training process using a small set of known actives to reliably select the optimal set of criteria for pharmacophore selection for each protein system. PMID:22397751

  10. Predicting protein subcellular locations with feature selection and analysis.

    PubMed

    Cai, Yudong; He, Jianfeng; Li, Xinlei; Feng, Kaiyan; Lu, Lin; Feng, Kairui; Kong, Xiangyin; Lu, Wencong

    2010-04-01

    In this paper, we propose a strategy to predict the subcellular locations of proteins by combining various feature selection methods. Firstly, proteins are coded by amino-acid composition and physicochemical properties, then these features are arranged by Minimum Redundancy Maximum Relevance method and further filtered by feature selection procedure. Nearest Neighbor Algorithm is used as a prediction model to predict the protein subcellular locations, and gains a correct prediction rate of 70.63%, evaluated by Jackknife cross-validation. Results of feature selection also enable us to identify the most important protein properties. The prediction software is available for public access on the website http://chemdata.shu.edu.cn/sub22/, which may play a important complementary role to a series of web-server predictors summarized recently in a review by Chou and Shen (Chou, K.C., Shen, H.B. Natural Science, 2009, 2, 63-92, http://www.scirp.org/journal/NS/).

  11. Modeling Natural Selection

    ERIC Educational Resources Information Center

    Bogiages, Christopher A.; Lotter, Christine

    2011-01-01

    In their research, scientists generate, test, and modify scientific models. These models can be shared with others and demonstrate a scientist's understanding of how the natural world works. Similarly, students can generate and modify models to gain a better understanding of the content, process, and nature of science (Kenyon, Schwarz, and Hug…

  12. Modeling Natural Selection

    ERIC Educational Resources Information Center

    Bogiages, Christopher A.; Lotter, Christine

    2011-01-01

    In their research, scientists generate, test, and modify scientific models. These models can be shared with others and demonstrate a scientist's understanding of how the natural world works. Similarly, students can generate and modify models to gain a better understanding of the content, process, and nature of science (Kenyon, Schwarz, and Hug…

  13. Selected System Models

    NASA Astrophysics Data System (ADS)

    Schmidt-Eisenlohr, F.; Puñal, O.; Klagges, K.; Kirsche, M.

    Apart from the general issue of modeling the channel, the PHY and the MAC of wireless networks, there are specific modeling assumptions that are considered for different systems. In this chapter we consider three specific wireless standards and highlight modeling options for them. These are IEEE 802.11 (as example for wireless local area networks), IEEE 802.16 (as example for wireless metropolitan networks) and IEEE 802.15 (as example for body area networks). Each section on these three systems discusses also at the end a set of model implementations that are available today.

  14. Selective Target Protein Degradation via Phthalimide Conjugation

    PubMed Central

    Winter, Georg E.; Buckley, Dennis L.; Paulk, Joshiawa; Roberts, Justin M.; Souza, Amanda; Dhe-Paganon, Sirano; Bradner, James E.

    2016-01-01

    Small-molecule antagonists disable discrete biochemical properties of protein targets. For multi-domain protein targets, the pharmacologic consequence of drug action is limited by selective disruption of one domain-specific activity. More broadly, target inhibition is kinetically limited by the durability and degree of target engagement. These features of traditional drug molecules are challenging to the development of inhibitors targeting transcription factors and chromatin-associated epigenetic proteins, which function as multi-domain biomolecular scaffolds and generally feature rapid association and dissociation kinetics. We therefore devised a chemical strategy to prompt ligand-dependent target protein degradation, via chemical conjugation with derivatized phthalimides that hijack the function of the Cereblon E3 ubiquitin ligase complex. Using this approach, we converted an acetyl-lysine competitive antagonist that displaces BET bromodomains from chromatin (JQ1) to a phthalimide-conjugated ligand that prompts immediate Cereblon-dependent BET protein degradation (dBET1). Expression proteomics confirms high specificity for BET family members BRD2, BRD3 and BRD4 among 7429 proteins detected. Degradation of BET bromodomains is associated with a more rapid and robust apoptotic response compared to bromodomain inhibition in primary human leukemic blasts, and dBET1 exhibits in vivo efficacy in a human leukemia xenograft. The reach of this approach is illustrated by a second series of probes that degrade the cytosolic signaling protein, FKBP12. Together, these findings identify a facile and general new strategy to control target protein stability, with implications for approaching previously intractable protein targets. PMID:25999370

  15. Individual influence on model selection.

    PubMed

    Sterba, Sonya K; Pek, Jolynn

    2012-12-01

    Researchers in psychology are increasingly using model selection strategies to decide among competing models, rather than evaluating the fit of a given model in isolation. However, such interest in model selection outpaces an awareness that one or a few cases can have disproportionate impact on the model ranking. Though case influence on the fit of a single model in isolation has been often studied, case influence on model selection results is greatly underappreciated in psychology. This article introduces the issue of case influence on model selection and proposes 3 influence diagnostics for commonly used selection indices: the chi-square difference test, Bayesian information criterion, and Akaike's information criterion. These 3 diagnostics can be obtained simply from the byproducts of full information maximum likelihood estimation without heavy computational burden. We provide practical information on the interpretation and behavior of these diagnostics for applied researchers and provide software code to facilitate their use. Simulated and empirical examples involving different kinds of model comparison scenarios encountered in cross-sectional, longitudinal, and multilevel research as well as involving different kinds of outcome distributions illustrate the generality of the proposed diagnostics. An awareness of how cases influence model selection results is shown to aid researchers in understanding how representative their sample level results are at the case level.

  16. Launch vehicle selection model

    NASA Technical Reports Server (NTRS)

    Montoya, Alex J.

    1990-01-01

    Over the next 50 years, humans will be heading for the Moon and Mars to build scientific bases to gain further knowledge about the universe and to develop rewarding space activities. These large scale projects will last many years and will require large amounts of mass to be delivered to Low Earth Orbit (LEO). It will take a great deal of planning to complete these missions in an efficient manner. The planning of a future Heavy Lift Launch Vehicle (HLLV) will significantly impact the overall multi-year launching cost for the vehicle fleet depending upon when the HLLV will be ready for use. It is desirable to develop a model in which many trade studies can be performed. In one sample multi-year space program analysis, the total launch vehicle cost of implementing the program reduced from 50 percent to 25 percent. This indicates how critical it is to reduce space logistics costs. A linear programming model has been developed to answer such questions. The model is now in its second phase of development, and this paper will address the capabilities of the model and its intended uses. The main emphasis over the past year was to make the model user friendly and to incorporate additional realistic constraints that are difficult to represent mathematically. We have developed a methodology in which the user has to be knowledgeable about the mission model and the requirements of the payloads. We have found a representation that will cut down the solution space of the problem by inserting some preliminary tests to eliminate some infeasible vehicle solutions. The paper will address the handling of these additional constraints and the methodology for incorporating new costing information utilizing learning curve theory. The paper will review several test cases that will explore the preferred vehicle characteristics and the preferred period of construction, i.e., within the next decade, or in the first decade of the next century. Finally, the paper will explore the interaction

  17. Launch vehicle selection model

    NASA Technical Reports Server (NTRS)

    Montoya, Alex J.

    1990-01-01

    Over the next 50 years, humans will be heading for the Moon and Mars to build scientific bases to gain further knowledge about the universe and to develop rewarding space activities. These large scale projects will last many years and will require large amounts of mass to be delivered to Low Earth Orbit (LEO). It will take a great deal of planning to complete these missions in an efficient manner. The planning of a future Heavy Lift Launch Vehicle (HLLV) will significantly impact the overall multi-year launching cost for the vehicle fleet depending upon when the HLLV will be ready for use. It is desirable to develop a model in which many trade studies can be performed. In one sample multi-year space program analysis, the total launch vehicle cost of implementing the program reduced from 50 percent to 25 percent. This indicates how critical it is to reduce space logistics costs. A linear programming model has been developed to answer such questions. The model is now in its second phase of development, and this paper will address the capabilities of the model and its intended uses. The main emphasis over the past year was to make the model user friendly and to incorporate additional realistic constraints that are difficult to represent mathematically. We have developed a methodology in which the user has to be knowledgeable about the mission model and the requirements of the payloads. We have found a representation that will cut down the solution space of the problem by inserting some preliminary tests to eliminate some infeasible vehicle solutions. The paper will address the handling of these additional constraints and the methodology for incorporating new costing information utilizing learning curve theory. The paper will review several test cases that will explore the preferred vehicle characteristics and the preferred period of construction, i.e., within the next decade, or in the first decade of the next century. Finally, the paper will explore the interaction

  18. The anti-TNF-α antibody infliximab inhibits the expression of fat-transporter-protein FAT/CD36 in a selective hepatic-radiation mouse model.

    PubMed

    Martius, Gesa; Cameron, Silke; Rave-Fränk, Margret; Hess, Clemens F; Wolff, Hendrik A; Malik, Ihtzaz A

    2015-03-02

    Previously, we reported a radiation-induced inflammation triggering fat-accumulation through fatty-acid-translocase/cluster of differentiation protein 36 (FAT/CD36) in rat liver. Furthermore, inhibition of radiation-induced FAT/CD36-expression by anti-tumor necrosis factor-α (anti-TNF-α) (infliximab) was shown in vitro. The current study investigates fat-accumulation in a mouse-model of single-dose liver-irradiation (25-Gray) and the effect of anti-TNF-α-therapy on FAT/CD36 gene-expression. Mice livers were selectively irradiated in vivo in presence or absence of infliximab. Serum- and hepatic-triglycerides, mRNA, and protein were analyzed by colorimetric assays, RT-PCR, Immunofluorescence and Western-Blot, respectively. Sudan-staining was used demonstrating fat-accumulation in tissue. In mice livers, early (1-3 h) induction of TNF-α-expression, a pro-inflammatory cytokine, was observed. It was followed by elevated hepatic-triglyceride level (6-12 h), compared to sham-irradiated controls. In contrast, serum-triglyceride level was decreased at these time points. Similar to triglyceride level in mice livers, Sudan staining of liver cryosections showed a quick (6-12 h) increase of fat-droplets after irradiation. Furthermore, expression of fat-transporter-protein FAT/CD36 was increased at protein level caused by radiation or TNF-α. TNF-α-blockage by anti-TNF-α showed an early inhibition of radiation-induced FAT/CD36 expression in mice livers. Immunohistochemistry showed basolateral and cytoplasmic expression of FAT/CD36 in hepatocytes. Moreover, co-localization of FAT/CD36 was detected with α-smooth muscle actin (α-SMA+) cells and F4/80+ macrophages. In summary, hepatic-radiation triggers fat-accumulation in mice livers, involving acute-phase-processes. Accordingly, anti-TNF-α-therapy prevented early radiation-induced expression of FAT/CD36 in vivo.

  19. Selective sweeps in Cryptocercus woodroach antifungal proteins.

    PubMed

    Velenovsky, Joseph F; Kalisch, Jessica; Bulmer, Mark S

    2016-10-01

    We identified the antifungal gene termicin in three species of Cryptocercus woodroaches. Cryptocercus represents the closest living cockroach lineage of termites, which suggests that the antifungal role of termicin evolved prior to the divergence of termites from other cockroaches. An analysis of Cryptocercus termicin and two β-1,3-glucanase genes (GNBP1 and GNBP2), which appear to work synergistically with termicin in termites, revealed evidence of selection in these proteins. We identified the signature of past selective sweeps within GNBP2 from Cryptocercus punctulatus and Cryptocercus wrighti. The signature of past selective sweeps was also found within termicin from Cryptocercus punctulatus and Cryptocercus darwini. Our analysis further suggests a phenotypically identical variant of GNBP2 was maintained within Cryptocercus punctulatus, Cryptocercus wrighti, and Cryptocercus darwini while synonymous sites diverged. Cryptocercus termicin and GNBP2 appear to have experienced similar selective pressure to that of their termite orthologues in Reticulitermes. This selective pressure may be a result of ubiquitous entomopathogenic fungal pathogens such as Metarhizium. This study further reveals the similarities between Cryptocercus woodroaches and termites.

  20. Sexual selection and the molecular evolution of ADAM proteins.

    PubMed

    Finn, Scott; Civetta, Alberto

    2010-09-01

    Rapid evolution has been identified for many reproductive genes and recent studies have combined phylogenetic tests and information on species mating systems to test sexual selection. Here we examined the molecular evolution of the ADAM gene family, a diverse group of 35 proteins capable of adhesion to and cleavage of other proteins, using sequence data from 25 mammalian genes. Out of the 25 genes analyzed, all those expressed in male reproductive tissue showed evidence of positive selection. Positively selected amino acids within the protein adhesion domain were only found in sperm surface ADAM proteins (ADAMs 1, 2, 3, 4, and 32) suggesting selection driven by male x female interactions. We tested heterogeneity in rates of evolution of the adhesion domain of ADAM proteins by using sequence data from Hominidae and macaques. The use of the branch and branch-site models (PAML) showed evidence of higher d (N)/d (S) and/or positive selection linked to branches experiencing high postmating selective pressures (chimpanzee and macaque) for Adams 2, 18, and 23. Moreover, we found consistent higher proportion of nonsynonymous relative to synonymous and noncoding sequence substitutions in chimpanzee and/or macaque only for Adams 2, 18, and 23. Our results suggest that lineage-specific sexual selection bouts might have driven the evolution of the adhesion sperm protein surface domains of ADAMs 2 and 18 in primates. Adams 2 and 18 are localized in chromosome 8 of primates and adjacent to each other, so their evolution might have also been influenced by their common genome localization.

  1. TAS-116, a highly selective inhibitor of heat shock protein 90α and β, demonstrates potent antitumor activity and minimal ocular toxicity in preclinical models.

    PubMed

    Ohkubo, Shuichi; Kodama, Yasuo; Muraoka, Hiromi; Hitotsumachi, Hiroko; Yoshimura, Chihoko; Kitade, Makoto; Hashimoto, Akihiro; Ito, Kenjiro; Gomori, Akira; Takahashi, Koichi; Shibata, Yoshihiro; Kanoh, Akira; Yonekura, Kazuhiko

    2015-01-01

    The molecular chaperone HSP90 plays a crucial role in cancer cell growth and survival by stabilizing cancer-related proteins. A number of HSP90 inhibitors have been developed clinically for cancer therapy; however, potential off-target and/or HSP90-related toxicities have proved problematic. The 4-(1H-pyrazolo[3,4-b]pyridine-1-yl)benzamide TAS-116 is a selective inhibitor of cytosolic HSP90α and β that does not inhibit HSP90 paralogs such as endoplasmic reticulum GRP94 or mitochondrial TRAP1. Oral administration of TAS-116 led to tumor shrinkage in human tumor xenograft mouse models accompanied by depletion of multiple HSP90 clients, demonstrating that the inhibition of HSP90α and β alone was sufficient to exert antitumor activity in certain tumor models. One of the most notable HSP90-related adverse events universally observed to differing degrees in the clinical setting is visual disturbance. A two-week administration of the isoxazole resorcinol NVP-AUY922, an HSP90 inhibitor, caused marked degeneration and disarrangement of the outer nuclear layer of the retina and induced photoreceptor cell death in rats. In contrast, TAS-116 did not produce detectable photoreceptor injury in rats, probably due to its lower distribution in retinal tissue. Importantly, in a rat model, the antitumor activity of TAS-116 was accompanied by a higher distribution of the compound in subcutaneously xenografted NCI-H1975 non-small cell lung carcinoma tumors than in retina. Moreover, TAS-116 showed activity against orthotopically transplanted NCI-H1975 lung tumors. Together, these data suggest that TAS-116 has a potential to maximize antitumor activity while minimizing adverse effects such as visual disturbances that are observed with other compounds of this class. ©2014 American Association for Cancer Research.

  2. Modeling Mercury in Proteins

    SciTech Connect

    Smith, Jeremy C; Parks, Jerry M

    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.

  3. Protein minimization by random fragmentation and selection.

    PubMed

    Rudgers, G W; Palzkill, T

    2001-07-01

    Protein-protein interactions are involved in most biological processes and are important targets for drug design. Over the past decade, there has been increased interest in the design of small molecules that mimic functional epitopes of protein inhibitors. BLIP is a 165 amino acid protein that is a potent inhibitor of TEM-1 beta-lactamase (K(i) = 0.1 nM). To aid in the development of new inhibitors of beta-lactamase, the gene encoding BLIP was randomly fragmented and DNA segments encoding peptides that retain the ability to bind TEM-1 beta-lactamase were isolated using phage display. The selected peptides revealed a common, overlapping region that includes BLIP residues C30-D49. Synthesis and binding analysis of the C30-D49 peptide indicate that this peptide inhibits TEM-1 beta-lactamase. Therefore, a peptide derivative of BLIP that has been reduced in size by 88% compared with wild-type BLIP retains the ability to bind and inhibit beta-lactamase.

  4. Modeling Mercury in Proteins.

    PubMed

    Parks, J M; Smith, J C

    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 nontoxic, other forms such as Hg(2+) 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 Hg(2+) can be methylated by certain bacteria and archaea to form methylmercury. Conversely, bacteria also demethylate methylmercury and reduce Hg(2+) 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 molecular picture and circumvent issues associated with toxicity. Here, we describe computational methods for investigating and characterizing how mercury binds to proteins, how inter- and intraprotein 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 confer mercury resistance in many bacteria. Lastly, we place work on mercury in proteins in the context of what is needed for a comprehensive multiscale model of environmental mercury cycling. © 2016 Elsevier Inc. All rights reserved.

  5. Multiscale modeling of proteins.

    PubMed

    Tozzini, Valentina

    2010-02-16

    The activity within a living cell is based on a complex network of interactions among biomolecules, exchanging information and energy through biochemical processes. These events occur on different scales, from the nano- to the macroscale, spanning about 10 orders of magnitude in the space domain and 15 orders of magnitude in the time domain. Consequently, many different modeling techniques, each proper for a particular time or space scale, are commonly used. In addition, a single process often spans more than a single time or space scale. Thus, the necessity arises for combining the modeling techniques in multiscale approaches. In this Account, I first review the different modeling methods for bio-systems, from quantum mechanics to the coarse-grained and continuum-like descriptions, passing through the atomistic force field simulations. Special attention is devoted to their combination in different possible multiscale approaches and to the questions and problems related to their coherent matching in the space and time domains. These aspects are often considered secondary, but in fact, they have primary relevance when the aim is the coherent and complete description of bioprocesses. Subsequently, applications are illustrated by means of two paradigmatic examples: (i) the green fluorescent protein (GFP) family and (ii) the proteins involved in the human immunodeficiency virus (HIV) replication cycle. The GFPs are currently one of the most frequently used markers for monitoring protein trafficking within living cells; nanobiotechnology and cell biology strongly rely on their use in fluorescence microscopy techniques. A detailed knowledge of the actions of the virus-specific enzymes of HIV (specifically HIV protease and integrase) is necessary to study novel therapeutic strategies against this disease. Thus, the insight accumulated over years of intense study is an excellent framework for this Account. The foremost relevance of these two biomolecular systems was

  6. Inferring Selection on Amino Acid Preference in Protein Domains

    PubMed Central

    Durbin, Richard

    2009-01-01

    Models that explicitly account for the effect of selection on new mutations have been proposed to account for “codon bias” or the excess of “preferred” codons that results from selection for translational efficiency and/or accuracy. In principle, such models can be applied to any mutation that results in a preferred allele, but in most cases, the fitness effect of a specific mutation cannot be predicted. Here we show that it is possible to assign preferred and unpreferred states to amino acid changing mutations that occur in protein domains. We propose that mutations that lead to more common amino acids (at a given position in a domain) can be considered “preferred alleles” just as are synonymous mutations leading to codons for more abundant tRNAs. We use genome-scale polymorphism data to show that alleles for preferred amino acids in protein domains occur at higher frequencies in the population, as has been shown for preferred codons. We show that this effect is quantitative, such that there is a correlation between the shift in frequency of preferred alleles and the predicted fitness effect. As expected, we also observe a reduction in the numbers of polymorphisms and substitutions at more important positions in domains, consistent with stronger selection at those positions. We examine the derived allele frequency distribution and polymorphism to divergence ratios of preferred and unpreferred differences and find evidence for both negative and positive selections acting to maintain protein domains in the human population. Finally, we analyze a model for selection on amino acid preferences in protein domains and find that it is consistent with the quantitative effects that we observe. PMID:19095755

  7. Electrostatic selectivity in protein-nanoparticle interactions.

    PubMed

    Chen, Kaimin; Xu, Yisheng; Rana, Subinoy; Miranda, Oscar R; Dubin, Paul L; Rotello, Vincent M; Sun, Lianhong; Guo, Xuhong

    2011-07-11

    The binding of bovine serum albumin (BSA) and β-lactoglobulin (BLG) to TTMA (a cationic gold nanoparticle coupled to 3,6,9,12-tetraoxatricosan-1-aminium, 23-mercapto-N,N,N-trimethyl) was studied by high-resolution turbidimetry (to observe a critical pH for binding), dynamic light scattering (to monitor particle growth), and isothermal titration calorimetry (to measure binding energetics), all as a function of pH and ionic strength. Distinctively higher affinities observed for BLG versus BSA, despite the lower pI of the latter, were explained in terms of their different charge anisotropies, namely, the negative charge patch of BLG. To confirm this effect, we studied two isoforms of BLG that differ in only two amino acids. Significantly stronger binding to BLGA could be attributed to the presence of the additional aspartates in the negative charge domain for the BLG dimer, best portrayed in DelPhi. This selectivity decreases at low ionic strength, at which both isoforms bind well below pI. Selectivity increases with ionic strength for BLG versus BSA, which binds above pI. This result points to the diminished role of long-range repulsions for binding above pI. Dynamic light scattering reveals a tendency for higher-order aggregation for TTMA-BSA at pH above the pI of BSA, due to its ability to bridge nanoparticles. In contrast, soluble BLG-TTMA complexes were stable over a range of pH because the charge anisotropy of this protein at makes it unable to bridge nanoparticles. Finally, isothermal titration calorimetry shows endoenthalpic binding for all proteins: the higher affinity of TTMA for BLGA versus BLGB comes from a difference in the dominant entropy term.

  8. Model selection for logistic regression models

    NASA Astrophysics Data System (ADS)

    Duller, Christine

    2012-09-01

    Model selection for logistic regression models decides which of some given potential regressors have an effect and hence should be included in the final model. The second interesting question is whether a certain factor is heterogeneous among some subsets, i.e. whether the model should include a random intercept or not. In this paper these questions will be answered with classical as well as with Bayesian methods. The application show some results of recent research projects in medicine and business administration.

  9. Selective Refinement and Molecular Dynamics Ranking Selection of Near-native Protein Structures

    NASA Astrophysics Data System (ADS)

    Zhang, Jiong; Zhang, Jingfen; Xu, Dong; Shang, Yi; Kosztin, Ioan

    2014-03-01

    In recent years in silico protein structure prediction reached a level where a variety of servers can generate large pools of near-native structures. However, the identification and further refinement of the best structures from the pool of decoys remain problematic. To address these issues, we have developed a selective refinement protocol (SRP), and a molecular dynamics (MD) simulation based ranking method (MDR). In SRP the refinement of structures is accomplished by using the relax mode of the Rosetta software package, subject to specific constraints determined by the type and complexity of the target. The final best models are selected with MDR by testing their relative stability against gradual heating during all atom MD simulations. We have implemented the selective refinement protocol and the MDR method in Mufold-MD, our fully automated protein structure prediction server. Mufold-MD was one of the top servers in the CASP10 competition.

  10. Hierarchy and extremes in selections from pools of randomized proteins

    PubMed Central

    Boyer, Sébastien; Biswas, Dipanwita; Kumar Soshee, Ananda; Scaramozzino, Natale; Nizak, Clément; Rivoire, Olivier

    2016-01-01

    Variation and selection are the core principles of Darwinian evolution, but quantitatively relating the diversity of a population to its capacity to respond to selection is challenging. Here, we examine this problem at a molecular level in the context of populations of partially randomized proteins selected for binding to well-defined targets. We built several minimal protein libraries, screened them in vitro by phage display, and analyzed their response to selection by high-throughput sequencing. A statistical analysis of the results reveals two main findings. First, libraries with the same sequence diversity but built around different “frameworks” typically have vastly different responses; second, the distribution of responses of the best binders in a library follows a simple scaling law. We show how an elementary probabilistic model based on extreme value theory rationalizes the latter finding. Our results have implications for designing synthetic protein libraries, estimating the density of functional biomolecules in sequence space, characterizing diversity in natural populations, and experimentally investigating evolvability (i.e., the potential for future evolution). PMID:26969726

  11. Recognition-then-Reaction Enables Site-Selective Bioconjugation to Proteins on Live-Cell Surfaces.

    PubMed

    Cui, Cheng; Zhang, Hui; Wang, Ruowen; Cansiz, Sena; Pan, Xiaoshu; Wan, Shuo; Hou, Weijia; Li, Long; Chen, Meiwan; Liu, Yuan; Chen, Xigao; Liu, Qiaoling; Tan, Weihong

    2017-09-18

    Site-selective protein modification is a key step in facilitating protein functionalization and manipulation. To accomplish this, genetically engineered proteins were previously required, but the procedure was laborious, complex, and technically challenging. Herein we report the development of aptamer-based recognition-then-reaction to guide site-selective protein/DNA conjugation in a single step with outstanding selectivity and efficiency. As models, several proteins, including human thrombin, PDGF-BB, Avidin, and His-tagged recombinant protein, were studied, and the results showed excellent selectivity under mild reaction conditions. Taking advantage of aptamers as recognition elements with extraordinary selectivity and affinity, this simple preparation method can tag a protein in a complex milieu. Thus, with the aptamer obtained from cell-SELEX, real-time modification of live-cell membrane proteins can be achieved in one step without any pre-treatment. © 2017 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.

  12. Comparative Protein Structure Modeling Using MODELLER

    PubMed Central

    Webb, Benjamin; Sali, Andrej

    2016-01-01

    Comparative protein structure modeling predicts the three-dimensional structure of a given protein sequence (target) based primarily on its alignment to one or more proteins of known structure (templates). The prediction process consists of fold assignment, target-template alignment, model building, and model evaluation. This unit describes how to calculate comparative models using the program MODELLER and how to use the ModBase database of such models, and discusses all four steps of comparative modeling, frequently observed errors, and some applications. Modeling lactate dehydrogenase from Trichomonas vaginalis (TvLDH) is described as an example. The download and installation of the MODELLER software is also described. PMID:27322406

  13. Entropic criterion for model selection

    NASA Astrophysics Data System (ADS)

    Tseng, Chih-Yuan

    2006-10-01

    Model or variable selection is usually achieved through ranking models according to the increasing order of preference. One of methods is applying Kullback-Leibler distance or relative entropy as a selection criterion. Yet that will raise two questions, why use this criterion and are there any other criteria. Besides, conventional approaches require a reference prior, which is usually difficult to get. Following the logic of inductive inference proposed by Caticha [Relative entropy and inductive inference, in: G. Erickson, Y. Zhai (Eds.), Bayesian Inference and Maximum Entropy Methods in Science and Engineering, AIP Conference Proceedings, vol. 707, 2004 (available from arXiv.org/abs/physics/0311093)], we show relative entropy to be a unique criterion, which requires no prior information and can be applied to different fields. We examine this criterion by considering a physical problem, simple fluids, and results are promising.

  14. Modeling Protein Self Assembly

    ERIC Educational Resources Information Center

    Baker, William P.; Jones, Carleton Buck; Hull, Elizabeth

    2004-01-01

    Understanding the structure and function of proteins is an important part of the standards-based science curriculum. Proteins serve vital roles within the cell and malfunctions in protein self assembly are implicated in degenerative diseases. Experience indicates that this topic is a difficult one for many students. We have found that the concept…

  15. Modeling Protein Domain Function

    ERIC Educational Resources Information Center

    Baker, William P.; Jones, Carleton "Buck"; Hull, Elizabeth

    2007-01-01

    This simple but effective laboratory exercise helps students understand the concept of protein domain function. They use foam beads, Styrofoam craft balls, and pipe cleaners to explore how domains within protein active sites interact to form a functional protein. The activity allows students to gain content mastery and an understanding of the…

  16. Modeling Protein Domain Function

    ERIC Educational Resources Information Center

    Baker, William P.; Jones, Carleton "Buck"; Hull, Elizabeth

    2007-01-01

    This simple but effective laboratory exercise helps students understand the concept of protein domain function. They use foam beads, Styrofoam craft balls, and pipe cleaners to explore how domains within protein active sites interact to form a functional protein. The activity allows students to gain content mastery and an understanding of the…

  17. Selected Tether Applications Cost Model

    NASA Technical Reports Server (NTRS)

    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.

  18. Protein Folding: Detailed Models

    NASA Astrophysics Data System (ADS)

    Pande, Vijay

    Proteins play a fundamental role in biology. With their ability to perform numerous biological roles, including acting as catalysts, antibodies, and molecular signals, proteins today realize many of the goals that modern nanotechnology aspires to. However, before proteins can carry out these remarkable molecular functions, they must perform another amazing feat — they must assemble themselves. This process of protein self-assembly into a particular shape, or "fold" is called protein folding. Due to the importance of the folded state in the biological activity of proteins, recent interest from misfolding related diseases [1], as well as a fascination of just how this process occurs [2-4], there has been much work performed in order to unravel the mechanism of protein folding [5].

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

    PubMed Central

    Wong, Wendy SW; Sainudiin, Raazesh; Nielsen, Rasmus

    2006-01-01

    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. PMID:16542458

  20. Selective protein covalent binding and target organ toxicity.

    PubMed

    Cohen, S D; Pumford, N R; Khairallah, E A; Boekelheide, K; Pohl, L R; Amouzadeh, H R; Hinson, J A

    1997-03-01

    Protein covalent binding by xenobiotic metabolites has long been associated with target organ toxicity but mechanistic involvement of such binding has not been widely demonstrated. Modern biochemical, molecular, and immunochemical approaches have facilitated identification of specific protein targets of xenobiotic covalent binding. Such studies have revealed that protein covalent binding is not random, but rather selective with respect to the proteins targeted. Selective binding to specific cellular target proteins may better correlate with toxicity than total protein covalent binding. Current research is directed at characterizing and identifying the targeted proteins and clarifying the effect of such binding on their structure, function, and potential roles in target organ toxicity. The approaches employed to detect and identify the tartgeted proteins are described. Metabolites of acetaminophen, halothane, and 2,5-hexanedione form covalently bound adducts to recently identified protein targets. The selective binding may influence homeostatic or other cellular responses which in turn contribute to drug toxicity, hypersensitivity, or autoimmunity.

  1. Selection responses for clinical mastitis and protein yield in two Norwegian dairy cattle selection experiments.

    PubMed

    Heringstad, B; Klemetsdal, G; Steine, T

    2003-09-01

    Inferences from two dairy cattle selection experiments, in which sires were selected from external sources, were drawn by using an animal model to analyze data from the entire population. The first selection experiment was carried out in the period from 1978 to 1989 and included groups selected for high milk production (HMP) and low milk production (LMP). Each year, the highest ranking proven sires for milk production, from the most recent group of Norwegian Dairy Cattle (NRF) test bulls, were selected and mated to the cows in the HMP group. A group of sires with low milk production indices from progeny testing in 1978 and 1979 were used as sires in the LMP group during the entire experiment. The second selection experiment, which started in 1989, included one high protein yield (HPY) group and one low clinical mastitis (LCM) group. The highest ranking proven NRF sires for protein yield and mastitis resistance were selected each year from the most recent group of progeny tested bulls and used as sires in the HPY and LCM groups, respectively. Genetic trends for protein yield were positive (as expected) for HMP and HPY cows, and negative for LMP and LCM cows. Estimates of annual genetic trends for clinical mastitis were +0.23, -0.02, +0.04, and -0.91% per year for HMP, LMP, HPY, and LCM cows, respectively. The difference in genetic trend of clinical mastitis between HMP and HPY groups, both selected for increased milk production, reflects the gradual change in the NRF breeding objective towards more weight on health relative to milk over the last 20 yr. After four cow generations, the genetic difference in mastitis between HMP and LMP group cows was 3.1% clinical mastitis, a correlated response to selection for increased milk production. The genetic difference between LCM and HPY cows of 8.6% clinical mastitis after three cow generations is mainly a result of direct selection against clinical mastitis in the LCM group. In the NRF population, an approximately flat

  2. Model selection for modified gravity.

    PubMed

    Kitching, T D; Simpson, F; Heavens, A F; Taylor, A N

    2011-12-28

    In this article, we review model selection predictions for modified gravity scenarios as an explanation for the observed acceleration of the expansion history of the Universe. We present analytical procedures for calculating expected Bayesian evidence values in two cases: (i) that modified gravity is a simple parametrized extension of general relativity (GR; two nested models), such that a Bayes' factor can be calculated, and (ii) that we have a class of non-nested models where a rank-ordering of evidence values is required. We show that, in the case of a minimal modified gravity parametrization, we can expect large area photometric and spectroscopic surveys, using three-dimensional cosmic shear and baryonic acoustic oscillations, to 'decisively' distinguish modified gravity models over GR (or vice versa), with odds of ≫1:100. It is apparent that the potential discovery space for modified gravity models is large, even in a simple extension to gravity models, where Newton's constant G is allowed to vary as a function of time and length scale. On the time and length scales where dark energy dominates, it is only through large-scale cosmological experiments that we can hope to understand the nature of gravity.

  3. Functional properties of select edible oilseed proteins.

    PubMed

    Sharma, Girdhari M; Su, Mengna; Joshi, Aditya U; Roux, Kenneth H; Sathe, Shridhar K

    2010-05-12

    Borate saline buffer (0.1 M, pH 8.45) solubilized proteins from almond, Brazil nut, cashew nut, hazelnut, macadamia, pine nut, pistachio, Spanish peanut, Virginia peanut, and soybean seeds were prepared from the corresponding defatted flour. The yield was in the range from 10.6% (macadamia) to 27.4% (almond). The protein content, on a dry weight basis, of the lyophilized preparations ranged from 69.23% (pine nut) to 94.80% (soybean). Isolated proteins from Brazil nut had the lightest and hazelnut the darkest color. Isolated proteins exhibited good solubility in aqueous media. Foaming capacity (<40% overrun) and stability (<1 h) of the isolated proteins were poor to fair. Almond proteins had the highest viscosity among the tested proteins. Oil-holding capacity of the isolated proteins ranged from 2.8 (macadamia) to 7 (soybean) g of oil/g of protein. Least gelation concentrations (% w/v) for almond, Brazil nut, cashew, hazelnut, macadamia, pine nut, pistachio, Spanish peanut, Virginia peanut, and soybean were, respectively, 6, 8, 8, 12, 20, 12, 10, 14, 14, and 16.

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

    PubMed

    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

    2016-04-19

    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. © 2016 Wiley Periodicals, Inc.

  5. Model selection for pion photoproduction

    DOE PAGES

    Landay, J.; Doring, M.; Fernandez-Ramirez, C.; ...

    2017-01-12

    Partial-wave analysis of meson and photon-induced reactions is needed to enable the comparison of many theoretical approaches to data. In both energy-dependent and independent parametrizations of partial waves, the selection of the model amplitude is crucial. Principles of the S matrix are implemented to a different degree in different approaches; but a many times overlooked aspect concerns the selection of undetermined coefficients and functional forms for fitting, leading to a minimal yet sufficient parametrization. We present an analysis of low-energy neutral pion photoproduction using the least absolute shrinkage and selection operator (LASSO) in combination with criteria from information theory andmore » K-fold cross validation. These methods are not yet widely known in the analysis of excited hadrons but will become relevant in the era of precision spectroscopy. As a result, the principle is first illustrated with synthetic data; then, its feasibility for real data is demonstrated by analyzing the latest available measurements of differential cross sections (dσ/dΩ), photon-beam asymmetries (Σ), and target asymmetry differential cross sections (dσT/d≡Tdσ/dΩ) in the low-energy regime.« less

  6. Model selection for pion photoproduction

    NASA Astrophysics Data System (ADS)

    Landay, J.; Döring, M.; Fernández-Ramírez, C.; Hu, B.; Molina, R.

    2017-01-01

    Partial-wave analysis of meson and photon-induced reactions is needed to enable the comparison of many theoretical approaches to data. In both energy-dependent and independent parametrizations of partial waves, the selection of the model amplitude is crucial. Principles of the S matrix are implemented to a different degree in different approaches; but a many times overlooked aspect concerns the selection of undetermined coefficients and functional forms for fitting, leading to a minimal yet sufficient parametrization. We present an analysis of low-energy neutral pion photoproduction using the least absolute shrinkage and selection operator (LASSO) in combination with criteria from information theory and K -fold cross validation. These methods are not yet widely known in the analysis of excited hadrons but will become relevant in the era of precision spectroscopy. The principle is first illustrated with synthetic data; then, its feasibility for real data is demonstrated by analyzing the latest available measurements of differential cross sections (d σ /d Ω ), photon-beam asymmetries (Σ ), and target asymmetry differential cross sections (d σT/d ≡T d σ /d Ω ) in the low-energy regime.

  7. A refined atomic scale model of the Saccharomyces cerevisiae K+-translocation protein Trk1p combined with experimental evidence confirms the role of selectivity filter glycines and other key residues.

    PubMed

    Zayats, Vasilina; Stockner, Thomas; Pandey, Saurabh Kumar; Wörz, Katharina; Ettrich, Rüdiger; Ludwig, Jost

    2015-05-01

    Potassium ion (K+) uptake in yeast is mediated mainly by the Trk1/2 proteins that enable cells to survive on external K+ concentration as low as a few μM. Fungal Trks are related to prokaryotic TRK and Ktr and plant HKT K+ transport systems. Overall sequence similarity is very low, thus requiring experimental verification of homology models. Here a refined structural model of the Saccharomyces cerevisiae Trk1 is presented that was obtained by combining homology modeling, molecular dynamics simulation and experimental verification through functional analysis of mutants. Structural models and experimental results showed that glycines within the selectivity filter, conserved among the K-channel/transporter family, are not only important for protein function, but are also required for correct folding/membrane targeting. A conserved aspartic acid in the PA helix (D79) and a lysine in the M2D helix (K1147) were proposed earlier to interact. Our results suggested individual roles of these residues in folding, structural integrity and function. While mutations of D79 completely abolished protein folding, mutations at position 1147 were tolerated to some extent. Intriguingly, a secondary interaction of D79 with R76 could enhance folding/stability of Trk1 and enable a fraction of Trk1[K1147A] to fold. The part of the ion permeation path containing the selectivity filter is shaped similar to that of ion channels. However below the selectivity filter it is obstructed or regulated by a proline containing loop. The presented model could provide the structural basis for addressing the long standing question if Trk1 is a passive or active ion-translocation system.

  8. Oregano Essential Oil Improves Intestinal Morphology and Expression of Tight Junction Proteins Associated with Modulation of Selected Intestinal Bacteria and Immune Status in a Pig Model.

    PubMed

    Zou, Yi; Xiang, Quanhang; Wang, Jun; Peng, Jian; Wei, Hongkui

    2016-01-01

    Oregano essential oil (OEO) has long been used to improve the health of animals, particularly the health of intestine, which is generally attributed to its antimicrobial and anti-inflammatory effects. However, how OEO acts in the intestine of pig is still unclear. This study was aimed at elucidating how OEO promotes the intestinal barrier integrity in a pig model. Pigs were fed a control diet alone or one supplemented with 25 mg/kg of OEO for 4 weeks. The OEO-treated pigs showed decreased (P < 0.05) endotoxin level in serum and increased (P < 0.05) villus height and expression of occludin and zonula occludens-1 (ZO-1) in the jejunum. These results demonstrated that the integrity of intestinal barrier was improved by OEO treatment. The OEO-treated pigs had a lower (P < 0.05) population of Escherichia coli in the jejunum, ileum, and colon than the control. This is in accordance with the greater inactivation (P < 0.05) of inflammation, which was reflected by the mitogen-activated protein kinase (MAPK), protein kinase B (Akt), and nuclear factor κB (NF-κB) signaling pathways and expression of inflammatory cytokines in the jejunum. Our results show that OEO promotes intestinal barrier integrity, probably through modulating intestinal bacteria and immune status in pigs.

  9. Oregano Essential Oil Improves Intestinal Morphology and Expression of Tight Junction Proteins Associated with Modulation of Selected Intestinal Bacteria and Immune Status in a Pig Model

    PubMed Central

    Zou, Yi; Xiang, Quanhang; Wang, Jun; Peng, Jian; Wei, Hongkui

    2016-01-01

    Oregano essential oil (OEO) has long been used to improve the health of animals, particularly the health of intestine, which is generally attributed to its antimicrobial and anti-inflammatory effects. However, how OEO acts in the intestine of pig is still unclear. This study was aimed at elucidating how OEO promotes the intestinal barrier integrity in a pig model. Pigs were fed a control diet alone or one supplemented with 25 mg/kg of OEO for 4 weeks. The OEO-treated pigs showed decreased (P < 0.05) endotoxin level in serum and increased (P < 0.05) villus height and expression of occludin and zonula occludens-1 (ZO-1) in the jejunum. These results demonstrated that the integrity of intestinal barrier was improved by OEO treatment. The OEO-treated pigs had a lower (P < 0.05) population of Escherichia coli in the jejunum, ileum, and colon than the control. This is in accordance with the greater inactivation (P < 0.05) of inflammation, which was reflected by the mitogen-activated protein kinase (MAPK), protein kinase B (Akt), and nuclear factor κB (NF-κB) signaling pathways and expression of inflammatory cytokines in the jejunum. Our results show that OEO promotes intestinal barrier integrity, probably through modulating intestinal bacteria and immune status in pigs. PMID:27314026

  10. Molecular modelling of protein-protein/protein-solvent interactions

    NASA Astrophysics Data System (ADS)

    Luchko, Tyler

    The inner workings of individual cells are based on intricate networks of protein-protein interactions. However, each of these individual protein interactions requires a complex physical interaction between proteins and their aqueous environment at the atomic scale. In this thesis, molecular dynamics simulations are used in three theoretical studies to gain insight at the atomic scale about protein hydration, protein structure and tubulin-tubulin (protein-protein) interactions, as found in microtubules. Also presented, in a fourth project, is a molecular model of solvation coupled with the Amber molecular modelling package, to facilitate further studies without the need of explicitly modelled water. Basic properties of a minimally solvated protein were calculated through an extended study of myoglobin hydration with explicit solvent, directly investigating water and protein polarization. Results indicate a close correlation between polarization of both water and protein and the onset of protein function. The methodology of explicit solvent molecular dynamics was further used to study tubulin and microtubules. Extensive conformational sampling of the carboxy-terminal tails of 8-tubulin was performed via replica exchange molecular dynamics, allowing the characterisation of the flexibility, secondary structure and binding domains of the C-terminal tails through statistical analysis methods. Mechanical properties of tubulin and microtubules were calculated with adaptive biasing force molecular dynamics. The function of the M-loop in microtubule stability was demonstrated in these simulations. The flexibility of this loop allowed constant contacts between the protofilaments to be maintained during simulations while the smooth deformation provided a spring-like restoring force. Additionally, calculating the free energy profile between the straight and bent tubulin configurations was used to test the proposed conformational change in tubulin, thought to cause microtubule

  11. Comparative protein structure modeling using MODELLER.

    PubMed

    Eswar, Narayanan; Webb, Ben; Marti-Renom, Marc A; Madhusudhan, M S; Eramian, David; Shen, Min-Yi; Pieper, Ursula; Sali, Andrej

    2007-11-01

    Functional characterization of a protein sequence is a common goal in biology, and is usually facilitated by having an accurate three-dimensional (3-D) structure of the studied protein. In the absence of an experimentally determined structure, comparative or homology modeling can sometimes provide a useful 3-D model for a protein that is related to at least one known protein structure. Comparative modeling predicts the 3-D structure of a given protein sequence (target) based primarily on its alignment to one or more proteins of known structure (templates). The prediction process consists of fold assignment, target-template alignment, model building, and model evaluation. This unit describes how to calculate comparative models using the program MODELLER and discusses all four steps of comparative modeling, frequently observed errors, and some applications. Modeling lactate dehydrogenase from Trichomonas vaginalis (TvLDH) is described as an example. The download and installation of the MODELLER software is also described. (c) 2007 by John Wiley & Sons, Inc.

  12. Selective incorporation of membrane proteins into proteoliposomes of different compositions.

    PubMed

    Eytan, G D; Racker, E

    1977-05-25

    1. Cytochrome oxidase was incorporated into preformed liposomes containing phosphatidylserine. When confronted with a mixture of liposomes, some containing phosphatidylserine and some without it, the enzyme was incorporated only into the phosphatidylserine-containing liposomes. 2. The hydrophobic proteins of the oligomycin-sensitive ATPase incubated in the presence of a mixture of liposomes with and without cytochrome oxidase were preferentially incorporated into cytochrome oxidase-containing liposomes. This selectivity was abolished by either cytochrome c or ascorbate. 3. Cytochrome oxidase incubated in the presence of a mixture of liposomes with and without the hydrophobic proteins of the ATPase was preferentially incorporated into liposomes that did not contain the hydrophobic proteins. 4. Cytochrome oxidase and the oligomycin-sensitive ATPase were preferentially incorporated into pure liposomes over bacteriorhodopsin-containing vesicles. 5. Reduced coenzyme Q (QH2)-cytochrome c reductase was incorporated randomly when incubated in the presence of a mixture of pure liposomes and liposomes containing the hydrophobic proteins of the ATPase complex. 6. The significance of the incorporation procedure as a model for membrane biogenesis is discussed.

  13. Metalloregulatory Proteins: Metal Selectivity and Allosteric Switching

    PubMed Central

    Caballero, Hermes Reyes; Campanello, Gregory C.; Giedroc, David P.

    2011-01-01

    Prokaryotic organisms have evolved an impressive capacity to quickly adapt to a changing and challenging microenvironment in which the availability of both biologically required and non-essential transition metal ions can vary dramatically. In all bacteria, a panel of metalloregulatory proteins control the expression of genes encoding membrane transporters and metal trafficking proteins, that collectively manage metal homeostasis and resistance. These “metal sensors” are specialized allosteric proteins, in which the direct binding of a specific or small number of “cognate” metal ion(s) drives a conformational change in the regulator that allosterically activates or inhibits operator DNA binding, or alternatively, distorts the promoter structure thereby converting a poor promoter to a strong one. In this review, we discuss our current understanding of the features that control metal specificity of the allosteric response in these systems, and the role that structure, thermodynamics and conformational dynamics play in mediating allosteric activation or inhibition of DNA binding. PMID:21511390

  14. Olfactory self-selection of protein-containing foods.

    PubMed

    Heinrichs, S C; Deutsch, J A; Moore, B O

    1990-03-01

    When one unflavored, nonprotein diet was available in two differently scented bins, rats fed a protein-free diet over four days ate more from the bin smelling of gluten, ovalbumin, yeast or fibrin, but not soy, casein or lactalbumin, than from the bin smelling of butter. Rats fed a protein-containing diet over the same four-day period had no such preference. This result demonstrates that protein-deprived rats can use odor cues in making their selection of certain proteins. Since the direction, speed, and size of preference for these protein odors, excepting soy, are remarkably similar to those previously observed when rats actually consumed the proteins, olfactory stimuli appear to elicit appropriate protein selection responses independently of other protein quality variables such as taste, texture or nutrient composition.

  15. Refinement and Selection of Near-native Protein Structures

    NASA Astrophysics Data System (ADS)

    Zhang, Jiong; Zhang, Jingfen; Shang, Yi; Xu, Dong; Kosztin, Ioan

    2013-03-01

    In recent years in silico protein structure prediction reached a level where a variety of servers can generate large pools of near-native structures. However, the identification and further refinement of the best structures from the pool of decoys continue to be problematic. To address these issues, we have developed a selective refinement protocol (based on the Rosetta software package), and a molecular dynamics (MD) simulation based ranking method (MDR). The refinement of the selected structures is done by employing Rosetta's relax mode, subject to certain constraints. The selection of the final best models is done with MDR by testing their relative stability against gradual heating during all atom MD simulations. We have implemented the selective refinement protocol and the MDR method in our fully automated server Mufold-MD. Assessments of the performance of the Mufold-MD server in the CASP10 competition and other tests will be presented. This work was supported by grants from NIH. Computer time was provided by the University of Missouri Bioinformatics Consortium.

  16. Generation and characterization of transgenic mice expressing mitochondrial targeted red fluorescent protein selectively in neurons: modeling mitochondriopathy in excitotoxicity and amyotrophic lateral sclerosis.

    PubMed

    Wang, Yi; Pan, Yan; Price, Ann; Martin, Lee J

    2011-11-02

    Mitochondria have roles or appear to have roles in the pathogenesis of several chronic age-related and acute neurological disorders, including Charcot-Marie-Tooth disease, amyotrophic lateral sclerosis, Parkinson's disease, and cerebral ischemia, and could be critical targets for development of rational mechanism-based, disease-modifying therapeutics for treating these disorders effectively. A deeper understanding of neural tissue mitochondria pathobiologies as definitive mediators of neural injury, disease, and cell death merits further study, and the development of additional tools to study neural mitochondria will help achieve this unmet need. We created transgenic mice that express the coral (Discosoma sp.) red fluorescent protein DsRed2 specifically in mitochondria of neurons using a construct engineered with a Thy1 promoter, specific for neuron expression, to drive expression of a fusion protein of DsRed2 with a mitochondrial targeting sequence. The biochemical and histological characterization of these mice shows the expression of mitochondrial-targeted DsRed2 to be specific for mitochondria and concentrated in distinct CNS regions, including cerebral cortex, hippocampus, thalamus, brainstem, and spinal cord. Red fluorescent mitochondria were visualized in cerebral cortical and hippocampal pyramidal neurons, ventrobasal thalamic neurons, subthalamic neurons, and spinal motor neurons. For the purpose of proof of principle application, these mice were used in excitotoxicity paradigms and double transgenic mice were generated by crossing Thy1-mitoDsRed2 mice with transgenic mice expressing enhanced-GFP (eGFP) under the control of the Hlxb9 promoter that drives eGFP expression specifically in motor neurons and by crossing Thy1-mitoDsRed2 mice to amyotrophic lateral sclerosis (ALS) mice expressing human mutant superoxide dismutase-1. These novel transgenic mice will be a useful tool for better understanding the biology of mitochondria in mouse and cellular models

  17. Metalloregulatory proteins: metal selectivity and allosteric switching.

    PubMed

    Reyes-Caballero, Hermes; Campanello, Gregory C; Giedroc, David P

    2011-07-01

    Prokaryotic organisms have evolved the capacity to quickly adapt to a changing and challenging microenvironment in which the availability of both biologically required and non-essential transition metal ions can vary dramatically. In all bacteria, a panel of metalloregulatory proteins controls the expression of genes encoding membrane transporters and metal trafficking proteins that collectively manage metal homeostasis and resistance. These "metal sensors" are specialized allosteric proteins, in which the direct binding of a specific or small number of "cognate" metal ion(s) drives a conformational change in the regulator that allosterically activates or inhibits operator DNA binding, or alternatively, distorts the promoter structure thereby converting a poor promoter to a strong one. In this review, we discuss our current understanding of the features that control metal specificity of the allosteric response in these systems, and the role that structure, thermodynamics and conformational dynamics play in mediating allosteric activation or inhibition of DNA binding. Copyright © 2011 Elsevier B.V. All rights reserved.

  18. Protein designs in HP models

    NASA Astrophysics Data System (ADS)

    Gupta, Arvind; Khodabakhshi, Alireza Hadj; Maňuch, Ján; Rafiey, Arash; Stacho, Ladislav

    2009-07-01

    The inverse protein folding problem is that of designing an amino acid sequence which folds into a prescribed shape. This problem arises in drug design where a particular structure is necessary to ensure proper protein-protein interactions and could have applications in nanotechnology. A major challenge in designing proteins with native folds that attain a specific shape is to avoid proteins that have multiple native folds (unstable proteins). In this technical note we present our results on protein designs in the variant of Hydrophobic-Polar (HP) model introduced by Dill [6] on 2D square lattice. The HP model distinguishes only polar and hydrophobic monomers and only counts the number of hydrophobic contacts in the energy function. To achieve better stability of our designs we use the Hydrophobic-Polar-Cysteine (HPC) model which distinguishes the third type of monomers called "cysteines" and incorporates also the disulfid bridges (SS-bridges) into the energy function. We present stable designs in 2D square lattice and 3D hexagonal prism lattice in the HPC model.

  19. Protein fold classification with genetic algorithms and feature selection.

    PubMed

    Chen, Peng; Liu, Chunmei; Burge, Legand; Mahmood, Mohammad; Southerland, William; Gloster, Clay

    2009-10-01

    Protein fold classification is a key step to predicting protein tertiary structures. This paper proposes a novel approach based on genetic algorithms and feature selection to classifying protein folds. Our dataset is divided into a training dataset and a test dataset. Each individual for the genetic algorithms represents a selection function of the feature vectors of the training dataset. A support vector machine is applied to each individual to evaluate the fitness value (fold classification rate) of each individual. The aim of the genetic algorithms is to search for the best individual that produces the highest fold classification rate. The best individual is then applied to the feature vectors of the test dataset and a support vector machine is built to classify protein folds based on selected features. Our experimental results on Ding and Dubchak's benchmark dataset of 27-class folds show that our approach achieves an accuracy of 71.28%, which outperforms current state-of-the-art protein fold predictors.

  20. Fuzzy Clustering-Based Modeling of Surface Interactions and Emulsions of Selected Whey Protein Concentrate Combined to i-Carrageenan and Gum Arabic Solutions

    USDA-ARS?s Scientific Manuscript database

    Gums and proteins are valuable ingredients with a wide spectrum of applications. Surface properties (surface tension, interfacial tension, emulsion activity index “EAI” and emulsion stability index “ESI”) of 4% whey protein concentrate (WPC) in a combination with '- carrageenan (0.05%, 0.1%, and 0.5...

  1. Mitochondrial genomes are retained by selective constraints on protein targeting

    PubMed Central

    Björkholm, Patrik; Harish, Ajith; Hagström, Erik; Ernst, Andreas M.; Andersson, Siv G. E.

    2015-01-01

    Mitochondria are energy-producing organelles in eukaryotic cells considered to be of bacterial origin. The mitochondrial genome has evolved under selection for minimization of gene content, yet it is not known why not all mitochondrial genes have been transferred to the nuclear genome. Here, we predict that hydrophobic membrane proteins encoded by the mitochondrial genomes would be recognized by the signal recognition particle and targeted to the endoplasmic reticulum if they were nuclear-encoded and translated in the cytoplasm. Expression of the mitochondrially encoded proteins Cytochrome oxidase subunit 1, Apocytochrome b, and ATP synthase subunit 6 in the cytoplasm of HeLa cells confirms export to the endoplasmic reticulum. To examine the extent to which the mitochondrial proteome is driven by selective constraints within the eukaryotic cell, we investigated the occurrence of mitochondrial protein domains in bacteria and eukaryotes. The accessory protein domains of the oxidative phosphorylation system are unique to mitochondria, indicating the evolution of new protein folds. Most of the identified domains in the accessory proteins of the ribosome are also found in eukaryotic proteins of other functions and locations. Overall, one-third of the protein domains identified in mitochondrial proteins are only rarely found in bacteria. We conclude that the mitochondrial genome has been maintained to ensure the correct localization of highly hydrophobic membrane proteins. Taken together, the results suggest that selective constraints on the eukaryotic cell have played a major role in modulating the evolution of the mitochondrial genome and proteome. PMID:26195779

  2. A Logistic Regression Model for Personnel Selection.

    ERIC Educational Resources Information Center

    Raju, Nambury S.; And Others

    1991-01-01

    A two-parameter logistic regression model for personnel selection is proposed. The model was tested with a database of 84,808 military enlistees. The probability of job success was related directly to trait levels, addressing such topics as selection, validity generalization, employee classification, selection bias, and utility-based fair…

  3. Selective sorting of alpha-granule proteins

    PubMed Central

    Italiano, J.E.; Battinelli, E. M.

    2010-01-01

    Summary One of the main functions of blood platelets is to secrete a variety of substances that can modify a developing thrombus, regulate the growth of the vasculature, promote wound repair, and contribute to cell-adhesive events. The majority of this vast array of secreted proteins is stored in alpha-granules. Until recently, it was assumed that platelets contained one homogeneous population of alpha-granules that undergo complete de-granulation during platelet activation. This review focuses on the mechanisms of alpha-granule biogenesis and secretion, with a particular emphasis on recent findings that clearly demonstrate that platelets contain distinct subpopulations of alpha-granules that undergo differential release during activation. We consider the implications of this new paradigm of platelet secretion, discuss mechanisms of alpha-granule biogenesis, and review the molecular basis of transport and delivery of alpha-granules to assembling platelets. PMID:19630794

  4. Surface selective binding of nanoclay particles to polyampholyte protein chains

    NASA Astrophysics Data System (ADS)

    Pawar, Nisha; Bohidar, H. B.

    2009-07-01

    Binding of nanoclay (Laponite) to gelatin-A and gelatin-B (both polyampholytes) molecules was investigated at room temperature (25 °C) both experimentally and theoretically. The stoichiometric binding ratio between gelatin and Laponite was found to be strongly dependent on the solution ionic strength. Large soluble complexes were formed at higher ionic strengths of the solution, a result supported by data obtained from light scattering, viscosity, and zeta potential measurements. The binding problem was theoretically modeled by choosing a suitable two-body screened Coulomb potential, U(R+)=(q-/2ɛ)[(Q-/R-)e-kR--(Q+/R+)e-kR+], where the protein dipole has charges Q+ and Q_ that are located at distances R+ and R_ from the point Laponite charge q- and the dispersion liquid has dielectric constant (ɛ). U(R+) accounted for electrostatic interactions between a dipole (protein molecule) and an effective charge (Laponite particle) located at an angular position θ. Gelatin-A and Laponite association was facilitated by a strong attractive interaction potential that led to preferential binding of the biopolymer chains to negatively charged face of Laponite particles. In the case of gelatin-B selective surf ace patch binding dominated the process where the positively charged rim and negatively charged face of the particles were selectively bound to the oppositely charged segments of the biopolymer. The equilibrium separation (Re) between the protein and nanoclay particle revealed monovalent salt concentration dependence given by Re˜[NaCl]α where α =0.6±0.2 for gelatin-A and α =0.4±0.2 for gelatin-B systems. The equilibrium separations were ≈30% less compared to the gelatin-A system implying preferential short-range ordering of the gelatin-B-nanoclay pair in the solvent.

  5. Selected Logistics Models and Techniques.

    DTIC Science & Technology

    1984-09-01

    Programmable Calculator LCC...Program 27 TI-59 Programmable Calculator LCC Model 30 Unmanned Spacecraft Cost Model 31 iv I: TABLE OF CONTENTS (CONT’D) (Subject Index) LOGISTICS...34"" - % - "° > - " ° .° - " .’ > -% > ]*° - LOGISTICS ANALYSIS MODEL/TECHNIQUE DATA MODEL/TECHNIQUE NAME: TI-59 Programmable Calculator LCC Model TYPE MODEL: Cost Estimating DEVELOPED BY:

  6. Modeling protein recognition of carbohydrates.

    PubMed

    Laederach, Alain; Reilly, Peter J

    2005-09-01

    We have a limited understanding of the details of molecular recognition of carbohydrates by proteins, which is critical to a multitude of biological processes. Furthermore, carbohydrate-modifying proteins such as glycosyl hydrolases and phosphorylases are of growing importance as potential drug targets. Interactions between proteins and carbohydrates have complex thermodynamics, and in general the specific positioning of only a few hydroxyl groups determines their binding affinities. A thorough understanding of both carbohydrate and protein structures is thus essential to predict these interactions. An atomic-level view of carbohydrate recognition through structures of carbohydrate-active enzymes complexed with transition-state inhibitors reveals some of the distinctive molecular features unique to protein-carbohydrate complexes. However, the inherent flexibility of carbohydrates and their often water-mediated hydrogen bonding to proteins makes simulation of their complexes difficult. Nonetheless, recent developments such as the parameterization of specific force fields and docking scoring functions have greatly improved our ability to predict protein-carbohydrate interactions. We review protein-carbohydrate complexes having defined molecular requirements for specific carbohydrate recognition by proteins, providing an overview of the different computational techniques available to model them. Copyright 2005 Wiley-Liss, Inc.

  7. Protein purification by polyelectrolyte coacervation: influence of protein charge anisotropy on selectivity.

    PubMed

    Xu, Yisheng; Mazzawi, Malek; Chen, Kaimin; Sun, Lianhong; Dubin, Paul L

    2011-05-09

    The effect of polyelectrolyte binding affinity on selective coacervation of proteins with the cationic polyelectrolyte, poly(diallyldimethylammonium chloride) (PDADMAC), was investigated for bovine serum albumin/β-lactoglobulin (BSA/BLG) and for the isoforms BLG-A/BLG-B. High-sensitivity turbidimetric titrations were used to define conditions of complex formation and coacervation (pH(c) and pH(ϕ), respectively) as a function of ionic strength. The resultant phase boundaries, essential for the choice of conditions for selective coacervation for the chosen protein pairs, are nonmonotonic with respect to ionic strength, for both pH(c) and pH(ϕ). These results are explained in the context of short-range attraction/long-range repulsion governing initial protein binding "on the wrong side of pI" and also subsequent phase separation due to charge neutralization. The stronger binding of BLG despite its higher isoelectric point, inferred from lower pH(c), is shown to result from the negative "charge patch" on BLG, absent for BSA, as visualized via computer modeling (DelPhi). The higher affinity of BLG versus BSA was also confirmed by isothermal titration calorimetry (ITC). The relative values of pH(ϕ) for the two proteins show complex salt dependence so that the choice of ionic strength determines the order of coacervation, whereas the choice of pH controls the yield of the target protein. Coacervation at I = 100 mM, pH 7, of BLG from a 1:1 (w/w) mixture with BSA was shown by SEC to provide 90% purity of BLG with a 20-fold increase in concentration. Ultrafiltration was shown to remove effectively the polymer from the target protein. The relationship between protein charge anisotropy and binding affinity and between binding affinity and selective coacervation, inferred from the results for BLG/BSA, was tested using the isoforms of BLG. Substitution of glycine in BLG-B by aspartate in BLG-A lowers pH(c) by 0.2, as anticipated on the basis of DelPhi modeling. The stronger

  8. Mitotic apparatus: the selective extraction of protein with mild acid.

    PubMed

    Bibring, T; Baxandall, J

    1968-07-26

    The treatment of isolated mitotic apparatus with mild (pH 3) hydrochloric acid results in the extraction of less than 10 percent of its protein, accompanied by the selective morphological disappearance of the microtubules. The same extraction can be shown to dissolve outer doublet microtubules from sperm flagella. A protein with points of similarity to the flagellar microtubule protein is the major component of the extract from mitotic apparatus.

  9. Estimation of Absolute Protein Quantities of Unlabeled Samples by Selected Reaction Monitoring Mass Spectrometry*

    PubMed Central

    Ludwig, Christina; Claassen, Manfred; Schmidt, Alexander; Aebersold, Ruedi

    2012-01-01

    For many research questions in modern molecular and systems biology, information about absolute protein quantities is imperative. This information includes, for example, kinetic modeling of processes, protein turnover determinations, stoichiometric investigations of protein complexes, or quantitative comparisons of different proteins within one sample or across samples. To date, the vast majority of proteomic studies are limited to providing relative quantitative comparisons of protein levels between limited numbers of samples. Here we describe and demonstrate the utility of a targeting MS technique for the estimation of absolute protein abundance in unlabeled and nonfractionated cell lysates. The method is based on selected reaction monitoring (SRM) mass spectrometry and the “best flyer” hypothesis, which assumes that the specific MS signal intensity of the most intense tryptic peptides per protein is approximately constant throughout a whole proteome. SRM-targeted best flyer peptides were selected for each protein from the peptide precursor ion signal intensities from directed MS data. The most intense transitions per peptide were selected from full MS/MS scans of crude synthetic analogs. We used Monte Carlo cross-validation to systematically investigate the accuracy of the technique as a function of the number of measured best flyer peptides and the number of SRM transitions per peptide. We found that a linear model based on the two most intense transitions of the three best flying peptides per proteins (TopPep3/TopTra2) generated optimal results with a cross-correlated mean fold error of 1.8 and a squared Pearson coefficient R2 of 0.88. Applying the optimized model to lysates of the microbe Leptospira interrogans, we detected significant protein abundance changes of 39 target proteins upon antibiotic treatment, which correlate well with literature values. The described method is generally applicable and exploits the inherent performance advantages of SRM

  10. [Nature's healing power--natural selection in protein turnover].

    PubMed

    Pirlet, K

    2003-10-01

    For millennia, medical practitioners, healthy people and the sick have known that there is an inner force that keeps us sound and also cures us. However, neither medicine nor natural sciences have been able to define this curative power. It can not be captured by experiments or assigned with dimensions or numbers. The answer can be found in the quintessential process of all living systems, the interaction of genes and proteins in every single cell. Genes encode proteins; proteins are the ubiquitous instruments of life. Protein molecules undergo rapid turnover: their median lifetime is about 2 days, and in every second so many proteins are synthesized in each of us that their number equals the number of seconds that have passed since the big bang 15 billion years ago. The biochemical dogma that says that after their synthesis proteins are degraded at random - blindly, quasi - is wrong. The experimental results have not been interpreted correctly. In the first instance, proteins are stable and functional, since they have to fulfill numerous tasks. Only aged, severely damaged, non-functioning proteins are discarded. Protein turnover is subject to selection, not to chance. By selecting fresh, functional proteins, the quality of cellular proteins is kept at a high level. The principle of selection in protein turnover is the crucial component in the scientific groundwork of naturopathy therapeutics. With natural influencies and measures (e.g. food processing, intermediate metabolism, exercise, light, warmth, coldness) the steady process of regeneration is accelerated - in contrast to medical therapy, which restricts and blocks protein activities. Natural healing power and self-organization energy are basic phenomena of a therapeutically used physiology. Naturopathy is natural science. Copyright 2003 S. Karger GmbH, Freiburg

  11. Enrichment of phosphorylated peptides and proteins by selective precipitation methods.

    PubMed

    Rainer, Matthias; Bonn, Günther K

    2015-01-01

    Protein phosphorylation is one of the most prominent post-translational modifications involved in the regulation of cellular processes. Fundamental understanding of biological processes requires appropriate bioanalytical methods for selectively enriching phosphorylated peptides and proteins. Most of the commonly applied enrichment approaches include chromatographic materials including Fe(3+)-immobilized metal-ion affinity chromatography or metal oxides. In the last years, the introduction of several non-chromatographic isolation technologies has increasingly attracted the interest of many scientists. Such approaches are based on the selective precipitation of phosphorylated peptides and proteins by applying various metal cations. The excellent performance of precipitation-based enrichment methods can be explained by the absence of any stationary phase, resin or sorbent, which usually leads to unspecific binding. This review provides an overview of recently published methods for the selective precipitation of phosphorylated peptides and proteins.

  12. Dockground: A comprehensive data resource for modeling of protein complexes.

    PubMed

    Kundrotas, Petras J; Anishchenko, Ivan; Dauzhenka, Taras; Kotthoff, Ian; Mnevets, Daniil; Copeland, Matthew M; Vakser, Ilya A

    2017-09-10

    Characterization of life processes at the molecular level requires structural details of protein interactions. The number of experimentally determined structures of protein-protein complexes accounts only for a fraction of known protein interactions. This gap in structural description of the interactome has to be bridged by modeling. An essential part of the development of structural modeling/docking techniques for protein interactions is databases of protein-protein complexes. They are necessary for studying protein interfaces, providing a knowledge base for docking algorithms, developing intermolecular potentials, search procedures, and scoring functions. Development of protein-protein docking techniques requires thorough benchmarking of different parts of the docking protocols on carefully curated sets of protein-protein complexes. We present a comprehensive description of the Dockground resource (http://dockground.compbio.ku.edu) for structural modeling of protein interactions, including previously unpublished unbound docking benchmark set 4, and the X-ray docking decoy set 2. The resource offers a variety of interconnected datasets of protein-protein complexes and other data for the development and testing of different aspects of protein docking methodologies. Based on protein-protein complexes extracted from the PDB biounit files, Dockground offers sets of X-ray unbound, simulated unbound, model, and docking decoy structures. All datasets are freely available for download, as a whole or selecting specific structures, through a user-friendly interface on one integrated website. This article is protected by copyright. All rights reserved. © 2017 The Protein Society.

  13. SWISS-MODEL: an automated protein homology-modeling server

    PubMed Central

    Schwede, Torsten; Kopp, Jürgen; Guex, Nicolas; Peitsch, Manuel C.

    2003-01-01

    SWISS-MODEL (http://swissmodel.expasy.org) is a server for automated comparative modeling of three-dimensional (3D) protein structures. It pioneered the field of automated modeling starting in 1993 and is the most widely-used free web-based automated modeling facility today. In 2002 the server computed 120 000 user requests for 3D protein models. SWISS-MODEL provides several levels of user interaction through its World Wide Web interface: in the ‘first approach mode’ only an amino acid sequence of a protein is submitted to build a 3D model. Template selection, alignment and model building are done completely automated by the server. In the ‘alignment mode’, the modeling process is based on a user-defined target-template alignment. Complex modeling tasks can be handled with the ‘project mode’ using DeepView (Swiss-PdbViewer), an integrated sequence-to-structure workbench. All models are sent back via email with a detailed modeling report. WhatCheck analyses and ANOLEA evaluations are provided optionally. The reliability of SWISS-MODEL is continuously evaluated in the EVA-CM project. The SWISS-MODEL server is under constant development to improve the successful implementation of expert knowledge into an easy-to-use server. PMID:12824332

  14. IRT Model Selection Methods for Dichotomous Items

    ERIC Educational Resources Information Center

    Kang, Taehoon; Cohen, Allan S.

    2007-01-01

    Fit of the model to the data is important if the benefits of item response theory (IRT) are to be obtained. In this study, the authors compared model selection results using the likelihood ratio test, two information-based criteria, and two Bayesian methods. An example illustrated the potential for inconsistency in model selection depending on…

  15. IRT Model Selection Methods for Dichotomous Items

    ERIC Educational Resources Information Center

    Kang, Taehoon; Cohen, Allan S.

    2007-01-01

    Fit of the model to the data is important if the benefits of item response theory (IRT) are to be obtained. In this study, the authors compared model selection results using the likelihood ratio test, two information-based criteria, and two Bayesian methods. An example illustrated the potential for inconsistency in model selection depending on…

  16. Model selection bias and Freedman's paradox

    USGS Publications Warehouse

    Lukacs, P.M.; Burnham, K.P.; Anderson, D.R.

    2010-01-01

    In situations where limited knowledge of a system exists and the ratio of data points to variables is small, variable selection methods can often be misleading. Freedman (Am Stat 37:152-155, 1983) demonstrated how common it is to select completely unrelated variables as highly "significant" when the number of data points is similar in magnitude to the number of variables. A new type of model averaging estimator based on model selection with Akaike's AIC is used with linear regression to investigate the problems of likely inclusion of spurious effects and model selection bias, the bias introduced while using the data to select a single seemingly "best" model from a (often large) set of models employing many predictor variables. The new model averaging estimator helps reduce these problems and provides confidence interval coverage at the nominal level while traditional stepwise selection has poor inferential properties. ?? The Institute of Statistical Mathematics, Tokyo 2009.

  17. Site-Selective Conjugation of Native Proteins with DNA.

    PubMed

    Trads, Julie B; Tørring, Thomas; Gothelf, Kurt V

    2017-06-20

    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 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. 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. In recent years, a number of chemical methods that target conjugation to specific sites at native proteins have become available, and an overview of these methods is provided in this Account. Our laboratory has investigated DNA-templated protein conjugation (DTPC), which offers an alternative approach to site-selective conjugation of DNA to proteins. The method is inspired by the concept of DNA-templated synthesis where functional groups conjugated to DNA strands are preorganized by DNA hybridization to dramatically increase the reaction rate. In DPTC, we target metal binding sites in proteins to template selective covalent conjugation reactions. By chelation of a DNA-metal complex with a metal binding site of the protein, an electrophile on a second DNA strand is aligned for reaction with a lysine side chain on the protein in the proximity of the metal binding site. The method is quite general because approximately one-third of all wild-type proteins contain metal-binding sites, including many IgG antibodies, and it is also applicable to His-tagged proteins. This

  18. Variable Selection in Semiparametric Regression Modeling.

    PubMed

    Li, Runze; Liang, Hua

    2008-01-01

    In this paper, we are concerned with how to select significant variables in semiparametric modeling. Variable selection for semiparametric regression models consists of two components: model selection for nonparametric components and select significant variables for parametric portion. Thus, it is much more challenging than that for parametric models such as linear models and generalized linear models because traditional variable selection procedures including stepwise regression and the best subset selection require model selection to nonparametric components for each submodel. This leads to very heavy computational burden. In this paper, we propose a class of variable selection procedures for semiparametric regression models using nonconcave penalized likelihood. The newly proposed procedures are distinguished from the traditional ones in that they delete insignificant variables and estimate the coefficients of significant variables simultaneously. This allows us to establish the sampling properties of the resulting estimate. We first establish the rate of convergence of the resulting estimate. With proper choices of penalty functions and regularization parameters, we then establish the asymptotic normality of the resulting estimate, and further demonstrate that the proposed procedures perform as well as an oracle procedure. Semiparametric generalized likelihood ratio test is proposed to select significant variables in the nonparametric component. We investigate the asymptotic behavior of the proposed test and demonstrate its limiting null distribution follows a chi-squared distribution, which is independent of the nuisance parameters. Extensive Monte Carlo simulation studies are conducted to examine the finite sample performance of the proposed variable selection procedures.

  19. A sliding selectivity scale for lipid binding to membrane proteins

    PubMed Central

    Landreh, Michael; Marty, Michael T.; Gault, Joseph; Robinson, Carol V.

    2017-01-01

    Biological membranes form barriers that are essential for cellular integrity and compartmentalisation. Proteins that reside in the membrane have co-evolved with their hydrophobic lipid environment which serves as a solvent for proteins with very diverse requirements. As a result, membrane protein-lipid interactions range from completely non-selective to highly discriminating. Mass spectrometry (MS), in combination with X-ray crystallography and molecular dynamics simulations, enables us to monitor how lipids interact with intact membrane protein complexes and assess their effects on structure and dynamics. Recent studies illustrate the ability to differentiate specific lipid binding, preferential interactions with lipid subsets, and nonselective annular contacts. In this review, we consider the biological implications of different lipid-binding scenarios and propose that binding occurs on a sliding selectivity scale, in line with the view of biological membranes as facilitators of dynamic protein and lipid organization. PMID:27155089

  20. Development of target protein-selective degradation inducer for protein knockdown.

    PubMed

    Itoh, Yukihiro; Ishikawa, Minoru; Kitaguchi, Risa; Sato, Shinichi; Naito, Mikihiko; Hashimoto, Yuichi

    2011-05-15

    Our previous technique for inducing selective degradation of target proteins with ester-type SNIPER (Specific and Nongenetic Inhibitor-of-apoptosis-proteins (IAPs)-dependent Protein ERaser) degrades both the target proteins and IAPs. Here, we designed a small-molecular amide-type SNIPER to overcome this issue. As proof of concept, we synthesized and biologically evaluated an amide-type SNIPER which induces selective degradation of cellular retinoic acid binding protein II (CRABP-II), but not IAPs. Such small-molecular, amide-type SNIPERs that induce target protein-selective degradation without affecting IAPs should be effective tools to study the biological roles of target proteins in living cells.

  1. Coaggregation of RNA-Binding Proteins in a Model of TDP-43 Proteinopathy with Selective RGG Motif Methylation and a Role for RRM1 Ubiquitination

    PubMed Central

    Dammer, Eric B.; Fallini, Claudia; Gozal, Yair M.; Duong, Duc M.; Rossoll, Wilfried; Xu, Ping; Lah, James J.; Levey, Allan I.; Peng, Junmin; Bassell, Gary J.; Seyfried, Nicholas T.

    2012-01-01

    TAR DNA-binding protein 43 (TDP-43) is a major component within ubiquitin-positive inclusions of a number of neurodegenerative diseases that increasingly are considered as TDP-43 proteinopathies. Identities of other inclusion proteins associated with TDP-43 aggregation remain poorly defined. In this study, we identify and quantitate 35 co-aggregating proteins in the detergent-resistant fraction of HEK-293 cells in which TDP-43 or a particularly aggregate prone variant, TDP-S6, were enriched following overexpression, using stable isotope-labeled (SILAC) internal standards and liquid chromatography coupled to tandem mass spectrometry (LC-MS/MS). We also searched for differential post-translational modification (PTM) sites of ubiquitination. Four sites of ubiquitin conjugation to TDP-43 or TDP-S6 were confirmed by dialkylated GST-TDP-43 external reference peptides, occurring on or near RNA binding motif (RRM) 1. RRM-containing proteins co-enriched in cytoplasmic granular structures in HEK-293 cells and primary motor neurons with insoluble TDP-S6, including cytoplasmic stress granule associated proteins G3BP, PABPC1, and eIF4A1. Proteomic evidence for TDP-43 co-aggregation with paraspeckle markers RBM14, PSF and NonO was also validated by western blot and by immunocytochemistry in HEK-293 cells. An increase in peptides from methylated arginine-glycine-glycine (RGG) RNA-binding motifs of FUS/TLS and hnRNPs was found in the detergent-insoluble fraction of TDP-overexpressing cells. Finally, TDP-43 and TDP-S6 detergent-insoluble species were reduced by mutagenesis of the identified ubiquitination sites, even following oxidative or proteolytic stress. Together, these findings define some of the aggregation partners of TDP-43, and suggest that TDP-43 ubiquitination influences TDP-43 oligomerization. PMID:22761693

  2. Model Selection Indices for Polytomous Items

    ERIC Educational Resources Information Center

    Kang, Taehoon; Cohen, Allan S.; Sung, Hyun-Jung

    2009-01-01

    This study examines the utility of four indices for use in model selection with nested and nonnested polytomous item response theory (IRT) models: a cross-validation index and three information-based indices. Four commonly used polytomous IRT models are considered: the graded response model, the generalized partial credit model, the partial credit…

  3. Binding-regulated click ligation for selective detection of proteins.

    PubMed

    Cao, Ya; Han, Peng; Wang, Zhuxin; Chen, Weiwei; Shu, Yongqian; Xiang, Yang

    2016-04-15

    Herein, a binding-regulated click ligation (BRCL) strategy for endowing selective detection of proteins is developed with the incorporation of small-molecule ligand and clickable DNA probes. The fundamental principle underlying the strategy is the regulating capability of specific protein-ligand binding against the ligation between clickable DNA probes, which could efficiently combine the detection of particular protein with enormous DNA-based sensing technologies. In this work, the feasibly of the BRCL strategy is first verified through agarose gel electrophoresis and electrochemical impedance spectroscopy measurements, and then confirmed by transferring it to a nanomaterial-assisted fluorescence assay. Significantly, the BRCL strategy-based assay is able to respond to target protein with desirable selectivity, attributing to the specific recognition between small-molecule ligand and its target. Further experiments validate the general applicability of the sensing method by tailoring the ligand toward different proteins (i.e., avidin and folate receptor), and demonstrate its usability in complex biological samples. To our knowledge, this work pioneers the practice of click chemistry in probing specific small-molecule ligand-protein binding, and therefore may pave a new way for selective detection of proteins. Copyright © 2015 Elsevier B.V. All rights reserved.

  4. MODEL SELECTION FOR SPECTROPOLARIMETRIC INVERSIONS

    SciTech Connect

    Asensio Ramos, A.; Manso Sainz, R.; Martinez Gonzalez, M. J.; Socas-Navarro, H.; Viticchie, B.

    2012-04-01

    Inferring magnetic and thermodynamic information from spectropolarimetric observations relies on the assumption of a parameterized model atmosphere whose parameters are tuned by comparison with observations. Often, the choice of the underlying atmospheric model is based on subjective reasons. In other cases, complex models are chosen based on objective reasons (for instance, the necessity to explain asymmetries in the Stokes profiles) but it is not clear what degree of complexity is needed. The lack of an objective way of comparing models has, sometimes, led to opposing views of the solar magnetism because the inferred physical scenarios are essentially different. We present the first quantitative model comparison based on the computation of the Bayesian evidence ratios for spectropolarimetric observations. Our results show that there is not a single model appropriate for all profiles simultaneously. Data with moderate signal-to-noise ratios (S/Ns) favor models without gradients along the line of sight. If the observations show clear circular and linear polarization signals above the noise level, models with gradients along the line are preferred. As a general rule, observations with large S/Ns favor more complex models. We demonstrate that the evidence ratios correlate well with simple proxies. Therefore, we propose to calculate these proxies when carrying out standard least-squares inversions to allow for model comparison in the future.

  5. Student learning using the natural selection model

    NASA Astrophysics Data System (ADS)

    Mesmer, Karen Luann

    Students often have difficulty in learning natural selection, a major model in biology. This study examines what middle school students are capable of learning when taught about natural selection using a modeling approach. Students were taught the natural selection model including the components of population, variation, selective advantage, survival, heredity and reproduction. They then used the model to solve three case studies. Their learning was evaluated from responses on a pretest, a posttest and interviews. The results suggest that middle school students can identify components of the natural selection model in a Darwinian explanation, explain the significance of the components and relate them to each other as well as solve evolutionary problems using the model.

  6. Selection against tandem splice sites affecting structured protein regions.

    PubMed

    Hiller, Michael; Szafranski, Karol; Huse, Klaus; Backofen, Rolf; Platzer, Matthias

    2008-03-21

    Alternative selection of splice sites in tandem donors and acceptors is a major mode of alternative splicing. Here, we analyzed whether in-frame tandem sites leading to subtle mRNA insertions/deletions of 3, 6, or 9 nucleotides are under natural selection. We found multiple lines of evidence that the human protein coding sequences are under selection against such in-frame tandem splice events, indicating that these events are often deleterious. The strength of selection is not homogeneous within the coding sequence as protein regions that fold into a fixed 3D structure (intrinsically ordered) are under stronger selection, especially against sites with a strong minor splice site. Investigating structures of functional protein domains, we found that tandem acceptors are preferentially located at the domain surface and outside structural elements such as helices and sheets. Using three-species comparisons, we estimate that more than half of all mutations that create NAGNAG acceptors in the coding region have been eliminated by selection. We estimate that ~2,400 introns are under selection against possessing a tandem site.

  7. State-selective metabolic labeling of cellular proteins.

    PubMed

    Ngo, John T; Babin, Brett M; Champion, Julie A; Schuman, Erin M; Tirrell, David A

    2012-08-17

    Transcriptional activity from a specified promoter can provide a useful marker for the physiological state of a cell. Here we introduce a method for selective tagging of proteins made in cells in which specified promoters are active. Tagged proteins can be modified with affinity reagents for enrichment or with fluorescent dyes for visualization. The method allows state-selective analysis of the proteome, whereby proteins synthesized in predetermined physiological states can be identified. The approach is demonstrated by proteome-wide labeling of bacterial proteins upon activation of the P(BAD) promoter and the SoxRS regulon and provides a basis for analysis of more complex systems including spatially heterogeneous microbial cultures and biofilms.

  8. Arginine selective reagents for ligation to peptides and proteins.

    PubMed

    Thompson, Darren A; Ng, Raymond; Dawson, Philip E

    2016-05-01

    A new class of arginine-specific bioconjugation reagents for protein labeling has been developed. This method utilizes a triazolyl-phenylglyoxal group on the probe molecule that reacts selectively with the guandinyl group of Arg residues in a protein or peptide. The reaction proceeds in neutral to basic bicarbonate buffers and is selective for arginine residues in peptides and folded proteins. Importantly, the triazolyl-phenylglyoxal group can be introduced into complex molecules containing alkyne groups using CuAAC chemistry, providing a robust approach for the generation of phenylglyoxal reactive groups into molecules to be covalently attached onto the surface of proteins. Copyright © 2016 European Peptide Society and John Wiley & Sons, Ltd. Copyright © 2016 European Peptide Society and John Wiley & Sons, Ltd.

  9. Site-Selective Surface-Enhanced Raman Detection of Proteins.

    PubMed

    Matteini, Paolo; Cottat, Maximilien; Tavanti, Francesco; Panfilova, Elizaveta; Scuderi, Mario; Nicotra, Giuseppe; Menziani, Maria Cristina; Khlebtsov, Nikolai; de Angelis, Marella; Pini, Roberto

    2017-01-24

    Strategies for protein detection via surface-enhanced Raman spectroscopy (SERS) currently exploit the formation of randomly generated hot spots at the interfaces of metal colloidal nanoparticles, which are clustered together by intrusive chemical or physical processes in the presence of the target biomolecule. We propose a different approach based on selective and quantitative gathering of protein molecules at regular hot spots generated on the corners of individual silver nanocubes in aqueous medium at physiological pH. Here, the protein, while keeping its native configuration, experiences an intense local E-field, which boosts SERS efficiency and detection sensitivity. Uncontrolled signal fluctuations caused by variable molecular adsorption to different particle areas or inside clustered nanoparticles are circumvented. Advanced electron microscopy analyses and computational simulations outline a strategy relying on a site-selective mechanism with superior Raman signal enhancement, which offers the perspective of highly controlled and reproducible routine SERS detection of proteins.

  10. A Computational Model of Selection by Consequences

    ERIC Educational Resources Information Center

    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…

  11. A Computational Model of Selection by Consequences

    ERIC Educational Resources Information Center

    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…

  12. Photo selective protein immobilization using bovine serum albumin

    NASA Astrophysics Data System (ADS)

    Kim, Wan-Joong; Kim, Ansoon; Huh, Chul; Park, Chan Woo; Ah, Chil Seong; Kim, Bong Kyu; Yang, Jong-Heon; Chung, Kwang Hyo; Choi, Yo Han; Hong, Jongcheol; Sung, Gun Yong

    2012-11-01

    A simple and selective technique which immobilizes protein onto a solid substrate by using UV illumination has been developed. In protein immobilization, a Bovine serum albumin (BSA) performed bifunctional role as a cross-linker between substrate and proteins and as a blocker inhibiting a nonspecific protein adsorption. A new photo-induced protein immobilization process has been investigated at each step by fluorescence microscopy, ellipsometry, and Fourier transform infrared (FT-IR) spectroscopy. A UV photomask has been used to induce selective protein immobilization on target regions of the surface of the SiO2 substrates under UV illumination with negligible nonspecific binding. The UV illumination also showed improved photostability than the conventional methods which employed bifunctional photo-crosslinker molecules of photo-reactive diazirine. This new UV illumination-based photo-addressable protein immobilization provides a new approach for developing novel protein microarrays for multiplexed sensing as well as other types of bio-immobilization in biomedical devices and biotechnologies.

  13. Selective Uptake and Refolding of Globular Proteins in Coacervate Microdroplets.

    PubMed

    Martin, Nicolas; Li, Mei; Mann, Stephen

    2016-06-14

    Intrinsic differences in the molecular sequestration of folded and unfolded proteins within poly(diallyldimethylammonium) (PDDA)/poly(acrylate) (PAA) coacervate microdroplets are exploited to establish membrane-free microcompartments that support protein refolding, facilitate the recovery of secondary structure and enzyme activity, and enable the selective uptake and exclusion of folded and unfolded biomolecules, respectively. Native bovine serum albumin, carbonic anhydrase, and α-chymotrypsin are preferentially sequestered within positively charged coacervate microdroplets, and the unfolding of these proteins in the presence of increasing amounts of urea results in an exponential decrease in the equilibrium partition constants as well as the kinetic release of unfolded molecules from the droplets into the surrounding continuous phase. Slow refolding in the presence of positively charged microdroplets leads to the resequestration of functional proteins and the restoration of enzymatic activity; however, fast refolding results in protein aggregation at the droplet surface. In contrast, slow and fast refolding in the presence of negatively charged PDDA/PAA droplets gives rise to reduced protein aggregation and misfolding by interactions at the droplet surface to give increased levels of protein renaturation. Together, our observations provide new insights into the bottom-up design and construction of self-assembling microcompartments capable of supporting the selective uptake and refolding of globular proteins.

  14. Zeolite Nanoparticles for Selective Sorption of Plasma Proteins

    PubMed Central

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

    2015-01-01

    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. PMID:26616161

  15. Zeolite Nanoparticles for Selective Sorption of Plasma Proteins.

    PubMed

    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.

  16. Zeolite Nanoparticles for Selective Sorption of Plasma Proteins

    NASA Astrophysics Data System (ADS)

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

    2015-11-01

    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.

  17. Estimating the Distribution of Selection Coefficients from Phylogenetic Data Using Sitewise Mutation-Selection Models

    PubMed Central

    Tamuri, Asif U.; dos Reis, Mario; Goldstein, Richard A.

    2012-01-01

    Estimation of the distribution of selection coefficients of mutations is a long-standing issue in molecular evolution. In addition to population-based methods, the distribution can be estimated from DNA sequence data by phylogenetic-based models. Previous models have generally found unimodal distributions where the probability mass is concentrated between mildly deleterious and nearly neutral mutations. Here we use a sitewise mutation–selection phylogenetic model to estimate the distribution of selection coefficients among novel and fixed mutations (substitutions) in a data set of 244 mammalian mitochondrial genomes and a set of 401 PB2 proteins from influenza. We find a bimodal distribution of selection coefficients for novel mutations in both the mitochondrial data set and for the influenza protein evolving in its natural reservoir, birds. Most of the mutations are strongly deleterious with the rest of the probability mass concentrated around mildly deleterious to neutral mutations. The distribution of the coefficients among substitutions is unimodal and symmetrical around nearly neutral substitutions for both data sets at adaptive equilibrium. About 0.5% of the nonsynonymous mutations and 14% of the nonsynonymous substitutions in the mitochondrial proteins are advantageous, with 0.5% and 24% observed for the influenza protein. Following a host shift of influenza from birds to humans, however, we find among novel mutations in PB2 a trimodal distribution with a small mode of advantageous mutations. PMID:22209901

  18. Protein Hydrogel Microbeads for Selective Uranium Mining from Seawater.

    PubMed

    Kou, Songzi; Yang, Zhongguang; Sun, Fei

    2017-01-25

    Practical methods for oceanic uranium extraction have yet to be developed in order to tap into the vast uranium reserve in the ocean as an alternative energy. Here we present a protein hydrogel system containing a network of recently engineered super uranyl binding proteins (SUPs) that is assembled through thiol-maleimide click chemistry under mild conditions. Monodisperse SUP hydrogel microbeads fabricated by a microfluidic device further enable uranyl (UO2(2+)) enrichment from natural seawater with great efficiency (enrichment index, K = 2.5 × 10(3)) and selectivity. Our results demonstrate the feasibility of using protein hydrogels to extract uranium from the ocean.

  19. Modeling HIV-1 Drug Resistance as Episodic Directional Selection

    PubMed Central

    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. PMID:22589711

  20. Evolution of Metal Selectivity in Templated Protein Interfaces

    PubMed Central

    Brodin, Jeffrey D.; Medina-Morales, Annette; Ni, Thomas; Salgado, Eric N.; Ambroggio, Xavier I.; Tezcan, F. Akif

    2010-01-01

    Selective binding by metalloproteins to their cognate metal ions is essential to cellular survival. How proteins originally acquired the ability to selectively bind metals and evolved a diverse array of metal-centered functions despite the availability of only a few metal-coordinating functionalities remains an open question. Using a rational design approach (Metal-Templated Interface Redesign), we describe the transformation of a monomeric electron transfer protein, cytochrome cb562, into a tetrameric assembly (C96RIDC-1) that stably and selectively binds Zn2+, and displays a metal-dependent conformational change reminiscent of a signaling protein. A thorough analysis of the metal binding properties of C96RIDC-14 reveals that it can also stably harbor other divalent metals with affinities that rival (Ni2+) or even exceed (Cu2+) those of Zn2+ on a per site basis. Nevertheless, this analysis suggests that our templating strategy also introduces an increased bias towards binding a higher number of Zn2+ ions (4 high affinity sites) versus Cu2+ or Ni2+ (2 high affinity sites), ultimately leading to the exclusive selectivity of C96RIDC-14 for Zn2 over those ions. More generally, our results indicate that an initial metal-driven nucleation event followed by the formation of a stable protein architecture around the metal provides a straightforward path for generating structural and functional diversity. PMID:20515031

  1. The Ouroboros Model, selected facets.

    PubMed

    Thomsen, Knud

    2011-01-01

    The Ouroboros Model features a biologically inspired cognitive architecture. At its core lies a self-referential recursive process with alternating phases of data acquisition and evaluation. Memory entries are organized in schemata. The activation at a time of part of a schema biases the whole structure and, in particular, missing features, thus triggering expectations. An iterative recursive monitor process termed 'consumption analysis' is then checking how well such expectations fit with successive activations. Mismatches between anticipations based on previous experience and actual current data are highlighted and used for controlling the allocation of attention. A measure for the goodness of fit provides feedback as (self-) monitoring signal. The basic algorithm works for goal directed movements and memory search as well as during abstract reasoning. It is sketched how the Ouroboros Model can shed light on characteristics of human behavior including attention, emotions, priming, masking, learning, sleep and consciousness.

  2. Engineering A-kinase anchoring protein (AKAP)-selective regulatory subunits of protein kinase A (PKA) through structure-based phage selection.

    PubMed

    Gold, Matthew G; Fowler, Douglas M; Means, Christopher K; Pawson, Catherine T; Stephany, Jason J; Langeberg, Lorene K; Fields, Stanley; Scott, John D

    2013-06-14

    PKA is retained within distinct subcellular environments by the association of its regulatory type II (RII) subunits with A-kinase anchoring proteins (AKAPs). Conventional reagents that universally disrupt PKA anchoring are patterned after a conserved AKAP motif. We introduce a phage selection procedure that exploits high-resolution structural information to engineer RII mutants that are selective for a particular AKAP. Selective RII (RSelect) sequences were obtained for eight AKAPs following competitive selection screening. Biochemical and cell-based experiments validated the efficacy of RSelect proteins for AKAP2 and AKAP18. These engineered proteins represent a new class of reagents that can be used to dissect the contributions of different AKAP-targeted pools of PKA. Molecular modeling and high-throughput sequencing analyses revealed the molecular basis of AKAP-selective interactions and shed new light on native RII-AKAP interactions. We propose that this structure-directed evolution strategy might be generally applicable for the investigation of other protein interaction surfaces.

  3. Estimation and Accuracy after Model Selection

    PubMed Central

    Efron, Bradley

    2013-01-01

    Classical statistical theory ignores model selection in assessing estimation accuracy. Here we consider bootstrap methods for computing standard errors and confidence intervals that take model selection into account. The methodology involves bagging, also known as bootstrap smoothing, to tame the erratic discontinuities of selection-based estimators. A useful new formula for the accuracy of bagging then provides standard errors for the smoothed estimators. Two examples, nonparametric and parametric, are carried through in detail: a regression model where the choice of degree (linear, quadratic, cubic, …) is determined by the Cp criterion, and a Lasso-based estimation problem. PMID:25346558

  4. Improving a consensus approach for protein structure selection by removing redundancy.

    PubMed

    Wang, Qingguo; Shang, Yi; Xu, Dong

    2011-01-01

    In protein tertiary structure prediction, a crucial step is to select near-native structures from a large number of predicted structural models. Over the years, extensive research has been conducted for the protein structure selection problem with most approaches focusing on developing more accurate energy or scoring functions. Despite significant advances in this area, the discerning power of current approaches is still unsatisfactory. In this paper, we propose a novel consensus-based algorithm for the selection of predicted protein structures. Given a set of predicted models, our method first removes redundant structures to derive a subset of reference models. Then, a structure is ranked based on its average pairwise similarity to the reference models. Using the CASP8 data set containing a large collection of predicted models for 122 targets, we compared our method with the best CASP8 quality assessment (QA) servers, which are all consensus based, and showed that our QA scores correlate better with the GDT-TSs than those of the CASP8 QA servers. We also compared our method with the state-of-the-art scoring functions and showed its improved performance for near-native model selection. The GDT-TSs of the top models picked by our method are on average more than 8 percent better than the ones selected by the best performing scoring function.

  5. Computational modeling of membrane proteins

    PubMed Central

    Leman, Julia Koehler; Ulmschneider, Martin B.; Gray, Jeffrey J.

    2014-01-01

    The determination of membrane protein (MP) structures has always trailed that of soluble proteins due to difficulties in their overexpression, reconstitution into membrane mimetics, and subsequent structure determination. The percentage of MP structures in the protein databank (PDB) has been at a constant 1-2% for the last decade. In contrast, over half of all drugs target MPs, only highlighting how little we understand about drug-specific effects in the human body. To reduce this gap, researchers have attempted to predict structural features of MPs even before the first structure was experimentally elucidated. In this review, we present current computational methods to predict MP structure, starting with secondary structure prediction, prediction of trans-membrane spans, and topology. Even though these methods generate reliable predictions, challenges such as predicting kinks or precise beginnings and ends of secondary structure elements are still waiting to be addressed. We describe recent developments in the prediction of 3D structures of both α-helical MPs as well as β-barrels using comparative modeling techniques, de novo methods, and molecular dynamics (MD) simulations. The increase of MP structures has (1) facilitated comparative modeling due to availability of more and better templates, and (2) improved the statistics for knowledge-based scoring functions. Moreover, de novo methods have benefitted from the use of correlated mutations as restraints. Finally, we outline current advances that will likely shape the field in the forthcoming decade. PMID:25355688

  6. Selective Inhibition on RAGE-binding AGEs Required by Bioactive Peptide Alpha-S2 Case in Protein from Goat Ethawah Breed Milk: Study of Biological Modeling

    PubMed Central

    Fatchiyah, Fatchiyah; Hardiyanti, Ferlany; Widodo, Nashi

    2015-01-01

    Background: Advanced Glycation End Products (AGE) play a pivotal role in the development various degenerative diseases such as diabetes, cardiovascular disease, stroke, neuropathy, and nephropathy. Different studies have been done to employ AGEs as drug targets for the diseases therapy. In previous study, we have found bioactive peptide from Ethawah goat milk for anti-diabetic that may work through inhibition of AGE receptor function. However, the mechanism of bioactive peptides inhibits AGE- AGE receptor (RAGE) bonding still not clear yet. Therefore we investigated the inhibition mechanism by calculate the potential energy binding among the peptides, AGEs and RAGE using molecular docking system. Methods: Modeling 3D-structure was predicted by SWISS-MODEL web server. The virtual interaction was analyzed by docking system using HEX 8.0, Pymol and Discovery Studio 4.0 software. Results: this study showed that AGEs (Argypirimidine, Imidazole, Pentosidine and Pyrraline) bind to C-domain of RAGE. The total energy binding of RAGE with Argypirimidine, Imidazole, Pentosidine and Pyrraline were 378.35kJ/mol, -74.57kJ/mol, -301.25kJ/mol and -400.72kJ/mol, respectively. We have found three peptides among eight peptides from Ethawah goat milk, which are able bind to C-domain of RAGE, there are CSN1S2 f41-47, CSN1S2 f182-189, and CSN1S2 f214-221. The CSN1S22 f41-47 at arginine residue 47 interacts with proline162, leusine163 and leusine158 of RAGE. The total binding energy between CSN1S2 f41-47, CSN1S2 f182-189, and CSN1S2 f214-221 with RAGE were -378.35 kJ/mol, -359.97kJ/mol, -356.78 kJ/mol, respectively. Total binding energy and binding pattern indicated that RAGE more prefer bind with peptide and block AGE bind to functional site of RAGE. Further analysis showed that complex peptide-RAGE shifted binding site of AGE on function domain RAGE. Conclusion: This study suggested that the peptides from Ethawah goat milk may act as an inhibitor of AGEs-RAGE interaction that impaired

  7. Review and selection of unsaturated flow models

    SciTech Connect

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

    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.

  8. Environment-sensitive amphiphilic fluorophore for selective sensing of protein.

    PubMed

    Ojha, Bimlesh; Das, Gopal

    2011-04-01

    Here we report the selective sensing of BSA (bovine serum albumin) by 8-(alkoxy)quinoline-based fluorescent probes, via non-covalent interactions. The weak fluorescence of these probes in aqueous solution showed a dramatic increase in quantum yield and lifetime upon binding with BSA, while the responses to various other proteins/enzymes used were negligible under similar experimental conditions. The emission of the probe is affected by the interplay with BSA but not with tryptophan amino acid suggesting that the microenvironment created by the macromolecule induces some change in their excited-state properties. Binding site assignment by a known site-selective binding ligand enabled us to conclude that the compounds predominantly bind at site I of BSA. The changes in fluorescence intensity and the position of emission maxima of compounds in presence of BSA along with the increase in steady state anisotropy values well reflect the nature of binding and location of the probe inside the protein environment. Compounds interact with BSA efficiently and exhibit site selectivity and thus have the potentiality to serve as an efficient and selective sensor of protein.

  9. Conformational selection of protein kinase A revealed by flexible-ligand flexible-protein docking.

    PubMed

    Huang, Zunnan; Wong, Chung F

    2009-03-01

    Protein kinases have high structural plasticity: their structure can change significantly, depending on what ligands are bound to them. Rigid-protein docking methods are not capable of describing such effects. Here, we present a new flexible-ligand flexible-protein docking model in which the protein can adopt conformations between two extremes observed experimentally. The model utilized a molecular dynamics-based simulated annealing cycling protocol and a distance-dependent dielectric model to perform docking. By testing this model on docking four diverse ligands to protein kinase A, we found that the ligands were able to dock successfully to the protein with the proper conformations of the protein induced. By imposing relatively soft conformational restraints to the protein during docking, this model reduced computational costs yet permitted essential conformational changes that were essential for these inhibitors to dock properly to the protein. For example, without adequate movement of the glycine-rich loop, it was difficult for the ligands to move from the surface of the protein to the binding site. In addition, these simulations called for better ways to compare simulation results with experiment other than using the popular root-mean-square deviation between the structure of a ligand in a docking pose and that in experiment because the structure of the protein also changed. In this work, we also calculated the correlation coefficient between protein-ligand/protein-protein distances in the docking structure and those in the crystal structure to check how well a ligand docked into the binding site of the protein and whether the proper conformation of the protein was induced.

  10. Graphical tools for model selection in generalized linear models.

    PubMed

    Murray, K; Heritier, S; Müller, S

    2013-11-10

    Model selection techniques have existed for many years; however, to date, simple, clear and effective methods of visualising the model building process are sparse. This article describes graphical methods that assist in the selection of models and comparison of many different selection criteria. Specifically, we describe for logistic regression, how to visualize measures of description loss and of model complexity to facilitate the model selection dilemma. We advocate the use of the bootstrap to assess the stability of selected models and to enhance our graphical tools. We demonstrate which variables are important using variable inclusion plots and show that these can be invaluable plots for the model building process. We show with two case studies how these proposed tools are useful to learn more about important variables in the data and how these tools can assist the understanding of the model building process.

  11. Analytical lessons learned from selected therapeutic protein drug comparability studies.

    PubMed

    Federici, Marcia; Lubiniecki, Anthony; Manikwar, Prakash; Volkin, David B

    2013-05-01

    The successful implementation of process and product changes for a therapeutic protein drug, both during clinical development and after commercialization, requires a detailed evaluation of their impact on the protein's structure and biological functionality. This analysis is called a comparability exercise and includes a data driven assessment of biochemical equivalence and biological characterization using a cadre of analytical methodologies. This review focuses on describing analytical results and lessons learned from selected published therapeutic protein comparability case studies both for bulk drug substance and final drug product. An overview of the currently available analytical methodologies typically used is presented as well as a discussion of new emerging analytical techniques. The potential utility of several novel analytical approaches to comparability studies is discussed including distribution and stability of protein drugs in vivo, and enhanced evaluation of higher-order protein structure in actual formulations using hydrogen/deuterium exchange mass spectrometry, two-dimensional nuclear magnetic resonance fingerprinting or empirical phase diagrams. In addition, new methods for detecting and characterizing protein aggregates and particles are presented as these degradants are of current industry-wide concern. The critical role that analytical methodologies play in elucidating the structure-function relationships for therapeutic protein products during the overall assessment of comparability is discussed.

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

    ERIC Educational Resources Information Center

    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…

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

    ERIC Educational Resources Information Center

    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…

  14. Coevolutionary information, protein folding landscapes, and the thermodynamics of natural selection

    PubMed Central

    Morcos, Faruck; Schafer, Nicholas P.; Cheng, Ryan R.; Onuchic, José N.; Wolynes, Peter G.

    2014-01-01

    The energy landscape used by nature over evolutionary timescales to select protein sequences is essentially the same as the one that folds these sequences into functioning proteins, sometimes in microseconds. We show that genomic data, physical coarse-grained free energy functions, and family-specific information theoretic models can be combined to give consistent estimates of energy landscape characteristics of natural proteins. One such characteristic is the effective temperature Tsel at which these foldable sequences have been selected in sequence space by evolution. Tsel quantifies the importance of folded-state energetics and structural specificity for molecular evolution. Across all protein families studied, our estimates for Tsel are well below the experimental folding temperatures, indicating that the energy landscapes of natural foldable proteins are strongly funneled toward the native state. PMID:25114242

  15. A novel method for protein-protein interaction site prediction using phylogenetic substitution models

    PubMed Central

    La, David; Kihara, Daisuke

    2011-01-01

    Protein-protein binding events mediate many critical biological functions in the cell. Typically, functionally important sites in proteins can be well identified by considering sequence conservation. However, protein-protein interaction sites exhibit higher sequence variation than other functional regions, such as catalytic sites of enzymes. Consequently, the mutational behavior leading to weak sequence conservation poses significant challenges to the protein-protein interaction site prediction. Here, we present a phylogenetic framework to capture critical sequence variations that favor the selection of residues essential for protein-protein binding. Through the comprehensive analysis of diverse protein families, we show that protein binding interfaces exhibit distinct amino acid substitution as compared with other surface residues. Based on this analysis, we have developed a novel method, BindML, which utilizes the substitution models to predict protein-protein binding sites of protein with unknown interacting partners. BindML estimates the likelihood that a phylogenetic tree of a local surface region in a query protein structure follows the substitution patterns of protein binding interface and non-binding surfaces. BindML is shown to perform well compared to alternative methods for protein binding interface prediction. The methodology developed in this study is very versatile in the sense that it can be generally applied for predicting other types of functional sites, such as DNA, RNA, and membrane binding sites in proteins. PMID:21989996

  16. Objective Bayesian model selection for Cox regression.

    PubMed

    Held, Leonhard; Gravestock, Isaac; Sabanés Bové, Daniel

    2016-12-20

    There is now a large literature on objective Bayesian model selection in the linear model based on the g-prior. The methodology has been recently extended to generalized linear models using test-based Bayes factors. In this paper, we show that test-based Bayes factors can also be applied to the Cox proportional hazards model. If the goal is to select a single model, then both the maximum a posteriori and the median probability model can be calculated. For clinical prediction of survival, we shrink the model-specific log hazard ratio estimates with subsequent calculation of the Breslow estimate of the cumulative baseline hazard function. A Bayesian model average can also be employed. We illustrate the proposed methodology with the analysis of survival data on primary biliary cirrhosis patients and the development of a clinical prediction model for future cardiovascular events based on data from the Second Manifestations of ARTerial disease (SMART) cohort study. Cross-validation is applied to compare the predictive performance with alternative model selection approaches based on Harrell's c-Index, the calibration slope and the integrated Brier score. Finally, a novel application of Bayesian variable selection to optimal conditional prediction via landmarking is described. Copyright © 2016 John Wiley & Sons, Ltd.

  17. A General Method for Insertion of Functional Proteins within Proteins via Combinatorial Selection of Permissive Junctions.

    PubMed

    Peng, Yingjie; Zeng, Wenwen; Ye, Hui; Han, Kyung Ho; Dharmarajan, Venkatasubramanian; Novick, Scott; Wilson, Ian A; Griffin, Patrick R; Friedman, Jeffrey M; Lerner, Richard A

    2015-08-20

    A major goal of modern protein chemistry is to create new proteins with different functions. One approach is to amalgamate secondary and tertiary structures from different proteins. This is difficult for several reasons, not the least of which is the fact that the junctions between secondary and tertiary structures are not degenerate and usually affect the function and folding of the entire complex. Here, we offer a solution to this problem by coupling a large combinatorial library of about 10(7) different N- and C-terminal junctions to a powerful system that selects for function. Using this approach, the entire Leptin and follicle-stimulating hormone (FSH) were inserted into an antibody. Complexes with full retention of function in vivo and in vitro, although rare, were found easily by using an autocrine selection system to search for hormonal activity. Such large diversity systems, when coupled to robust selection systems, should enable construction of novel therapeutic proteins.

  18. Comparing Model Selection and Regularization Approaches to Variable Selection in Model-Based Clustering.

    PubMed

    Celeux, Gilles; Martin-Magniette, Marie-Laure; Maugis-Rabusseau, Cathy; Raftery, Adrian E

    2014-01-01

    We compare two major approaches to variable selection in clustering: model selection and regularization. Based on previous results, we select the method of Maugis et al. (2009b), which modified the method of Raftery and Dean (2006), as a current state of the art model selection method. We select the method of Witten and Tibshirani (2010) as a current state of the art regularization method. We compared the methods by simulation in terms of their accuracy in both classification and variable selection. In the first simulation experiment all the variables were conditionally independent given cluster membership. We found that variable selection (of either kind) yielded substantial gains in classification accuracy when the clusters were well separated, but few gains when the clusters were close together. We found that the two variable selection methods had comparable classification accuracy, but that the model selection approach had substantially better accuracy in selecting variables. In our second simulation experiment, there were correlations among the variables given the cluster memberships. We found that the model selection approach was substantially more accurate in terms of both classification and variable selection than the regularization approach, and that both gave more accurate classifications than K-means without variable selection. But the model selection approach is not available in a very high dimension context.

  19. Coarsening of protein clusters on subcellular drops exhibits strong and sudden size selectivity

    NASA Astrophysics Data System (ADS)

    Brown, Aidan; Rutenberg, Andrew

    2015-03-01

    Autophagy is an important process for the degradation of cellular components, with receptor proteins targeting substrates to downstream autophagy machinery. An important question is how receptor protein interactions lead to their selective accumulation on autophagy substrates. Receptor proteins have recently been observed in clusters, raising the possibility that clustering could affect autophagy selectivity. We investigate the clustering dynamics of the autophagy receptor protein NBR1. In addition to standard receptor protein domains, NBR1 has a ``J'' domain that anchors it to membranes, and a coiled-coil domain that enhances self-interaction. We model coarsening clusters of NBR1 on the surfaces of a polydisperse collection of drops, representing organelles. Despite the disconnected nature of the drop surfaces, we recover dynamical scaling of cluster sizes. Significantly, we find that at a well-defined time after coarsening begins, clusters evaporate from smaller drops and grow on larger drops. Thus, coarsening-driven size selection will localize protein clusters to larger substrates, leaving smaller substrates without clusters. This provides a possible physical mechanism for autophagy selectivity, and can explain reports of size selection during peroxisome degradation.

  20. Protein-protein docking with a reduced protein model accounting for side-chain flexibility.

    PubMed

    Zacharias, Martin

    2003-06-01

    A protein-protein docking approach has been developed based on a reduced protein representation with up to three pseudo atoms per amino acid residue. Docking is performed by energy minimization in rotational and translational degrees of freedom. The reduced protein representation allows an efficient search for docking minima on the protein surfaces within. During docking, an effective energy function between pseudo atoms has been used based on amino acid size and physico-chemical character. Energy minimization of protein test complexes in the reduced representation results in geometries close to experiment with backbone root mean square deviations (RMSDs) of approximately 1 to 3 A for the mobile protein partner from the experimental geometry. For most test cases, the energy-minimized experimental structure scores among the top five energy minima in systematic docking studies when using both partners in their bound conformations. To account for side-chain conformational changes in case of using unbound protein conformations, a multicopy approach has been used to select the most favorable side-chain conformation during the docking process. The multicopy approach significantly improves the docking performance, using unbound (apo) binding partners without a significant increase in computer time. For most docking test systems using unbound partners, and without accounting for any information about the known binding geometry, a solution within approximately 2 to 3.5 A RMSD of the full mobile partner from the experimental geometry was found among the 40 top-scoring complexes. The approach could be extended to include protein loop flexibility, and might also be useful for docking of modeled protein structures.

  1. Ion selective transistor modelling for behavioural simulations.

    PubMed

    Daniel, M; Janicki, M; Wroblewski, W; Dybko, A; Brzozka, Z; Napieralski, A

    2004-01-01

    Computer aided design and simulation of complex silicon microsystems oriented for environment monitoring requires efficient and accurate models of ion selective sensors, compatible with the existing behavioural simulators. This paper concerns sensors based on the back-side contact Ion Sensitive Field Effect Transistors (ISFETs). The ISFETs with silicon nitride gate are sensitive to hydrogen ion concentration. When the transistor gate is additionally covered with a special ion selective membrane, selectivity to other than hydrogen ions can be achieved. Such sensors are especially suitable for flow analysis of solutions containing various ions. The problem of ion selective sensor modelling is illustrated here on a practical example of an ammonium sensitive membrane. The membrane is investigated in the presence of some interfering ions and appropriate selectivity coefficients are determined. Then, the model of the whole sensor is created and used in subsequent electrical simulations. Providing that appropriate selectivity coefficients are known, the proposed model is applicable for any membrane, and can be straightforwardly implemented for behavioural simulation of water monitoring microsystems. The model has been already applied in a real on-line water pollution monitoring system for detection of various contaminants.

  2. Screening of selective histone deacetylase inhibitors by proteochemometric modeling

    PubMed Central

    2012-01-01

    Background Histone deacetylase (HDAC) is a novel target for the treatment of cancer and it can be classified into three classes, i.e., classes I, II, and IV. The inhibitors selectively targeting individual HDAC have been proved to be the better candidate antitumor drugs. To screen selective HDAC inhibitors, several proteochemometric (PCM) models based on different combinations of three kinds of protein descriptors, two kinds of ligand descriptors and multiplication cross-terms were constructed in our study. Results The results show that structure similarity descriptors are better than sequence similarity descriptors and geometry descriptors in the leftacterization of HDACs. Furthermore, the predictive ability was not improved by introducing the cross-terms in our models. Finally, a best PCM model based on protein structure similarity descriptors and 32-dimensional general descriptors was derived (R2 = 0.9897, Qtest2 = 0.7542), which shows a powerful ability to screen selective HDAC inhibitors. Conclusions Our best model not only predict the activities of inhibitors for each HDAC isoform, but also screen and distinguish class-selective inhibitors and even more isoform-selective inhibitors, thus it provides a potential way to discover or design novel candidate antitumor drugs with reduced side effect. PMID:22913517

  3. Detergent selection for enhanced extraction of membrane proteins.

    PubMed

    Arachea, Buenafe T; Sun, Zhen; Potente, Nina; Malik, Radhika; Isailovic, Dragan; Viola, Ronald E

    2012-11-01

    Generating stable conditions for membrane proteins after extraction from their lipid bilayer environment is essential for subsequent characterization. Detergents are the most widely used means to obtain this stable environment; however, different types of membrane proteins have been found to require detergents with varying properties for optimal extraction efficiency and stability after extraction. The extraction profiles of several detergent types have been examined for membranes isolated from bacteria and yeast, and for a set of recombinant target proteins. The extraction efficiencies of these detergents increase at higher concentrations, and were shown to correlate with their respective CMC values. Two alkyl sugar detergents, octyl-β-d-glucoside (OG) and 5-cyclohexyl-1-pentyl-β-d-maltoside (Cymal-5), and a zwitterionic surfactant, N-decylphosphocholine (Fos-choline-10), were generally effective in the extraction of a broad range of membrane proteins. However, certain detergents were more effective than others in the extraction of specific classes of integral membrane proteins, offering guidelines for initial detergent selection. The differences in extraction efficiencies among this small set of detergents supports the value of detergent screening and optimization to increase the yields of targeted membrane proteins. Copyright © 2012 Elsevier Inc. All rights reserved.

  4. Protein models: The Grand Challenge of protein docking

    PubMed Central

    Anishchenko, Ivan; Kundrotas, Petras J.; Tuzikov, Alexander V.; Vakser, Ilya A.

    2016-01-01

    Characterization of life processes at the molecular level requires structural details of protein–protein interactions (PPIs). The number of experimentally determined protein structures accounts only for a fraction of known proteins. This gap has to be bridged by modeling, typically using experimentally determined structures as templates to model related proteins. The fraction of experimentally determined PPI structures is even smaller than that for the individual proteins, due to a larger number of interactions than the number of individual proteins, and a greater difficulty of crystallizing protein–protein complexes. The approaches to structural modeling of PPI (docking) often have to rely on modeled structures of the interactors, especially in the case of large PPI networks. Structures of modeled proteins are typically less accurate than the ones determined by X-ray crystallography or nuclear magnetic resonance. Thus the utility of approaches to dock these structures should be assessed by thorough benchmarking, specifically designed for protein models. To be credible, such benchmarking has to be based on carefully curated sets of structures with levels of distortion typical for modeled proteins. This article presents such a suite of models built for the benchmark set of the X-ray structures from the Dockground resource (http://dockground.bioinformatics.ku.edu) by a combination of homology modeling and Nudged Elastic Band method. For each monomer, six models were generated with predefined Cα root mean square deviation from the native structure (1, 2, . . ., 6 Å). The sets and the accompanying data provide a comprehensive resource for the development of docking methodology for modeled proteins. PMID:23934791

  5. Structural basis of PROTAC cooperative recognition for selective protein degradation

    PubMed Central

    Chan, Kwok-Ho; Chen, Wenzhang; Lamont, Douglas J.; Zengerle, Michael; Ciulli, Alessio

    2017-01-01

    Inducing macromolecular interactions with small molecules to activate cellular signaling is a challenging goal. PROTACs (proteolysis-targeting chimaeras) are bifunctional molecules that recruit a target protein in proximity to an E3 ubiquitin ligase to trigger protein degradation. Structural elucidation of the key ternary ligase:PROTAC:target species and how this impacts target degradation selectivity remains elusive. We solved the crystal structure of Brd4-degrader MZ1 in complex with human VHL and the Brd4 bromodomain (Brd4BD2). The ligand folds into itself to allow formation of specific intermolecular interactions in the ternary complex. Isothermal titration calorimetry studies, supported by surface mutagenesis and proximity assays, are consistent with pronounced cooperative formation of ternary complexes with Brd4BD2. Structure-based-designed compound AT1 exhibits highly selective depletion of Brd4 in cells. Our results elucidate how PROTAC-induced de novo contacts dictate preferential recruitment of a target protein into a stable and cooperative complex with an E3 ligase for selective degradation. PMID:28288108

  6. Structural basis of PROTAC cooperative recognition for selective protein degradation.

    PubMed

    Gadd, Morgan S; Testa, Andrea; Lucas, Xavier; Chan, Kwok-Ho; Chen, Wenzhang; Lamont, Douglas J; Zengerle, Michael; Ciulli, Alessio

    2017-03-13

    Inducing macromolecular interactions with small molecules to activate cellular signaling is a challenging goal. PROTACs (proteolysis-targeting chimeras) are bifunctional molecules that recruit a target protein in proximity to an E3 ubiquitin ligase to trigger protein degradation. Structural elucidation of the key ternary ligase-PROTAC-target species and its impact on target degradation selectivity remain elusive. We solved the crystal structure of Brd4 degrader MZ1 in complex with human VHL and the Brd4 bromodomain (Brd4(BD2)). The ligand folds into itself to allow formation of specific intermolecular interactions in the ternary complex. Isothermal titration calorimetry studies, supported by surface mutagenesis and proximity assays, are consistent with pronounced cooperative formation of ternary complexes with Brd4(BD2). Structure-based-designed compound AT1 exhibits highly selective depletion of Brd4 in cells. Our results elucidate how PROTAC-induced de novo contacts dictate preferential recruitment of a target protein into a stable and cooperative complex with an E3 ligase for selective degradation.

  7. Riboflavin-binding protein exhibits selective sweet suppression toward protein sweeteners.

    PubMed

    Maehashi, Kenji; Matano, Mami; Kondo, Azusa; Yamamoto, Yasushi; Udaka, Shigezo

    2007-02-01

    Riboflavin-binding protein (RBP) is well known as a riboflavin carrier protein in chicken egg and serum. A novel function of RBP was found as a sweet-suppressing protein. RBP, purified from hen egg white, suppressed the sweetness of protein sweeteners such as thaumatin, monellin, and lysozyme, whereas it did not suppress the sweetness of low molecular weight sweeteners such as sucrose, glycine, D-phenylalanine, saccharin, cyclamate, aspartame, and stevioside. Therefore, the sweet-suppressing activity of RBP was apparently selective to protein sweeteners. The sweet suppression by RBP was independent of binding of riboflavin with its molecule. Yolk RBP, with minor structural differences compared with egg white RBP, also elicited a weaker sweet suppression. However, other commercially available proteins including ovalbumin, ovomucoid, beta-lactogloblin, myoglobin, and albumin did not substantially alter the sweetness of protein sweeteners. Because a prerinse with RBP reduced the subsequent sweetness of protein sweeteners, whereas the enzymatic activity of lysozyme and the elution profile of lysozyme on gel permeation chromatography were not affected by RBP, it is suggested that the sweet suppression is caused by an interaction of RBP with a sweet taste receptor rather than with the protein sweeteners themselves. The selectivity in the sweet suppression by RBP is consistent with the existence of multiple interaction sites within a single sweet taste receptor.

  8. Evolution of the folding ability of proteins through functional selection

    PubMed Central

    Saito, Seiji; Sasai, Masaki; Yomo, Tetsuya

    1997-01-01

    An evolutionary process is simulated with a simple spin-glass-like model of proteins to examine the origin of folding ability. At each generation, sequences are randomly mutated and subjected to a simulation of the folding process based on the model. According to the frequency of local configurations at the active sites, sequences are selected and passed to the next generation. After a few hundred generations, a sequence capable of folding globally into a native conformation emerges. Moreover, the selected sequence has a distinct energy minimum and an anisotropic funnel on the energy surface, which are the imperative features for fast folding of proteins. The proposed model reveals that the functional selection on the local configurations leads a sequence to fold globally into a conformation at a faster rate. PMID:9326608

  9. Anti-idiotypic protein domains selected from protein A-based affibody libraries.

    PubMed

    Eklund, Malin; Axelsson, Lars; Uhlén, Mathias; Nygren, Per-Ake

    2002-08-15

    Three pairs of small protein domains showing binding behavior in analogy with anti-idiotypic antibodies have been selected using phage display technology. From an affibody protein library constructed by combinatorial variegation of the Fc binding surface of the 58 residue staphylococcal protein A (SPA)-derived domain Z, affibody variants have been selected to the parental SPA scaffold and to two earlier identified SPA-derived affibodies. One selected affibody (Z(SPA-1)) was shown to recognize each of the five domains of wild-type SPA with dissociation constants (K(D)) in the micromolar range. The binding of the Z(SPA-1) affibody to its parental structure was shown to involve the Fc binding site of SPA, while the Fab-binding site was not involved. Similarly, affibodies showing anti-idiotypic binding characteristics were also obtained when affibodies previously selected for binding to Taq DNA polymerase and human IgA, respectively, were used as targets for selections. The potential applications for these types of affinity pairs were exemplified by one-step protein recovery using affinity chromatography employing the specific interactions between the respective protein pair members. These experiments included the purification of the Z(SPA-1) affibody from a total Escherichia coli cell lysate using protein A-Sepharose, suggesting that this protein A/antiprotein A affinity pair could provide a basis for novel affinity gene fusion systems. The use of this type of small, robust, and easily expressed anti-idiotypic affibody pair for affinity technology applications, including self-assembled protein networks, is discussed.

  10. Selective Sorting of Cargo Proteins into Bacterial Membrane Vesicles*

    PubMed Central

    Haurat, M. Florencia; Aduse-Opoku, Joseph; Rangarajan, Minnie; Dorobantu, Loredana; Gray, Murray R.; Curtis, Michael A.; Feldman, Mario F.

    2011-01-01

    In contrast to the well established multiple cellular roles of membrane vesicles in eukaryotic cell biology, outer membrane vesicles (OMV) produced via blebbing of prokaryotic membranes have frequently been regarded as cell debris or microscopy artifacts. Increasingly, however, bacterial membrane vesicles are thought to play a role in microbial virulence, although it remains to be determined whether OMV result from a directed process or from passive disintegration of the outer membrane. Here we establish that the human oral pathogen Porphyromonas gingivalis has a mechanism to selectively sort proteins into OMV, resulting in the preferential packaging of virulence factors into OMV and the exclusion of abundant outer membrane proteins from the protein cargo. Furthermore, we show a critical role for lipopolysaccharide in directing this sorting mechanism. The existence of a process to package specific virulence factors into OMV may significantly alter our current understanding of host-pathogen interactions. PMID:21056982

  11. Two mechanisms of ion selectivity in protein binding sites.

    PubMed

    Yu, Haibo; Noskov, Sergei Yu; Roux, Benoît

    2010-11-23

    A theoretical framework is presented to clarify the molecular determinants of ion selectivity in protein binding sites. The relative free energy of a bound ion is expressed in terms of the main coordinating ligands coupled to an effective potential of mean force representing the influence of the rest of the protein. The latter is separated into two main contributions. The first includes all the forces keeping the ion and the coordinating ligands confined to a microscopic subvolume but does not prevent the ligands from adapting to a smaller or larger ion. The second regroups all the remaining forces that control the precise geometry of the coordinating ligands best adapted to a given ion. The theoretical framework makes it possible to delineate two important limiting cases. In the limit where the geometric forces are dominant (rigid binding site), ion selectivity is controlled by the ion-ligand interactions within the matching cavity size according to the familiar "snug-fit" mechanism of host-guest chemistry. In the limit where the geometric forces are negligible, the ion and ligands behave as a "confined microdroplet" that is free to fluctuate and adapt to ions of different sizes. In this case, ion selectivity is set by the interplay between ion-ligand and ligand-ligand interactions and is controlled by the number and the chemical type of ion-coordinating ligands. The framework is illustrated by considering the ion-selective binding sites in the KcsA channel and the LeuT transporter.

  12. Modelling with words: Narrative and natural selection.

    PubMed

    Dimech, Dominic K

    2017-02-18

    I argue that verbal models should be included in a philosophical account of the scientific practice of modelling. Weisberg (2013) has directly opposed this thesis on the grounds that verbal structures, if they are used in science, only merely describe models. I look at examples from Darwin's On the Origin of Species (1859) of verbally constructed narratives that I claim model the general phenomenon of evolution by natural selection. In each of the cases I look at, a particular scenario is described that involves at least some fictitious elements but represents the salient causal components of natural selection. I pronounce the importance of prioritising observation of scientific practice for the philosophy of modelling and I suggest that there are other likely model types that are excluded from philosophical accounts.

  13. Improving enzyme regulatory protein classification by means of SVM-RFE feature selection.

    PubMed

    Fernandez-Lozano, Carlos; Fernández-Blanco, Enrique; Dave, Kirtan; Pedreira, Nieves; Gestal, Marcos; Dorado, Julián; Munteanu, Cristian R

    2014-05-01

    Enzyme regulation proteins are very important due to their involvement in many biological processes that sustain life. The complexity of these proteins, the impossibility of identifying direct quantification molecular properties associated with the regulation of enzymatic activities, and their structural diversity creates the necessity for new theoretical methods that can predict the enzyme regulatory function of new proteins. The current work presents the first classification model that predicts protein enzyme regulators using the Markov mean properties. These protein descriptors encode the topological information of the amino acid into contact networks based on amino acid distances and physicochemical properties. MInD-Prot software calculated these molecular descriptors for 2415 protein chains (350 enzyme regulators) using five atom physicochemical properties (Mulliken electronegativity, Kang-Jhon polarizability, vdW area, atom contribution to P) and the protein 3D regions. The best classification models to predict enzyme regulators have been obtained with machine learning algorithms from Weka using 18 features. K* has been demonstrated to be the most accurate algorithm for this protein function classification. Wrapper Subset Evaluator and SVM-RFE approaches were used to perform a feature subset selection with the best results obtained from SVM-RFE. Classification performance employing all the available features can be reached using only the 8 most relevant features selected by SVM-RFE. Thus, the current work has demonstrated the possibility of predicting new molecular targets involved in enzyme regulation using fast theoretical algorithms.

  14. An Ss Model with Adverse Selection.

    ERIC Educational Resources Information Center

    House, Christopher L.; Leahy, John V.

    2004-01-01

    We present a model of the market for a used durable in which agents face fixed costs of adjustment, the magnitude of which depends on the degree of adverse selection in the secondary market. We find that, unlike typical models, the sS bands in our model contract as the variance of the shock increases. We also analyze a dynamic version of the model…

  15. An Ss Model with Adverse Selection.

    ERIC Educational Resources Information Center

    House, Christopher L.; Leahy, John V.

    2004-01-01

    We present a model of the market for a used durable in which agents face fixed costs of adjustment, the magnitude of which depends on the degree of adverse selection in the secondary market. We find that, unlike typical models, the sS bands in our model contract as the variance of the shock increases. We also analyze a dynamic version of the model…

  16. Kinetic rate constant prediction supports the conformational selection mechanism of protein binding.

    PubMed

    Moal, Iain H; Bates, Paul A

    2012-01-01

    The prediction of protein-protein kinetic rate constants provides a fundamental test of our understanding of molecular recognition, and will play an important role in the modeling of complex biological systems. In this paper, a feature selection and regression algorithm is applied to mine a large set of molecular descriptors and construct simple models for association and dissociation rate constants using empirical data. Using separate test data for validation, the predicted rate constants can be combined to calculate binding affinity with accuracy matching that of state of the art empirical free energy functions. The models show that the rate of association is linearly related to the proportion of unbound proteins in the bound conformational ensemble relative to the unbound conformational ensemble, indicating that the binding partners must adopt a geometry near to that of the bound prior to binding. Mirroring the conformational selection and population shift mechanism of protein binding, the models provide a strong separate line of evidence for the preponderance of this mechanism in protein-protein binding, complementing structural and theoretical studies.

  17. Surfaces for competitive selective bacterial capture from protein solutions.

    PubMed

    Fang, Bing; Gon, Saugata; Nüsslein, Klaus; Santore, Maria M

    2015-05-20

    Active surfaces that form the basis for bacterial sensors for threat detection, food safety, or certain diagnostic applications rely on bacterial adhesion. However, bacteria capture from complex fluids on the active surfaces can be reduced by the competing adsorption of proteins and other large molecules. Such adsorption can also interfere with device performance. As a result, multiple upstream processing steps are frequently employed to separate macromolecules from any cells, which remain in the buffer. Here, we present an economical approach to capture bacteria, without competitive adsorption by proteins, on engineered surfaces that do not employ biomolecular recognition, antibodies, or other molecules with engineered sequences. The surfaces are based on polyethylene glycol (PEG) brushes that, on their own, repel both proteins and bacteria. These PEG brushes backfill the surface around sparsely adsorbed cationic polymer coils (here, poly-L-lysine (PLL)). The PLL coils are effectively embedded within the brush and produce locally cationic nanoscale regions that attract negatively charged regions of proteins or cells against the steric background repulsion from the PEG brush. By carefully designing the surfaces to include just enough PLL to capture bacteria, but not enough to capture proteins, we achieve sharp selectivity where S. aureus is captured from albumin- or fibrinogen-containing solutions, but free albumin or fibrinogen molecules are rejected from the surface. Bacterial adhesion on these surfaces is not reduced by competitive protein adsorption, in contrast to performance of more uniformly cationic surfaces. Also, protein adsorption to the bacteria does not interfere with capture, at least for the case of S. aureus, to which fibrinogen binds through a specific receptor.

  18. Bayesian variable selection for latent class models.

    PubMed

    Ghosh, Joyee; Herring, Amy H; Siega-Riz, Anna Maria

    2011-09-01

    In this article, we develop a latent class model with class probabilities that depend on subject-specific covariates. One of our major goals is to identify important predictors of latent classes. We consider methodology that allows estimation of latent classes while allowing for variable selection uncertainty. We propose a Bayesian variable selection approach and implement a stochastic search Gibbs sampler for posterior computation to obtain model-averaged estimates of quantities of interest such as marginal inclusion probabilities of predictors. Our methods are illustrated through simulation studies and application to data on weight gain during pregnancy, where it is of interest to identify important predictors of latent weight gain classes.

  19. On spatial mutation-selection models

    SciTech Connect

    Kondratiev, Yuri; Kutoviy, Oleksandr E-mail: kutovyi@mit.edu; Minlos, Robert Pirogov, Sergey

    2013-11-15

    We discuss the selection procedure in the framework of mutation models. We study the regulation for stochastically developing systems based on a transformation of the initial Markov process which includes a cost functional. The transformation of initial Markov process by cost functional has an analytic realization in terms of a Kimura-Maruyama type equation for the time evolution of states or in terms of the corresponding Feynman-Kac formula on the path space. The state evolution of the system including the limiting behavior is studied for two types of mutation-selection models.

  20. On spatial mutation-selection models

    SciTech Connect

    Kondratiev, Yuri; Kutoviy, Oleksandr E-mail: kutovyi@mit.edu; Minlos, Robert Pirogov, Sergey

    2013-11-15

    We discuss the selection procedure in the framework of mutation models. We study the regulation for stochastically developing systems based on a transformation of the initial Markov process which includes a cost functional. The transformation of initial Markov process by cost functional has an analytic realization in terms of a Kimura-Maruyama type equation for the time evolution of states or in terms of the corresponding Feynman-Kac formula on the path space. The state evolution of the system including the limiting behavior is studied for two types of mutation-selection models.

  1. An experimental framework for improved selection of binding proteins using SNAP display.

    PubMed

    Houlihan, Gillian; Gatti-Lafranconi, Pietro; Kaltenbach, Miriam; Lowe, David; Hollfelder, Florian

    2014-03-01

    Display technologies (e.g. phage and ribosome display) are powerful tools for selecting and evolving protein binders against various target molecules. SNAP display is a DNA display technology that is conducted entirely in vitro: DNA encoding a library of variants is encapsulated in water-in-oil droplets wherein in vitro protein expression and covalent coupling to the encoding DNA occurs. Here, we explore critical factors for the successful performance of SNAP display based on a set of experiments designed to measure and quantify to what extent they affect selection efficiency. We find that, in SNAP display, the reconstituted cell free expression system PURExpress led to 1.5-fold more active protein and achieved 3.5-fold greater DNA recovery in model selections compared to the RTS 100 Escherichia coli lysate based expression system. We report on the influence parameters including droplet occupancy, valency and selection stringency have on recovery and enrichment. An improved procedure involving bivalent display and stringent selection against a model target, Her2, led to a 10(7)-fold enrichment of a DARPin (H10-2-G3, known to bind Her2 with picomolar affinity) over a non-binding DARPin after three rounds of selection. Furthermore, when spiked into a mixture of DARPins with different affinities, DARPin H10-2-G3 outcompeted all other variants demonstrating SNAP display's ability to efficiently resolve clones with affinities in the nano- to picomolar range. These data establish SNAP display as an in vitro protein engineering tool for isolating protein binders and provide a framework for troubleshooting affinity selections.

  2. Selective Microtubule-Based Transport of Dendritic Membrane Proteins Arises in Concert with Axon Specification

    PubMed Central

    Petersen, Jennifer D.; Kaech, Stefanie

    2014-01-01

    The polarized distribution of membrane proteins to axonal or somatodendritic neuronal compartments is fundamental to nearly every aspect of neuronal function. The polarity of dendritic proteins depends on selective microtubule-based transport; the vesicles that carry these proteins are transported into dendrites but do not enter the axon. We used live-cell imaging of fluorescently tagged dendritic and axonal proteins combined with immunostaining for initial segment and cytoskeletal markers to evaluate different models of dendrite-selective transport in cultured rat hippocampal neurons. In mature neurons, dendritic vesicles that entered the base of the axon stopped at the proximal edge of the axon initial segment, defined by immunostaining for ankyrinG, rather than moving into the initial segment itself. In contrast, axonal vesicles passed through the initial segment without impediment. During development, dendrite-selective transport was detected shortly after axons formed, several days before initial segment assembly, before the appearance of a dense actin meshwork in the initial segment, and before dendrites acquire microtubules of mixed polarity orientation. Indeed, some elements of selective transport were detected even before axon specification. These findings are inconsistent with models for selective transport that depend on the presence of an F-actin-based cytoplasmic filter in the initial segment or that posit that transport into dendrites is mediated by dyneins translocating along minus-end out microtubules. Instead our results suggest that selective transport involves the coordinated regulation of the different motor proteins that mediate dendritic vesicle transport and that the selectivity of motor-microtubule interactions is one facet of this process. PMID:24647935

  3. Polarizable protein model for Dissipative Particle Dynamics

    NASA Astrophysics Data System (ADS)

    Peter, Emanuel; Lykov, Kirill; Pivkin, Igor

    2015-11-01

    In this talk, we present a novel polarizable protein model for the Dissipative Particle Dynamics (DPD) simulation technique, a coarse-grained particle-based method widely used in modeling of fluid systems at the mesoscale. We employ long-range electrostatics and Drude oscillators in combination with a newly developed polarizable water model. The protein in our model is resembled by a polarizable backbone and a simplified representation of the sidechains. We define the model parameters using the experimental structures of 2 proteins: TrpZip2 and TrpCage. We validate the model on folding of five other proteins and demonstrate that it successfully predicts folding of these proteins into their native conformations. As a perspective of this model, we will give a short outlook on simulations of protein aggregation in the bulk and near a model membrane, a relevant process in several Amyloid diseases, e.g. Alzheimer's and Diabetes II.

  4. Standard Codon Substitution Models Overestimate Purifying Selection for Nonstationary Data

    PubMed Central

    Yap, Von Bing; Huttley, Gavin A.

    2017-01-01

    Estimation of natural selection on protein-coding sequences is a key comparative genomics approach for de novo prediction of lineage-specific adaptations. Selective pressure is measured on a per-gene basis by comparing the rate of nonsynonymous substitutions to the rate of synonymous substitutions. All published codon substitution models have been time-reversible and thus assume that sequence composition does not change over time. We previously demonstrated that if time-reversible DNA substitution models are applied in the presence of changing sequence composition, the number of substitutions is systematically biased towards overestimation. We extend these findings to the case of codon substitution models and further demonstrate that the ratio of nonsynonymous to synonymous rates of substitution tends to be underestimated over three data sets of mammals, vertebrates, and insects. Our basis for comparison is a nonstationary codon substitution model that allows sequence composition to change. Goodness-of-fit results demonstrate that our new model tends to fit the data better. Direct measurement of nonstationarity shows that bias in estimates of natural selection and genetic distance increases with the degree of violation of the stationarity assumption. Additionally, inferences drawn under time-reversible models are systematically affected by compositional divergence. As genomic sequences accumulate at an accelerating rate, the importance of accurate de novo estimation of natural selection increases. Our results establish that our new model provides a more robust perspective on this fundamental quantity. PMID:28175284

  5. Bayesian model selection and isocurvature perturbations

    NASA Astrophysics Data System (ADS)

    Beltrán, María; García-Bellido, Juan; Lesgourgues, Julien; Liddle, Andrew R.; Slosar, Anže

    2005-03-01

    Present cosmological data are well explained assuming purely adiabatic perturbations, but an admixture of isocurvature perturbations is also permitted. We use a Bayesian framework to compare the performance of cosmological models including isocurvature modes with the purely adiabatic case; this framework automatically and consistently penalizes models which use more parameters to fit the data. We compute the Bayesian evidence for fits to a data set comprised of WMAP and other microwave anisotropy data, the galaxy power spectrum from 2dFGRS and SDSS, and Type Ia supernovae luminosity distances. We find that Bayesian model selection favors the purely adiabatic models, but so far only at low significance.

  6. The RNA-binding protein SERBP1 interacts selectively with the signaling protein RACK1.

    PubMed

    Bolger, Graeme B

    2017-03-04

    The RACK1 protein interacts with numerous proteins involved in signal transduction, the cytoskeleton, and mRNA splicing and translation. We used the 2-hybrid system to identify additional proteins interacting with RACK1 and isolated the RNA-binding protein SERBP1. SERPB1 shares amino acid sequence homology with HABP4 (also known as Ki-1/57), a component of the RNA spicing machinery that has been shown previously to interact with RACK1. Several different isoforms of SERBP1, generated by alternative mRNA splicing, interacted with RACK1 with indistinguishable interaction strength, as determined by a 2-hybrid beta-galactosidase assay. Analysis of deletion constructs of SERBP1 showed that the C-terminal third of the SERBP1 protein, which contains one of its two substrate sites for protein arginine N-methyltransferase 1 (PRMT1), is necessary and sufficient for it to interact with RACK1. Analysis of single amino acid substitutions in RACK1, identified in a reverse 2-hybrid screen, showed very substantial overlap with those implicated in the interaction of RACK1 with the cAMP-selective phosphodiesterase PDE4D5. These data are consistent with SERBP1 interacting selectively with RACK1, mediated by an extensive interaction surface on both proteins.

  7. Site selectivity for protein tyrosine nitration: insights from features of structure and topological network.

    PubMed

    Cheng, Shangli; Lian, Baofeng; Liang, Juan; Shi, Ting; Xie, Lu; Zhao, Yi-Lei

    2013-11-01

    Tyrosine nitration is a covalent post-translational modification, which regulates protein functions such as hindering tyrosine phosphorylation and affecting essential signal transductions in cells. Based on up-to-date proteomics data, tyrosine nitration appears to be a highly selective process since not all tyrosine residues in proteins or all proteins are nitrated in vivo. Quite a few investigations included the protein structural information from the RCSB PDB database, where near 100,000 high-quality three-dimensional structures are available. In this work, we analyzed the local protein structures and amino acid topological networks of the nitrated and non-nitrated tyrosine sites in nitrated proteins, including neighboring atomic distribution, amino acid pair (AAP) and amino acid triangle (AAT). It has been found that aromatic and aliphatic residues, particularly with large volume, aromatic, aliphatic, or acidic side chains, are disfavored for the nitration. After integrating these structural features and topological network features with traditional sequence features, the predictive model achieves a sensitivity of 63.30% and a specificity of 92.24%, resulting in a much better accuracy compared to the previous models with only protein sequence information. Our investigation implies that the site selectivity may stem from a more open, hydrophilic and high-pH chemical environment around the tyrosine residue.

  8. Sparse model selection via integral terms

    NASA Astrophysics Data System (ADS)

    Schaeffer, Hayden; McCalla, Scott G.

    2017-08-01

    Model selection and parameter estimation are important for the effective integration of experimental data, scientific theory, and precise simulations. In this work, we develop a learning approach for the selection and identification of a dynamical system directly from noisy data. The learning is performed by extracting a small subset of important features from an overdetermined set of possible features using a nonconvex sparse regression model. The sparse regression model is constructed to fit the noisy data to the trajectory of the dynamical system while using the smallest number of active terms. Computational experiments detail the model's stability, robustness to noise, and recovery accuracy. Examples include nonlinear equations, population dynamics, chaotic systems, and fast-slow systems.

  9. Synthetic silvestrol analogues as potent and selective protein synthesis inhibitors.

    PubMed

    Liu, Tao; Nair, Somarajan J; Lescarbeau, André; Belani, Jitendra; Peluso, Stéphane; Conley, James; Tillotson, Bonnie; O'Hearn, Patrick; Smith, Sherri; Slocum, Kelly; West, Kip; Helble, Joseph; Douglas, Mark; Bahadoor, Adilah; Ali, Janid; McGovern, Karen; Fritz, Christian; Palombella, Vito J; Wylie, Andrew; Castro, Alfredo C; Tremblay, Martin R

    2012-10-25

    Misregulation of protein translation plays a critical role in human cancer pathogenesis at many levels. Silvestrol, a cyclopenta[b]benzofuran natural product, blocks translation at the initiation step by interfering with assembly of the eIF4F translation complex. Silvestrol has a complex chemical structure whose functional group requirements have not been systematically investigated. Moreover, silvestrol has limited development potential due to poor druglike properties. Herein, we sought to develop a practical synthesis of key intermediates of silvestrol and explore structure-activity relationships around the C6 position. The ability of silvestrol and analogues to selectively inhibit the translation of proteins with high requirement on the translation-initiation machinery (i.e., complex 5'-untranslated region UTR) relative to simple 5'UTR was determined by a cellular reporter assay. Simplified analogues of silvestrol such as compounds 74 and 76 were shown to have similar cytotoxic potency and better ADME characteristics relative to those of silvestrol.

  10. A model for plant lighting system selection

    NASA Technical Reports Server (NTRS)

    Ciolkosz, D. E.; Albright, L. D.; Sager, J. C.; Langhans, R. W.

    2002-01-01

    A decision model is presented that compares lighting systems for a plant growth scenario and chooses the most appropriate system from a given set of possible choices. The model utilizes a Multiple Attribute Utility Theory approach, and incorporates expert input and performance simulations to calculate a utility value for each lighting system being considered. The system with the highest utility is deemed the most appropriate system. The model was applied to a greenhouse scenario, and analyses were conducted to test the model's output for validity. Parameter variation indicates that the model performed as expected. Analysis of model output indicates that differences in utility among the candidate lighting systems were sufficiently large to give confidence that the model's order of selection was valid.

  11. Functionalized periodic mesoporous organosilicas for selective adsorption of proteins

    NASA Astrophysics Data System (ADS)

    Zhu, Ling; Liu, Xiaoyan; Chen, Tong; Xu, Zhigang; Yan, Wenfu; Zhang, Haixia

    2012-07-01

    The periodic mesoporous organosilicas (PMO) with an organobridged (sbnd CH2sbnd ) was synthesized and functionalized with amino or carboxylic groups by post-synthesis methods. The functionalized PMO by changing the hydrophilic/hydrophobic property and the net charge could be used to selectively adsorb and purify proteins with different shapes and different isoelectric points (pI). The experimental result showed that Bovine serum albumin (BSA) was adsorbed quicker than hemoglobin (Hb) on the materials, and lysozyme (Lys) could not be adsorbed on these PMO materials at all. The adsorption capacity of amino groups modified PMO (PMO-(NH2)2) for BSA was 44.67 mg/g and 300.0 mg/gfor Hb on carboxylic groups modified PMO (PMO-(COOH)2). The adsorption behavior of proteins was affected strongly by the interaction among different constituents in the mixture of proteins. In addition, it is found that the adsorption rate of (PMO-(NH2)2 for adsorption of proteins was much slower than PMO-(COOH)2.

  12. Selected Reaction Monitoring Mass Spectrometry for Absolute Protein Quantification.

    PubMed

    Manes, Nathan P; Mann, Jessica M; Nita-Lazar, Aleksandra

    2015-08-17

    Absolute quantification of target proteins within complex biological samples is critical to a wide range of research and clinical applications. This protocol provides step-by-step instructions for the development and application of quantitative assays using selected reaction monitoring (SRM) mass spectrometry (MS). First, likely quantotypic target peptides are identified based on numerous criteria. This includes identifying proteotypic peptides, avoiding sites of posttranslational modification, and analyzing the uniqueness of the target peptide to the target protein. Next, crude external peptide standards are synthesized and used to develop SRM assays, and the resulting assays are used to perform qualitative analyses of the biological samples. Finally, purified, quantified, heavy isotope labeled internal peptide standards are prepared and used to perform isotope dilution series SRM assays. Analysis of all of the resulting MS data is presented. This protocol was used to accurately assay the absolute abundance of proteins of the chemotaxis signaling pathway within RAW 264.7 cells (a mouse monocyte/macrophage cell line). The quantification of Gi2 (a heterotrimeric G-protein α-subunit) is described in detail.

  13. Electronic Delivery Systems: A Selection Model.

    ERIC Educational Resources Information Center

    Pallesen, Peter J.; Haley, Paul; Jones, Edward S.; Moore, Bobbie; Widlake, Dina E.; Medsker, Karen L.

    1999-01-01

    Discussion of electronic learning delivery systems focuses on a delivery system selection model that is designed for use by performance improvement professionals who are choosing between satellite networks, teleconferencing, Internet/Intranet networks, desktop multimedia, electronic performance support systems, transportable audio/video, and the…

  14. Observability in strategic models of viability selection.

    PubMed

    Gámez, M; Carreño, R; Kósa, A; Varga, Z

    2003-10-01

    Strategic models of frequency-dependent viability selection, in terms of mathematical systems theory, are considered as a dynamic observation system. Using a general sufficient condition for observability of nonlinear systems with invariant manifold, it is studied whether, observing certain phenotypic characteristics of the population, the development of its genetic state can be recovered, at least near equilibrium.

  15. A Theoretical Model for Selective Exposure Research.

    ERIC Educational Resources Information Center

    Roloff, Michael E.; Noland, Mark

    This study tests the basic assumptions underlying Fishbein's Model of Attitudes by correlating an individual's selective exposure to types of television programs (situation comedies, family drama, and action/adventure) with the attitudinal similarity between individual attitudes and attitudes characterized on the programs. Twenty-three college…

  16. Residue-specific immobilization of protein molecules by size-selected clusters

    PubMed Central

    Prisco, Umberto; Leung, Carl; Xirouchaki, Chrisa; Jones, Celine H; Heath, John K; Palmer, Richard E

    2005-01-01

    The atomic force microscope (AFM), operating in contact mode, has been employed in buffer solution to study two proteins; (i) green fluorescent protein (GFP), from the hydromedusan jellyfish Aequorea victoria; and (ii) human oncostatin M (OSM), in the presence of size-selected gold nanoclusters pinned on to a highly oriented pyrolytic graphite substrate. The AFM images have revealed immobilization of single molecules of OSM, which are strongly bound to the gold nanoclusters. Conversely, no strong immobilization has been observed for the GFP, as these molecules were easily displaced by the scanning tip. The contrasting behaviour of the two proteins can be explained by the exposed molecular surface area of their cysteine residues as modelled on the basis of their respective X-ray crystallographic data structures. GFP contains two cysteine residues, but neither is readily available to chemisorb on the gold clusters, because the cysteines are largely inaccessible from the surface of the protein. In contrast, OSM has a total of five cysteine residues, with different degrees of accessibility, which make the protein amenable to anchoring on the nanoclusters. Statistical analysis of the height of the OSM molecules bound to the nanoclusters is in accordance with crystallographic data, and suggests various configurations of the proteins on the clusters, associated with the presence of different cysteine anchoring sites. These results suggest that the three-dimensional conformation of protein molecules is preserved when they are chemisorbed to size-selected gold clusters, thus opening a new route towards oriented immobilization of individual protein molecules. PMID:16849177

  17. A computational model of selection by consequences.

    PubMed Central

    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 over wide ranges in these experiments, and many of the qualitative features of the model also were varied. The digital organism consistently showed a hyperbolic relation between response and reinforcement rates, and this hyperbolic description of the data was consistently better than the description provided by other, similar, function forms. In addition, the parameters of the hyperbola varied systematically with the quantitative, and some of the qualitative, properties of the model in ways that were consistent with findings from biological organisms. These results suggest that the material events responsible for an organism's responding on RI schedules are computationally equivalent to Darwinian selection by consequences. They also suggest that the computational model developed here is worth pursuing further as a possible dynamic account of behavior. PMID:15357512

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

    NASA Astrophysics Data System (ADS)

    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.

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

    PubMed

    Dasmeh, Pouria; Serohijos, Adrian W R; Kepp, Kasper P; Shakhnovich, Eugene I

    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 of Mbs is not significantly different among mammals (with typical oxygenation constants of ∼0.8-1.2 µM(-1)), folding stabilities of cetacean Mbs are ∼2-4 kcal/mol higher than for terrestrial Mbs. Using ancestral sequence reconstruction, maximum likelihood and bayesian tests to describe the evolution of cetacean Mbs, and experimentally calibrated computation of stability effects of mutations, we observe accelerated evolution in cetaceans and identify seven positively selected sites in Mb. Overall, these sites contribute to Mb stabilization with a conditional probability of 0.8. We observe a correlation 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 sites that occur later act against other destabilizing mutations to maintain stability across the clade, except for the shallow divers, where late stability relaxation occurs, probably due to the shorter aerobic dive limits of these species. The three main positively selected sites 66, 5, and 35 undergo changes that favor hydrophobic folding, structural integrity, and intra-helical hydrogen bonds.

  20. Maltodextrin-modified magnetic microspheres for selective enrichment of maltose binding proteins.

    PubMed

    Zheng, Jin; Ma, Chongjun; Sun, Yangfei; Pan, Miaorong; Li, Li; Hu, Xiaojian; Yang, Wuli

    2014-03-12

    In this work, maltodextrin-modified magnetic microspheres Fe3O4@SiO2-Maltodextrin (Fe3O4@SiO2-MD) with uniform size and fine morphology were synthesized through a facile and low-cost method. As the maltodextrins on the surface of microspheres were combined with maltose binding proteins (MBP), the magnetic microspheres could be applied to enriching standard MBP fused proteins. Then, the application of Fe3O4@SiO2-MD in one-step purification and immobilization of MBP fused proteins was demonstrated. For the model protein we examined, Fe3O4@SiO2-MD showed excellent binding selectivity and capacity against other Escherichia coli proteins in the crude cell lysate. Additionally, the maltodextrin-modified magnetic microspheres can be recycled for several times without significant loss of binding capacity.

  1. Regulation of cardiomyocyte signaling by RGS proteins: differential selectivity towards G proteins and susceptibility to regulation.

    PubMed

    Hao, Jianming; Michalek, Christina; Zhang, Wei; Zhu, Ming; Xu, Xiaomei; Mende, Ulrike

    2006-07-01

    Many signals that regulate cardiomyocyte growth, differentiation and function are mediated via heterotrimeric G proteins, which are under the control of RGS proteins (Regulators of G protein Signaling). Several RGS proteins are expressed in the heart, but so far little is known about their function and regulation. Using adenoviral gene transfer, we conducted the first comprehensive analysis of the capacity and selectivity of the major cardiac RGS proteins (RGS2-RGS5) to regulate central G protein-mediated signaling pathways in adult ventricular myocytes (AVM). All four RGS proteins potently inhibited Gq/11-mediated phospholipase C beta stimulation and cell growth (assessed in neonatal myocytes). Importantly, RGS2 selectively inhibited Gq/11 signaling, whereas RGS3, RGS4 and RGS5 had the capacity to regulate both Gq/11 and Gi/o signaling (carbachol-induced cAMP inhibition). Gs signaling was unaffected, and, contrary to reports in other cell lines, RGS2-RGS5 did not appear to regulate adenylate cyclase directly in AVM. Since RGS proteins can be highly regulated in their expression by many different stimuli, we also tested the hypothesis that RGS expression is subject to G protein-mediated regulation in AVM and determined the specificity with which enhanced G protein signaling alters endogenous RGS expression in AVM. RGS2 mRNA and protein were markedly but transiently up-regulated by enhanced Gq/11 signaling (alpha1-adrenergic stimulation or Galphaq* overexpression), possibly by a negative feedback mechanism. In contrast, the other negative regulators of Gq/11 signaling (RGS3-RGS5) were unchanged. Endogenous RGS2 (but not RGS3-RGS5) expression was also up-regulated in cells with enhanced AC signaling (beta-adrenergic or forskolin stimulation). Taken together, these findings suggest diverse roles of RGS proteins in regulating myocyte signaling. RGS2 emerged as the only selective and highly regulated inhibitor of Gq/11 signaling that could potentially become a promising

  2. The Role of Evolutionary Selection in the Dynamics of Protein Structure Evolution.

    PubMed

    Gilson, Amy I; Marshall-Christensen, Ahmee; Choi, Jeong-Mo; Shakhnovich, Eugene I

    2017-04-11

    Homology modeling is a powerful tool for predicting a protein's structure. This approach is successful because proteins whose sequences are only 30% identical still adopt the same structure, while structure similarity rapidly deteriorates beyond the 30% threshold. By studying the divergence of protein structure as sequence evolves in real proteins and in evolutionary simulations, we show that this nonlinear sequence-structure relationship emerges as a result of selection for protein folding stability in divergent evolution. Fitness constraints prevent the emergence of unstable protein evolutionary intermediates, thereby enforcing evolutionary paths that preserve protein structure despite broad sequence divergence. However, on longer timescales, evolution is punctuated by rare events where the fitness barriers obstructing structure evolution are overcome and discovery of new structures occurs. We outline biophysical and evolutionary rationale for broad variation in protein family sizes, prevalence of compact structures among ancient proteins, and more rapid structure evolution of proteins with lower packing density. Copyright © 2017 Biophysical Society. Published by Elsevier Inc. All rights reserved.

  3. Selecting representative model micro-organisms

    PubMed Central

    Holland, BR; Schmid, J

    2005-01-01

    Background Micro-biological research relies on the use of model organisms that act as representatives of their species or subspecies, these are frequently well-characterized laboratory strains. However, it has often become apparent that the model strain initially chosen does not represent important features of the species. For micro-organisms, the diversity of their genomes is such that even the best possible choice of initial strain for sequencing may not assure that the genome obtained adequately represents the species. To acquire information about a species' genome as efficiently as possible, we require a method to choose strains for analysis on the basis of how well they represent the species. Results We develop the Best Total Coverage (BTC) method for selecting one or more representative model organisms from a group of interest, given that rough genetic distances between the members of the group are known. Software implementing a "greedy" version of the method can be used with large data sets, its effectiveness is tested using both constructed and biological data sets. Conclusion In both the simulated and biological examples the greedy-BTC method outperformed random selection of model organisms, and for two biological examples it outperformed selection of model strains based on phylogenetic structure. Although the method was designed with microbial species in mind, and is tested here on three microbial data sets, it will also be applicable to other types of organism. PMID:15904495

  4. Conformational selection in a protein-protein interaction revealed by dynamic pathway analysis

    SciTech Connect

    Chakrabarti, Kalyan S.; Agafonov, Roman V.; Pontiggia, Francesco; Otten, Renee; Higgins, Matthew K.; Schertler, Gebhard F. X.; Oprian, Daniel D.; Kern, Dorothee

    2015-12-24

    Molecular recognition plays a central role in biology, and protein dynamics has been acknowledged to be important in this process. However, it is highly debated whether conformational changes happen before ligand binding to produce a binding-competent state (conformational selection) or are caused in response to ligand binding (induced fit). Proposals for both mechanisms in protein/protein recognition have been primarily based on structural arguments. However, the distinction between them is a question of the probabilities of going via these two opposing pathways. Here we present a direct demonstration of exclusive conformational selection in protein/protein recognition by measuring the flux for rhodopsin kinase binding to its regulator recoverin, an important molecular recognition in the vision system. Using NMR spectroscopy, stopped-flow kinetics and isothermal titration calorimetry we show that recoverin populates a minor conformation in solution that exposes a hydrophobic binding pocket responsible for binding rhodopsin kinase. Lastly, protein dynamics in free recoverin limits the overall rate of binding.

  5. Conformational selection in a protein-protein interaction revealed by dynamic pathway analysis

    DOE PAGES

    Chakrabarti, Kalyan S.; Agafonov, Roman V.; Pontiggia, Francesco; ...

    2015-12-24

    Molecular recognition plays a central role in biology, and protein dynamics has been acknowledged to be important in this process. However, it is highly debated whether conformational changes happen before ligand binding to produce a binding-competent state (conformational selection) or are caused in response to ligand binding (induced fit). Proposals for both mechanisms in protein/protein recognition have been primarily based on structural arguments. However, the distinction between them is a question of the probabilities of going via these two opposing pathways. Here we present a direct demonstration of exclusive conformational selection in protein/protein recognition by measuring the flux for rhodopsinmore » kinase binding to its regulator recoverin, an important molecular recognition in the vision system. Using NMR spectroscopy, stopped-flow kinetics and isothermal titration calorimetry we show that recoverin populates a minor conformation in solution that exposes a hydrophobic binding pocket responsible for binding rhodopsin kinase. Lastly, protein dynamics in free recoverin limits the overall rate of binding.« less

  6. Conformational selection in a protein-protein interaction revealed by dynamic pathway analysis

    DOE PAGES

    Chakrabarti, Kalyan S.; Agafonov, Roman V.; Pontiggia, Francesco; ...

    2015-12-24

    Molecular recognition plays a central role in biology, and protein dynamics has been acknowledged to be important in this process. However, it is highly debated whether conformational changes happen before ligand binding to produce a binding-competent state (conformational selection) or are caused in response to ligand binding (induced fit). Proposals for both mechanisms in protein/protein recognition have been primarily based on structural arguments. However, the distinction between them is a question of the probabilities of going via these two opposing pathways. Here we present a direct demonstration of exclusive conformational selection in protein/protein recognition by measuring the flux for rhodopsinmore » kinase binding to its regulator recoverin, an important molecular recognition in the vision system. Using NMR spectroscopy, stopped-flow kinetics and isothermal titration calorimetry we show that recoverin populates a minor conformation in solution that exposes a hydrophobic binding pocket responsible for binding rhodopsin kinase. Lastly, protein dynamics in free recoverin limits the overall rate of binding.« less

  7. Model Selection Methods for Mixture Dichotomous IRT Models

    ERIC Educational Resources Information Center

    Li, Feiming; Cohen, Allan S.; Kim, Seock-Ho; Cho, Sun-Joo

    2009-01-01

    This study examines model selection indices for use with dichotomous mixture item response theory (IRT) models. Five indices are considered: Akaike's information coefficient (AIC), Bayesian information coefficient (BIC), deviance information coefficient (DIC), pseudo-Bayes factor (PsBF), and posterior predictive model checks (PPMC). The five…

  8. The effect of protein dielectric coefficient on the ionic selectivity of a calcium channel.

    PubMed

    Boda, Dezso; Valiskó, Mónika; Eisenberg, Bob; Nonner, Wolfgang; Henderson, Douglas; Gillespie, Dirk

    2006-07-21

    Calcium-selective ion channels are known to have carboxylate-rich selectivity filters, a common motif that is primarily responsible for their high Ca(2+) affinity. Different Ca(2+) affinities ranging from micromolar (the L-type Ca channel) to millimolar (the ryanodine receptor channel) are closely related to the different physiological functions of these channels. To understand the physical mechanism for this range of affinities given similar amino acids in their selectivity filters, we use grand canonical Monte Carlo simulations to assess the binding of monovalent and divalent ions in the selectivity filter of a model Ca channel. We use a reduced model where the electolyte is modeled by hard-sphere ions embedded in a continuum dielectric solvent, while the interior of protein surrounding the channel is allowed to have a dielectric coefficient different from that of the electrolyte. The induced charges that appear on the protein/lumen interface are calculated by the induced charge computation method [Boda et al., Phys. Rev. E 69, 046702 (2004)]. It is shown that decreasing the dielectric coefficient of the protein attracts more cations into the pore because the protein's carboxyl groups induce negative charges on the dielectric boundary. As the density of the hard-sphere ions increases in the filter, Ca(2+) is absorbed into the filter with higher probability than Na(+) because Ca(2+) provides twice the charge to neutralize the negative charge of the pore (both structural carboxylate oxygens and induced charges) than Na(+) while occupying about the same space (the charge/space competition mechanism). As a result, Ca(2+) affinity is improved an order of magnitude by decreasing the protein dielectric coefficient from 80 to 5. Our results indicate that adjusting the dielectric properties of the protein surrounding the permeation pathway is a possible way for evolution to regulate the Ca(2+) affinity of the common four-carboxylate motif.

  9. Model selection criterion in survival analysis

    NASA Astrophysics Data System (ADS)

    Karabey, Uǧur; Tutkun, Nihal Ata

    2017-07-01

    Survival analysis deals with time until occurrence of an event of interest such as death, recurrence of an illness, the failure of an equipment or divorce. There are various survival models with semi-parametric or parametric approaches used in medical, natural or social sciences. The decision on the most appropriate model for the data is an important point of the analysis. In literature Akaike information criteria or Bayesian information criteria are used to select among nested models. In this study,the behavior of these information criterion is discussed for a real data set.

  10. SCAN: A Scalable Model of Attentional Selection.

    PubMed

    Hudson, Patrick T.W.; van den Herik, H Jaap; Postma, Eric O.

    1997-08-01

    This paper describes the SCAN (Signal Channelling Attentional Network) model, a scalable neural network model for attentional scanning. The building block of SCAN is a gating lattice, a sparsely-connected neural network defined as a special case of the Ising lattice from statistical mechanics. The process of spatial selection through covert attention is interpreted as a biological solution to the problem of translation-invariant pattern processing. In SCAN, a sequence of pattern translations combines active selection with translation-invariant processing. Selected patterns are channelled through a gating network, formed by a hierarchical fractal structure of gating lattices, and mapped onto an output window. We show how the incorporation of an expectation-generating classifier network (e.g. Carpenter and Grossberg's ART network) into SCAN allows attentional selection to be driven by expectation. Simulation studies show the SCAN model to be capable of attending and identifying object patterns that are part of a realistically sized natural image. Copyright 1997 Elsevier Science Ltd.

  11. Using genetic algorithms to select most predictive protein features.

    PubMed

    Kernytsky, Andrew; Rost, Burkhard

    2009-04-01

    Many important characteristics of proteins such as biochemical activity and subcellular localization present a challenge to machine-learning methods: it is often difficult to encode the appropriate input features at the residue level for the purpose of making a prediction for the entire protein. The problem is usually that the biophysics of the connection between a machine-learning method's input (sequence feature) and its output (observed phenomenon to be predicted) remains unknown; in other words, we may only know that a certain protein is an enzyme (output) without knowing which region may contain the active site residues (input). The goal then becomes to dissect a protein into a vast set of sequence-derived features and to correlate those features with the desired output. We introduce a framework that begins with a set of global sequence features and then vastly expands the feature space by generically encoding the coexistence of residue-based features. It is this combination of individual features, that is the step from the fractions of serine and buried (input space 20 + 2) to the fraction of buried serine (input space 20 * 2) that implicitly shifts the search space from global feature inputs to features that can capture very local evidence such as a the individual residues of a catalytic triad. The vast feature space created is explored by a genetic algorithm (GA) paired with neural networks and support vector machines. We find that the GA is critical for selecting combinations of features that are neither too general resulting in poor performance, nor too specific, leading to overtraining. The final framework manages to effectively sample a feature space that is far too large for exhaustive enumeration. We demonstrate the power of the concept by applying it to prediction of protein enzymatic activity. (c) 2008 Wiley-Liss, Inc.

  12. On Model Selection Criteria in Multimodel Analysis

    NASA Astrophysics Data System (ADS)

    Meyer, P. D.; Ye, M.; Neuman, S. P.

    2007-12-01

    Hydrologic systems are open and complex, rendering them prone to multiple conceptualizations and mathematical descriptions. There has been a growing tendency to postulate several alternative hydrologic models for a site and use model selection criteria to (a) rank these models, (b) eliminate some of them and/or (c) weigh and average predictions and statistics generated by multiple models. This has led to some debate among hydrogeologists about the merits and demerits of common model selection (also known as model discrimination or information) criteria such as AIC, AICc, BIC, and KIC and some lack of clarity about the proper interpretation and mathematical representation of each criterion. In particular, whereas we [Neuman, 2003; Ye et al., 2004, 2005; Meyer et al., 2007] have based our approach to multimodel hydrologic ranking and inference on the Bayesian criterion KIC (which reduces asymptotically to BIC), Poeter and Anderson [2005] have voiced a strong preference for the information-theoretic criterion AICc (which reduces asymptotically to AIC). Their preference stems in part from a perception that KIC and BIC require a "true" or "quasi-true" model to be in the set of alternatives while AIC and AICc are free of such an unreasonable requirement. We examine the model selection literature to find that (a) all published rigorous derivations of AIC and AICc require that the (true) model having generated the observational data be in the set of candidate models; (b) though BIC and KIC were originally derived by assuming that such a model is in the set, BIC has been rederived by Cavanaugh and Neath [1999] without the need for such an assumption; (c) KIC reduces to BIC as the number of observations becomes large relative to the number of adjustable model parameters, implying that it likewise does not require the existence of a true model in the set of alternatives; (d) if a true model is in the set, BIC and KIC select with probability one the true model as sample size

  13. Diamidine Compounds for Selective Inhibition of Protein Arginine Methyltransferase 1

    PubMed Central

    2015-01-01

    Protein arginine methylation is a posttranslational modification critical for a variety of biological processes. Misregulation of protein arginine methyltransferases (PRMTs) has been linked to many pathological conditions. Most current PRMT inhibitors display limited specificity and selectivity, indiscriminately targeting many methyltransferase enzymes that use S-adenosyl-l-methionine as a cofactor. Here we report diamidine compounds for specific inhibition of PRMT1, the primary type I enzyme. Docking, molecular dynamics, and MM/PBSA analysis together with biochemical assays were conducted to understand the binding modes of these inhibitors and the molecular basis of selective inhibition for PRMT1. Our data suggest that 2,5-bis(4-amidinophenyl)furan (1, furamidine, DB75), one leading inhibitor, targets the enzyme active site and is primarily competitive with the substrate and noncompetitive toward the cofactor. Furthermore, cellular studies revealed that 1 is cell membrane permeable and effectively inhibits intracellular PRMT1 activity and blocks cell proliferation in leukemia cell lines with different genetic lesions. PMID:24564570

  14. Uncovering Molecular Bases Underlying Bone Morphogenetic Protein Receptor Inhibitor Selectivity

    PubMed Central

    Alsamarah, Abdelaziz; LaCuran, Alecander E.; Oelschlaeger, Peter; Hao, Jijun; Luo, Yun

    2015-01-01

    Abnormal alteration of bone morphogenetic protein (BMP) signaling is implicated in many types of diseases including cancer and heterotopic ossifications. Hence, small molecules targeting BMP type I receptors (BMPRI) to interrupt BMP signaling are believed to be an effective approach to treat these diseases. However, lack of understanding of the molecular determinants responsible for the binding selectivity of current BMP inhibitors has been a big hindrance to the development of BMP inhibitors for clinical use. To address this issue, we carried out in silico experiments to test whether computational methods can reproduce and explain the high selectivity of a small molecule BMP inhibitor DMH1 on BMPRI kinase ALK2 vs. the closely related TGF-β type I receptor kinase ALK5 and vascular endothelial growth factor receptor type 2 (VEGFR2) tyrosine kinase. We found that, while the rigid docking method used here gave nearly identical binding affinity scores among the three kinases; free energy perturbation coupled with Hamiltonian replica-exchange molecular dynamics (FEP/H-REMD) simulations reproduced the absolute binding free energies in excellent agreement with experimental data. Furthermore, the binding poses identified by FEP/H-REMD led to a quantitative analysis of physical/chemical determinants governing DMH1 selectivity. The current work illustrates that small changes in the binding site residue type (e.g. pre-hinge region in ALK2 vs. ALK5) or side chain orientation (e.g. Tyr219 in caALK2 vs. wtALK2), as well as a subtle structural modification on the ligand (e.g. DMH1 vs. LDN193189) will cause distinct binding profiles and selectivity among BMP inhibitors. Therefore, the current computational approach represents a new way of investigating BMP inhibitors. Our results provide critical information for designing exclusively selective BMP inhibitors for the development of effective pharmacotherapy for diseases caused by aberrant BMP signaling. PMID:26133550

  15. Review and selection of unsaturated flow models

    SciTech Connect

    1993-09-10

    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 ground-water flow models; to conduct performance assessments; and to develop performance assessment models, where necessary. In the area of scientific modeling, the M&O CRWMS has the following responsibilities: To provide overall management and integration of modeling activities. To provide a framework for focusing modeling and model development. To identify areas that require increased or decreased emphasis. To ensure that the tools necessary to conduct performance assessment are available. These responsibilities are being initiated through a three-step process. It consists of a thorough review of existing models, testing of models which best fit the established requirements, and making recommendations for future development that should be conducted. Future model enhancement will then focus on the models selected during this activity. Furthermore, in order to manage future model development, particularly in those areas requiring substantial enhancement, the three-step process will be updated and reported periodically in the future.

  16. Model selection in systems and synthetic biology.

    PubMed

    Kirk, Paul; Thorne, Thomas; Stumpf, Michael P H

    2013-08-01

    Developing mechanistic models has become an integral aspect of systems biology, as has the need to differentiate between alternative models. Parameterizing mathematical models has been widely perceived as a formidable challenge, which has spurred the development of statistical and optimisation routines for parameter inference. But now focus is increasingly shifting to problems that require us to choose from among a set of different models to determine which one offers the best description of a given biological system. We will here provide an overview of recent developments in the area of model selection. We will focus on approaches that are both practical as well as build on solid statistical principles and outline the conceptual foundations and the scope for application of such methods in systems biology. Copyright © 2013 Elsevier Ltd. All rights reserved.

  17. Application of RGS box proteins to evaluate G-protein selectivity in receptor-promoted signaling.

    PubMed

    Hains, Melinda D; Siderovski, David P; Harden, T Kendall

    2004-01-01

    Regulator of G-protein signaling (RGS) domains bind directly to GTP-bound Galpha subunits and accelerate their intrinsic GTPase activity by up to several thousandfold. The selectivity of RGS proteins for individual Galpha subunits has been illustrated. Thus, the expression of RGS proteins can be used to inhibit signaling pathways activated by specific G protein-coupled receptors (GPCRs). This article describes the use of specific RGS domain constructs to discriminate among G(i/o), Gq-and G(12/13)-mediated activation of phospholipase C (PLC) isozymes in COS-7 cells. Overexpression of the N terminus of GRK2 (amino acids 45-178) or p115 RhoGEF (amino acids 1-240) elicited selective inhibition of Galphaq- or Galpha(12/13)-mediated signaling to PLC activation, respectively. In contrast, RGS2 overexpression was found to inhibit PLC activation by both G(i/o)- and Gq-coupled GPCRs. RGS4 exhibited dramatic receptor selectivity in its inhibitory actions; of the G(i/o)- and Gq-coupled GPCRs tested (LPA1, LPA2, P2Y1, S1P3), only the Gq-coupled lysophosphatidic acid-activated LPA2 receptor was found to be inhibited by RGS4 overexpression.

  18. MODBASE, a database of annotated comparative protein structure models.

    PubMed

    Pieper, Ursula; Eswar, Narayanan; Stuart, Ashley C; Ilyin, Valentin A; Sali, Andrej

    2002-01-01

    MODBASE (http://guitar.rockefeller.edu/modbase) is a relational database of annotated comparative protein structure models for all available protein sequences matched to at least one known protein structure. The models are calculated by MODPIPE, an automated modeling pipeline that relies on PSI-BLAST, IMPALA and MODELLER. MODBASE uses the MySQL relational database management system for flexible and efficient querying, and the MODVIEW Netscape plugin for viewing and manipulating multiple sequences and structures. It is updated regularly to reflect the growth of the protein sequence and structure databases, as well as improvements in the software for calculating the models. For ease of access, MODBASE is organized into different datasets. The largest dataset contains models for domains in 304 517 out of 539 171 unique protein sequences in the complete TrEMBL database (23 March 2001); only models based on significant alignments (PSI-BLAST E-value < 10(-4)) and models assessed to have the correct fold are included. Other datasets include models for target selection and structure-based annotation by the New York Structural Genomics Research Consortium, models for prediction of genes in the Drosophila melanogaster genome, models for structure determination of several ribosomal particles and models calculated by the MODWEB comparative modeling web server.

  19. Bayesian Model Selection for Group Studies

    PubMed Central

    Stephan, Klaas Enno; Penny, Will D.; Daunizeau, Jean; Moran, Rosalyn J.; Friston, Karl J.

    2009-01-01

    Bayesian model selection (BMS) is a powerful method for determining the most likely among a set of competing hypotheses about the mechanisms that generated observed data. BMS has recently found widespread application in neuroimaging, particularly in the context of dynamic causal modelling (DCM). However, so far, combining BMS results from several subjects has relied on simple (fixed effects) metrics, e.g. the group Bayes factor (GBF), that do not account for group heterogeneity or outliers. In this paper, we compare the GBF with two random effects methods for BMS at the between-subject or group level. These methods provide inference on model-space using a classical and Bayesian perspective respectively. First, a classical (frequentist) approach uses the log model evidence as a subject-specific summary statistic. This enables one to use analysis of variance to test for differences in log-evidences over models, relative to inter-subject differences. We then consider the same problem in Bayesian terms and describe a novel hierarchical model, which is optimised to furnish a probability density on the models themselves. This new variational Bayes method rests on treating the model as a random variable and estimating the parameters of a Dirichlet distribution which describes the probabilities for all models considered. These probabilities then define a multinomial distribution over model space, allowing one to compute how likely it is that a specific model generated the data of a randomly chosen subject as well as the exceedance probability of one model being more likely than any other model. Using empirical and synthetic data, we show that optimising a conditional density of the model probabilities, given the log-evidences for each model over subjects, is more informative and appropriate than both the GBF and frequentist tests of the log-evidences. In particular, we found that the hierarchical Bayesian approach is considerably more robust than either of the other

  20. Development of a selective inhibitor of Protein Arginine Deiminase 2.

    PubMed

    Muth, Aaron; Subramanian, Venkataraman; Beaumont, Edward; Nagar, Mitesh; Kerry, Philip; McEwan, Paul; Srinath, Hema; Clancy, Kathleen Wanda; Parelkar, Sangram S; Thompson, Paul R

    2017-03-22

    Protein arginine deiminase 2 (PAD2) plays a key role in the onset and progression of multiple sclerosis, rheumatoid arthritis and breast cancer. To date, no PAD2-selective inhibitor has been developed. Such a compound will be critical for elucidating the biological roles of this isozyme and may ultimately be useful for treating specific diseases in which PAD2 activity is dysregulated. To achieve this goal, we synthesized a series of benzimidazole-based derivatives of Cl-amidine, hypothesizing that this scaffold would allow access to a series of PAD2-selective inhibitors with enhanced cellular efficacy. Herein, we demonstrate that substitutions at both the N-terminus and C-terminus of Cl-amidine result in >100-fold increases in PAD2 potency and selectivity (30a, 41a, and 49a) as well as cellular efficacy 30a. Notably, these compounds use the far less reactive fluoroacetamidine warhead. In total, we predict that 30a will be a critical tool for understanding cellular PAD2 function and sets the stage for treating diseases in which PAD2 activity is dysregulated.

  1. A Theoretical Lower Bound for Selection on the Expression Levels of Proteins

    DOE PAGES

    Price, Morgan N.; Arkin, Adam P.

    2016-06-11

    We use simple models of the costs and benefits of microbial gene expression to show that changing a protein's expression away from its optimum by 2-fold should reduce fitness by at least [Formula: see text], where P is the fraction the cell's protein that the gene accounts for. As microbial genes are usually expressed at above 5 parts per million, and effective population sizes are likely to be above 10(6), this implies that 2-fold changes to gene expression levels are under strong selection, as [Formula: see text], where Ne is the effective population size and s is the selection coefficient.more » Thus, most gene duplications should be selected against. On the other hand, we predict that for most genes, small changes in the expression will be effectively neutral.« less

  2. A Theoretical Lower Bound for Selection on the Expression Levels of Proteins

    SciTech Connect

    Price, Morgan N.; Arkin, Adam P.

    2016-06-11

    We use simple models of the costs and benefits of microbial gene expression to show that changing a protein's expression away from its optimum by 2-fold should reduce fitness by at least [Formula: see text], where P is the fraction the cell's protein that the gene accounts for. As microbial genes are usually expressed at above 5 parts per million, and effective population sizes are likely to be above 10(6), this implies that 2-fold changes to gene expression levels are under strong selection, as [Formula: see text], where Ne is the effective population size and s is the selection coefficient. Thus, most gene duplications should be selected against. On the other hand, we predict that for most genes, small changes in the expression will be effectively neutral.

  3. Structural Basis for Binding and Selectivity of Antimalarial and Anticancer Ethylenediamine Inhibitors to Protein Farnesyltransferase

    SciTech Connect

    Hast, Michael A.; Fletcher, Steven; Cummings, Christopher G.; Pusateri, Erin E.; Blaskovich, Michelle A.; Rivas, Kasey; Gelb, Michael H.; Voorhis, Wesley C.Van; Sebti, Said M.; Hamilton, Andrew D.; Beese, Lorena S. ); ); ); )

    2009-03-20

    Protein farnesyltransferase (FTase) catalyzes an essential posttranslational lipid modification of more than 60 proteins involved in intracellular signal transduction networks. FTase inhibitors have emerged as a significant target for development of anticancer therapeutics and, more recently, for the treatment of parasitic diseases caused by protozoan pathogens, including malaria (Plasmodium falciparum). We present the X-ray crystallographic structures of complexes of mammalian FTase with five inhibitors based on an ethylenediamine scaffold, two of which exhibit over 1000-fold selective inhibition of P. falciparum FTase. These structures reveal the dominant determinants in both the inhibitor and enzyme that control binding and selectivity. Comparison to a homology model constructed for the P. falciparum FTase suggests opportunities for further improving selectivity of a new generation of antimalarial inhibitors.

  4. Stochastic model for protein flexibility analysis.

    PubMed

    Xia, Kelin; Wei, Guo-Wei

    2013-12-01

    Protein flexibility is an intrinsic property and plays a fundamental role in protein functions. Computational analysis of protein flexibility is crucial to protein function prediction, macromolecular flexible docking, and rational drug design. Most current approaches for protein flexibility analysis are based on Hamiltonian mechanics. We introduce a stochastic model to study protein flexibility. The essential idea is to analyze the free induction decay of a perturbed protein structural probability, which satisfies the master equation. The transition probability matrix is constructed by using probability density estimators including monotonically decreasing radial basis functions. We show that the proposed stochastic model gives rise to some of the best predictions of Debye-Waller factors or B factors for three sets of protein data introduced in the literature.

  5. ModelOMatic: fast and automated model selection between RY, nucleotide, amino acid, and codon substitution models.

    PubMed

    Whelan, Simon; Allen, James E; Blackburne, Benjamin P; Talavera, David

    2015-01-01

    Molecular phylogenetics is a powerful tool for inferring both the process and pattern of evolution from genomic sequence data. Statistical approaches, such as maximum likelihood and Bayesian inference, are now established as the preferred methods of inference. The choice of models that a researcher uses for inference is of critical importance, and there are established methods for model selection conditioned on a particular type of data, such as nucleotides, amino acids, or codons. A major limitation of existing model selection approaches is that they can only compare models acting upon a single type of data. Here, we extend model selection to allow comparisons between models describing different types of data by introducing the idea of adapter functions, which project aggregated models onto the originally observed sequence data. These projections are implemented in the program ModelOMatic and used to perform model selection on 3722 families from the PANDIT database, 68 genes from an arthropod phylogenomic data set, and 248 genes from a vertebrate phylogenomic data set. For the PANDIT and arthropod data, we find that amino acid models are selected for the overwhelming majority of alignments; with progressively smaller numbers of alignments selecting codon and nucleotide models, and no families selecting RY-based models. In contrast, nearly all alignments from the vertebrate data set select codon-based models. The sequence divergence, the number of sequences, and the degree of selection acting upon the protein sequences may contribute to explaining this variation in model selection. Our ModelOMatic program is fast, with most families from PANDIT taking fewer than 150 s to complete, and should therefore be easily incorporated into existing phylogenetic pipelines. ModelOMatic is available at https://code.google.com/p/modelomatic/.

  6. Computational protein structure modeling and analysis of UV-B stress protein in Synechocystis PCC 6803.

    PubMed

    Rahman, Md Akhlaqur; Chaturvedi, Navaneet; Sinha, Sukrat; Pandey, Paras Nath; Gupta, Dwijendra Kumar; Sundaram, Shanthy; Tripathi, Ashutosh

    2013-01-01

    This study focuses on Ultra Violet stress (UVS) gene product which is a UV stress induced protein from cyanobacteria, Synechocystis PCC 6803. Three dimensional structural modeling of target UVS protein was carried out by homology modeling method. 3F2I pdb from Nostoc sp. PCC 7120 was selected as a suitable template protein structure. Ultimately, the detection of active binding regions was carried out for characterization of functional sites in modeled UV-B stress protein. The top five probable ligand binding sites were predicted and the common binding residues between target and template protein was analyzed. It has been validated for the first time that modeled UVS protein structure from Synechocystis PCC 6803 was structurally and functionally similar to well characterized UVS protein of another cyanobacterial species, Nostoc sp PCC 7120 because of having same structural motif and fold with similar protein topology and function. Investigations revealed that UVS protein from Synechocystis sp. might play significant role during ultraviolet resistance. Thus, it could be a potential biological source for remediation for UV induced stress.

  7. Selective molecular transport through the protein shell of a bacterial microcompartment organelle

    SciTech Connect

    Chowdhury, Chiranjit; Chun, Sunny; Pang, Allan; Sawaya, Michael R.; Sinha, Sharmistha; Yeates, Todd O.; Bobik, Thomas A.

    2015-02-23

    Bacterial microcompartments are widespread prokaryotic organelles that have important and diverse roles ranging from carbon fixation to enteric pathogenesis. Current models for microcompartment function propose that their outer protein shell is selectively permeable to small molecules, but whether a protein shell can mediate selective permeability and how this occurs are unresolved questions. In this paper, biochemical and physiological studies of structure-guided mutants are used to show that the hexameric PduA shell protein of the 1,2-propanediol utilization (Pdu) microcompartment forms a selectively permeable pore tailored for the influx of 1,2-propanediol (the substrate of the Pdu microcompartment) while restricting the efflux of propionaldehyde, a toxic intermediate of 1,2-propanediol catabolism. Crystal structures of various PduA mutants provide a foundation for interpreting the observed biochemical and phenotypic data in terms of molecular diffusion across the shell. Finally and overall, these studies provide a basis for understanding a class of selectively permeable channels formed by nonmembrane proteins.

  8. Selective molecular transport through the protein shell of a bacterial microcompartment organelle

    DOE PAGES

    Chowdhury, Chiranjit; Chun, Sunny; Pang, Allan; ...

    2015-02-23

    Bacterial microcompartments are widespread prokaryotic organelles that have important and diverse roles ranging from carbon fixation to enteric pathogenesis. Current models for microcompartment function propose that their outer protein shell is selectively permeable to small molecules, but whether a protein shell can mediate selective permeability and how this occurs are unresolved questions. In this paper, biochemical and physiological studies of structure-guided mutants are used to show that the hexameric PduA shell protein of the 1,2-propanediol utilization (Pdu) microcompartment forms a selectively permeable pore tailored for the influx of 1,2-propanediol (the substrate of the Pdu microcompartment) while restricting the efflux ofmore » propionaldehyde, a toxic intermediate of 1,2-propanediol catabolism. Crystal structures of various PduA mutants provide a foundation for interpreting the observed biochemical and phenotypic data in terms of molecular diffusion across the shell. Finally and overall, these studies provide a basis for understanding a class of selectively permeable channels formed by nonmembrane proteins.« less

  9. Modeling spatial oscillations of Min proteins in round bacteria

    NASA Astrophysics Data System (ADS)

    Huang, Kerwyn; Wingreen, Ned

    2004-03-01

    In the rod-shaped bacterium phE. coli, the Min proteins oscillate from pole to pole every ˜40 seconds. This internal spatial oscillator plays an essential role in the high accuracy of phE. coli's cell division. Homologs of the Min proteins also exist in round cells (cocci) such as phNeisseria gonorrhoeae. While oscillations have not been directly observed in phN. gonorrhoeae cells because of their small size ( ˜1 micron in diameter), evidence is accumulating that the Min proteins do oscillate in these cells. For example, the Min proteins are observed to oscillate in round mutants of phE. coli, and the phN. gonorrhoeae Min proteins oscillate when expressed in rod-shaped phE. coli. Adding to the evidence for Min-protein oscillations in phN. gonorrhoeae, we report that a numerical model for Min-protein oscillations in rod-shaped cells also produces oscillations in round cells. Our results moreover explain why the rings of MinE protein found in wild-type phE. coli are absent in round phE. coli mutants. Importantly, we find that there is a minimum radius below which oscillations do not occur. Finally, we show that Min-protein oscillations are able to select the longest axis of nearly round cells. This sensitivity of Min-protein oscillations to cell geometry suggests a role for the oscillations in selecting the plane of cell division.

  10. Optimal parametrization of electrodynamical battery model using model selection criteria

    NASA Astrophysics Data System (ADS)

    Suárez-García, Andrés; Alfonsín, Víctor; Urréjola, Santiago; Sánchez, Ángel

    2015-07-01

    This paper describes the mathematical parametrization of an electrodynamical battery model using different model selection criteria. A good modeling technique is needed by the battery management units in order to increase battery lifetime. The elements of battery models can be mathematically parametrized to enhance their implementation in simulation environments. In this work, the best mathematical parametrizations are selected using three model selection criteria: the coefficient of determination (R2), the Akaike Information Criterion (AIC) and the Bayes Information Criterion (BIC). The R2 criterion only takes into account the error of the mathematical parametrizations, whereas AIC and BIC consider complexity. A commercial 40 Ah lithium iron phosphate (LiFePO4) battery is modeled and then simulated for contrasting. The OpenModelica open-source modeling and simulation environment is used for doing the battery simulations. The mean percent error of the simulations is 0.0985% for the models parametrized with R2 , 0.2300% for the AIC ones, and 0.3756% for the BIC ones. As expected, the R2 selected the most precise, complex and slowest mathematical parametrizations. The AIC criterion chose parametrizations with similar accuracy, but simpler and faster than the R2 ones.

  11. Novel, potent and selective inhibitors of protein kinase C show oral anti-inflammatory activity.

    PubMed

    Nixon, J S; Bishop, J; Bradshaw, D; Davis, P D; Hill, C H; Elliott, L H; Kumar, H; Lawton, G; Lewis, E J; Mulqueen, M

    1991-01-01

    Clarification of the precise role of protein kinase C (PKC) in cellular functional responses has been hampered by a lack of potent, selective inhibitors. The structural lead provided by staurosporine, a potent but non-selective protein kinase (PK) inhibitor, was used to derive a series of bis(indolyl)maleimides of which the most potent, Ro 31-8425 (I50: PKC = 8 nM) showed 350-fold selectivity for PKC over cAMP-dependent protein kinase. Ro 31-8425 antagonised cellular processes triggered by phorbol esters (potent, specific PKC activators) and inhibited the allogeneic mixed lymphocyte reaction, suggesting a role for PKC in T-cell activation. Methylation of the primary amine in Ro 31-8425 produced an analogue. Ro 31-8830 which, when administered orally, produced a dose-dependent inhibition of a phorbol ester-induced paw oedema in mice (minimum effective dose = 15 mg/kg). Ro 31-8830 also selectively inhibited the secondary inflammation in a developing adjuvant arthritis model in the rat. The results presented here suggest that these selective inhibitors of PKC may have therapeutic value in the treatment of T-cell-mediated autoimmune diseases.

  12. Protein fragment reconstruction using various modeling techniques

    NASA Astrophysics Data System (ADS)

    Boniecki, Michal; Rotkiewicz, Piotr; Skolnick, Jeffrey; Kolinski, Andrzej

    2003-11-01

    Recently developed reduced models of proteins with knowledge-based force fields have been applied to a specific case of comparative modeling. From twenty high resolution protein structures of various structural classes, significant fragments of their chains have been removed and treated as unknown. The remaining portions of the structures were treated as fixed - i.e., as templates with an exact alignment. Then, the missed fragments were reconstructed using several modeling tools. These included three reduced types of protein models: the lattice SICHO (Side Chain Only) model, the lattice CABS (Cα + Cβ + Side group) model and an off-lattice model similar to the CABS model and called REFINER. The obtained reduced models were compared with more standard comparative modeling tools such as MODELLER and the SWISS-MODEL server. The reduced model results are qualitatively better for the higher resolution lattice models, clearly suggesting that these are now mature, competitive and complementary (in the range of sparse alignments) to the classical tools of comparative modeling. Comparison between the various reduced models strongly suggests that the essential ingredient for the sucessful and accurate modeling of protein structures is not the representation of conformational space (lattice, off-lattice, all-atom) but, rather, the specificity of the force fields used and, perhaps, the sampling techniques employed. These conclusions are encouraging for the future application of the fast reduced models in comparative modeling on a genomic scale.

  13. Variable selection in semi-parametric models

    PubMed Central

    Zhang, Hongmei; Maity, Arnab; Arshad, Hasan; Holloway, John; Karmaus, Wilfried

    2014-01-01

    We propose Bayesian variable selection methods in semi-parametric models in the framework of partially linear Gaussian and problit regressions. Reproducing kernels are utilized to evaluate possibly non-linear joint effect of a set of variables. Indicator variables are introduced into the reproducing kernels for the inclusion or exclusion of a variable. Different scenarios based on posterior probabilities of including a variable are proposed to select important variables. Simulations are used to demonstrate and evaluate the methods. It was found that the proposed methods can efficiently select the correct variables regardless of the feature of the effects, linear or non-linear in an unknown form. The proposed methods are applied to two real data sets to identify cytosine phosphate guanine methylation sites associated with maternal smoking and cytosine phosphate guanine sites associated with cotinine levels with creatinine levels adjusted. The selected methylation sites have the potential to advance our understanding of the underlying mechanism for the impact of smoking exposure on health outcomes, and consequently benefit medical research in disease intervention. PMID:23990355

  14. Image Discrimination Models With Stochastic Channel Selection

    NASA Technical Reports Server (NTRS)

    Ahumada, Albert J., Jr.; Beard, Bettina L.; Null, Cynthia H. (Technical Monitor)

    1995-01-01

    Many models of human image processing feature a large fixed number of channels representing cortical units varying in spatial position (visual field direction and eccentricity) and spatial frequency (radial frequency and orientation). The values of these parameters are usually sampled at fixed values selected to ensure adequate overlap considering the bandwidth and/or spread parameters, which are usually fixed. Even high levels of overlap does not always ensure that the performance of the model will vary smoothly with image translation or scale changes. Physiological measurements of bandwidth and/or spread parameters result in a broad distribution of estimated parameter values and the prediction of some psychophysical results are facilitated by the assumption that these parameters also take on a range of values. Selecting a sample of channels from a continuum of channels rather than using a fixed set can make model performance vary smoothly with changes in image position, scale, and orientation. It also facilitates the addition of spatial inhomogeneity, nonlinear feature channels, and focus of attention to channel models.

  15. Image Discrimination Models With Stochastic Channel Selection

    NASA Technical Reports Server (NTRS)

    Ahumada, Albert J., Jr.; Beard, Bettina L.; Null, Cynthia H. (Technical Monitor)

    1995-01-01

    Many models of human image processing feature a large fixed number of channels representing cortical units varying in spatial position (visual field direction and eccentricity) and spatial frequency (radial frequency and orientation). The values of these parameters are usually sampled at fixed values selected to ensure adequate overlap considering the bandwidth and/or spread parameters, which are usually fixed. Even high levels of overlap does not always ensure that the performance of the model will vary smoothly with image translation or scale changes. Physiological measurements of bandwidth and/or spread parameters result in a broad distribution of estimated parameter values and the prediction of some psychophysical results are facilitated by the assumption that these parameters also take on a range of values. Selecting a sample of channels from a continuum of channels rather than using a fixed set can make model performance vary smoothly with changes in image position, scale, and orientation. It also facilitates the addition of spatial inhomogeneity, nonlinear feature channels, and focus of attention to channel models.

  16. Inflation model selection meets dark radiation

    NASA Astrophysics Data System (ADS)

    Tram, Thomas; Vallance, Robert; Vennin, Vincent

    2017-01-01

    We investigate how inflation model selection is affected by the presence of additional free-streaming relativistic degrees of freedom, i.e. dark radiation. We perform a full Bayesian analysis of both inflation parameters and cosmological parameters taking reheating into account self-consistently. We compute the Bayesian evidence for a few representative inflation scenarios in both the standard ΛCDM model and an extension including dark radiation parametrised by its effective number of relativistic species Neff. Using a minimal dataset (Planck low-l polarisation, temperature power spectrum and lensing reconstruction), we find that the observational status of most inflationary models is unchanged. The exceptions are potentials such as power-law inflation that predict large values for the scalar spectral index that can only be realised when Neff is allowed to vary. Adding baryon acoustic oscillations data and the B-mode data from BICEP2/Keck makes power-law inflation disfavoured, while adding local measurements of the Hubble constant H0 makes power-law inflation slightly favoured compared to the best single-field plateau potentials. This illustrates how the dark radiation solution to the H0 tension would have deep consequences for inflation model selection.

  17. Model selection for radiochromic film dosimetry.

    PubMed

    Méndez, I

    2015-05-21

    The purpose of this study was to find the most accurate model for radiochromic film dosimetry by comparing different channel independent perturbation models. A model selection approach based on (algorithmic) information theory was followed, and the results were validated using gamma-index analysis on a set of benchmark test cases. Several questions were addressed: (a) whether incorporating the information of the non-irradiated film, by scanning prior to irradiation, improves the results; (b) whether lateral corrections are necessary when using multichannel models; (c) whether multichannel dosimetry produces better results than single-channel dosimetry; (d) which multichannel perturbation model provides more accurate film doses. It was found that scanning prior to irradiation and applying lateral corrections improved the accuracy of the results. For some perturbation models, increasing the number of color channels did not result in more accurate film doses. Employing Truncated Normal perturbations was found to provide better results than using Micke-Mayer perturbation models. Among the models being compared, the triple-channel model with Truncated Normal perturbations, net optical density as the response and subject to the application of lateral corrections was found to be the most accurate model. The scope of this study was circumscribed by the limits under which the models were tested. In this study, the films were irradiated with megavoltage radiotherapy beams, with doses from about 20-600 cGy, entire (8 inch  × 10 inch) films were scanned, the functional form of the sensitometric curves was a polynomial and the different lots were calibrated using the plane-based method.

  18. Models of blockage-induced selectivity

    NASA Astrophysics Data System (ADS)

    Barré, C.; Talbot, J.

    2017-04-01

    We examine blockage-induced selectivity in a particulate stream flowing through a channel. Each component of the mixture is characterized by the transit time, τ, necessary to pass through the channel. The model is motivated by filtration and other processes involving blockage. The transit time distribution of exiting particles depends on the entering particle distribution, \\psi(τ) , the intensity, λ, of the entering stream, and the blocking rule. With the simple rule that a blockage occurs whenever two particles are present in the channel, the properties of the exiting stream are directly related to the Laplace transform of the entering distribution, \\tilde\\psi(λ) . For any entering distribution, the exiting stream is enriched in faster moving components. The selectivity of a species in a binary mixture can be mapped to a thermodynamic system, namely a hard rod mixture at a given pressure and temperature that can model the adsorption of gas mixtures in nanopores. We also examine an alternative rule according to which blocking only occurs if a faster moving particle catches up to a slower one in the channel. The selectivity is quantitatively different compared to the simple blocking rule. In a binary mixture the majority component in the entering stream is further enhanced in the exiting stream, independently of the transit times.

  19. Design of protein-interaction specificity affords selective bZIP-binding peptides

    PubMed Central

    Grigoryan, Gevorg; Reinke, Aaron W.; Keating, Amy E.

    2009-01-01

    Interaction specificity is a required feature of biological networks and a necessary characteristic of protein or small-molecule reagents and therapeutics. The ability to alter or inhibit protein interactions selectively would advance basic and applied molecular science. Assessing or modelling interaction specificity requires treating multiple competing complexes, which presents computational and experimental challenges. Here we present a computational framework for designing protein interaction specificity and use it to identify specific peptide partners for human bZIP transcription factors. Protein microarrays were used to characterize designed, synthetic ligands for all but one of 20 bZIP families. The bZIP proteins share strong sequence and structural similarities and thus are challenging targets to bind specifically. Yet many of the designs, including examples that bind the oncoproteins cJun, cFos and cMaf, were selective for their targets over all 19 other families. Collectively, the designs exhibit a wide range of novel interaction profiles, demonstrating that human bZIPs have only sparsely sampled the possible interaction space accessible to them. Our computational method provides a way to systematically analyze tradeoffs between stability and specificity and is suitable for use with many types of structure-scoring functions; thus it may prove broadly useful as a tool for protein design. PMID:19370028

  20. Protein turnover measurement using selected reaction monitoring-mass spectrometry (SRM-MS).

    PubMed

    Holman, Stephen W; Hammond, Dean E; Simpson, Deborah M; Waters, John; Hurst, Jane L; Beynon, Robert J

    2016-10-28

    Protein turnover represents an important mechanism in the functioning of cells, with deregulated synthesis and degradation of proteins implicated in many diseased states. Therefore, proteomics strategies to measure turnover rates with high confidence are of vital importance to understanding many biological processes. In this study, the more widely used approach of non-targeted precursor ion signal intensity (MS1) quantification is compared with selected reaction monitoring (SRM), a data acquisition strategy that records data for specific peptides, to determine if improved quantitative data would be obtained using a targeted quantification approach. Using mouse liver as a model system, turnover measurement of four tricarboxylic acid cycle proteins was performed using both MS1 and SRM quantification strategies. SRM outperformed MS1 in terms of sensitivity and selectivity of measurement, allowing more confident determination of protein turnover rates. SRM data are acquired using cheaper and more widely available tandem quadrupole mass spectrometers, making the approach accessible to a larger number of researchers than MS1 quantification, which is best performed on high mass resolution instruments. SRM acquisition is ideally suited to focused studies where the turnover of tens of proteins is measured, making it applicable in determining the dynamics of proteins complexes and complete metabolic pathways.This article is part of the themed issue 'Quantitative mass spectrometry'.

  1. Protein turnover measurement using selected reaction monitoring-mass spectrometry (SRM-MS)

    PubMed Central

    Holman, Stephen W.; Hammond, Dean E.; Simpson, Deborah M.; Waters, John; Hurst, Jane L.

    2016-01-01

    Protein turnover represents an important mechanism in the functioning of cells, with deregulated synthesis and degradation of proteins implicated in many diseased states. Therefore, proteomics strategies to measure turnover rates with high confidence are of vital importance to understanding many biological processes. In this study, the more widely used approach of non-targeted precursor ion signal intensity (MS1) quantification is compared with selected reaction monitoring (SRM), a data acquisition strategy that records data for specific peptides, to determine if improved quantitative data would be obtained using a targeted quantification approach. Using mouse liver as a model system, turnover measurement of four tricarboxylic acid cycle proteins was performed using both MS1 and SRM quantification strategies. SRM outperformed MS1 in terms of sensitivity and selectivity of measurement, allowing more confident determination of protein turnover rates. SRM data are acquired using cheaper and more widely available tandem quadrupole mass spectrometers, making the approach accessible to a larger number of researchers than MS1 quantification, which is best performed on high mass resolution instruments. SRM acquisition is ideally suited to focused studies where the turnover of tens of proteins is measured, making it applicable in determining the dynamics of proteins complexes and complete metabolic pathways. This article is part of the themed issue ‘Quantitative mass spectrometry’. PMID:27644981

  2. Resampling methods for model fitting and model selection.

    PubMed

    Babu, G Jogesh

    2011-11-01

    Resampling procedures for fitting models and model selection are considered in this article. Nonparametric goodness-of-fit statistics are generally based on the empirical distribution function. The distribution-free property of these statistics does not hold in the multivariate case or when some of the parameters are estimated. Bootstrap methods to estimate the underlying distributions are discussed in such cases. The results hold not only in the case of one-dimensional parameter space, but also for the vector parameters. Bootstrap methods for inference, when the data is from an unknown distribution that may or may not belong to a specified family of distributions, are also considered. Most of the information criteria-based model selection procedures such as the Akaike information criterion, Bayesian information criterion, and minimum description length use estimation of bias. The bias, which is inevitable in model selection problems, arises mainly from estimating the distance between the "true" model and an estimated model. A jackknife type procedure for model selection is discussed, which instead of bias estimation is based on bias reduction.

  3. Preventing fibril formation of a protein by selective mutation

    PubMed Central

    Maisuradze, Gia G.; Medina, Jordi; Kachlishvili, Khatuna; Krupa, Pawel; Mozolewska, Magdalena A.; Martin-Malpartida, Pau; Maisuradze, Luka; Macias, Maria J.; Scheraga, Harold A.

    2015-01-01

    The origins of formation of an intermediate state involved in amyloid formation and ways to prevent it are illustrated with the example of the Formin binding protein 28 (FBP28) WW domain, which folds with biphasic kinetics. Molecular dynamics of protein folding trajectories are used to examine local and global motions and the time dependence of formation of contacts between Cαs and Cβs of selected pairs of residues. Focus is placed on the WT FBP28 WW domain and its six mutants (L26D, L26E, L26W, E27Y, T29D, and T29Y), which have structures that are determined by high-resolution NMR spectroscopy. The origins of formation of an intermediate state are elucidated, viz. as formation of hairpin 1 by a hydrophobic collapse mechanism causing significant delay of formation of both hairpins, especially hairpin 2, which facilitates the emergence of an intermediate state. It seems that three-state folding is a major folding scenario for all six mutants and WT. Additionally, two-state and downhill folding scenarios were identified in ∼15% of the folding trajectories for L26D and L26W, in which both hairpins are formed by the Matheson–Scheraga mechanism much faster than in three-state folding. These results indicate that formation of hairpins connecting two antiparallel β-strands determines overall folding. The correlations between the local and global motions identified for all folding trajectories lead to the identification of the residues making the main contributions in the formation of the intermediate state. The presented findings may provide an understanding of protein folding intermediates in general and lead to a procedure for their prevention. PMID:26483482

  4. Select host cell proteins coelute with monoclonal antibodies in protein A chromatography.

    PubMed

    Nogal, Bartek; Chhiba, Krishan; Emery, Jefferson C

    2012-01-01

    The most significant factor contributing to the presence of host cell protein (HCP) impurities in Protein A chromatography eluates is their association with the product monoclonal antibodies (mAbs) has been reported previously, and it has been suggested that more efficacious column washes may be developed by targeting the disruption of the mAbs-HCP interaction. However, characterization of this interaction is not straight forward as it is likely to involve multiple proteins and/or types of interaction. This work is an attempt to begin to understand the contribution of HCP subpopulations and/or mAb interaction propensity to the variability in HCP levels in the Protein A eluate. We performed a flowthrough (FT) recycling study with product respiking using two antibody molecules of apparently different HCP interaction propensities. In each case, the ELISA assay showed depletion of select subpopulations of HCP in Protein A eluates in subsequent column runs, while the feedstock HCP in the FTs remained unchanged from its native harvested cell culture fluid (HCCF) levels. In a separate study, the final FT from each molecule's recycling study was cross-spiked with various mAbs. In this case, Protein A eluate levels remained low for all but two molecules which were known as having high apparent HCP interaction propensity. The results of these studies suggest that mAbs may preferentially bind to select subsets of HCPs, and the degree of interaction and/or identity of the associated HCPs may vary depending on the mAb.

  5. Mechanical Strength of 17 134 Model Proteins and Cysteine Slipknots

    PubMed Central

    2009-01-01

    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 cystein slipknots, is identified as one that is expected to lead to the strongest force clamps known. This class is characterized through molecular dynamics simulations. PMID:19876372

  6. Identifying relevant positions in proteins by Critical Variable Selection.

    PubMed

    Grigolon, Silvia; Franz, Silvio; Marsili, Matteo

    2016-06-21

    Evolution in its course has found a variety of solutions to the same optimisation problem. The advent of high-throughput genomic sequencing has made available extensive data from which, in principle, one can infer the underlying structure on which biological functions rely. In this paper, we present a new method aimed at the extraction of sites encoding structural and functional properties from a set of protein primary sequences, namely a multiple sequence alignment. The method, called critical variable selection, is based on the idea that subsets of relevant sites correspond to subsequences that occur with a particularly broad frequency distribution in the dataset. By applying this algorithm to in silico sequences, to the response regulator receiver and to the voltage sensor domain of ion channels, we show that this procedure recovers not only the information encoded in single site statistics and pairwise correlations but also captures dependencies going beyond pairwise correlations. The method proposed here is complementary to statistical coupling analysis, in that the most relevant sites predicted by the two methods differ markedly. We find robust and consistent results for datasets as small as few hundred sequences that reveal a hidden hierarchy of sites that are consistent with the present knowledge on biologically relevant sites and evolutionary dynamics. This suggests that critical variable selection is capable of identifying a core of sites encoding functional and structural information in a multiple sequence alignment.

  7. Probing protein flexibility reveals a mechanism for selective promiscuity

    PubMed Central

    Pabon, Nicolas A; Camacho, Carlos J

    2017-01-01

    Many eukaryotic regulatory proteins adopt distinct bound and unbound conformations, and use this structural flexibility to bind specifically to multiple partners. However, we lack an understanding of how an interface can select some ligands, but not others. Here, we present a molecular dynamics approach to identify and quantitatively evaluate the interactions responsible for this selective promiscuity. We apply this approach to the anticancer target PD-1 and its ligands PD-L1 and PD-L2. We discover that while unbound PD-1 exhibits a hard-to-drug hydrophilic interface, conserved specific triggers encoded in the cognate ligands activate a promiscuous binding pathway that reveals a flexible hydrophobic binding cavity. Specificity is then established by additional contacts that stabilize the PD-1 cavity into distinct bound-like modes. Collectively, our studies provide insight into the structural basis and evolution of multiple binding partners, and also suggest a biophysical approach to exploit innate binding pathways to drug seemingly undruggable targets. DOI: http://dx.doi.org/10.7554/eLife.22889.001 PMID:28432789

  8. Modeling chain folding in protein-constrained circular DNA.

    PubMed Central

    Martino, J A; Olson, W K

    1998-01-01

    An efficient method for sampling equilibrium configurations of DNA chains binding one or more DNA-bending proteins is presented. The technique is applied to obtain the tertiary structures of minimal bending energy for a selection of dinucleosomal minichromosomes that differ in degree of protein-DNA interaction, protein spacing along the DNA chain contour, and ring size. The protein-bound portions of the DNA chains are represented by tight, left-handed supercoils of fixed geometry. The protein-free regions are modeled individually as elastic rods. For each random spatial arrangement of the two nucleosomes assumed during a stochastic search for the global minimum, the paths of the flexible connecting DNA segments are determined through a numerical solution of the equations of equilibrium for torsionally relaxed elastic rods. The minimal energy forms reveal how protein binding and spacing and plasmid size differentially affect folding and offer new insights into experimental minichromosome systems. PMID:9591675

  9. Recursive protein modeling: a divide and conquer strategy for Protein Structure Prediction and its case study in CASP9.

    PubMed

    Cheng, Jianlin; Eickholt, Jesse; Wang, Zheng; Deng, Xin

    2012-06-01

    After decades of research, protein structure prediction remains a very challenging problem. In order to address the different levels of complexity of structural modeling, two types of modeling techniques--template-based modeling and template-free modeling--have been developed. Template-based modeling can often generate a moderate- to high-resolution model when a similar, homologous template structure is found for a query protein but fails if no template or only incorrect templates are found. Template-free modeling, such as fragment-based assembly, may generate models of moderate resolution for small proteins of low topological complexity. Seldom have the two techniques been integrated together to improve protein modeling. Here we develop a recursive protein modeling approach to selectively and collaboratively apply template-based and template-free modeling methods to model template-covered (i.e. certain) and template-free (i.e. uncertain) regions of a protein. A preliminary implementation of the approach was tested on a number of hard modeling cases during the 9th Critical Assessment of Techniques for Protein Structure Prediction (CASP9) and successfully improved the quality of modeling in most of these cases. Recursive modeling can significantly reduce the complexity of protein structure modeling and integrate template-based and template-free modeling to improve the quality and efficiency of protein structure prediction.

  10. RECURSIVE PROTEIN MODELING: A DIVIDE AND CONQUER STRATEGY FOR PROTEIN STRUCTURE PREDICTION AND ITS CASE STUDY IN CASP9

    PubMed Central

    CHENG, JIANLIN; EICKHOLT, JESSE; WANG, ZHENG; DENG, XIN

    2013-01-01

    After decades of research, protein structure prediction remains a very challenging problem. In order to address the different levels of complexity of structural modeling, two types of modeling techniques — template-based modeling and template-free modeling — have been developed. Template-based modeling can often generate a moderate- to high-resolution model when a similar, homologous template structure is found for a query protein but fails if no template or only incorrect templates are found. Template-free modeling, such as fragment-based assembly, may generate models of moderate resolution for small proteins of low topological complexity. Seldom have the two techniques been integrated together to improve protein modeling. Here we develop a recursive protein modeling approach to selectively and collaboratively apply template-based and template-free modeling methods to model template-covered (i.e. certain) and template-free (i.e. uncertain) regions of a protein. A preliminary implementation of the approach was tested on a number of hard modeling cases during the 9th Critical Assessment of Techniques for Protein Structure Prediction (CASP9) and successfully improved the quality of modeling in most of these cases. Recursive modeling can signicantly reduce the complexity of protein structure modeling and integrate template-based and template-free modeling to improve the quality and efficiency of protein structure prediction. PMID:22809379

  11. Protein selection and export via outer membrane vesicles.

    PubMed

    Bonnington, K E; Kuehn, M J

    2014-08-01

    Outer membrane vesicles (OMVs) are constitutively produced by all Gram-negative bacteria. OMVs form when buds from the outer membrane (OM) of cells encapsulate periplasmic material and pinch off from the OM to form spheroid particles approximately 10 to 300nm in diameter. OMVs accomplish a diversity of functional roles yet the OMV's utility is ultimately determined by its unique composition. Inclusion into OMVs may impart a variety of benefits to the protein cargo, including: protection from proteolytic degradation, enhancement of long-distance delivery, specificity in host-cell targeting, modulation of the immune response, coordinated secretion with other bacterial effectors, and/or exposure to a unique function-promoting environment. Many enriched OMV-associated components are virulence factors, aiding in host cell destruction, immune system evasion, host cell invasion, or antibiotic resistance. Although the mechanistic details of how proteins become enriched as OMV cargo remain elusive, recent data on OM biogenesis and relationships between LPS structure and OMV-cargo inclusion rates shed light on potential models for OM organization and consequent OMV budding. In this review, mechanisms based on pre-existing OM microdomains are proposed to explain how cargo may experience differing levels of enrichment in OMVs and degrees of association with OMVs during extracellular export. This article is part of a Special Issue entitled: Protein trafficking and secretion in bacteria. Guest Editors: Anastassios Economou and Ross Dalbey. Copyright © 2014 Elsevier B.V. All rights reserved.

  12. Selective separation of the major whey proteins using ion exchange membranes.

    PubMed

    Goodall, S; Grandison, A S; Jauregi, P J; Price, J

    2008-01-01

    Synthetic microporous membranes with functional groups covalently attached were used to selectively separate beta-lactoglobulin, BSA, and alpha-lactalbumin from rennet whey. The selectivity and membrane performance of strong (quaternary ammonium) and weak (diethylamine) ion-exchange membranes were studied using breakthrough curves, measurement of binding capacity, and protein composition of the elution fraction to determine the binding behavior of each membrane. When the weak and strong anion exchange membranes were saturated with whey, they were both selective primarily for beta-lactoglobulin with less than 1% of the eluate consisting of alpha-lactalbumin or BSA. The binding capacity of a pure beta-lactoglobulin solution was in excess of 1.5 mg/cm2 of membrane. This binding capacity was reduced to approximately 1.2 mg/cm2 when using a rennet whey solution (pH 6.4). This reduction in protein binding capacity can be explained by both the competitive effects of other whey proteins and the effect of ions present in whey. Using binary solution breakthrough curves and rennet whey breakthrough curves, it was shown that alpha-lactalbumin and BSA were displaced from the strong and weak anion exchange membranes by beta-lactoglobulin. Finally, the effect of ionic strength on the binding capacity of individual proteins for each membrane was determined by comparing model protein solutions in milk permeate (pH 6.4) and a 10 mM sodium phosphate buffer (pH 6.4). Binding capacities of beta-lactoglobulin, alpha-lactalbumin, and BSA in milk permeate were reduced by as much as 50%. This reduction in capacity coupled with the low binding capacity of current ion exchange membranes are 2 serious considerations for selectively separating complex and concentrated protein solutions.

  13. Appropriate model selection methods for nonstationary generalized extreme value models

    NASA Astrophysics Data System (ADS)

    Kim, Hanbeen; Kim, Sooyoung; Shin, Hongjoon; Heo, Jun-Haeng

    2017-04-01

    Several evidences of hydrologic data series being nonstationary in nature have been found to date. This has resulted in the conduct of many studies in the area of nonstationary frequency analysis. Nonstationary probability distribution models involve parameters that vary over time. Therefore, it is not a straightforward process to apply conventional goodness-of-fit tests to the selection of an appropriate nonstationary probability distribution model. Tests that are generally recommended for such a selection include the Akaike's information criterion (AIC), corrected Akaike's information criterion (AICc), Bayesian information criterion (BIC), and likelihood ratio test (LRT). In this study, the Monte Carlo simulation was performed to compare the performances of these four tests, with regard to nonstationary as well as stationary generalized extreme value (GEV) distributions. Proper model selection ratios and sample sizes were taken into account to evaluate the performances of all the four tests. The BIC demonstrated the best performance with regard to stationary GEV models. In case of nonstationary GEV models, the AIC proved to be better than the other three methods, when relatively small sample sizes were considered. With larger sample sizes, the AIC, BIC, and LRT presented the best performances for GEV models which have nonstationary location and/or scale parameters, respectively. Simulation results were then evaluated by applying all four tests to annual maximum rainfall data of selected sites, as observed by the Korea Meteorological Administration.

  14. Model selection versus model averaging in dose finding studies.

    PubMed

    Schorning, Kirsten; Bornkamp, Björn; Bretz, Frank; Dette, Holger

    2016-09-30

    A key objective of Phase II dose finding studies in clinical drug development is to adequately characterize the dose response relationship of a new drug. An important decision is then on the choice of a suitable dose response function to support dose selection for the subsequent Phase III studies. In this paper, we compare different approaches for model selection and model averaging using mathematical properties as well as simulations. We review and illustrate asymptotic properties of model selection criteria and investigate their behavior when changing the sample size but keeping the effect size constant. In a simulation study, we investigate how the various approaches perform in realistically chosen settings. Finally, the different methods are illustrated with a recently conducted Phase II dose finding study in patients with chronic obstructive pulmonary disease. Copyright © 2016 John Wiley & Sons, Ltd.

  15. PPIcons: identification of protein-protein interaction sites in selected organisms.

    PubMed

    Sriwastava, Brijesh K; Basu, Subhadip; Maulik, Ujjwal; Plewczynski, Dariusz

    2013-09-01

    The physico-chemical properties of interaction interfaces have a crucial role in characterization of protein-protein interactions (PPI). In silico prediction of participating amino acids helps to identify interface residues for further experimental verification using mutational analysis, or inhibition studies by screening library of ligands against given protein. Given the unbound structure of a protein and the fact that it forms a complex with another known protein, the objective of this work is to identify the residues that are involved in the interaction. We attempt to predict interaction sites in protein complexes using local composition of amino acids together with their physico-chemical characteristics. The local sequence segments (LSS) are dissected from the protein sequences using a sliding window of 21 amino acids. The list of LSSs is passed to the support vector machine (SVM) predictor, which identifies interacting residue pairs considering their inter-atom distances. We have analyzed three different model organisms of Escherichia coli, Saccharomyces Cerevisiae and Homo sapiens, where the numbers of considered hetero-complexes are equal to 40, 123 and 33 respectively. Moreover, the unified multi-organism PPI meta-predictor is also developed under the current work by combining the training databases of above organisms. The PPIcons interface residues prediction method is measured by the area under ROC curve (AUC) equal to 0.82, 0.75, 0.72 and 0.76 for the aforementioned organisms and the meta-predictor respectively.

  16. Models of globular proteins in aqueous solutions

    NASA Astrophysics Data System (ADS)

    Wentzel, Nathaniel James

    Protein crystallization is a continuing area of research. Currently, there is no universal theory for the conditions required to crystallize proteins. A better understanding of protein crystallization will be helpful in determining protein structure and preventing and treating certain diseases. In this thesis, we will extend the understanding of globular proteins in aqueous solutions by analyzing various models for protein interactions. Experiments have shown that the liquid-liquid phase separation curves for lysozyme in solution with salt depend on salt type and salt concentration. We analyze a simple square well model for this system whose well depth depends on salt type and salt concentration, to determine the phase coexistence surfaces from experimental data. The surfaces, calculated from a single Monte Carlo simulation and a simple scaling argument, are shown as a function of temperature, salt concentration and protein concentration for two typical salts. Urate Oxidase from Asperigillus flavus is a protein used for studying the effects of polymers on the crystallization of large proteins. Experiments have determined some aspects of the phase diagram. We use Monte Carlo techniques and perturbation theory to predict the phase diagram for a model of urate oxidase in solution with PEG. The model used includes an electrostatic interaction, van der Waals attraction, and a polymerinduced depletion interaction. The results agree quantitatively with experiments. Anisotropy plays a role in globular protein interactions, including the formation of hemoglobin fibers in sickle cell disease. Also, the solvent conditions have been shown to play a strong role in the phase behavior of some aqueous protein solutions. Each has previously been treated separately in theoretical studies. Here we propose and analyze a simple, combined model that treats both anisotropy and solvent effects. We find that this model qualitatively explains some phase behavior, including the existence of

  17. Improved modeling of GPS selective availability

    NASA Technical Reports Server (NTRS)

    Braasch, Michael S.; Fink, Annmarie; Duffus, Keith

    1994-01-01

    Selective Availability (SA) represents the dominant error source for stand-alone users of the Global Positioning System (GPS). Even for DGPS, SA mandates the update rate required for a desired level of accuracy in realtime applications. As was witnessed in the recent literature, the ability to model this error source is crucial to the proper evaluation of GPS-based systems. A variety of SA models were proposed to date; however, each has its own shortcomings. Most of these models were based on limited data sets or data which were corrupted by additional error sources. A comprehensive treatment of the problem is presented. The phenomenon of SA is discussed and a technique is presented whereby both clock and orbit components of SA are identifiable. Extensive SA data sets collected from Block 2 satellites are presented. System Identification theory then is used to derive a robust model of SA from the data. This theory also allows for the statistical analysis of SA. The stationarity of SA over time and across different satellites is analyzed and its impact on the modeling problem is discussed.

  18. Modeling protein-protein and protein-peptide complexes: CAPRI 6th edition.

    PubMed

    Lensink, Marc F; Velankar, Sameer; Wodak, Shoshana J

    2017-03-01

    We present the sixth report evaluating the performance of methods for predicting the atomic resolution structures of protein complexes offered as targets to the community-wide initiative on the Critical Assessment of Predicted Interactions (CAPRI). The evaluation is based on a total of 20,670 predicted models for 8 protein-peptide complexes, a novel category of targets in CAPRI, and 12 protein-protein targets in CAPRI prediction Rounds held during the years 2013-2016. For two of the protein-protein targets, the focus was on the prediction of side-chain conformation and positions of interfacial water molecules. Seven of the protein-protein targets were particularly challenging owing to their multicomponent nature, to conformational changes at the binding site, or to a combination of both. Encouragingly, the very large multiprotein complex with the nucleosome was correctly predicted, and correct models were submitted for the protein-peptide targets, but not for some of the challenging protein-protein targets. Models of acceptable quality or better were obtained for 14 of the 20 targets, including medium quality models for 13 targets and high quality models for 8 targets, indicating tangible progress of present-day computational methods in modeling protein complexes with increased accuracy. Our evaluation suggests that the progress stems from better integration of different modeling tools with docking procedures, as well as the use of more sophisticated evolutionary information to score models. Nonetheless, adequate modeling of conformational flexibility in interacting proteins remains an important area with a crucial need for improvement. Proteins 2017; 85:359-377. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

  19. Computational protein design: the Proteus software and selected applications.

    PubMed

    Simonson, Thomas; Gaillard, Thomas; Mignon, David; Schmidt am Busch, Marcel; Lopes, Anne; Amara, Najette; Polydorides, Savvas; Sedano, Audrey; Druart, Karen; Archontis, Georgios

    2013-10-30

    We describe an automated procedure for protein design, implemented in a flexible software package, called Proteus. System setup and calculation of an energy matrix are done with the XPLOR modeling program and its sophisticated command language, supporting several force fields and solvent models. A second program provides algorithms to search sequence space. It allows a decomposition of the system into groups, which can be combined in different ways in the energy function, for both positive and negative design. The whole procedure can be controlled by editing 2-4 scripts. Two applications consider the tyrosyl-tRNA synthetase enzyme and its successful redesign to bind both O-methyl-tyrosine and D-tyrosine. For the latter, we present Monte Carlo simulations where the D-tyrosine concentration is gradually increased, displacing L-tyrosine from the binding pocket and yielding the binding free energy difference, in good agreement with experiment. Complete redesign of the Crk SH3 domain is presented. The top 10000 sequences are all assigned to the correct fold by the SUPERFAMILY library of Hidden Markov Models. Finally, we report the acid/base behavior of the SNase protein. Sidechain protonation is treated as a form of mutation; it is then straightforward to perform constant-pH Monte Carlo simulations, which yield good agreement with experiment. Overall, the software can be used for a wide range of application, producing not only native-like sequences but also thermodynamic properties with errors that appear comparable to other current software packages. Copyright © 2013 Wiley Periodicals, Inc.

  20. Deciphering Supramolecular Structures with Protein-Protein Interaction Network Modeling

    PubMed Central

    Tsuji, Toshiyuki; Yoda, Takao; Shirai, Tsuyoshi

    2015-01-01

    Many biological molecules are assembled into supramolecules that are essential to perform complicated functions in the cell. However, experimental information about the structures of supramolecules is not sufficient at this point. We developed a method of predicting and modeling the structures of supramolecules in a biological network by combining structural data of the Protein Data Bank (PDB) and interaction data in IntAct databases. Templates for binary complexes in IntAct were extracted from PDB. Modeling was attempted by assembling binary complexes with superposed shared subunits. A total of 3,197 models were constructed, and 1,306 (41% of the total) contained at least one subunit absent from experimental structures. The models also suggested 970 (25% of the total) experimentally undetected subunit interfaces, and 41 human disease-related amino acid variants were mapped onto these model-suggested interfaces. The models demonstrated that protein-protein interaction network modeling is useful to fill the information gap between biological networks and structures. PMID:26549015

  1. Kernel-based feature selection techniques for transport proteins based on star graph topological indices.

    PubMed

    Fernandez-Lozano, Carlos; Gestal, Marcos; Pedreira-Souto, Nieves; Postelnicu, Lucian; Dorado, Julián; Munteanu, Cristian Robert

    2013-01-01

    The transport of the molecules inside cells is a very important topic, especially in Drug Metabolism. The experimental testing of the new proteins for the transporter molecular function is expensive and inefficient due to the large amount of new peptides. Therefore, there is a need for cheap and fast theoretical models to predict the transporter proteins. In the current work, the primary structure of a protein is represented as a molecular Star graph, characterized by a series of topological indices. The dataset was made up of 2,503 protein chains, out of which 413 have transporter molecular function and 2,090 have no transporter function. These indices were used as input to several classification techniques to find the best Quantitative Structure Activity Relationship (QSAR) model that can evaluate the transporter function of a new protein chain. Among several feature selection techniques, the Support Vector Machine Recursive Feature Elimination allows us to obtain a classification model based on 20 attributes with a true positive rate of 83% and a false positive rate of 16.7%.

  2. Self-organized models of selectivity in calcium channels

    NASA Astrophysics Data System (ADS)

    Giri, Janhavi; Fonseca, James E.; Boda, Dezső; Henderson, Douglas; Eisenberg, Bob

    2011-04-01

    The role of flexibility in the selectivity of calcium channels is studied using a simple model with two parameters that accounts for the selectivity of calcium (and sodium) channels in many ionic solutions of different compositions and concentrations using two parameters with unchanging values. We compare the distribution of side chains (oxygens) and cations (Na+ and Ca2+) and integrated quantities. We compare the occupancies of cations Ca2+/Na+ and linearized conductance of Na+. The distributions show a strong dependence on the locations of fixed side chains and the flexibility of the side chains. Holding the side chains fixed at certain predetermined locations in the selectivity filter distorts the distribution of Ca2+ and Na+ in the selectivity filter. However, integrated quantities—occupancy and normalized conductance—are much less sensitive. Our results show that some flexibility of side chains is necessary to avoid obstruction of the ionic pathway by oxygen ions in 'unfortunate' fixed positions. When oxygen ions are mobile, they adjust 'automatically' and move 'out of the way', so they can accommodate the permeable cations in the selectivity filter. Structure is the computed consequence of the forces in this model. The structures are self-organized, at their free energy minimum. The relationship of ions and side chains varies with an ionic solution. Monte Carlo simulations are particularly well suited to compute induced-fit, self-organized structures because the simulations yield an ensemble of structures near their free energy minimum. The exact location and mobility of oxygen ions has little effect on the selectivity behavior of calcium channels. Seemingly, nature has chosen a robust mechanism to control selectivity in calcium channels: the first-order determinant of selectivity is the density of charge in the selectivity filter. The density is determined by filter volume along with the charge and excluded volume of structural ions confined within it

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

    PubMed

    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+.

  4. Various expression-augmenting DNA elements benefit from STAR-Select, a novel high stringency selection system for protein expression.

    PubMed

    Otte, Arie P; Kwaks, Ted H J; van Blokland, Rik J M; Sewalt, Richard G A B; Verhees, John; Klaren, Vincent N A; Siersma, Tjalling K; Korse, Hans W M; Teunissen, Nannette C; Botschuijver, Sara; van Mer, Charl; Man, Sue Y

    2007-01-01

    The creation of highly productive mammalian cell lines often requires the screening of large numbers of clones, and even then expression levels are often low. Previously, we identified DNA elements, anti-repressor or STAR elements, that increase protein expression levels. These positive effects of STAR elements are most apparent when stable clones are established under high selection stringency. We therefore developed a very high selection system, STAR-Select, that allows the formation of few but highly productive clones. Here we compare the influence of STAR and other expression-augmenting DNA elements on protein expression levels in CHO-K1 cells. The comparison is done in the context of the often-used cotransfection selection procedure and in the context of the STAR-Select system. We show that STAR elements, as well as MAR elements induce the highest protein expression levels with both selection systems. Furthermore, in trans cotransfection of multiple copies of STAR and MAR elements also results in higher protein expression levels. However, highest expression levels are achieved with the STAR-Select selection system, when STAR elements or MARs are incorporated in a single construct. Our results also show that the novel STAR-Select selection system, which was developed in the context of STAR elements, is also very beneficial for the use of MAR elements.

  5. The Influence of Selection for Protein Stability on dN/dS Estimations

    PubMed Central

    Dasmeh, Pouria; Serohijos, Adrian W.R.; Kepp, Kasper P.; Shakhnovich, Eugene I.

    2014-01-01

    Understanding the relative contributions of various evolutionary processes—purifying selection, neutral drift, and adaptation—is fundamental to evolutionary biology. A common metric to distinguish these processes is the ratio of nonsynonymous to synonymous substitutions (i.e., dN/dS) interpreted from the neutral theory as a null model. However, from biophysical considerations, mutations have non-negligible effects on the biophysical properties of proteins such as folding stability. In this work, we investigated how stability affects the rate of protein evolution in phylogenetic trees by using simulations that combine explicit protein sequences with associated stability changes. We first simulated myoglobin evolution in phylogenetic trees with a biophysically realistic approach that accounts for 3D structural information and estimates of changes in stability upon mutation. We then compared evolutionary rates inferred directly from simulation to those estimated using maximum-likelihood (ML) methods. We found that the dN/dS estimated by ML methods (ωML) is highly predictive of the per gene dN/dS inferred from the simulated phylogenetic trees. This agreement is strong in the regime of high stability where protein evolution is neutral. At low folding stabilities and under mutation-selection balance, we observe deviations from neutrality (per gene dN/dS > 1 and dN/dS < 1). We showed that although per gene dN/dS is robust to these deviations, ML tests for positive selection detect statistically significant per site dN/dS > 1. Altogether, we show how protein biophysics affects the dN/dS estimations and its subsequent interpretation. These results are important for improving the current approaches for detecting positive selection. PMID:25355808

  6. The influence of selection for protein stability on dN/dS estimations.

    PubMed

    Dasmeh, Pouria; Serohijos, Adrian W R; Kepp, Kasper P; Shakhnovich, Eugene I

    2014-10-28

    Understanding the relative contributions of various evolutionary processes-purifying selection, neutral drift, and adaptation-is fundamental to evolutionary biology. A common metric to distinguish these processes is the ratio of nonsynonymous to synonymous substitutions (i.e., dN/dS) interpreted from the neutral theory as a null model. However, from biophysical considerations, mutations have non-negligible effects on the biophysical properties of proteins such as folding stability. In this work, we investigated how stability affects the rate of protein evolution in phylogenetic trees by using simulations that combine explicit protein sequences with associated stability changes. We first simulated myoglobin evolution in phylogenetic trees with a biophysically realistic approach that accounts for 3D structural information and estimates of changes in stability upon mutation. We then compared evolutionary rates inferred directly from simulation to those estimated using maximum-likelihood (ML) methods. We found that the dN/dS estimated by ML methods (ωML) is highly predictive of the per gene dN/dS inferred from the simulated phylogenetic trees. This agreement is strong in the regime of high stability where protein evolution is neutral. At low folding stabilities and under mutation-selection balance, we observe deviations from neutrality (per gene dN/dS > 1 and dN/dS < 1). We showed that although per gene dN/dS is robust to these deviations, ML tests for positive selection detect statistically significant per site dN/dS > 1. Altogether, we show how protein biophysics affects the dN/dS estimations and its subsequent interpretation. These results are important for improving the current approaches for detecting positive selection. © The Author(s) 2014. Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution.

  7. A model of protein conformational substates

    PubMed Central

    Stein, D. L.

    1985-01-01

    Many proteins have been observed to exist in a large number of conformations that are believed to play an important role in their dynamics. A model of protein conformational substates that incorporates the ideas of frustration and disorder in analogy to glasses and spin glasses is proposed. Applications to x-ray diffraction, Mössbauer studies, and recombination experiments are discussed. PMID:16593568

  8. Topical liposome targeting of dyes, melanins, genes, and proteins selectively to hair follicles.

    PubMed

    Hoffman, R M

    1998-01-01

    For therapeutic and cosmetic modification of hair, we have developed a hair-follicle-selective macromolecule and small molecule targeting system with topical application of phosphatidylcholine-based liposomes. Liposome-entrapped melanins, proteins, genes, and small-molecules have been selectively targeted to the hair follicle and hair shafts of mice. Liposomal delivery of these molecules is time dependent. Negligible amounts of delivered molecules enter the dermis, epidermis, or bloodstream thereby demonstrating selective follicle delivery. Naked molecules are trapped in the stratum corneum and are unable to enter the follicle. The potential of the hair-follicle liposome delivery system for therapeutic use for hair disease as well as for cosmesis has been demonstrated in 3-dimensional histoculture of hair-growing skin and mouse in vivo models. Topical liposome selective delivery to hair follicles has demonstrated the ability to color hair with melanin, the delivery of the active lac-Z gene to hair matrix cells and delivery of proteins as well. Liposome-targeting of molecules to hair follicles has also been achieved in human scalp in histoculture. Liposomes thus have high potential in selective hair follicle targeting of large and small molecules, including genes, opening the field of gene therapy and other molecular therapy of the hair process to restore hair growth, physiologically restore or alter hair pigment, and to prevent or accelerate hair loss.

  9. Parallel cascade selection molecular dynamics for efficient conformational sampling and free energy calculation of proteins

    NASA Astrophysics Data System (ADS)

    Kitao, Akio; Harada, Ryuhei; Nishihara, Yasutaka; Tran, Duy Phuoc

    2016-12-01

    Parallel Cascade Selection Molecular Dynamics (PaCS-MD) was proposed as an efficient conformational sampling method to investigate conformational transition pathway of proteins. In PaCS-MD, cycles of (i) selection of initial structures for multiple independent MD simulations and (ii) conformational sampling by independent MD simulations are repeated until the convergence of the sampling. The selection is conducted so that protein conformation gradually approaches a target. The selection of snapshots is a key to enhance conformational changes by increasing the probability of rare event occurrence. Since the procedure of PaCS-MD is simple, no modification of MD programs is required; the selections of initial structures and the restart of the next cycle in the MD simulations can be handled with relatively simple scripts with straightforward implementation. Trajectories generated by PaCS-MD were further analyzed by the Markov state model (MSM), which enables calculation of free energy landscape. The combination of PaCS-MD and MSM is reported in this work.

  10. Active surfaces engineered by immobilizing protein-polymer nanoreactors for selectively detecting sugar alcohols.

    PubMed

    Zhang, Xiaoyan; Lomora, Mihai; Einfalt, Tomaz; Meier, Wolfgang; Klein, Noreen; Schneider, Dirk; Palivan, Cornelia G

    2016-05-01

    We introduce active surfaces generated by immobilizing protein-polymer nanoreactors on a solid support for sensitive sugar alcohols detection. First, such selective nanoreactors were engineered in solution by simultaneous encapsulation of specific enzymes in copolymer polymersomes, and insertion of membrane proteins for selective conduct of sugar alcohols. Despite the artificial surroundings, and the thickness of the copolymer membrane, functionality of reconstituted Escherichia coli glycerol facilitator (GlpF) was preserved, and allowed selective diffusion of sugar alcohols to the inner cavity of the polymersome, where encapsulated ribitol dehydrogenase (RDH) enzymes served as biosensing entities. Ribitol, selected as a model sugar alcohol, was detected quantitatively by the RDH-nanoreactors with GlpF-mediated permeability in a concentration range of 1.5-9 mM. To obtain "active surfaces" for detecting sugar alcohols, the nanoreactors optimized in solution were then immobilized on a solid support: aldehyde groups exposed at the compartment external surface reacted via an aldehyde-amino reaction with glass surfaces chemically modified with amino groups. The nanoreactors preserved their architecture and activity after immobilization on the glass surface, and represent active biosensing surfaces for selective detection of sugar alcohols, with high sensitivity. Copyright © 2016 Elsevier Ltd. All rights reserved.

  11. Protein selection and export via outer membrane vesicles

    PubMed Central

    Bonnington, K. E.; Kuehn, M. J.

    2014-01-01

    Outer membrane vesicles (OMVs) are constitutively produced by all Gram-negative bacteria. OMVs form when buds from the outer membrane (OM) of cells encapsulate periplasmic material and pinch off from the OM to form spheroid particles approximately 10 to 300 nm in diameter. OMVs accomplish a diversity of functional roles yet the OMV’s utility is ultimately determined by its unique composition. Inclusion into OMVs may impart a variety of benefits to the protein cargo, including: protection from proteolytic degradation, enhancement of long-distance delivery, specificity in host-cell targeting, modulation of the immune response, coordinated secretion with other bacterial effectors, and/or exposure to a unique function-promoting environment. Many enriched OMV-associated components are virulence factors, aiding in host cell destruction, immune system evasion, host cell invasion, or antibiotic resistance. Although the mechanistic details of how proteins become enriched as OMV cargo remain elusive, recent data on OM biogenesis and relationships between LPS structure and OMV-cargo inclusion rates shed light on potential models for OM organization and consequent OMV budding. In this review, mechanisms based on pre-existing OM microdomains are proposed to explain how cargo may experience differing levels of enrichment in OMVs and degrees of association with OMVs during extracellular export. PMID:24370777

  12. ProSelection: A novel algorithm to select proper protein structure subsets for in silico target identification and drug discovery research.

    PubMed

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

    2017-10-10

    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 ProSelection algorithm, protein structures of "weak selector" are filtered out whereas structures of "strong selector" 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 selector" from "weak selector" 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 FDA-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. Targeting prion-like protein doppel selectively suppresses tumor angiogenesis

    PubMed Central

    Al-Hilal, Taslim A.; Chung, Seung Woo; Choi, Jeong Uk; Kim, Seong Who; Kim, Sang Yoon; Ahsan, Fakhrul; Kim, In-San

    2016-01-01

    Controlled and site-specific regulation of growth factor signaling remains a major challenge for current antiangiogenic therapies, as these antiangiogenic agents target normal vasculature as well tumor vasculature. In this article, we identified the prion-like protein doppel as a potential therapeutic target for tumor angiogenesis. We investigated the interactions between doppel and VEGFR2 and evaluated whether blocking the doppel/VEGFR2 axis suppresses the process of angiogenesis. We discovered that tumor endothelial cells (TECs), but not normal ECs, express doppel; tumors from patients and mouse xenografts expressed doppel in their vasculatures. Induced doppel overexpression in ECs enhanced vascularization, whereas doppel constitutively colocalized and complexed with VEGFR2 in TECs. Doppel inhibition depleted VEGFR2 from the cell membrane, subsequently inducing the internalization and degradation of VEGFR2 and thereby attenuating VEGFR2 signaling. We also synthesized an orally active glycosaminoglycan (LHbisD4) that specifically binds with doppel. We determined that LHbisD4 concentrates over the tumor site and that genetic loss of doppel in TECs decreases LHbisD4 binding and targeting both in vitro and in vivo. Moreover, LHbisD4 eliminated VEGFR2 from the cell membrane, prevented VEGF binding in TECs, and suppressed tumor growth. Together, our results demonstrate that blocking doppel can control VEGF signaling in TECs and selectively inhibit tumor angiogenesis. PMID:26950422

  14. Disulfide bridge regulates ligand-binding site selectivity in liver bile acid-binding proteins.

    PubMed

    Cogliati, Clelia; Tomaselli, Simona; Assfalg, Michael; Pedò, Massimo; Ferranti, Pasquale; Zetta, Lucia; Molinari, Henriette; Ragona, Laura

    2009-10-01

    Bile acid-binding proteins (BABPs) are cytosolic lipid chaperones that play central roles in driving bile flow, as well as in the adaptation to various pathological conditions, contributing to the maintenance of bile acid homeostasis and functional distribution within the cell. Understanding the mode of binding of bile acids with their cytoplasmic transporters is a key issue in providing a model for the mechanism of their transfer from the cytoplasm to the nucleus, for delivery to nuclear receptors. A number of factors have been shown to modulate bile salt selectivity, stoichiometry, and affinity of binding to BABPs, e.g. chemistry of the ligand, protein plasticity and, possibly, the formation of disulfide bridges. Here, the effects of the presence of a naturally occurring disulfide bridge on liver BABP ligand-binding properties and backbone dynamics have been investigated by NMR. Interestingly, the disulfide bridge does not modify the protein-binding stoichiometry, but has a key role in modulating recognition at both sites, inducing site selectivity for glycocholic and glycochenodeoxycholic acid. Protein conformational changes following the introduction of a disulfide bridge are small and located around the inner binding site, whereas significant changes in backbone motions are observed for several residues distributed over the entire protein, both in the apo form and in the holo form. Site selectivity appears, therefore, to be dependent on protein mobility rather than being governed by steric factors. The detected properties further establish a parallelism with the behaviour of human ileal BABP, substantiating the proposal that BABPs have parallel functions in hepatocytes and enterocytes.

  15. Advances in Homology Protein Structure Modeling

    PubMed Central

    Xiang, Zhexin

    2007-01-01

    Homology modeling plays a central role in determining protein structure in the structural genomics project. The importance of homology modeling has been steadily increasing because of the large gap that exists between the overwhelming number of available protein sequences and experimentally solved protein structures, and also, more importantly, because of the increasing reliability and accuracy of the method. In fact, a protein sequence with over 30% identity to a known structure can often be predicted with an accuracy equivalent to a low-resolution X-ray structure. The recent advances in homology modeling, especially in detecting distant homologues, aligning sequences with template structures, modeling of loops and side chains, as well as detecting errors in a model, have contributed to reliable prediction of protein structure, which was not possible even several years ago. The ongoing efforts in solving protein structures, which can be time-consuming and often difficult, will continue to spur the development of a host of new computational methods that can fill in the gap and further contribute to understanding the relationship between protein structure and function. PMID:16787261

  16. Modeling the paramyxovirus hemagglutinin-neuraminidase protein.

    PubMed

    Epa, V C

    1997-11-01

    The paramyxovirus hemagglutinin-neuraminidase (HN) protein exhibits neuraminidase activity and has an active site functionally similar to that in influenza neuraminidases. Earlier work identified conserved amino acids among HN sequences and proposed similarity between HN and influenza neuraminidase sequences. In this work we identify the three-dimensional fold and develop a more detailed model for the HN protein, in the process we examine a variety of protein structure prediction methods. We use the known structures of viral and bacterial neuraminidases as controls in testing the success of protein structure prediction and modeling methods, including knowledge-based threading, discrete three-dimensional environmental profiles, hidden Markov models, neural network secondary structure prediction, pattern matching, and hydropathy plots. The results from threading show that the HN protein sequence has a 6 beta-sheet propellor fold and enable us to assign the locations of the individual beta-strands. The three-dimensional environmental profile and hidden Markov model methods were not successful in this work. The model developed in this work helps to understand better the biological function of the HN protein and design inhibitors of the enzyme and serves as an assessment of some protein structure prediction methods, especially after the x-ray crystallographic solution of its structure.

  17. Quantitative Structure-Activity Relationship Modeling of Kinase Selectivity Profiles.

    PubMed

    Kothiwale, Sandeepkumar; Borza, Corina; Pozzi, Ambra; Meiler, Jens

    2017-09-19

    The discovery of selective inhibitors of biological target proteins is the primary goal of many drug discovery campaigns. However, this goal has proven elusive, especially for inhibitors targeting the well-conserved orthosteric adenosine triphosphate (ATP) binding pocket of kinase enzymes. The human kinome is large and it is rather difficult to profile early lead compounds against around 500 targets to gain an upfront knowledge on selectivity. Further, selectivity can change drastically during derivatization of an initial lead compound. Here, we have introduced a computational model to support the profiling of compounds early in the drug discovery pipeline. On the basis of the extensive profiled activity of 70 kinase inhibitors against 379 kinases, including 81 tyrosine kinases, we developed a quantitative structure-activity relation (QSAR) model using artificial neural networks, to predict the activity of these kinase inhibitors against the panel of 379 kinases. The model's performance in predicting activity ranges from 0.6 to 0.8 depending on the kinase, from the area under the curve (AUC) of the receiver operating characteristics (ROC). The profiler is available online at http://www.meilerlab.org/index.php/servers/show?s_id=23.

  18. An aerodynamic drag model for protein ions.

    PubMed

    Douglas, D J

    1994-01-01

    The energy losses of protein ions passing through a collision cell filled with inert gas have been modeled as the aerodynamic drag on a projectile at high Knudsen number. When applied to the energy loss data of Covey and Douglas (J. Am. Soc. Mass Spectrom. 1993, 4, 616-623) with drag coefficients from the gas dynamics literature, derived protein cross sections are ∼0.8 of those found with the simple collision model used by Covey and Douglas.

  19. Image Modeling and Enhancement via Structured Sparse Model Selection

    DTIC Science & Technology

    2010-01-01

    f − f kM‖2 +T 2M ) + 32σ2 KN , (9) where f kM = ∑ M m=1〈 f ,φ km〉φ km. Theorem 1 is derived from the general model selection result of Barron ... Michel Morel for the inspiring discussions. 5. REFERENCES [1] M. Aharon, M. Elad, and A. Bruckstein. K-SVD: An algorithm for designing overcomplete...Model-based compressive sensing. Submitted to IEEE Trans on Information The- ory, 2008. [3] A. Barron , L. Birgé, and P. Massart. Risk bounds for model

  20. Transformation model selection by multiple hypotheses testing

    NASA Astrophysics Data System (ADS)

    Lehmann, Rüdiger

    2014-12-01

    Transformations between different geodetic reference frames are often performed such that first the transformation parameters are determined from control points. If in the first place we do not know which of the numerous transformation models is appropriate then we can set up a multiple hypotheses test. The paper extends the common method of testing transformation parameters for significance, to the case that also constraints for such parameters are tested. This provides more flexibility when setting up such a test. One can formulate a general model with a maximum number of transformation parameters and specialize it by adding constraints to those parameters, which need to be tested. The proper test statistic in a multiple test is shown to be either the extreme normalized or the extreme studentized Lagrange multiplier. They are shown to perform superior to the more intuitive test statistics derived from misclosures. It is shown how model selection by multiple hypotheses testing relates to the use of information criteria like AICc and Mallows' , which are based on an information theoretic approach. Nevertheless, whenever comparable, the results of an exemplary computation almost coincide.

  1. Fuzzy modelling for selecting headgear types.

    PubMed

    Akçam, M Okan; Takada, Kenji

    2002-02-01

    The purpose of this study was to develop a computer-assisted inference model for selecting appropriate types of headgear appliance for orthodontic patients and to investigate its clinical versatility as a decision-making aid for inexperienced clinicians. Fuzzy rule bases were created for degrees of overjet, overbite, and mandibular plane angle variables, respectively, according to subjective criteria based on the clinical experience and knowledge of the authors. The rules were then transformed into membership functions and the geometric mean aggregation was performed to develop the inference model. The resultant fuzzy logic was then tested on 85 cases in which the patients had been diagnosed as requiring headgear appliances. Eight experienced orthodontists judged each of the cases, and decided if they 'agreed', 'accepted', or 'disagreed' with the recommendations of the computer system. Intra-examiner agreements were investigated using repeated judgements of a set of 30 orthodontic cases and the kappa statistic. All of the examiners exceeded a kappa score of 0.7, allowing them to participate in the test run of the validity of the proposed inference model. The examiners' agreement with the system's recommendations was evaluated statistically. The average satisfaction rate of the examiners was 95.6 per cent and, for 83 out of the 85 cases, 97.6 per cent. The majority of the examiners (i.e. six or more out of the eight) were satisfied with the recommendations of the system. Thus, the usefulness of the proposed inference logic was confirmed.

  2. Modeling selective local interactions with memory

    NASA Astrophysics Data System (ADS)

    Galante, Amanda; Levy, Doron

    2013-10-01

    Recently we developed a stochastic particle system describing local interactions between cyanobacteria. We focused on the common freshwater cyanobacteria Synechocystis sp., which are coccoidal bacteria that utilize group dynamics to move toward a light source, a motion referred to as phototaxis. We were particularly interested in the local interactions between cells that were located in low to medium density areas away from the front. The simulations of our stochastic particle system in 2D replicated many experimentally observed phenomena, such as the formation of aggregations and the quasi-random motion of cells. In this paper, we seek to develop a better understanding of group dynamics produced by this model. To facilitate this study, we replace the stochastic model with a system of ordinary differential equations describing the evolution of particles in 1D. Unlike many other models, our emphasis is on particles that selectively choose one of their neighbors as the preferred direction of motion. Furthermore, we incorporate memory by allowing persistence in the motion. We conduct numerical simulations which allow us to efficiently explore the space of parameters, in order to study the stability, size, and merging of aggregations.

  3. Bayesian model selection analysis of WMAP3

    SciTech Connect

    Parkinson, David; Mukherjee, Pia; Liddle, Andrew R.

    2006-06-15

    We present a Bayesian model selection analysis of WMAP3 data using our code CosmoNest. We focus on the density perturbation spectral index n{sub S} and the tensor-to-scalar ratio r, which define the plane of slow-roll inflationary models. We find that while the Bayesian evidence supports the conclusion that n{sub S}{ne}1, the data are not yet powerful enough to do so at a strong or decisive level. If tensors are assumed absent, the current odds are approximately 8 to 1 in favor of n{sub S}{ne}1 under our assumptions, when WMAP3 data is used together with external data sets. WMAP3 data on its own is unable to distinguish between the two models. Further, inclusion of r as a parameter weakens the conclusion against the Harrison-Zel'dovich case (n{sub S}=1, r=0), albeit in a prior-dependent way. In appendices we describe the CosmoNest code in detail, noting its ability to supply posterior samples as well as to accurately compute the Bayesian evidence. We make a first public release of CosmoNest, now available at www.cosmonest.org.

  4. Selecting a model of supersymmetry breaking mediation

    SciTech Connect

    AbdusSalam, S. S.; Allanach, B. C.; Dolan, M. J.; Feroz, F.; Hobson, M. P.

    2009-08-01

    We study the problem of selecting between different mechanisms of supersymmetry breaking in the minimal supersymmetric standard model using current data. We evaluate the Bayesian evidence of four supersymmetry breaking scenarios: mSUGRA, mGMSB, mAMSB, and moduli mediation. The results show a strong dependence on the dark matter assumption. Using the inferred cosmological relic density as an upper bound, minimal anomaly mediation is at least moderately favored over the CMSSM. Our fits also indicate that evidence for a positive sign of the {mu} parameter is moderate at best. We present constraints on the anomaly and gauge mediated parameter spaces and some previously unexplored aspects of the dark matter phenomenology of the moduli mediation scenario. We use sparticle searches, indirect observables and dark matter observables in the global fit and quantify robustness with respect to prior choice. We quantify how much information is contained within each constraint.

  5. Model selection and assessment for multi-species occupancy models.

    PubMed

    Broms, Kristin M; Hooten, Mevin B; Fitzpatrick, Ryan M

    2016-07-01

    While multi-species occupancy models (MSOMs) are emerging as a popular method for analyzing biodiversity data, formal checking and validation approaches for this class of models have lagged behind. Concurrent with the rise in application of MSOMs among ecologists, a quiet regime shift is occurring in Bayesian statistics where predictive model comparison approaches are experiencing a resurgence. Unlike single-species occupancy models that use integrated likelihoods, MSOMs are usually couched in a Bayesian framework and contain multiple levels. Standard model checking and selection methods are often unreliable in this setting and there is only limited guidance in the ecological literature for this class of models. We examined several different contemporary Bayesian hierarchical approaches for checking and validating MSOMs and applied these methods to a freshwater aquatic study system in Colorado, USA, to better understand the diversity and distributions of plains fishes. Our findings indicated distinct differences among model selection approaches, with cross-validation techniques performing the best in terms of prediction. © 2016 by the Ecological Society of America.

  6. Target selection of soluble protein complexes for structural proteomics studies

    PubMed Central

    Shen, Weiping; Yun, Steven; Tam, Bonny; Dalal, Kush; Pio, Frederic F

    2005-01-01

    Background Protein expression in E. coli is the most commonly used system to produce protein for structural studies, because it is fast and inexpensive and can produce large quantity of proteins. However, when proteins from other species such as mammalian are produced in this system, problems of protein expression and solubility arise [1]. Structural genomics project are currently investigating proteomics pipelines that would produce sufficient quantities of recombinant proteins for structural studies of protein complexes. To investigate how the E. coli protein expression system could be used for this purpose, we purified apoptotic binary protein complexes formed between members of the Caspase Associated Recruitment Domain (CARD) family. Results A combinatorial approach to the generation of protein complexes was performed between members of the CARD domain protein family that have the ability to form hetero-dimers between each other. In our method, each gene coding for a specific protein partner is cloned in pET-28b (Novagen) and PGEX2T (Amersham) expression vectors. All combinations of protein complexes are then obtained by reconstituting complexes from purified components in native conditions, after denaturation-renaturation or co-expression. Our study applied to 14 soluble CARD domain proteins revealed that co-expression studies perform better than native and denaturation-renaturation methods. In this study, we confirm existing interactions obtained in vivoin mammalian cells and also predict new interactions. Conclusion The simplicity of this screening method could be easily scaled up to identify soluble protein complexes for structural genomic projects. This study reports informative statistics on the solubility of human protein complexes expressed in E.coli belonging to the human CARD protein family. PMID:15904526

  7. PREAL: prediction of allergenic protein by maximum Relevance Minimum Redundancy (mRMR) feature selection

    PubMed Central

    2013-01-01

    Background Assessment of potential allergenicity of protein is necessary whenever transgenic proteins are introduced into the food chain. Bioinformatics approaches in allergen prediction have evolved appreciably in recent years to increase sophistication and performance. However, what are the critical features for protein's allergenicity have been not fully investigated yet. Results We presented a more comprehensive model in 128 features space for allergenic proteins prediction by integrating various properties of proteins, such as biochemical and physicochemical properties, sequential features and subcellular locations. The overall accuracy in the cross-validation reached 93.42% to 100% with our new method. Maximum Relevance Minimum Redundancy (mRMR) method and Incremental Feature Selection (IFS) procedure were applied to obtain which features are essential for allergenicity. Results of the performance comparisons showed the superior of our method to the existing methods used widely. More importantly, it was observed that the features of subcellular locations and amino acid composition played major roles in determining the allergenicity of proteins, particularly extracellular/cell surface and vacuole of the subcellular locations for wheat and soybean. To facilitate the allergen prediction, we implemented our computational method in a web application, which can be available at http://gmobl.sjtu.edu.cn/PREAL/index.php. Conclusions Our new approach could improve the accuracy of allergen prediction. And the findings may provide novel insights for the mechanism of allergies. PMID:24565053

  8. Autocrine selection of a GLP-1R G-protein biased agonist with potent antidiabetic effects

    PubMed Central

    Zhang, Hongkai; Sturchler, Emmanuel; Zhu, Jiang; Nieto, Ainhoa; Cistrone, Philip A.; Xie, Jia; He, LinLing; Yea, Kyungmoo; Jones, Teresa; Turn, Rachel; Di Stefano, Peter S.; Griffin, Patrick R.; Dawson, Philip E.; McDonald, Patricia H.; Lerner, Richard A.

    2015-01-01

    Glucagon-like peptide-1 (GLP-1) receptor (GLP-1R) agonists have emerged as treatment options for type 2 diabetes mellitus (T2DM). GLP-1R signals through G-protein-dependent, and G-protein-independent pathways by engaging the scaffold protein β-arrestin; preferential signalling of ligands through one or the other of these branches is known as ‘ligand bias'. Here we report the discovery of the potent and selective GLP-1R G-protein-biased agonist, P5. We identified P5 in a high-throughput autocrine-based screening of large combinatorial peptide libraries, and show that P5 promotes G-protein signalling comparable to GLP-1 and Exendin-4, but exhibited a significantly reduced β-arrestin response. Preclinical studies using different mouse models of T2DM demonstrate that P5 is a weak insulin secretagogue. Nevertheless, chronic treatment of diabetic mice with P5 increased adipogenesis, reduced adipose tissue inflammation as well as hepatic steatosis and was more effective at correcting hyperglycaemia and lowering haemoglobin A1c levels than Exendin-4, suggesting that GLP-1R G-protein-biased agonists may provide a novel therapeutic approach to T2DM. PMID:26621478

  9. Discrete and continuous models of protein sorting in the Golgi

    NASA Astrophysics Data System (ADS)

    Gong, Haijun; Schwartz, Russell

    2009-03-01

    The Golgi apparatus plays an important role in processing and sorting proteins and lipids. Golgi compartments constantly exchange material with each other and with other cellular components, allowing them to maintain and reform distinct identities despite dramatic changes in structure and size during cell division, development and osmotic stress. We have developed two minimal models of membrane and protein exchange in the Golgi --- a discrete, stochastic model [1] and a continuous ordinary differential equation (ODE) model --- both based on two fundamental mechanisms: vesicle-coat-mediated selective concentration of soluble N-ethylmaleimide-sensitive factor attachment protein receptor (SNARE) proteins during vesicle formation and SNARE-mediated selective fusion of vesicles. Both show similar ability to establish and maintain distinct identities over broad parameter ranges, but they diverge in extreme conditions where Golgi collapse and reassembly may be observed. By exploring where the models differ, we hope to better identify those features essential to minimal models of various Golgi behaviors. [1] H. Gong, D. Sengupta, A. D. Linstedt, R. Schwartz. Biophys J. 95: 1674-1688, 2008.

  10. Model study of protein unfolding by interfaces.

    PubMed

    Chakarova, S D; Carlsson, A E

    2004-02-01

    We study interface-induced protein unfolding on hydrophobic and polar interfaces by means of a two-dimensional lattice model and an exhaustive enumeration ground-state structure search, for a set of model proteins of length 20 residues. We compare the effects of the two types of interfaces, and search for criteria that influence the retention of a protein's native-state structure upon adsorption. We find that the unfolding proceeds by a large, sudden loss of native contacts. The unfolding at polar interfaces exhibits similar behavior to that at hydrophobic interfaces but with a much weaker interface coupling strength. Further, we find that the resistance of proteins to unfolding in our model is positively correlated with the magnitude of the folding energy in the native-state structure, the thermal stability (or energy gap) for that structure, and the interface energy for native-state adsorption. We find these factors to be of roughly equal importance.

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

    PubMed Central

    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

  12. Automated hydrophobic interaction chromatography column selection for use in protein purification.

    PubMed

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

    2011-09-21

    which HIC media 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.

  13. Mixture models for protein structure ensembles.

    PubMed

    Hirsch, Michael; Habeck, Michael

    2008-10-01

    Protein structure ensembles provide important insight into the dynamics and function of a protein and contain information that is not captured with a single static structure. However, it is not clear a priori to what extent the variability within an ensemble is caused by internal structural changes. Additional variability results from overall translations and rotations of the molecule. And most experimental data do not provide information to relate the structures to a common reference frame. To report meaningful values of intrinsic dynamics, structural precision, conformational entropy, etc., it is therefore important to disentangle local from global conformational heterogeneity. We consider the task of disentangling local from global heterogeneity as an inference problem. We use probabilistic methods to infer from the protein ensemble missing information on reference frames and stable conformational sub-states. To this end, we model a protein ensemble as a mixture of Gaussian probability distributions of either entire conformations or structural segments. We learn these models from a protein ensemble using the expectation-maximization algorithm. Our first model can be used to find multiple conformers in a structure ensemble. The second model partitions the protein chain into locally stable structural segments or core elements and less structured regions typically found in loops. Both models are simple to implement and contain only a single free parameter: the number of conformers or structural segments. Our models can be used to analyse experimental ensembles, molecular dynamics trajectories and conformational change in proteins. The Python source code for protein ensemble analysis is available from the authors upon request.

  14. Going against the Tide: Selective Cellular Protein Synthesis during Virally Induced Host Shutoff.

    PubMed

    Cao, Shuai; Dhungel, Pragyesh; Yang, Zhilong

    2017-09-01

    Many viral infections cause host shutoff, a state in which host protein synthesis is globally inhibited. Emerging evidence from vaccinia and influenza A virus infections indicates that subsets of cellular proteins are resistant to host shutoff and continue to be synthesized. Remarkably, the proteins of oxidative phosphorylation, the cellular-energy-generating machinery, are selectively synthesized in both cases. Identifying mechanisms that drive selective protein synthesis should facilitate understanding both viral replication and fundamental cell biology. Copyright © 2017 American Society for Microbiology.

  15. Selective translational control of the Alzheimer amyloid precursor protein transcript by iron regulatory protein-1.

    PubMed

    Cho, Hyun-Hee; Cahill, Catherine M; Vanderburg, Charles R; Scherzer, Clemens R; Wang, Bin; Huang, Xudong; Rogers, Jack T

    2010-10-08

    Iron influx increases the translation of the Alzheimer amyloid precursor protein (APP) via an iron-responsive element (IRE) RNA stem loop in its 5'-untranslated region. Equal modulated interaction of the iron regulatory proteins (IRP1 and IRP2) with canonical IREs controls iron-dependent translation of the ferritin subunits. However, our immunoprecipitation RT-PCR and RNA binding experiments demonstrated that IRP1, but not IRP2, selectively bound the APP IRE in human neural cells. This selective IRP1 interaction pattern was evident in human brain and blood tissue from normal and Alzheimer disease patients. We computer-predicted an optimal novel RNA stem loop structure for the human, rhesus monkey, and mouse APP IREs with reference to the canonical ferritin IREs but also the IREs encoded by erythroid heme biosynthetic aminolevulinate synthase and Hif-2α mRNAs, which preferentially bind IRP1. Selective 2'-hydroxyl acylation analyzed by primer extension analysis was consistent with a 13-base single-stranded terminal loop and a conserved GC-rich stem. Biotinylated RNA probes deleted of the conserved CAGA motif in the terminal loop did not bind to IRP1 relative to wild type probes and could no longer base pair to form a predicted AGA triloop. An AGU pseudo-triloop is key for IRP1 binding to the canonical ferritin IREs. RNA probes encoding the APP IRE stem loop exhibited the same high affinity binding to rhIRP1 as occurs for the H-ferritin IRE (35 pm). Intracellular iron chelation increased binding of IRP1 to the APP IRE, decreasing intracellular APP expression in SH-SY5Y cells. Functionally, shRNA knockdown of IRP1 caused increased expression of neural APP consistent with IRP1-APP IRE-driven translation.

  16. Improved selection of LMW over HMW proteins from human plasma by mesoporous silica particles with external modification.

    PubMed

    Qi, Yanxia; Wei, Junying; Wang, Hua; Zhang, Yangjun; Xu, Jing; Qian, Xiaohong; Guan, Yafeng

    2009-12-15

    Selective extraction of low molecular weight (LMW) proteins and peptides from complex biological samples plays an important role in the discovery of useful biomarkers and signaling molecules. Various methods, such as solid-phase extraction (SPE), ultrafiltration, and size-exclusion chromatography have been developed for such extraction purpose. In this study, we present, to our knowledge, the first demonstration of alkyl-diol@SiO(2) mesoporous material MCM-41 (alkyl-diol group on the external surface of mesoporous material) for selective extraction of LMW proteins and peptides from complex biological samples. The adsorption kinetics of LMW proteins, the influence of pH on adsorption and the desorption recovery by different elution solvents were investigated by using standard proteins as model samples. It was demonstrated that the modification of alkyl-diol group on the external surface could efficiently decrease the adsorption of HMW protein and increase the desorption recovery of LMW protein. Furthermore, the mesoporous materials were applied to selectively extract LMW proteins and peptides (<10 kDa) from crude human plasma. And the modified MCM-41 material had much better extraction selectivity and efficiency for LMW proteins and peptides than unmodified one.

  17. Aromatic Functionality of Target Proteins Influences Monomer Selection for Creating Artificial Antibodies on Plasmonic Biosensors.

    PubMed

    Hu, Rong; Luan, Jingyi; Kharasch, Evan D; Singamaneni, Srikanth; Morrissey, Jeremiah J

    2017-01-11

    Natural antibodies used as biorecognition elements suffer from numerous shortcomings, such as limited chemical and environmental stability and cost. Artificial antibodies based on molecular imprinting are an attractive alternative to natural antibodies. We investigated the role of aromatic interactions in target recognition capabilities of artificial antibodies. Three proteins with different aromatic amino acid content were employed as model targets. Artificial antibodies were formed on nanostructures using combinations of silane monomers of varying aromatic functionality. We employed refractive index sensitivity of plasmonic nanostructures as a transduction platform for monitoring various steps in the imprinting process and to quantify the target recognition capabilities of the artificial antibodies. The sensitivity of the artificial antibodies with aromatic interactions exhibited a protein-dependent enhancement. Selectivity and sensitivity enhancement due to the presence of aromatic groups in imprinted polymer matrix was found to be higher for target proteins with higher aromatic amino acid content. Our results indicate that tailoring the monomer composition based on the amino acid content of the target protein can improve the sensitivity of plasmonic biosensors based on artificial antibodies without affecting the selectivity.

  18. Fluorescent probes for selective protein labeling in lysosomes: a case of α-galactosidase A.

    PubMed

    Bohl, Cornelius; Pomorski, Adam; Seemann, Susanne; Knospe, Anne-Marie; Zheng, Chaonan; Krężel, Artur; Rolfs, Arndt; Lukas, Jan

    2017-08-15

    Fluorescence-based live-cell imaging (LCI) of lysosomal glycosidases is often hampered by unfavorable pH and redox conditions that reduce fluorescence output. Moreover, most lysosomal glycosidases are low-mass soluble proteins that do not allow for bulky fluorescent protein fusions. We selected α-galactosidase A (GALA) as a model lysosomal glycosidase involved in Anderson-Fabry disease (AFD) for the current LCI approach. Examination of the subcellular localization of AFD-causing mutants can reveal the mechanism underlying cellular trafficking deficits. To minimize genetic GALA modification, we employed a biarsenical labeling protocol with tetracysteine (TC-tag) detection. We tested the efficiency of halogen substituted biarsenical probes to interact with C-terminally TC-tagged GALA peptide at pH 4.5 in vitro and identified F2FlAsH-EDT2 as a superior detection reagent for GALA. This probe provides improved signal/noise ratio in labeled COS-7 cells transiently expressing TC-tagged GALA. The investigated fluorescence-based LCI technology of TC-tagged lysosomal protein using an improved biarsenical probe can be used to identify novel compounds that promote proper trafficking of mutant GALA to lysosomal compartments and rescue the mutant phenotype.-Bohl, C., Pomorski, A., Seemann, S., Knospe, A.-M., Zheng, C., Krężel, A., Rolfs, A., Lukas, J. Fluorescent probes for selective protein labeling in lysosomes: a case of α-galactosidase A. © FASEB.

  19. Learning generative models for protein fold families.

    PubMed

    Balakrishnan, Sivaraman; Kamisetty, Hetunandan; Carbonell, Jaime G; Lee, Su-In; Langmead, Christopher James

    2011-04-01

    We introduce a new approach to learning statistical models from multiple sequence alignments (MSA) of proteins. Our method, called GREMLIN (Generative REgularized ModeLs of proteINs), learns an undirected probabilistic graphical model of the amino acid composition within the MSA. The resulting model encodes both the position-specific conservation statistics and the correlated mutation statistics between sequential and long-range pairs of residues. Existing techniques for learning graphical models from MSA either make strong, and often inappropriate assumptions about the conditional independencies within the MSA (e.g., Hidden Markov Models), or else use suboptimal algorithms to learn the parameters of the model. In contrast, GREMLIN makes no a priori assumptions about the conditional independencies within the MSA. We formulate and solve a convex optimization problem, thus guaranteeing that we find a globally optimal model at convergence. The resulting model is also generative, allowing for the design of new protein sequences that have the same statistical properties as those in the MSA. We perform a detailed analysis of covariation statistics on the extensively studied WW and PDZ domains and show that our method out-performs an existing algorithm for learning undirected probabilistic graphical models from MSA. We then apply our approach to 71 additional families from the PFAM database and demonstrate that the resulting models significantly out-perform Hidden Markov Models in terms of predictive accuracy.

  20. A Photoactivatable GFP for Selective Photolabeling of Proteins and Cells

    NASA Astrophysics Data System (ADS)

    Patterson, George H.; Lippincott-Schwartz, Jennifer

    2002-09-01

    We report a photoactivatable variant of the Aequorea victoria green fluorescent protein (GFP) that, after intense irradiation with 413-nanometer light, increases fluorescence 100 times when excited by 488-nanometer light and remains stable for days under aerobic conditions. These characteristics offer a new tool for exploring intracellular protein dynamics by tracking photoactivated molecules that are the only visible GFPs in the cell. Here, we use the photoactivatable GFP both as a free protein to measure protein diffusion across the nuclear envelope and as a chimera with a lysosomal membrane protein to demonstrate rapid interlysosomal membrane exchange.

  1. A photoactivatable GFP for selective photolabeling of proteins and cells.

    PubMed

    Patterson, George H; Lippincott-Schwartz, Jennifer

    2002-09-13

    We report a photoactivatable variant of the Aequorea victoria green fluorescent protein (GFP) that, after intense irradiation with 413-nanometer light, increases fluorescence 100 times when excited by 488-nanometer light and remains stable for days under aerobic conditions. These characteristics offer a new tool for exploring intracellular protein dynamics by tracking photoactivated molecules that are the only visible GFPs in the cell. Here, we use the photoactivatable GFP both as a free protein to measure protein diffusion across the nuclear envelope and as a chimera with a lysosomal membrane protein to demonstrate rapid interlysosomal membrane exchange.

  2. Improving randomness characterization through Bayesian model selection.

    PubMed

    Díaz Hernández Rojas, Rafael; Solís, Aldo; Angulo Martínez, Alí M; U'Ren, Alfred B; Hirsch, Jorge G; Marsili, Matteo; Pérez Castillo, Isaac

    2017-06-08

    Random number generation plays an essential role in technology with important applications in areas ranging from cryptography to Monte Carlo methods, and other probabilistic algorithms. All such applications require high-quality sources of random numbers, yet effective methods for assessing whether a source produce truly random sequences are still missing. Current methods either do not rely on a formal description of randomness (NIST test suite) on the one hand, or are inapplicable in principle (the characterization derived from the Algorithmic Theory of Information), on the other, for they require testing all the possible computer programs that could produce the sequence to be analysed. Here we present a rigorous method that overcomes these problems based on Bayesian model selection. We derive analytic expressions for a model's likelihood which is then used to compute its posterior distribution. Our method proves to be more rigorous than NIST's suite and Borel-Normality criterion and its implementation is straightforward. We applied our method to an experimental device based on the process of spontaneous parametric downconversion to confirm it behaves as a genuine quantum random number generator. As our approach relies on Bayesian inference our scheme transcends individual sequence analysis, leading to a characterization of the source itself.

  3. Growth rate modeling for selective tungsten LPCVD

    NASA Astrophysics Data System (ADS)

    Wolf, H.; Streiter, R.; Schulz, S. E.; Gessner, T.

    1995-10-01

    Selective chemical vapor deposition of tungsten plugs on sputtered tungsten was performed in a single-wafer cold-wall reactor using silane (SiH 4) and tungsten hexafluoride (WF 6). Extensive SEM measurements of film thickness were carried out to study the dependence of growth rates on various process conditions, wafer loading, and via dimensions. The results have been interpreted by numerical calculations based on a simulation model which is also presented. Both continuum fluid dynamics and the ballistic line-of-sight approach are used for transport modeling. The reaction rate is described by an empirical rate expression using coefficients fitted from experimental data. In the range 0.2 < p( SiH 4) /p( WF 6) < 0.75 , the reaction order was determined as 1.55 and -0.55 with respect to SiH 4 and WF 6, respectively. For higher partial pressure ratios the second-order rate dependence on p(SiH 4) and the minus first-order dependence on p(WF 6) were confirmed.

  4. A Selective Review of Group Selection in High-Dimensional Models.

    PubMed

    Huang, Jian; Breheny, Patrick; Ma, Shuangge

    2012-01-01

    Grouping structures arise naturally in many statistical modeling problems. Several methods have been proposed for variable selection that respect grouping structure in variables. Examples include the group LASSO and several concave group selection methods. In this article, we give a selective review of group selection concerning methodological developments, theoretical properties and computational algorithms. We pay particular attention to group selection methods involving concave penalties. We address both group selection and bi-level selection methods. We describe several applications of these methods in nonparametric additive models, semiparametric regression, seemingly unrelated regressions, genomic data analysis and genome wide association studies. We also highlight some issues that require further study.

  5. Selective Pressure to Increase Charge in Immunodominant Epitopes of the H3 Hemagglutinin Influenza Protein

    PubMed Central

    Pan, Keyao; Long, Jinxue; Sun, Haoxin; Tobin, Gregory J.; Nara, Peter L.

    2010-01-01

    The evolutionary speed and the consequent immune escape of H3N2 influenza A virus make it an interesting evolutionary system. Charged amino acid residues are often significant contributors to the free energy of binding for protein–protein interactions, including antibody–antigen binding and ligand–receptor binding. We used Markov chain theory and maximum likelihood estimation to model the evolution of the number of charged amino acids on the dominant epitope in the hemagglutinin protein of circulating H3N2 virus strains. The number of charged amino acids increased in the dominant epitope B of the H3N2 virus since introduction in humans in 1968. When epitope A became dominant in 1989, the number of charged amino acids increased in epitope A and decreased in epitope B. Interestingly, the number of charged residues in the dominant epitope of the dominant circulating strain is never fewer than that in the vaccine strain. We propose these results indicate selective pressure for charged amino acids that increase the affinity of the virus epitope for water and decrease the affinity for host antibodies. The standard PAM model of generic protein evolution is unable to capture these trends. The reduced alphabet Markov model (RAMM) model we introduce captures the increased selective pressure for charged amino acids in the dominant epitope of hemagglutinin of H3N2 influenza (R2 > 0.98 between 1968 and 1988). The RAMM model calibrated to historical H3N2 influenza virus evolution in humans fit well to the H3N2/Wyoming virus evolution data from Guinea pig animal model studies. Electronic supplementary material The online version of this article (doi:10.1007/s00239-010-9405-4) contains supplementary material, which is available to authorized users. PMID:21086120

  6. An evaluation of selected in silico models for the assessment ...

    EPA Pesticide Factsheets

    Skin sensitization remains an important endpoint for consumers, manufacturers and regulators. Although the development of alternative approaches to assess skin sensitization potential has been extremely active over many years, the implication of regulations such as REACH and the Cosmetics Directive in EU has provided a much stronger impetus to actualize this research into practical tools for decision making. Thus there has been considerable focus on the development, evaluation, and integration of alternative approaches for skin sensitization hazard and risk assessment. This includes in silico approaches such as (Q)SARs and expert systems. This study aimed to evaluate the predictive performance of a selection of in silico models and then to explore whether combining those models led to an improvement in accuracy. A dataset of 473 substances that had been tested in the local lymph node assay (LLNA) was compiled. This comprised 295 sensitizers and 178 non-sensitizers. Four freely available models were identified - 2 statistical models VEGA and MultiCASE model A33 for skin sensitization (MCASE A33) from the Danish National Food Institute and two mechanistic models Toxtree’s Skin sensitization Reaction domains (Toxtree SS Rxn domains) and the OASIS v1.3 protein binding alerts for skin sensitization from the OECD Toolbox (OASIS). VEGA and MCASE A33 aim to predict sensitization as a binary score whereas the mechanistic models identified reaction domains or structura

  7. Solubilization and electrophoretic characterization of select edible nut seed proteins.

    PubMed

    Sathe, Shridhar K; Venkatachalam, Mahesh; Sharma, Girdhari M; Kshirsagar, Harshal H; Teuber, Suzanne S; Roux, Kenneth H

    2009-09-09

    The solubility of almond, Brazil nut, cashew nut, hazelnut, macadamia, pecan, pine nut, pistachio, walnut, and peanut proteins in several aqueous solvents was qualitatively and quantitatively assessed. In addition, the effects of extraction time and ionic strength on protein solubility were also investigated. Electrophoresis and protein determination (Lowry, Bradford, and micro-Kjeldahl) methods were used for qualitative and quantitative assessment of proteins, respectively. Depending on the seed, buffer type and ionic strength significantly affected protein solubility. The results suggest that buffered sodium borate (BSB; 0.1 M H(3)BO(3), 0.025 M Na(2)B(4)O(7), 0.075 M NaCl, pH 8.45) optimally solubilizes nut seed proteins. Qualitative differences in seed protein electrophoretic profiles were revealed. For a specific seed type, these differences were dependent on the solvent(s) used to solubilize the seed proteins. SDS-PAGE results suggest the polypeptide molecular mass range for the tree nut seed proteins to be 3-100 kDa. The results of native IEF suggested that the proteins were mainly acidic, with a pI range from >4.5 to <7.0. Western immunoblotting experiments indicated that rabbit polyclonal antibodies recognized substantially the same polypeptides as those recognized by the corresponding pooled patient sera IgE.

  8. Selective, rapid and optically switchable regulation of protein function in live mammalian cells.

    PubMed

    Tsai, Yu-Hsuan; Essig, Sebastian; James, John R; Lang, Kathrin; Chin, Jason W

    2015-07-01

    The rapid and selective regulation of a target protein within living cells that contain closely related family members is an outstanding challenge. Here we introduce genetically directed bioorthogonal ligand tethering (BOLT) and demonstrate selective inhibition (iBOLT) of protein function. In iBOLT, inhibitor-conjugate/target protein pairs are created where the target protein contains a genetically encoded unnatural amino acid with bioorthogonal reactivity and the inhibitor conjugate contains a complementary bioorthogonal group. iBOLT enables the first rapid and specific inhibition of MEK isozymes, and introducing photoisomerizable linkers in the inhibitor conjugate enables reversible, optical regulation of protein activity (photo-BOLT) in live mammalian cells. We demonstrate that a pan kinase inhibitor conjugate allows selective and rapid inhibition of the lymphocyte specific kinase, indicating the modularity and scalability of BOLT. We anticipate that BOLT will enable the rapid and selective regulation of diverse proteins for which no selective small-molecule ligands exist.

  9. A multi-template combination algorithm for protein comparative modeling

    PubMed Central

    Cheng, Jianlin

    2008-01-01

    Background Multiple protein templates are commonly used in manual protein structure prediction. However, few automated algorithms of selecting and combining multiple templates are available. Results Here we develop an effective multi-template combination algorithm for protein comparative modeling. The algorithm selects templates according to the similarity significance of the alignments between template and target proteins. It combines the whole template-target alignments whose similarity significance score is close to that of the top template-target alignment within a threshold, whereas it only takes alignment fragments from a less similar template-target alignment that align with a sizable uncovered region of the target. We compare the algorithm with the traditional method of using a single top template on the 45 comparative modeling targets (i.e. easy template-based modeling targets) used in the seventh edition of Critical Assessment of Techniques for Protein Structure Prediction (CASP7). The multi-template combination algorithm improves the GDT-TS scores of predicted models by 6.8% on average. The statistical analysis shows that the improvement is significant (p-value < 10-4). Compared with the ideal approach that always uses the best template, the multi-template approach yields only slightly better performance. During the CASP7 experiment, the preliminary implementation of the multi-template combination algorithm (FOLDpro) was ranked second among 67 servers in the category of high-accuracy structure prediction in terms of GDT-TS measure. Conclusion We have developed a novel multi-template algorithm to improve protein comparative modeling. PMID:18366648

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

    NASA Astrophysics Data System (ADS)

    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.

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

    PubMed Central

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

    2015-01-01

    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. PMID:26608097

  12. Model study of protein unfolding by interfaces

    NASA Astrophysics Data System (ADS)

    Chakarova, S. D.; Carlsson, A. E.

    2004-02-01

    We study interface-induced protein unfolding on hydrophobic and polar interfaces by means of a two-dimensional lattice model and an exhaustive enumeration ground-state structure search, for a set of model proteins of length 20 residues. We compare the effects of the two types of interfaces, and search for criteria that influence the retention of a protein’s native-state structure upon adsorption. We find that the unfolding proceeds by a large, sudden loss of native contacts. The unfolding at polar interfaces exhibits similar behavior to that at hydrophobic interfaces but with a much weaker interface coupling strength. Further, we find that the resistance of proteins to unfolding in our model is positively correlated with the magnitude of the folding energy in the native-state structure, the thermal stability (or energy gap) for that structure, and the interface energy for native-state adsorption. We find these factors to be of roughly equal importance.

  13. Characterization and modeling of protein protein interaction networks

    NASA Astrophysics Data System (ADS)

    Colizza, Vittoria; Flammini, Alessandro; Maritan, Amos; Vespignani, Alessandro

    2005-07-01

    The recent availability of high-throughput gene expression and proteomics techniques has created an unprecedented opportunity for a comprehensive study of the structure and dynamics of many biological networks. Global proteomic interaction data, in particular, are synthetically represented as undirected networks exhibiting features far from the random paradigm which has dominated past effort in network theory. This evidence, along with the advances in the theory of complex networks, has triggered an intense research activity aimed at exploiting the evolutionary and biological significance of the resulting network's topology. Here we present a review of the results obtained in the characterization and modeling of the yeast Saccharomyces Cerevisiae protein interaction networks obtained with different experimental techniques. We provide a comparative assessment of the topological properties and discuss possible biases in interaction networks obtained with different techniques. We report on dynamical models based on duplication mechanisms that cast the protein interaction networks in the family of dynamically growing complex networks. Finally, we discuss various results and analysis correlating the networks’ topology with the biological function of proteins.

  14. Modelling of DNA-protein recognition

    NASA Technical Reports Server (NTRS)

    Rein, R.; Garduno, R.; Colombano, S.; Nir, S.; Haydock, K.; Macelroy, R. D.

    1980-01-01

    Computer model-building procedures using stereochemical principles together with theoretical energy calculations appear to be, at this stage, the most promising route toward the elucidation of DNA-protein binding schemes and recognition principles. A review of models and bonding principles is conducted and approaches to modeling are considered, taking into account possible di-hydrogen-bonding schemes between a peptide and a base (or a base pair) of a double-stranded nucleic acid in the major groove, aspects of computer graphic modeling, and a search for isogeometric helices. The energetics of recognition complexes is discussed and several models for peptide DNA recognition are presented.

  15. Modelling of DNA-protein recognition

    NASA Technical Reports Server (NTRS)

    Rein, R.; Garduno, R.; Colombano, S.; Nir, S.; Haydock, K.; Macelroy, R. D.

    1980-01-01

    Computer model-building procedures using stereochemical principles together with theoretical energy calculations appear to be, at this stage, the most promising route toward the elucidation of DNA-protein binding schemes and recognition principles. A review of models and bonding principles is conducted and approaches to modeling are considered, taking into account possible di-hydrogen-bonding schemes between a peptide and a base (or a base pair) of a double-stranded nucleic acid in the major groove, aspects of computer graphic modeling, and a search for isogeometric helices. The energetics of recognition complexes is discussed and several models for peptide DNA recognition are presented.

  16. Structural modeling of snow flea antifreeze protein.

    PubMed

    Lin, Feng-Hsu; Graham, Laurie A; Campbell, Robert L; Davies, Peter L

    2007-03-01

    The glycine-rich antifreeze protein recently discovered in snow fleas exhibits strong freezing point depression activity without significantly changing the melting point of its solution (thermal hysteresis). BLAST searches did not detect any protein with significant similarity in current databases. Based on its circular dichroism spectrum, discontinuities in its tripeptide repeat pattern, and intramolecular disulfide bonding, a detailed theoretical model is proposed for the 6.5-kDa isoform. In the model, the 81-residue protein is organized into a bundle of six short polyproline type II helices connected (with one exception) by proline-containing turns. This structure forms two sheets of three parallel helices, oriented antiparallel to each other. The central helices are particularly rich in glycines that facilitate backbone carbonyl-amide hydrogen bonding to four neighboring helices. The modeled structure has similarities to polyglycine II proposed by Crick and Rich in 1955 and is a close match to the polyproline type II antiparallel sheet structure determined by Traub in 1969 for (Pro-Gly-Gly)(n). Whereas the latter two structures are formed by intermolecular interactions, the snow flea antifreeze is stabilized by intramolecular interactions between the helices facilitated by the regularly spaced turns and disulfide bonds. Like several other antifreeze proteins, this modeled protein is amphipathic with a putative hydrophobic ice-binding face.

  17. Structural Modeling of Snow Flea Antifreeze Protein

    PubMed Central

    Lin, Feng-Hsu; Graham, Laurie A.; Campbell, Robert L.; Davies, Peter L.

    2007-01-01

    The glycine-rich antifreeze protein recently discovered in snow fleas exhibits strong freezing point depression activity without significantly changing the melting point of its solution (thermal hysteresis). BLAST searches did not detect any protein with significant similarity in current databases. Based on its circular dichroism spectrum, discontinuities in its tripeptide repeat pattern, and intramolecular disulfide bonding, a detailed theoretical model is proposed for the 6.5-kDa isoform. In the model, the 81-residue protein is organized into a bundle of six short polyproline type II helices connected (with one exception) by proline-containing turns. This structure forms two sheets of three parallel helices, oriented antiparallel to each other. The central helices are particularly rich in glycines that facilitate backbone carbonyl-amide hydrogen bonding to four neighboring helices. The modeled structure has similarities to polyglycine II proposed by Crick and Rich in 1955 and is a close match to the polyproline type II antiparallel sheet structure determined by Traub in 1969 for (Pro-Gly-Gly)n. Whereas the latter two structures are formed by intermolecular interactions, the snow flea antifreeze is stabilized by intramolecular interactions between the helices facilitated by the regularly spaced turns and disulfide bonds. Like several other antifreeze proteins, this modeled protein is amphipathic with a putative hydrophobic ice-binding face. PMID:17158562

  18. Selective release from cultured mammalian cells of heat-shock (stress) proteins that resemble glia-axon transfer proteins.

    PubMed

    Hightower, L E; Guidon, P T

    1989-02-01

    Cultured rat embryo cells were stimulated to rapidly release a small group of proteins that included several heat-shock proteins (hsp110, hsp71, hscp73) and nonmuscle actin. The extracellular proteins were analyzed by two-dimensional polyacrylamide gel electrophoresis. Heat-shocked cells released the same set of proteins as control cells with the addition of the stress-inducible hsp110 and hsp71. Release of these proteins was not blocked by either monensin or colchicine, inhibitors of the common secretory pathway. A small amount of the glucose-regulated protein grp78 was externalized by this pathway. The extracellular accumulation of these proteins was inhibited after they were synthesized in the presence of the lysine analogue aminoethyl cysteine. It is likely that the analogue-substituted proteins were misfolded and could not be released from cells, supporting our conclusion that a selective release mechanism is involved. Remarkably, actin and the squid heat-shock proteins homologous to rat hsp71 and hsp110 are also among a select group of proteins transferred from glial cells to the squid giant axon, where they have been implicated in neuronal stress responses (Tytell et al.: Brain Res., 363:161-164, 1986). Based in part on the similarities between these two sets of proteins, we hypothesized that these proteins were released from labile cortical regions of animal cells in response to perturbations of homeostasis in cells as evolutionarily distinct as cultured rat embryo cells and squid glial cells.

  19. Oxyanion selectivity in sulfate and molybdate transport proteins: an ab initio/CDM study.

    PubMed

    Dudev, Todor; Lim, Carmay

    2004-08-25

    A striking feature of sulfate (SO(4)(2-)) and molybdate (MoO(4)(2-)) transport proteins, such as SBP and ModA, which specifically bind SO(4)(2-) and MoO(4)(2-), respectively, is their ability to discriminate very similar anions with the same net charge, geometry, and hydrogen-bonding properties. Here, we determine to what extent (1) oxyanion-solvent interactions, (2) oxyanion-amino acid interactions, and (3) the anion-binding pocket sizes of the cognate protein contribute to the anion selectivity process in SO(4)(2-) and MoO(4)(2-) transport proteins by computing the free energies for replacing SO(4)(2-) with MoO(4)(2)(-)/WO(4)(2-) in model SO(4)(2-)-binding sites of varying degrees of solvent exposure using a combined quantum mechanical/continuum dielectric approach. The calculations reveal that MoO(4)(2-) transport proteins, such as ModA, specifically bind MoO(4)(2-)/WO(4)(2-) but not SO(4)(2-), mainly because the desolvation penalty of MoO(4)(2-)/WO(4)(2-) is significantly less than that of SO(4)(2-) and, to a lesser extent, because the large and rigid cavity in these proteins attenuates ligand interactions with SO(4)(2-), as compared to MoO(4)(2-). On the other hand, SO(4)(2-) transport proteins prefer SO(4)(2-) to MoO(4)(2-)/WO(4)(2-) because the small anion-binding pocket characteristic of these proteins inhibits binding of the larger MoO(4)(2-) and WO(4)(2-) anions. The calculations also help to explain the absence of positively charged Lys/Arg side chains in the anion-binding sites of SBP and ModA. During evolution, these transport proteins may have excluded cationic ligands from their binding sites because, on one hand, Lys/Arg do not contribute to the selectivity of the binding pocket and, on the other, they substantially stabilize the complex between the oxyanion and protein ligands, which in turn would prohibit the rapid release of the bound oxyanion at a certain stage during the transport process.

  20. Unraveling protein misfolding diseases using model systems

    PubMed Central

    Peffer, Sara; Cope, Kimberly; Morano, Kevin A

    2015-01-01

    Experimental model systems have long been used to probe the causes, consequences and mechanisms of pathology leading to human disease. Ideally, such information can be exploited to inform the development of therapeutic strategies or treatments to combat disease progression. In the case of protein misfolding diseases, a wide range of model systems have been developed to investigate different aspects of disorders including Huntington's disease, Parkinson's disease, Alzheimer's disease as well as amyotrophic lateral sclerosis. Utility of these systems broadly correlates with evolutionary complexity: small animal models such as rodents and the fruit fly are appropriate for pharmacological modeling and cognitive/behavioral assessment, the roundworm Caenorhabditis elegans allows analysis of tissue-specific disease features, and unicellular organisms such as the yeast Saccharomyces cerevisiae and the bacterium Escherichia coli are ideal for molecular studies. In this chapter, we highlight key advances in our understanding of protein misfolding/unfolding disease provided by model systems. PMID:28031870

  1. Unraveling protein misfolding diseases using model systems.

    PubMed

    Peffer, Sara; Cope, Kimberly; Morano, Kevin A

    2015-09-01

    Experimental model systems have long been used to probe the causes, consequences and mechanisms of pathology leading to human disease. Ideally, such information can be exploited to inform the development of therapeutic strategies or treatments to combat disease progression. In the case of protein misfolding diseases, a wide range of model systems have been developed to investigate different aspects of disorders including Huntington's disease, Parkinson's disease, Alzheimer's disease as well as amyotrophic lateral sclerosis. Utility of these systems broadly correlates with evolutionary complexity: small animal models such as rodents and the fruit fly are appropriate for pharmacological modeling and cognitive/behavioral assessment, the roundworm Caenorhabditis elegans allows analysis of tissue-specific disease features, and unicellular organisms such as the yeast Saccharomyces cerevisiae and the bacterium Escherichia coli are ideal for molecular studies. In this chapter, we highlight key advances in our understanding of protein misfolding/unfolding disease provided by model systems.

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

    SciTech Connect

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

    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.

  3. Assessing Model Selection Uncertainty Using a Bootstrap Approach: An update.

    PubMed

    Lubke, Gitta H; Campbell, Ian; McArtor, Dan; Miller, Patrick; Luningham, Justin; van den Berg, Stéphanie M

    2017-01-01

    Model comparisons in the behavioral sciences often aim at selecting the model that best describes the structure in the population. Model selection is usually based on fit indices such as AIC or BIC, and inference is done based on the selected best-fitting model. This practice does not account for the possibility that due to sampling variability, a different model might be selected as the preferred model in a new sample from the same population. A previous study illustrated a bootstrap approach to gauge this model selection uncertainty using two empirical examples. The current study consists of a series of simulations to assess the utility of the proposed bootstrap approach in multi-group and mixture model comparisons. These simulations show that bootstrap selection rates can provide additional information over and above simply relying on the size of AIC and BIC differences in a given sample.

  4. Principles Governing Metal Ion Selectivity in Ion Channel Proteins

    NASA Astrophysics Data System (ADS)

    Lim, Carmay

    2014-03-01

    Our research interests are to (i) unravel the principles governing biological processes and use them to identify novel drug targets and guide drug design, and (ii) develop new methods for studying macromolecular interactions. This talk will provide an overview of our work in these two areas and an example of how our studies have helped to unravel the principles underlying the conversion of Ca2+-selective to Na+-selective channels. Ion selectivity of four-domain voltage-gated Ca2+(Cav) and sodium (Nav) channels, which is controlled by the selectivity filter (SF, the narrowest region of an open pore), is crucial for electrical signaling. Over billions of years of evolution, mutation of the Glu from domain II/III in the EEEE/DEEA SF of Ca2+-selective Cav channels to Lys made these channels Na+-selective. This talk will delineate the physical principles why Lys is sufficient for Na+/Ca2+selectivity and why the DEKA SF is more Na+-selective than the DKEA one.

  5. Selective detection of target proteins by peptide-enabled graphene biosensor.

    PubMed

    Khatayevich, Dmitriy; Page, Tamon; Gresswell, Carolyn; Hayamizu, Yuhei; Grady, William; Sarikaya, Mehmet

    2014-04-24

    Direct molecular detection of biomarkers is a promising approach for diagnosis and monitoring of numerous diseases, as well as a cornerstone of modern molecular medicine and drug discovery. Currently, clinical applications of biomarkers are limited by the sensitivity, complexity and low selectivity of available indirect detection methods. Electronic 1D and 2D nano-materials such as carbon nanotubes and graphene, respectively, offer unique advantages as sensing substrates for simple, fast and ultrasensitive detection of biomolecular binding. Versatile methods, however, have yet to be developed for simultaneous functionalization and passivation of the sensor surface to allow for enhanced detection and selectivity of the device. Herein, we demonstrate selective detection of a model protein against a background of serum protein using a graphene sensor functionalized via self-assembling multifunctional short peptides. The two peptides are engineered to bind to graphene and undergo co-assembly in the form of an ordered monomolecular film on the substrate. While the probe peptide displays the bioactive molecule, the passivating peptide prevents non-specific protein adsorption onto the device surface, ensuring target selectivity. In particular, we demonstrate a graphene field effect transistor (gFET) biosensor which can detect streptavidin against a background of serum bovine albumin at less than 50 ng/ml. Our nano-sensor design, allows us to restore the graphene surface and utilize each sensor in multiple experiments. The peptide-enabled gFET device has great potential to address a variety of bio-sensing problems, such as studying ligand-receptor interactions, or detection of biomarkers in a clinical setting.

  6. Selectable sets of novel proteins: catalytic and other properties. Final report, 1 July-30 September 1988

    SciTech Connect

    Kauffman, S.A.

    1989-05-08

    This proposal aimed at the development of mathematical, computer, recombinant DNA, selection and screening procedures to attain adaptive evolution of entirely novel peptides or fusion proteins with useful catalytic, ligand binding, structural, or other features. Potential uses range from industrial catalysis to production of new drugs and vaccines. A broad purpose of the experimental effort is to obtain novel peptides that can mimic the biological effects of almost arbitrary signal molecules such as hormones, growth factors, even pathogenic antigens. Mathematical models were developed to explore rugged 'adaptive landscapes' associated with protein evolution. The fundamental importance of this work includes analysis of the distribution of function in peptide space and opening the way towards a technology of applied molecular evolution.

  7. Selective extraction of isolated mitotic apparatus. Evidence that typical microtubule protein is extracted by organic mercurial.

    PubMed

    Bibring, T; Baxandall, J

    1971-02-01

    Mitotic apparatus isolated from sea urchin eggs has been treated with meralluride sodium under conditions otherwise resembling those of its isolation. The treatment causes a selective morphological disappearance of microtubules while extracting a major protein fraction, probably consisting of two closely related proteins, which constitutes about 10% of mitotic apparatus protein. Extraction of other cell particulates under similar conditions yields much less of this protein. The extracted protein closely resembles outer doublet microtubule protein from sea urchin sperm tail in properties considered typical of microtubule proteins: precipitation by calcium ion and vinblastine, electrophoretic mobility in both acid and basic polyacrylamide gels, sedimentation coefficient, molecular weight, and, according to a preliminary determination, amino acid composition. An antiserum against a preparation of sperm tail outer doublet microtubules cross-reacts with the extract from mitotic apparatus. On the basis of these findings it appears that microtubule protein is selectively extracted from isolated mitotic apparatus by treatment with meralluride, and is a typical microtubule protein.

  8. Computational Protein Design Is a Challenge for Implicit Solvation Models

    PubMed Central

    Jaramillo, Alfonso; Wodak, Shoshana J.

    2005-01-01

    Increasingly complex schemes for representing solvent effects in an implicit fashion are being used in computational analyses of biological macromolecules. These schemes speed up the calculations by orders of magnitude and are assumed to compromise little on essential features of the solvation phenomenon. In this work we examine this assumption. Five implicit solvation models, a surface area-based empirical model, two models that approximate the generalized Born treatment and a finite difference Poisson-Boltzmann method are challenged in situations differing from those where these models were calibrated. These situations are encountered in automatic protein design procedures, whose job is to select sequences, which stabilize a given protein 3D structure, from a large number of alternatives. To this end we evaluate the energetic cost of burying amino acids in thousands of environments with different solvent exposures belonging, respectively, to decoys built with random sequences and to native protein crystal structures. In addition we perform actual sequence design calculations. Except for the crudest surface area-based procedure, all the tested models tend to favor the burial of polar amino acids in the protein interior over nonpolar ones, a behavior that leads to poor performance in protein design calculations. We show, on the other hand, that three of the examined models are nonetheless capable of discriminating between the native fold and many nonnative alternatives, a test commonly used to validate force fields. It is concluded that protein design is a particularly challenging test for implicit solvation models because it requires accurate estimates of the solvation contribution of individual residues. This contrasts with native recognition, which depends less on solvation and more on other nonbonded contributions. PMID:15377512

  9. The initiation of eukaryotic and prokaryotic protein synthesis: a selective accessibility and multisubstrate enzyme reaction.

    PubMed

    Nakamoto, Tokumasa

    2007-11-15

    An extension of our unique accessibility hypothesis for the initiation of protein synthesis is proposed following a review of the initiation of protein synthesis. The E. coli model initiation sequence generated by computer from 68 initiation sequences and the eukaryotic consensus initiation sequence derived by non-computer analysis of 211 initiation sequences do not contain a specific base in any position; they are only assigned preferred bases. The initiation site, in other words, is a varied sequence of preferred bases and its sequence is non-unique. This indicates that the ribosomal recognition of the initiation site may be the result of multiple interactions that are cooperative and cumulative and typical of multisubstrate enzymes. Because of this characteristic, the model of multisubstrate enzymes with broad substrate specificity is proposed as a paradigm for the initiation of protein synthesis. As predicted by this model, changes in the leader and downstream sequences that improve the agreement with the preferred base sequence do indeed enhance the rate of protein synthesis. The eukaryotic/prokaryotic hybrid studies show a considerable overlap in the specificities of the two groups of ribosomes. The scanning of the mRNA from the 5'-end postulated by the scanning hypothesis is not a necessary step since eukaryotic ribosomes are able to bind to internal mRNA sites and initiate synthesis. Our unique accessibility hypothesis, which is extended by coupling cooperative and cumulative specificity in ribosomal function, is referred to for brevity as the cumulative specificity hypothesis. The hypothesis actually postulates a selective accessibility and cooperative-cumulative specificity mechanism; it is able to account for the behavior of both eukaryotic and prokaryotic initiation of protein synthesis. From another perspective, the hypothesis can be regarded as providing a mechanism that enables ribosomes to recognize the IS in the absence of a unique initiation

  10. The Ebola virus matrix protein VP40 selectively induces vesiculation from phosphatidylserine-enriched membranes.

    PubMed

    Soni, Smita P; Stahelin, Robert V

    2014-11-28

    Ebola virus is from the Filoviridae family of viruses and is one of the most virulent pathogens known with ∼ 60% clinical fatality. The Ebola virus negative sense RNA genome encodes seven proteins including viral matrix protein 40 (VP40), which is the most abundant protein found in the virions. Within infected cells VP40 localizes at the inner leaflet of the plasma membrane (PM), binds lipids, and regulates formation of new virus particles. Expression of VP40 in mammalian cells is sufficient to form virus-like particles that are nearly indistinguishable from the authentic virions. However, how VP40 interacts with the PM and forms virus-like particles is for the most part unknown. To investigate VP40 lipid specificity in a model of viral egress we employed giant unilamellar vesicles with different lipid compositions. The results demonstrate VP40 selectively induces vesiculation from membranes containing phosphatidylserine (PS) at concentrations of PS that are representative of the PM inner leaflet content. The formation of intraluminal vesicles was not significantly detected in the presence of other important PM lipids including cholesterol and polyvalent phosphoinositides, further demonstrating PS selectivity. Taken together, these studies suggest that PM phosphatidylserine may be an important component of Ebola virus budding and that VP40 may be able to mediate PM scission.

  11. Selective loss of fine tuning of Gq/11 signaling by RGS2 protein exacerbates cardiomyocyte hypertrophy.

    PubMed

    Zhang, Wei; Anger, Thomas; Su, Jialin; Hao, Jianming; Xu, Xiaomei; Zhu, Ming; Gach, Agnieszka; Cui, Lei; Liao, Ronglih; Mende, Ulrike

    2006-03-03

    Alterations in cardiac G protein-mediated signaling, most prominently G(q/11) signaling, are centrally involved in hypertrophy and heart failure development. Several RGS proteins that can act as negative regulators of G protein signaling are expressed in the heart, but their functional roles are still poorly understood. RGS expression changes have been described in hypertrophic and failing hearts. In this study, we report a marked decrease in RGS2 (but not other major cardiac RGS proteins (RGS3-RGS5)) that occurs prior to hypertrophy development in different models with enhanced G(q/11) signaling (transgenic expression of activated Galpha(q)(*) and pressure overload due to aortic constriction). To assess functional consequences of selective down-regulation of endogenous RGS2, we identified targeting sequences for effective RGS2 RNA interference and used lipid-based transfection to achieve uptake of fluorescently labeled RGS2 small interfering RNA in >90% of neonatal and adult ventricular myocytes. Endogenous RGS2 expression was dose-dependently suppressed (up to 90%) with no major change in RGS3-RGS5. RGS2 knockdown increased phenylephrine- and endothelin-1-induced phospholipase Cbeta stimulation in both cell types and exacerbated the hypertrophic effect (increase in cell size and radiolabeled protein) in neonatal myocytes, with no major change in G(q/11)-mediated ERK1/2, p38, or JNK activation. Taken together, this study demonstrates that endogenous RGS2 exerts functionally important inhibitory restraint on G(q/11)-mediated phospholipase Cbeta activation and hypertrophy in ventricular myocytes. Our findings point toward a potential pathophysiological role of loss of fine tuning due to selective RGS2 down-regulation in G(q/11)-mediated remodeling. Furthermore, this study shows the feasibility of effective RNA interference in cardiomyocytes using lipid-based small interfering RNA transfection.

  12. Protein scaffolds for selective enrichment of metal ions

    DOEpatents

    He, Chuan; Zhou, Lu; Bosscher, Michael

    2016-02-09

    Polypeptides comprising high affinity for the uranyl ion are provided. Methods for binding uranyl using such proteins are likewise provided and can be used, for example, in methods for uranium purification or removal.

  13. Antibody orientation enhanced by selective polymer-protein noncovalent interactions.

    PubMed

    Clarizia, Lisa-Jo A; Sok, Davin; Wei, Ming; Mead, Joey; Barry, Carol; McDonald, Melisenda J

    2009-03-01

    A unique interaction has been found between protein G' (a truncated recombinant bacterial "alphabet" protein which aligns by noncovalent attachment to the antibody stem) and poly(methyl methacrylate), a thermoplastic polymer substrate, which can be easily fabricated using high-rate processes. Significantly improved orientation efficiency with traditional passive adsorption for this system (termed ALYGNSA) has been achieved as compared to the same assay performed on a polystyrene substrate with protein G'. Results were consistent with an average alignment of 80% of the human immunoglobulin G capture antibody which translated into a 30% to 50% improved alignment over an array of industry standards tested. Laser scanning confocal microscopy confirmed the immunological results. Studies of additional poly(methyl methacrylate) polymer derivatives and protein biolinker (A and AG) combinations have been conducted and have revealed different degrees of antibody alignment. These findings may lead to additional novel noncovalent methods of antibody orientation and greater sensitivity in immunological assays.

  14. Site-Selective Modification of Proteins with Oxetanes.

    PubMed

    Boutureira, Omar; Martínez-Sáez, Nuria; Brindle, Kevin M; Neves, André A; Corzana, Francisco; Bernardes, Gonçalo J L

    2017-03-05

    Oxetanes are four-membered ring oxygen heterocycles that are advantageously used in medicinal chemistry as modulators of physicochemical properties of small molecules. Herein, we present a simple method for the incorporation of oxetanes into proteins through chemoselective alkylation of cysteine. We demonstrate a broad substrate scope by reacting proteins used as apoptotic markers and in drug formulation, and a therapeutic antibody with a series of 3-oxetane bromides, enabling the identification of novel handles (S-to-S/N rigid, non-aromatic, and soluble linker) and reactivity modes (temporary cysteine protecting group), while maintaining their intrinsic activity. The possibility to conjugate oxetane motifs into full-length proteins has potential to identify novel drug candidates as the next-generation of peptide/protein therapeutics with improved physicochemical and biological properties.

  15. Food Protein Functionality--A New Model.

    PubMed

    Foegeding, E Allen

    2015-12-01

    Proteins in foods serve dual roles as nutrients and structural building blocks. The concept of protein functionality has historically been restricted to nonnutritive functions--such as creating emulsions, foams, and gels--but this places sole emphasis on food quality considerations and potentially overlooks modifications that may also alter nutritional quality or allergenicity. A new model is proposed that addresses the function of proteins in foods based on the length scale(s) responsible for the function. Properties such as flavor binding, color, allergenicity, and digestibility are explained based on the structure of individual molecules; placing this functionality at the nano/molecular scale. At the next higher scale, applications in foods involving gelation, emulsification, and foam formation are based on how proteins form secondary structures that are seen at the nano and microlength scales, collectively called the mesoscale. The macroscale structure represents the arrangements of molecules and mesoscale structures in a food. Macroscale properties determine overall product appearance, stability, and texture. The historical approach of comparing among proteins based on forming and stabilizing specific mesoscale structures remains valid but emphasis should be on a common means for structure formation to allow for comparisons across investigations. For applications in food products, protein functionality should start with identification of functional needs across scales. Those needs are then evaluated relative to how processing and other ingredients could alter desired molecular scale properties, or proper formation of mesoscale structures. This allows for a comprehensive approach to achieving the desired function of proteins in foods.

  16. Fast loop modeling for protein structures

    NASA Astrophysics Data System (ADS)

    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.

  17. Tungsten exposure causes a selective loss of histone demethylase protein.

    PubMed

    Laulicht-Glick, Freda; Wu, Feng; Zhang, Xiaoru; Jordan, Ashley; Brocato, Jason; Kluz, Thomas; Sun, Hong; Costa, Max

    2017-02-20

    In the course of our investigations into the toxicity of tungstate, we discovered that cellular exposure resulted in the loss of the histone demethylase protein. We specifically investigated the loss of two histone demethylase dioxygenases, JARID1A and JMJD1A. Both of these proteins were degraded in the presence of tungstate and this resulted in increased global levels of H3K4me3 and H3K9me2, the substrates of JARID1A and JMJD1A, respectively. Treatment with MG132 completely inhibited the loss of the demethylase proteins induced by tungstate treatment, suggesting that tungstate activated the proteasomal degradation of these proteins. The changes in global histone marks and loss of histone demethylase protein persisted for at least 48 h after removing sodium tungstate from the culture. The increase in global histone methylation remained when cells were cultured in methionine-free media, indicating that the increased histone methylation did not depend upon any de novo methylation process, but rather was due to the loss of the demethylase protein. Similar increases of H3K4me3 and H3K9me2 were observed in the livers of the mice that were acutely exposed to tungstate via their drinking water. Taken together, our results indicated that tungstate exposure specifically reduced histone demethylase JARID1A and JMJD1A via proteasomal degradation, leading to increased histone methylation.

  18. Role of Water in the Selection of Stable Proteins at Ambient and Extreme Thermodynamic Conditions

    NASA Astrophysics Data System (ADS)

    Bianco, Valentino; Franzese, Giancarlo; Dellago, Christoph; Coluzza, Ivan

    2017-04-01

    Proteins that are functional at ambient conditions do not necessarily work at extreme conditions of temperature T and pressure P . Furthermore, there are limits of T and P above which no protein has a stable functional state. Here, we show that these limits and the selection mechanisms for working proteins depend on how the properties of the surrounding water change with T and P . We find that proteins selected at high T are superstable and are characterized by a nonextreme segregation of a hydrophilic surface and a hydrophobic core. Surprisingly, a larger segregation reduces the stability range in T and P . Our computer simulations, based on a new protein design protocol, explain the hydropathy profile of proteins as a consequence of a selection process influenced by water. Our results, potentially useful for engineering proteins and drugs working far from ambient conditions, offer an alternative rationale to the evolutionary action exerted by the environment in extreme conditions.

  19. Increasing selection response by Bayesian modeling of heterogeneous environmental variances

    USDA-ARS?s Scientific Manuscript database

    Heterogeneity of environmental variance among genotypes reduces selection response because genotypes with higher variance are more likely to be selected than low-variance genotypes. Modeling heterogeneous variances to obtain weighted means corrected for heterogeneous variances is difficult in likel...

  20. Physicochemical descriptors to discriminate protein-protein interactions in permanent and transient complexes selected by means of machine learning algorithms.

    PubMed

    Block, Peter; Paern, Juri; Hüllermeier, Eyke; Sanschagrin, Paul; Sotriffer, Christoph A; Klebe, Gerhard

    2006-11-15

    Analyzing protein-protein interactions at the atomic level is critical for our understanding of the principles governing the interactions involved in protein-protein recognition. For this purpose, descriptors explaining the nature of different protein-protein complexes are desirable. In this work, the authors introduced Epic Protein Interface Classification as a framework handling the preparation, processing, and analysis of protein-protein complexes for classification with machine learning algorithms. We applied four different machine learning algorithms: Support Vector Machines, C4.5 Decision Trees, K Nearest Neighbors, and Naïve Bayes algorithm in combination with three feature selection methods, Filter (Relief F), Wrapper, and Genetic Algorithms, to extract discriminating features from the protein-protein complexes. To compare protein-protein complexes to each other, the authors represented the physicochemical characteristics of their interfaces in four different ways, using two different atomic contact vectors, DrugScore pair potential vectors and SFCscore descriptor vectors. We classified two different datasets: (A) 172 protein-protein complexes comprising 96 monomers, forming contacts enforced by the crystallographic packing environment (crystal contacts), and 76 biologically functional homodimer complexes; (B) 345 protein-protein complexes containing 147 permanent complexes and 198 transient complexes. We were able to classify up to 94.8% of the packing enforced/functional and up to 93.6% of the permanent/transient complexes correctly. Furthermore, we were able to extract relevant features from the different protein-protein complexes and introduce an approach for scoring the importance of the extracted features. (c) 2006 Wiley-Liss, Inc.

  1. Differential Evolutionary Selection and Natural Evolvability Observed in ALT Proteins of Human Filarial Parasites.

    PubMed

    Devoe, Neil C; Corbett, Ian J; Barker, Linsey; Chang, Robert; Gudis, Polyxeni; Mullen, Nathan; Perez, Kailey; Raposo, Hugo; Scholz, John; May, Meghan

    2016-01-01

    The abundant larval transcript (ALT-2) protein is present in all members of the Filarioidea, and has been reported as a potential candidate antigen for a subunit vaccine against lymphatic filariasis. To assess the potential for vaccine escape or heterologous protection, we examined the evolutionary selection acting on ALT-2. The ratios of nonsynonymous (K(a)) to synonymous (K(s)) mutation frequencies (ω) were calculated for the alt-2 genes of the lymphatic filariasis agents Brugia malayi and Wuchereria bancrofti and the agents of river blindness and African eyeworm disease Onchocerca volvulus and Loa loa. Two distinct Bayesian models of sequence evolution showed that ALT-2 of W. bancrofti and L. loa were under significant (P<0.05; P < 0.001) diversifying selection, while ALT-2 of B. malayi and O. volvulus were under neutral to stabilizing selection. Diversifying selection as measured by ω values was notably strongest on the region of ALT-2 encoding the signal peptide of L. loa and was elevated in the variable acidic domain of L. loa and W. bancrofti. Phylogenetic analysis indicated that the ALT-2 consensus sequences formed three clades: the first consisting of B. malayi, the second consisting of W. bancrofti, and the third containing both O. volvulus and L. loa. ALT-2 selection was therefore not predictable by phylogeny or pathology, as the two species parasitizing the eye were selected differently, as were the two species parasitizing the lymphatic system. The most immunogenic regions of L. loa and W. bancrofti ALT-2 sequence as modeled by antigenicity prediction analysis did not correspond with elevated levels of diversifying selection, and were not selected differently than predicted antigenic epitopes in B. malayi and O. volvulus. Measurements of ALT-2 evolvability made by χ2 analysis between alleles that were stable (O. volvulus and B. malayi) and those that were under diversifying selection (W. bancrofti and L. loa) indicated significant (P<0

  2. Differential Evolutionary Selection and Natural Evolvability Observed in ALT Proteins of Human Filarial Parasites

    PubMed Central

    Devoe, Neil C.; Corbett, Ian J.; Barker, Linsey; Chang, Robert; Gudis, Polyxeni; Mullen, Nathan; Perez, Kailey; Raposo, Hugo; Scholz, John; May, Meghan

    2016-01-01

    The abundant larval transcript (ALT-2) protein is present in all members of the Filarioidea, and has been reported as a potential candidate antigen for a subunit vaccine against lymphatic filariasis. To assess the potential for vaccine escape or heterologous protection, we examined the evolutionary selection acting on ALT-2. The ratios of nonsynonymous (K(a)) to synonymous (K(s)) mutation frequencies (ω) were calculated for the alt-2 genes of the lymphatic filariasis agents Brugia malayi and Wuchereria bancrofti and the agents of river blindness and African eyeworm disease Onchocerca volvulus and Loa loa. Two distinct Bayesian models of sequence evolution showed that ALT-2 of W. bancrofti and L. loa were under significant (P<0.05; P < 0.001) diversifying selection, while ALT-2 of B. malayi and O. volvulus were under neutral to stabilizing selection. Diversifying selection as measured by ω values was notably strongest on the region of ALT-2 encoding the signal peptide of L. loa and was elevated in the variable acidic domain of L. loa and W. bancrofti. Phylogenetic analysis indicated that the ALT-2 consensus sequences formed three clades: the first consisting of B. malayi, the second consisting of W. bancrofti, and the third containing both O. volvulus and L. loa. ALT-2 selection was therefore not predictable by phylogeny or pathology, as the two species parasitizing the eye were selected differently, as were the two species parasitizing the lymphatic system. The most immunogenic regions of L. loa and W. bancrofti ALT-2 sequence as modeled by antigenicity prediction analysis did not correspond with elevated levels of diversifying selection, and were not selected differently than predicted antigenic epitopes in B. malayi and O. volvulus. Measurements of ALT-2 evolvability made by χ2 analysis between alleles that were stable (O. volvulus and B. malayi) and those that were under diversifying selection (W. bancrofti and L. loa) indicated significant (P<0

  3. Protein Synthesis with Ribosomes Selected for the Incorporation of β-Amino Acids

    PubMed Central

    2016-01-01

    In an earlier study, β3-puromycin was used for the selection of modified ribosomes, which were utilized for the incorporation of five different β-amino acids into Escherichia coli dihydrofolate reductase (DHFR). The selected ribosomes were able to incorporate structurally disparate β-amino acids into DHFR, in spite of the use of a single puromycin for the selection of the individual clones. In this study, we examine the extent to which the structure of the β3-puromycin employed for ribosome selection influences the regio- and stereochemical preferences of the modified ribosomes during protein synthesis; the mechanistic probe was a single suppressor tRNACUA activated with each of four methyl-β-alanine isomers (1–4). The modified ribosomes were found to incorporate each of the four isomeric methyl-β-alanines into DHFR but exhibited a preference for incorporation of 3(S)-methyl-β-alanine (β-mAla; 4), i.e., the isomer having the same regio- and stereochemistry as the O-methylated β-tyrosine moiety of β3-puromycin. Also conducted were a selection of clones that are responsive to β2-puromycin and a demonstration of reversal of the regio- and stereochemical preferences of these clones during protein synthesis. These results were incorporated into a structural model of the modified regions of 23S rRNA, which included in silico prediction of a H-bonding network. Finally, it was demonstrated that incorporation of 3(S)-methyl-β-alanine (β-mAla; 4) into a short α-helical region of the nucleic acid binding domain of hnRNP LL significantly stabilized the helix without affecting its DNA binding properties. PMID:25982410

  4. Protein Synthesis with Ribosomes Selected for the Incorporation of β-Amino Acids.

    PubMed

    Maini, Rumit; Chowdhury, Sandipan Roy; Dedkova, Larisa M; Roy, Basab; Daskalova, Sasha M; Paul, Rakesh; Chen, Shengxi; Hecht, Sidney M

    2015-06-16

    In an earlier study, β³-puromycin was used for the selection of modified ribosomes, which were utilized for the incorporation of five different β-amino acids into Escherichia coli dihydrofolate reductase (DHFR). The selected ribosomes were able to incorporate structurally disparate β-amino acids into DHFR, in spite of the use of a single puromycin for the selection of the individual clones. In this study, we examine the extent to which the structure of the β³-puromycin employed for ribosome selection influences the regio- and stereochemical preferences of the modified ribosomes during protein synthesis; the mechanistic probe was a single suppressor tRNA(CUA) activated with each of four methyl-β-alanine isomers (1-4). The modified ribosomes were found to incorporate each of the four isomeric methyl-β-alanines into DHFR but exhibited a preference for incorporation of 3(S)-methyl-β-alanine (β-mAla; 4), i.e., the isomer having the same regio- and stereochemistry as the O-methylated β-tyrosine moiety of β³-puromycin. Also conducted were a selection of clones that are responsive to β²-puromycin and a demonstration of reversal of the regio- and stereochemical preferences of these clones during protein synthesis. These results were incorporated into a structural model of the modified regions of 23S rRNA, which included in silico prediction of a H-bonding network. Finally, it was demonstrated that incorporation of 3(S)-methyl-β-alanine (β-mAla; 4) into a short α-helical region of the nucleic acid binding domain of hnRNP LL significantly stabilized the helix without affecting its DNA binding properties.

  5. Mining flexible-receptor docking experiments to select promising protein receptor snapshots

    PubMed Central

    2010-01-01

    Background Molecular docking simulation is the Rational Drug Design (RDD) step that investigates the affinity between protein receptors and ligands. Typically, molecular docking algorithms consider receptors as rigid bodies. Receptors are, however, intrinsically flexible in the cellular environment. The use of a time series of receptor conformations is an approach to explore its flexibility in molecular docking computer simulations, but it is extensively time-consuming. Hence, selection of the most promising conformations can accelerate docking experiments and, consequently, the RDD efforts. Results We previously docked four ligands (NADH, TCL, PIF and ETH) to 3,100 conformations of the InhA receptor from M. tuberculosis. Based on the receptor residues-ligand distances we preprocessed all docking results to generate appropriate input to mine data. Data preprocessing was done by calculating the shortest interatomic distances between the ligand and the receptor’s residues for each docking result. They were the predictive attributes. The target attribute was the estimated free-energy of binding (FEB) value calculated by the AutodDock3.0.5 software. The mining inputs were submitted to the M5P model tree algorithm. It resulted in short and understandable trees. On the basis of the correlation values, for NADH, TCL and PIF we obtained more than 95% correlation while for ETH, only about 60%. Post processing the generated model trees for each of its linear models (LMs), we calculated the average FEB for their associated instances. From these values we considered a LM as representative if its average FEB was smaller than or equal the average FEB of the test set. The instances in the selected LMs were considered the most promising snapshots. It totalized 1,521, 1,780, 2,085 and 902 snapshots, for NADH, TCL, PIF and ETH respectively. Conclusions By post processing the generated model trees we were able to propose a criterion of selection of linear models which, in turn, is

  6. Selective destruction of abnormal proteins by ubiquitin-mediated protein quality control degradation.

    PubMed

    Fredrickson, Eric K; Gardner, Richard G

    2012-07-01

    Misfolded proteins are continuously produced in the cell and present an escalating detriment to cellular physiology if not managed effectively. As such, all organisms have evolved mechanisms to address misfolded proteins. One primary way eukaryotic cells handle the complication of misfolded proteins is by destroying them through the ubiquitin-proteasome system. To do this, eukaryotes possess specialized ubiquitin-protein ligases that have the capacity to recognize misfolded proteins over normally folded proteins. The strategies used by these Protein Quality Control (PQC) ligases to target the wide variety of misfolded proteins in the cell will likely be different than those used by ubiquitin-protein ligases that function in regulated degradation to target normally folded proteins. In this review, we highlight what is known about how misfolded proteins are recognized by PQC ubiquitin-protein ligases. Copyright © 2012 Elsevier Ltd. All rights reserved.

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

    PubMed Central

    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/. PMID:27907159

  8. Segmental, Domain-Selective Perdeuteration and Small-Angle Neutron Scattering for Structural Analysis of Multi-Domain Proteins.

    PubMed

    Sonntag, Miriam; Jagtap, Pravin Kumar Ankush; Simon, Bernd; Appavou, Marie-Sousai; Geerlof, Arie; Stehle, Ralf; Gabel, Frank; Hennig, Janosch; Sattler, Michael

    2017-08-01

    Multi-domain proteins play critical roles in fine-tuning essential processes in cellular signaling and gene regulation. Typically, multiple globular domains that are connected by flexible linkers undergo dynamic rearrangements upon binding to protein, DNA or RNA ligands. RNA binding proteins (RBPs) represent an important class of multi-domain proteins, which regulate gene expression by recognizing linear or structured RNA sequence motifs. Here, we employ segmental perdeuteration of the three RNA recognition motif (RRM) domains in the RBP TIA-1 using Sortase A mediated protein ligation. We show that domain-selective perdeuteration combined with contrast-matched small-angle neutron scattering (SANS), SAXS and computational modeling provides valuable information to precisely define relative domain arrangements. The approach is generally applicable to study conformational arrangements of individual domains in multi-domain proteins and changes induced by ligand binding. © 2017 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.

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

    USGS Publications Warehouse

    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

  10. Fold assessment for comparative protein structure modeling.

    PubMed

    Melo, Francisco; Sali, Andrej

    2007-11-01

    Accurate and automated assessment of both geometrical errors and incompleteness of comparative protein structure models is necessary for an adequate use of the models. Here, we describe a composite score for discriminating between models with the correct and incorrect fold. To find an accurate composite score, we designed and applied a genetic algorithm method that searched for a most informative subset of 21 input model features as well as their optimized nonlinear transformation into the composite score. The 21 input features included various statistical potential scores, stereochemistry quality descriptors, sequence alignment scores, geometrical descriptors, and measures of protein packing. The optimized composite score was found to depend on (1) a statistical potential z-score for residue accessibilities and distances, (2) model compactness, and (3) percentage sequence identity of the alignment used to build the model. The accuracy of the composite score was compared with the accuracy of assessment by single and combined features as well as by other commonly used assessment methods. The testing set was representative of models produced by automated comparative modeling on a genomic scale. The composite score performed better than any other tested score in terms of the maximum correct classification rate (i.e., 3.3% false positives and 2.5% false negatives) as well as the sensitivity and specificity across the whole range of thresholds. The composite score was implemented in our program MODELLER-8 and was used to assess models in the MODBASE database that contains comparative models for domains in approximately 1.3 million protein sequences.

  11. Fold assessment for comparative protein structure modeling

    PubMed Central

    Melo, Francisco; Sali, Andrej

    2007-01-01

    Accurate and automated assessment of both geometrical errors and incompleteness of comparative protein structure models is necessary for an adequate use of the models. Here, we describe a composite score for discriminating between models with the correct and incorrect fold. To find an accurate composite score, we designed and applied a genetic algorithm method that searched for a most informative subset of 21 input model features as well as their optimized nonlinear transformation into the composite score. The 21 input features included various statistical potential scores, stereochemistry quality descriptors, sequence alignment scores, geometrical descriptors, and measures of protein packing. The optimized composite score was found to depend on (1) a statistical potential z-score for residue accessibilities and distances, (2) model compactness, and (3) percentage sequence identity of the alignment used to build the model. The accuracy of the composite score was compared with the accuracy of assessment by single and combined features as well as by other commonly used assessment methods. The testing set was representative of models produced by automated comparative modeling on a genomic scale. The composite score performed better than any other tested score in terms of the maximum correct classification rate (i.e., 3.3% false positives and 2.5% false negatives) as well as the sensitivity and specificity across the whole range of thresholds. The composite score was implemented in our program MODELLER-8 and was used to assess models in the MODBASE database that contains comparative models for domains in approximately 1.3 million protein sequences. PMID:17905832

  12. Investigating protein haptenation mechanisms of skin sensitisers using human serum albumin as a model protein.

    PubMed

    Aleksic, Maja; Pease, Camilla K; Basketter, David A; Panico, Maria; Morris, Howard R; Dell, Anne

    2007-06-01

    Covalent modification of skin proteins by electrophiles is a key event in the induction of skin sensitisation but not skin irritation although the exact nature of the binding mechanisms has not been determined empirically for the vast majority of sensitisers. It is also unknown whether immunologically relevant protein targets exist in the skin contributing to effecting skin sensitisation. To determine the haptenation mechanism(s) and spectra of amino acid reactivity in an intact protein for two sensitisers expected to react by different mechanisms, human serum albumin (HSA) was chosen as a model protein. The aim of this work was also to verify for selected non-sensitisers and irritants that no protein haptenation occurs even under forcing conditions. HSA was incubated with chemicals and the resulting complexes were digested with trypsin and analysed deploying matrix-assisted laser desorption/ionization mass spectrometry, reverse phase high performance liquid chromatography and nano-electrospray tandem mass spectrometry. The data confirmed that different residues (lysine, cysteine, histidine and tyrosine) are covalently modified in a highly selective and differential manner by the sensitisers 2,4-dinitro-1-chlorobenzene and phenyl salicylate. Additionally, non-sensitisers 2,4-dichloro-1-nitrobenzene, butyl paraben and benzaldehyde and irritants benzalkonium chloride and sodium dodecyl sulphate did not covalently modify HSA under any conditions. The data indicate that covalent haptenation is a prerequisite of skin sensitisation but not irritation. The data also suggest that protein modifications are targeted to certain amino acids residing in chemical microenvironments conducive to reactivity within an intact protein. Deriving such information is relevant to our understanding of antigen formation in the immunobiology of skin sensitisation and in the development of in vitro protein haptenation assays.

  13. An in vitro selected binding protein (affibody) shows conformation-dependent recognition of the respiratory syncytial virus (RSV) G protein.

    PubMed

    Hansson, M; Ringdahl, J; Robert, A; Power, U; Goetsch, L; Nguyen, T N; Uhlén, M; Ståhl, S; Nygren, P A

    1999-03-01

    Using phage-display technology, a novel binding protein (Z-affibody) showing selective binding to the RSV (Long strain) G protein was selected from a combinatorial library of a small alpha-helical protein domain (Z), derived from staphylococcal protein A (SPA). Biopanning of the Z-library against a recombinant fusion protein comprising amino acids 130-230 of the G protein from RSV-subgroup A, resulted in the selection of a Z-affibody (Z(RSV1)) which showed G protein specific binding. Using biosensor technology, the affinity (K(D)) between Z(RSV1) and the recombinant protein was determined to be in the micromolar range (10(-6) M). Interestingly, the Z(RSV1) affibody was demonstrated to also recognize the partially (54%) homologous G protein of RSV subgroup B with similar affinity. Using different recombinant RSV G protein derived fragments, the binding was found to be dependent on the presence of the cysteinyl residues proposed to be involved in the formation of an intramolecular disulfide-constrained loop structure, indicating a conformation-dependent binding. Results from epitope mapping studies, employing a panel of monoclonal antibodies directed to different RSV G protein subfragments, suggest that the Z(RSV1) affibody binding site is located within the region of amino acids 164-186 of the G protein. This region contains a 13 amino acid residue sequence which is totally conserved between subgroups A and B of RSV and extends into the cystein loop region (amino acids 173-186). The potential use of the RSV G protein-specific Z(RSV1) affibody in diagnostic and therapeutic applications is discussed.

  14. Analyzing models for interactions of aptamers to proteins

    NASA Astrophysics Data System (ADS)

    Silva, Dilson; Missailidis, Sotiris

    2014-10-01

    We have devised an experimental and theoretical model, based on fluorescent spectroscopy and molecular modelling, to describe the interaction of aptamer (selected against various protein targets) with proteins and albumins in particular. This model, described in this work, has allowed us to decipher the nature of the interactions between aptamers and albumins, the binding site of the aptamers to albumins, the potential role of primer binding to the albumin and expand to the ability of albumin to carry aptamers in the bloodstream, providing data to better understand the level of free aptamer for target binding. We are presenting the study of a variety of aptamers, including those against the MUC1 tumour marker, heparanase and human kallikrein 6 with bovine and human serum albumins and the effect these interactions may have on the bioavailability of the aptamer for target-specific binding and therapeutic activity.

  15. Model for personal computer system selection.

    PubMed

    Blide, L

    1987-12-01

    Successful computer software and hardware selection is best accomplished by following an organized approach such as the one described in this article. The first step is to decide what you want to be able to do with the computer. Secondly, select software that is user friendly, well documented, bug free, and that does what you want done. Next, you select the computer, printer and other needed equipment from the group of machines on which the software will run. Key factors here are reliability and compatibility with other microcomputers in your facility. Lastly, you select a reliable vendor who will provide good, dependable service in a reasonable time. The ability to correctly select computer software and hardware is a key skill needed by medical record professionals today and in the future. Professionals can make quality computer decisions by selecting software and systems that are compatible with other computers in their facility, allow for future net-working, ease of use, and adaptability for expansion as new applications are identified. The key to success is to not only provide for your present needs, but to be prepared for future rapid expansion and change in your computer usage as technology and your skills grow.

  16. Light scattering evidence of selective protein fouling on biocompatible block copolymer micelles.

    PubMed

    Giacomelli, Fernando C; Stepánek, Petr; Schmidt, Vanessa; Jäger, Eliézer; Jäger, Alessandro; Giacomelli, Cristiano

    2012-08-07

    Selective protein fouling on block copolymer micelles with well-known potential for tumour-targeting drug delivery was evidenced by using dynamic light scattering measurements. The stability and interaction of block copolymer micelles with model proteins (BSA, IgG, lysozyme and CytC) is reported for systems featuring a hydrophobic (poly[2-(diisopropylamino)-ethyl methacrylate]) (PDPA) core and hydrophilic coronas comprising poly(ethylene oxide)/poly(glycerol monomethacrylate) (PEO-b-PG2MA) or poly[2-(methacryloyloxy)ethyl phosphorylcholine] (PMPC). The results revealed that protein size and hydrophilic chain density play important roles in the observed interactions. The PEO(113)-b-PG2MA(30)-b-PDPA(50) nanoparticles are stable and protein adsorption is prevented at all investigated protein environments. The successful protein-repellent characteristic of these nanoparticles is attributed to a high hydrophilic surface chain density (>0.1 chains per nm(2)) and to the length of the hydrophilic chains. On the other hand, although PMPC also has protein-repellent characteristics, the low surface chain density of the hydrophilic shell is supposed to enable interactions with small proteins. The PMPC(40)-b-PDPA(70) micelles are stable in BSA and IgG environments due to weak repulsion forces between PMPC and the proteins, to the hydration layer, and particularly to a size-effect where the large BSA (R(H) = 4.2 nm) and IgG (R(H) = 7.0 nm) do not easily diffuse within the PMPC shell. Conversely, a clear interaction was observed with the 2.1 nm radius lysozyme. The lysozyme protein can diffuse within the PMPC micellar shell towards the PDPA hydrophobic core in a process favored by its smaller size and the low hydrophilic PMPC surface chain density (∼0.049 chains per nm(2)) as compared to PEO-b-PG2MA (∼0.110 chains per nm(2)). The same behavior was not evidenced with the 2.3 nm radius positively charged CytC, probably due to its higher surface hydrophilicity and the consequent

  17. Sex-selective hippocampal alterations after adolescent nicotine administration: effects on neurospecific proteins.

    PubMed

    Xu, Zengjun; Seidler, Frederic J; Tate, Charlotte A; Garcia, Stephanie J; Slikker, William; Slotkin, Theodore A

    2003-12-01

    Nicotine is a neuroteratogen that targets cell development and synaptic function into adolescence, when smoking typically commences. We used a rat model of adolescent nicotine exposure to characterize the types of cells involved in hippocampal alterations. Nicotine was given to adolescent rats by minipump infusions from postnatal day (PN) 30 to PN47.5, using a dose rate (6 mg/kg/day) that replicates the plasma nicotine levels found in smokers. We examined specific neuronal and astrocyte proteins in the posttreatment period (PN50, PN60), when deficits in neurotransmission first appear: glial fibrillary acidic protein (GFAP), a marker for astrocytes; neurofilament 68-kDa protein (NF68), which is concentrated in the neuronal perikaryon and proximal neurites; and neurofilament 200-kDa protein (NF200), which is found in axonal projections distal to the perikaryon. Adolescent nicotine treatment evoked a significant decrease across all three markers, with the effect restricted to females and showing intensification between PN50 and PN60. These changes correspond to the sex-selectivity and temporal course over which other biomarkers indicate hippocampal cell damage and alterations in synaptic function. We conclude that administration of nicotine to adolescent rats alters neuroproteins in the female hippocampus during withdrawal, effects that could contribute to neurobehavioral deficits.

  18. Detecting experimental techniques and selecting relevant documents for protein-protein interactions from biomedical literature

    PubMed Central

    2011-01-01

    Background The selection of relevant articles for curation, and linking those articles to experimental techniques confirming the findings became one of the primary subjects of the recent BioCreative III contest. The contest’s Protein-Protein Interaction (PPI) task consisted of two sub-tasks: Article Classification Task (ACT) and Interaction Method Task (IMT). ACT aimed to automatically select relevant documents for PPI curation, whereas the goal of IMT was to recognise the methods used in experiments for identifying the interactions in full-text articles. Results We proposed and compared several classification-based methods for both tasks, employing rich contextual features as well as features extracted from external knowledge sources. For IMT, a new method that classifies pair-wise relations between every text phrase and candidate interaction method obtained promising results with an F1 score of 64.49%, as tested on the task’s development dataset. We also explored ways to combine this new approach and more conventional, multi-label document classification methods. For ACT, our classifiers exploited automatically detected named entities and other linguistic information. The evaluation results on the BioCreative III PPI test datasets showed that our systems were very competitive: one of our IMT methods yielded the best performance among all participants, as measured by F1 score, Matthew’s Correlation Coefficient and AUC iP/R; whereas for ACT, our best classifier was ranked second as measured by AUC iP/R, and also competitive according to other metrics. Conclusions Our novel approach that converts the multi-class, multi-label classification problem to a binary classification problem showed much promise in IMT. Nevertheless, on the test dataset the best performance was achieved by taking the union of the output of this method and that of a multi-class, multi-label document classifier, which indicates that the two types of systems complement each other in terms

  19. A model of autophagy size selectivity by receptor clustering on peroxisomes

    NASA Astrophysics Data System (ADS)

    Brown, Aidan I.; Rutenberg, Andrew D.

    2017-05-01

    Selective autophagy must not only select the correct type of organelle, but also must discriminate between individual organelles of the same kind so that some but not all of the organelles are removed. We propose that physical clustering of autophagy receptor proteins on the organelle surface can provide an appropriate all-or-none signal for organelle degradation. We explore this proposal using a computational model restricted to peroxisomes and the relatively well characterized pexophagy receptor proteins NBR1 and p62. We find that larger peroxisomes nucleate NBR1 clusters first and lose them last through competitive coarsening. This results in significant size-selectivity that favors large peroxisomes, and can explain the increased catalase signal that results from siRNA inhibition of p62. Excess ubiquitin, resulting from damaged organelles, suppresses size-selectivity but not cluster formation. Our proposed selectivity mechanism thus allows all damaged organelles to be degraded, while otherwise selecting only a portion of organelles for degradation.

  20. A Simple Model for Protein Folding

    NASA Astrophysics Data System (ADS)

    Henry, Eric R.; Eaton, William A.

    We describe a simple Ising-like statistical mechanical model for folding proteins based on the α-carbon contact map of the native structure. In this model residues can adopt two microscopic states corresponding to the native and non-native conformations. In order to exactly enumerate the large number of possible configurations, structure is considered to grow as continuous sequences of native residues, with no more than two sequences in each molecule. Inter-residue contacts can only form within each sequence and between residues of the two native sequences. As structure grows there is a tradeoff between the stabilizing effect of inter-residue contacts and the entropy losses from ordering residues in their native conformation and from forming a disordered loop to connect two continuous sequences. Folding kinetics are calculated from the dynamics on the free energy profile, as in Kramers' reaction rate theory. Although non-native interactions responsible for roughness in the energy landscape are not explicitly considered in the model, they are implicitly included by determining the absolute rates for motion on the free energy profile. With the exception of α-helical proteins, the kinetic progress curves exhibit single exponential time courses, consistent with two state behavior, as observed experimentally. The calculated folding rates are in remarkably good agreement with the measured values for the 25 two-state proteins investigated, with a correlation coefficient of 0.8. With its coarse-grained description of both the energy and entropy, and only three independently adjustable parameters, the model may be regarded as the simplest possible analytical model of protein folding capable of predicting experimental properties of specific proteins.

  1. Validation subset selections for extrapolation oriented QSPAR models.

    PubMed

    Szántai-Kis, Csaba; Kövesdi, István; Kéri, György; Orfi, László

    2003-01-01

    One of the most important features of QSPAR models is their predictive ability. The predictive ability of QSPAR models should be checked by external validation. In this work we examined three different types of external validation set selection methods for their usefulness in in-silico screening. The usefulness of the selection methods was studied in such a way that: 1) We generated thousands of QSPR models and stored them in 'model banks'. 2) We selected a final top model from the model banks based on three different validation set selection methods. 3) We predicted large data sets, which we called 'chemical universe sets', and calculated the corresponding SEPs. The models were generated from small fractions of the available water solubility data during a GA Variable Subset Selection procedure. The external validation sets were constructed by random selections, uniformly distributed selections or by perimeter-oriented selections. We found that the best performing models on the perimeter-oriented external validation sets usually gave the best validation results when the remaining part of the available data was overwhelmingly large, i.e., when the model had to make a lot of extrapolations. We also compared the top final models obtained from external validation set selection methods in three independent and different sizes of 'chemical universe sets'.

  2. Improved machine learning models for predicting selective compounds.

    PubMed

    Ning, Xia; Walters, Michael; Karypis, George; Karypisxy, George

    2012-01-23

    The identification of small potent compounds that selectively bind to the target under consideration with high affinities is a critical step toward successful drug discovery. However, there is still a lack of efficient and accurate computational methods to predict compound selectivity properties. In this paper, we propose a set of machine learning methods to do compound selectivity prediction. In particular, we propose a novel cascaded learning method and a multitask learning method. The cascaded method decomposes the selectivity prediction into two steps, one model for each step, so as to effectively filter out nonselective compounds. The multitask method incorporates both activity and selectivity models into one multitask model so as to better differentiate compound selectivity properties. We conducted a comprehensive set of experiments and compared the results with those of other conventional selectivity prediction methods, and our results demonstrated that the cascaded and multitask methods significantly improve the selectivity prediction performance.

  3. Hyperspectral band selection using statistical models

    NASA Astrophysics Data System (ADS)

    Maerker, Jochen; Groß, Wolfgang; Middelmann, Wolfgang; Ebert, Alfons

    2011-06-01

    Hyperspectral sensors are delivering a data cube consisting of hundreds of images gathered in adjacent frequency bands. Processing such data requires solutions to handle the computational complexity and the information redundancy. In principle, there are two different approaches deployable. Data compression merges this imagery to some few images. Hereby only the essential information is preserved. Small variations are treated as disturbances and hence removed. Band selection eliminates superfluous bands, leaving the others unmodified. Thus even minor deviations are preserved. In our paper, we present a novel band selection method especially developed for surveillance purposes. Hereby, the capability to detect even small variations poses an essential requirement, only fulfilled by the second approach. The computational complexity and the performance of such an algorithm depend on the available information. If complete knowledge about the targets and the background is available, contrast maximization establishes a perfect band selection. Without any knowledge the selection has to be performed by exploiting the band attributes often resulting in a poor choice. In order to avoid this, the developed algorithm incorporates the accessible information from the monitoring scene. In particular, features (e.g. anomalies) based on proximity relations are extracted in each band. Subsequently, an assessment of their suitability is accomplished by means of the value margins and the associated distributions. The final selection is then based on the inspection of the variations caused by the illumination and other external effects. We demonstrate and evaluate the appropriateness of this new method with a practical example.

  4. Modeling and minimizing CAPRI round 30 symmetrical protein complexes from CASP-11 structural models.

    PubMed

    El Houasli, Marwa; Maigret, Bernard; Devignes, Marie-Dominique; Ghoorah, Anisah W; Grudinin, Sergei; Ritchie, David W

    2017-03-01

    Many of the modeling targets in the blind CASP-11/CAPRI-30 experiment were protein homo-dimers and homo-tetramers. Here, we perform a retrospective docking-based analysis of the perfectly symmetrical CAPRI Round 30 targets whose crystal structures have been published. Starting from the CASP "stage-2" fold prediction models, we show that using our recently developed "SAM" polar Fourier symmetry docking algorithm combined with NAMD energy minimization often gives acceptable or better 3D models of the target complexes. We also use SAM to analyze the overall quality of all CASP structural models for the selected targets from a docking-based perspective. We demonstrate that docking only CASP "center" structures for the selected targets provides a fruitful and economical docking strategy. Furthermore, our results show that many of the CASP models are dockable in the sense that they can lead to acceptable or better models of symmetrical complexes. Even though SAM is very fast, using docking and NAMD energy minimization to pull out acceptable docking models from a large ensemble of docked CASP models is computationally expensive. Nonetheless, thanks to our SAM docking algorithm, we expect that applying our docking protocol on a modern computer cluster will give us the ability to routinely model 3D structures of symmetrical protein complexes from CASP-quality models. Proteins 2017; 85:463-469. © 2016 Wiley Periodicals, Inc.

  5. Detection of peptides, proteins, and drugs that selectively interact with protein targets.

    PubMed

    Serebriiskii, Ilya G; Mitina, Olga; Pugacheva, Elena N; Benevolenskaya, Elizaveta; Kotova, Elena; Toby, Garabet G; Khazak, Vladimir; Kaelin, William G; Chernoff, Jonathan; Golemis, Erica A

    2002-11-01

    Genome sequencing has been completed for multiple organisms, and pilot proteomic analyses reported for yeast and higher eukaryotes. This work has emphasized the facts that proteins are frequently engaged in multiple interactions, and that governance of protein interaction specificity is a primary means of regulating biological systems. In particular, the ability to deconvolute complex protein interaction networks to identify which interactions govern specific signaling pathways requires the generation of biological tools that allow the distinction of critical from noncritical interactions. We report the application of an enhanced Dual Bait two-hybrid system to allow detection and manipulation of highly specific protein-protein interactions. We summarize the use of this system to detect proteins and peptides that target well-defined specific motifs in larger protein structures, to facilitate rapid identification of specific interactors from a pool of putative interacting proteins obtained in a library screen, and to score specific drug-mediated disruption of protein-protein interaction.

  6. Expression Trend of Selected Ribosomal Protein Genes in Nasopharyngeal Carcinoma

    PubMed Central

    Ma, Xiang-Ru; Sim, Edmund Ui-Hang; Ling, Teck-Yee; Tiong, Thung-Sing; Subramaniam, Selva Kumar; Khoo, Alan Soo-Beng

    2012-01-01

    Background: Ribosomal proteins are traditionally associated with protein biosynthesis until recent studies that implicated their extraribosomal functions in human diseases and cancers. Our previous studies using GeneFishing™ DEG method and microarray revealed underexpression of three ribosomal protein genes, RPS26, RPS27, and RPL32 in cancer of the nasopharynx. Herein, we investigated the expression pattern and nucleotide sequence integrity of these genes in nasopharyngeal carcinoma to further delineate their involvement in tumourigenesis. The relationship of expression level with clinicopathologic factors was also statistically studied. Methods: Quantitative Polymerase Chain Reaction was performed on nasopharyngeal carcinoma and their paired normal tissues. Expression and sequence of these three genes were analysed. Results: All three ribosomal protein genes showed no significant difference in transcript expressions and no association could be established with clinicopathologic factors studied. No nucleotide aberrancy was detected in the coding regions of these genes. Conclusion: There is no early evidence to substantiate possible involvement of RPS26, RPS27, and RPL32 genes in NPC tumourigenesis. PMID:23613646

  7. Hydration dynamics near a model protein surface

    SciTech Connect

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

    2003-09-01

    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.

  8. Positive Darwinian selection drives the evolution of several female reproductive proteins in mammals

    PubMed Central

    Swanson, Willie J.; Yang, Ziheng; Wolfner, Mariana F.; Aquadro, Charles F.

    2001-01-01

    Rapid evolution driven by positive Darwinian selection is a recurrent theme in male reproductive protein evolution. In contrast, positive selection has never been demonstrated for female reproductive proteins. Here, we perform phylogeny-based tests on three female mammalian fertilization proteins and demonstrate positive selection promoting their divergence. Two of these female fertilization proteins, the zona pellucida glycoproteins ZP2 and ZP3, are part of the mammalian egg coat. Several sites identified in ZP3 as likely to be under positive selection are located in a region previously demonstrated to be involved in species-specific sperm-egg interaction, suggesting the selective pressure is related to male-female interaction. The results provide long-sought evidence for two evolutionary hypotheses: sperm competition and sexual conflict. PMID:11226269

  9. Genetic selection system for improving recombinant membrane protein expression in E. coli

    PubMed Central

    Massey-Gendel, Elizabeth; Zhao, Anni; Boulting, Gabriella; Kim, Hye-Yeon; Balamotis, Michael A; Seligman, Len M; Nakamoto, Robert K; Bowie, James U

    2009-01-01

    A major barrier to the physical characterization and structure determination of membrane proteins is low yield in recombinant expression. To address this problem, we have designed a selection strategy to isolate mutant strains of Escherichia coli that improve the expression of a targeted membrane protein. In this method, the coding sequence of the membrane protein of interest is fused to a C-terminal selectable marker, so that the production of the selectable marker and survival on selective media is linked to expression of the targeted membrane protein. Thus, mutant strains with improved expression properties can be directly selected. We also introduce a rapid method for curing isolated strains of the plasmids used during the selection process, in which the plasmids are removed by in vivo digestion with the homing endonuclease I-CreI. We tested this selection system on a rhomboid family protein from Mycobacterium tuberculosis (Rv1337) and were able to isolate mutants, which we call EXP strains, with up to 75-fold increased expression. The EXP strains also improve the expression of other membrane proteins that were not the target of selection, in one case roughly 90-fold. PMID:19165721

  10. Affinity-Guided Oxime Chemistry for Selective Protein Acylation in Live Tissue Systems.

    PubMed

    Tamura, Tomonori; Song, Zhining; Amaike, Kazuma; Lee, Shin; Yin, Sifei; Kiyonaka, Shigeki; Hamachi, Itaru

    2017-09-27

    Catalyst-mediated protein modification is a powerful approach for the imaging and engineering of natural proteins. We have previously developed affinity-guided 4-dimethylaminopyridine (AGD) chemistry as an efficient protein modification method using a catalytic acyl transfer reaction. However, because of the high electrophilicity of the thioester acyl donor molecule, AGD chemistry suffers from nonspecific reactions to proteins other than the target protein in crude biological environments, such as cell lysates, live cells, and tissue samples. To overcome this shortcoming, we here report a new acyl donor/organocatalyst system that allows more specific and efficient protein modification. In this method, a highly nucleophilic pyridinium oxime (PyOx) catalyst is conjugated to a ligand specific to the target protein. The ligand-tethered PyOx selectively binds to the target protein and facilitates the acyl transfer reaction of a mild electrophilic N-acyl-N-alkylsulfonamide acyl donor on the protein surface. We demonstrated that the new catalytic system, called AGOX (affinity-guided oxime) chemistry, can modify target proteins, both in test tubes and cell lysates, more selectively and efficiently than AGD chemistry. Low-background fluorescence labeling of the endogenous cell-membrane proteins, carbonic anhydrase XII and the folate receptor, in live cells allowed for the precise quantification of diffusion coefficients in the protein's native environment. Furthermore, the excellent biocompatibility and bioorthogonality of AGOX chemistry were demonstrated by the selective labeling of an endogenous neurotransmitter receptor in mouse brain slices, which are highly complicated tissue samples.

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

    PubMed

    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.

  12. Mutation-selection models of coding sequence evolution with site-heterogeneous amino acid fitness profiles

    PubMed Central

    Rodrigue, Nicolas; Philippe, Hervé; Lartillot, Nicolas

    2010-01-01

    Modeling the interplay between mutation and selection at the molecular level is key to evolutionary studies. To this end, codon-based evolutionary models have been proposed as pertinent means of studying long-range evolutionary patterns and are widely used. However, these approaches have not yet consolidated results from amino acid level phylogenetic studies showing that selection acting on proteins displays strong site-specific effects, which translate into heterogeneous amino acid propensities across the columns of alignments; related codon-level studies have instead focused on either modeling a single selective context for all codon columns, or a separate selective context for each codon column, with the former strategy deemed too simplistic and the latter deemed overparameterized. Here, we integrate recent developments in nonparametric statistical approaches to propose a probabilistic model that accounts for the heterogeneity of amino acid fitness profiles across the coding positions of a gene. We apply the model to a dozen real protein-coding gene alignments and find it to produce biologically plausible inferences, for instance, as pertaining to site-specific amino acid constraints, as well as distributions of scaled selection coefficients. In their account of mutational features as well as the heterogeneous regimes of selection at the amino acid level, the modeling approaches studied here can form a backdrop for several extensions, accounting for other selective features, for variable population size, or for subtleties of mutational features, all with parameterizations couched within population-genetic theory. PMID:20176949

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

    PubMed Central

    2010-01-01

    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. PMID:20149245

  14. Changes in Binding of [(123)I]CLINDE, a High-Affinity Translocator Protein 18 kDa (TSPO) Selective Radioligand in a Rat Model of Traumatic Brain Injury.

    PubMed

    Donat, Cornelius K; Gaber, Khaled; Meixensberger, Jürgen; Brust, Peter; Pinborg, Lars H; Hansen, Henrik H; Mikkelsen, Jens D

    2016-06-01

    After traumatic brain injury (TBI), secondary injuries develop, including neuroinflammatory processes that contribute to long-lasting impairments. These secondary injuries represent potential targets for treatment and diagnostics. The translocator protein 18 kDa (TSPO) is expressed in activated microglia cells and upregulated in response to brain injury and therefore a potential biomarker of the neuroinflammatory processes. Second-generation radioligands of TSPO, such as [(123)I]CLINDE, have a higher signal-to-noise ratio as the prototype ligand PK11195. [(123)I]CLINDE has been employed in human studies using single-photon emission computed tomography to image the neuroinflammatory response after stroke. In this study, we used the same tracer in a rat model of TBI to determine changes in TSPO expression. Adult Sprague-Dawley rats were subjected to moderate controlled cortical impact injury and sacrificed at 6, 24, 72 h and 28 days post surgery. TSPO expression was assessed in brain sections employing [(123)I]CLINDE in vitro autoradiography. From 24 h to 28 days post surgery, injured animals exhibited a marked and time-dependent increase in [(123)I]CLINDE binding in the ipsilateral motor, somatosensory and parietal cortex, as well as in the hippocampus and thalamus. Interestingly, binding was also significantly elevated in the contralateral M1 motor cortex following TBI. Craniotomy without TBI caused a less marked increase in [(123)I]CLINDE binding, restricted to the ipsilateral hemisphere. Radioligand binding was consistent with an increase in TSPO mRNA expression and CD11b immunoreactivity at the contusion site. This study demonstrates the applicability of [(123)I]CLINDE for detailed regional and quantitative assessment of glial activity in experimental models of TBI.

  15. Spatially-dependent Bayesian model selection for disease mapping.

    PubMed

    Carroll, Rachel; Lawson, Andrew B; Faes, Christel; Kirby, Russell S; Aregay, Mehreteab; Watjou, Kevin

    2016-01-01

    In disease mapping where predictor effects are to be modeled, it is often the case that sets of predictors are fixed, and the aim is to choose between fixed model sets. Model selection methods, both Bayesian model selection and Bayesian model averaging, are approaches within the Bayesian paradigm for achieving this aim. In the spatial context, model selection could have a spatial component in the sense that some models may be more appropriate for certain areas of a study region than others. In this work, we examine the use of spatially referenced Bayesian model averaging and Bayesian model selection via a large-scale simulation study accompanied by a small-scale case study. Our results suggest that BMS performs well when a strong regression signature is found.

  16. Mammalian cell surface display for monoclonal antibody-based FACS selection of viral envelope proteins.

    PubMed

    Bruun, Tim-Henrik; Grassmann, Veronika; Zimmer, Benjamin; Asbach, Benedikt; Peterhoff, David; Kliche, Alexander; Wagner, Ralf

    2017-08-17

    The elicitation of broadly and efficiently neutralizing antibodies in humans by active immunization is still a major obstacle in the development of vaccines against pathogens such as the human immunodeficiency virus (HIV), influenza virus, hepatitis C virus or cytomegalovirus. Here, we describe a mammalian cell surface display and monoclonal antibody (mAb)-mediated panning technology that allows affinity-based selection of envelope (Env) variants from libraries. To this end, we established an experimental setup featuring: 1) single and site specific integration of Env to link genotype and phenotype, 2) inducible Env expression to avoid cytotoxicity effects, 3) translational coupling of Env and enhanced green fluorescent protein expression to normalize for Env protein levels, and 4) display on HEK cells to ensure native folding and mammalian glycosylation. For proof of concept, we applied our method to a chimeric HIV-1 Env model library comprising variants with differential binding affinities to the V3-loop-directed mAbs 447-52D and HGN194. Fluorescence-activated cell sorting selectively enriched a high affinity variant up to 56- and 55-fold for 447-52D and HGN194, respectively, after only a single round of panning. Similarly, the low affinity variants for each antibody could be selectively enriched up to 237-fold. The binding profiles of membrane-bound gp145 and soluble gp140 chimeras showed identical affinity ranking, suggesting that the technology can guide the identification of Env variants with optimized antigenic properties for subsequent use as vaccine candidates. Finally, our mAb-based cellular display and selection strategy may also prove useful for the development of prophylactic vaccines against pathogens other than HIV.

  17. HABITAT MODELING APPROACHES FOR RESTORATION SITE SELECTION

    EPA Science Inventory

    Numerous modeling approaches have been used to develop predictive models of species-environment and species-habitat relationships. These models have been used in conservation biology and habitat or species management, but their application to restoration efforts has been minimal...

  18. Selective Immobilization of Proteins onto Solid Supports Through Split-Intein Mediated Protein Trans-Splicing

    SciTech Connect

    Kwon, Y; Coleman, M A; Camarero, J A

    2005-08-23

    Protein microarrays have emerged as important tools for screening protein-protein interactions and hold great potential for various applications including proteomics research, drug discovery, and diagnostics. This work describes a novel method for the traceless immobilization of proteins to a solid support through split-intein mediated protein trans-splicing. This method has been successfully used for the immobilization of biologically active proteins from very diluted samples ({approx}1{micro}M) and it does not require the purification of the protein to be attached. This makes possible the direct immobilization of proteins from complex mixtures such as cellular lysates and it can also be easily interfaced with cell-free expression systems for high-throughput production of protein microarrays.

  19. Selection originating from protein stability/foldability: Relationships between protein folding free energy, sequence ensemble, and fitness.

    PubMed

    Miyazawa, Sanzo

    2017-11-21

    Assuming that mutation and fixation processes are reversible Markov processes, we prove that the equilibrium ensemble of sequences obeys a Boltzmann distribution with exp(4Nem(1-1/(2N))), where m is Malthusian fitness and Ne and N are effective and actual population sizes. On the other hand, the probability distribution of sequences with maximum entropy that satisfies a given amino acid composition at each site and a given pairwise amino acid frequency at each site pair is a Boltzmann distribution with exp(-ψN), where the evolutionary statistical energy ψN is represented as the sum of one body (h) (compositional) and pairwise (J) (covariational) interactions over all sites and site pairs. A protein folding theory based on the random energy model (REM) indicates that the equilibrium ensemble of natural protein sequences is well represented by a canonical ensemble characterized by exp(-ΔGND/kBTs) or by exp(-GN/kBTs) if an amino acid composition is kept constant, where ΔGND≡GN-GD,GN and GD are the native and denatured free energies, and Ts is the effective temperature representing the strength of selection pressure. Thus, 4Nem(1-1/(2N)),-ΔψND(≡-ψN+ψD), and -ΔGND/kBTs must be equivalent to each other. With h and J estimated by the DCA program, the changes (ΔψN) of ψN due to single nucleotide nonsynonymous substitutions are analyzed. The results indicate that the standard deviation of ΔGN(=kBTsΔψN) is approximately constant irrespective of protein families, and therefore can be used to estimate the relative value of Ts. Glass transition temperature Tg and ΔGND are estimated from estimated Ts and experimental melting temperature (Tm) for 14 protein domains. The estimates of ΔGND agree with their experimental values for 5 proteins, and those of Ts and Tg are all within a reasonable range. In addition, approximating the probability density function (PDF) of ΔψN by a log-normal distribution, PDFs of ΔψN and Ka/Ks, which is the ratio of nonsynonymous

  20. Model-building codes for membrane proteins.

    SciTech Connect

    Shirley, David Noyes; Hunt, Thomas W.; Brown, W. Michael; Schoeniger, Joseph S.; Slepoy, Alexander; Sale, Kenneth L.; Young, Malin M.; Faulon, Jean-Loup Michel; Gray, Genetha Anne

    2005-01-01

    We have developed a novel approach to modeling the transmembrane spanning helical bundles of integral membrane proteins using only a sparse set of distance constraints, such as those derived from MS3-D, dipolar-EPR and FRET experiments. Algorithms have been written for searching the conformational space of membrane protein folds matching the set of distance constraints, which provides initial structures for local conformational searches. Local conformation search is achieved by optimizing these candidates against a custom penalty function that incorporates both measures derived from statistical analysis of solved membrane protein structures and distance constraints obtained from experiments. This results in refined helical bundles to which the interhelical loops and amino acid side-chains are added. Using a set of only 27 distance constraints extracted from the literature, our methods successfully recover the structure of dark-adapted rhodopsin to within 3.2 {angstrom} of the crystal structure.

  1. pH-selective mutagenesis of protein-protein interfaces: in silico design of therapeutic antibodies with prolonged half-life.

    PubMed

    Spassov, Velin Z; Yan, Lisa

    2013-04-01

    Understanding the effects of mutation on pH-dependent protein binding affinity is important in protein design, especially in the area of protein therapeutics. We propose a novel method for fast in silico mutagenesis of protein-protein complexes to calculate the effect of mutation as a function of pH. The free energy differences between the wild type and mutants are evaluated from a molecular mechanics model, combined with calculations of the equilibria of proton binding. The predicted pH-dependent energy profiles demonstrate excellent agreement with experimentally measured pH-dependency of the effect of mutations on the dissociation constants for the complex of turkey ovomucoid third domain (OMTKY3) and proteinase B. The virtual scanning mutagenesis identifies all hotspots responsible for pH-dependent binding of immunoglobulin G (IgG) to neonatal Fc receptor (FcRn) and the results support the current understanding of the salvage mechanism of the antibody by FcRn based on pH-selective binding. The method can be used to select mutations that change the pH-dependent binding profiles of proteins and guide the time consuming and expensive protein engineering experiments. As an application of this method, we propose a computational strategy to search for mutations that can alter the pH-dependent binding behavior of IgG to FcRn with the aim of improving the half-life of therapeutic antibodies in the target organism. Copyright © 2013 Wiley Periodicals, Inc.

  2. Proteomics reveals selective regulation of proteins in response to memory-related serotonin stimulation in Aplysia californica ganglia.

    PubMed

    Monje, Francisco J; Birner-Gruenberger, Ruth; Darnhofer, Barbara; Divisch, Isabella; Pollak, Daniela D; Lubec, Gert

    2012-02-01

    The marine mollusk Aplysia californica (Aplysia) is a powerful model for learning and memory due to its minimalistic nervous system. Key proteins, identified to be regulated by the neurotransmitter serotonin in Aplysia, have been successfully translated to mammalian models of learning and memory. Based upon a recently published large-scale analysis of Aplysia proteomic data, the current study investigated the regulation of protein levels 24 and 48 h after treatment with serotonin in Aplysia ganglia using a 2-D gel electrophoresis approach. Protein spots were quantified and protein-level changes of selected proteins were verified by Western blotting. Among those were Rab GDP dissociation inhibitor alpha (RabGDIα), synaptotagmin-1 and deleted in azoospermia-associated protein (DAZAP-1) in cerebral ganglia, calreticulin, RabGDIα, DAZAP-1, heterogeneous nuclear ribonucleoprotein F (hnRNPF), RACK-1 and actin-depolymerizing factor (ADF) in pleural ganglia and DAZAP-1, hnRNPF and ADF in pedal ganglia. Protein identity of the majority of spots was confirmed by a gel-based mass spectrometrical method (FT-MS). Taken together, protein-level changes induced by the learning-related neurotransmitter serotonin in Aplysia ganglia are described and a role for the abovementioned proteins in synaptic plasticity is proposed.

  3. Cognitive Niches: An Ecological Model of Strategy Selection

    ERIC Educational Resources Information Center

    Marewski, Julian N.; Schooler, Lael J.

    2011-01-01

    How do people select among different strategies to accomplish a given task? Across disciplines, the strategy selection problem represents a major challenge. We propose a quantitative model that predicts how selection emerges through the interplay among strategies, cognitive capacities, and the environment. This interplay carves out for each…

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

    PubMed Central

    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 molecules onto model proteins and to label the surface of living cells. PMID:23181702

  5. Insights into Protein-Ligand Interactions: Mechanisms, Models, and Methods.

    PubMed

    Du, Xing; Li, Yi; Xia, Yuan-Ling; Ai, Shi-Meng; Liang, Jing; Sang, Peng; Ji, Xing-Lai; Liu, Shu-Qun

    2016-01-26

    Molecular recognition, which is the process of biological macromolecules interacting with each other or various small molecules with a high specificity and affinity to form a specific complex, constitutes the basis of all processes in living organisms. Proteins, an important class of biological macromolecules, realize their functions through binding to themselves or other molecules. A detailed understanding of the protein-ligand interactions is therefore central to understanding biology at the molecular level. Moreover, knowledge of the mechanisms responsible for the protein-ligand recognition and binding will also facilitate the discovery, design, and development of drugs. In the present review, first, the physicochemical mechanisms underlying protein-ligand binding, including the binding kinetics, thermodynamic concepts and relationships, and binding driving forces, are introduced and rationalized. Next, three currently existing protein-ligand binding models--the "lock-and-key", "induced fit", and "conformational selection"--are described and their underlying thermodynamic mechanisms are discussed. Finally, the methods available for investigating protein-ligand binding affinity, including experimental and theoretical/computational approaches, are introduced, and their advantages, disadvantages, and challenges are discussed.

  6. Selective Sensitization of Zinc Finger Protein Oxidation by Reactive Oxygen Species through Arsenic Binding*

    PubMed Central

    Zhou, Xixi; Cooper, Karen L.; Sun, Xi; Liu, Ke J.; Hudson, Laurie G.

    2015-01-01

    Cysteine oxidation induced by reactive oxygen species (ROS) on redox-sensitive targets such as zinc finger proteins plays a critical role in redox signaling and subsequent biological outcomes. We found that arsenic exposure led to oxidation of certain zinc finger proteins based on arsenic interaction with zinc finger motifs. Analysis of zinc finger proteins isolated from arsenic-exposed cells and zinc finger peptides by mass spectrometry demonstrated preferential oxidation of C3H1 and C4 zinc finger configurations. C2H2 zinc finger proteins that do not bind arsenic were not oxidized by arsenic-generated ROS in the cellular environment. The findings suggest that selectivity in arsenic binding to zinc fingers with three or more cysteines defines the target proteins for oxidation by ROS. This represents a novel mechanism of selective protein oxidation and demonstrates how an environmental factor may sensitize certain target proteins for oxidation, thus altering the oxidation profile and redox regulation. PMID:26063799

  7. Novel approach for selecting the best predictor for identifying the binding sites in DNA binding proteins.

    PubMed

    Nagarajan, R; Ahmad, Shandar; Gromiha, M Michael

    2013-09-01

    Protein-DNA complexes play vital roles in many cellular processes by the interactions of amino acids with DNA. Several computational methods have been developed for predicting the interacting residues in DNA-binding proteins using sequence and/or structural information. These methods showed different levels of accuracies, which may depend on the choice of data sets used in training, the feature sets selected for developing a predictive model, the ability of the models to capture information useful for prediction or a combination of these factors. In many cases, different methods are likely to produce similar results, whereas in others, the predictors may return contradictory predictions. In this situation, a priori estimates of prediction performance applicable to the system being investigated would be helpful for biologists to choose the best method for designing their experiments. In this work, we have constructed unbiased, stringent and diverse data sets for DNA-binding proteins based on various biologically relevant considerations: (i) seven structural classes, (ii) 86 folds, (iii) 106 superfamilies, (iv) 194 families, (v) 15 binding motifs, (vi) single/double-stranded DNA, (vii) DNA conformation (A, B, Z, etc.), (viii) three functions and (ix) disordered regions. These data sets were culled as non-redundant with sequence identities of 25 and 40% and used to evaluate the performance of 11 different methods in which online services or standalone programs are available. We observed that the best performing methods for each of the data sets showed significant biases toward the data sets selected for their benchmark. Our analysis revealed important data set features, which could be used to estimate these context-specific biases and hence suggest the best method to be used for a given problem. We have developed a web server, which considers these features on demand and displays the best method that the investigator should use. The web server is freely available at

  8. Modeling of chemical inhibition from amyloid protein aggregation kinetics

    PubMed Central

    2014-01-01

    Backgrounds The process of amyloid proteins aggregation causes several human neuropathologies. In some cases, e.g. fibrillar deposits of insulin, the problems are generated in the processes of production and purification of protein and in the pump devices or injectable preparations for diabetics. Experimental kinetics and adequate modelling of chemical inhibition from amyloid aggregation are of practical importance in order to study the viable processing, formulation and storage as well as to predict and optimize the best conditions to reduce the effect of protein nucleation. Results In this manuscript, experimental data of insulin, Aβ42 amyloid protein and apomyoglobin fibrillation from recent bibliography were selected to evaluate the capability of a bivariate sigmoid equation to model them. The mathematical functions (logistic combined with Weibull equation) were used in reparameterized form and the effect of inhibitor concentrations on kinetic parameters from logistic equation were perfectly defined and explained. The surfaces of data were accurately described by proposed model and the presented analysis characterized the inhibitory influence on the protein aggregation by several chemicals. Discrimination between true and apparent inhibitors was also confirmed by the bivariate equation. EGCG for insulin (working at pH = 7.4/T = 37°C) and taiwaniaflavone for Aβ42 were the compounds studied that shown the greatest inhibition capacity. Conclusions An accurate, simple and effective model to investigate the inhibition of chemicals on amyloid protein aggregation has been developed. The equation could be useful for the clear quantification of inhibitor potential of chemicals and rigorous comparison among them. PMID:24572069

  9. Selective sorting and destruction of mitochondrial membrane proteins in aged yeast

    PubMed Central

    Hughes, Adam L; Hughes, Casey E; Henderson, Kiersten A; Yazvenko, Nina; Gottschling, Daniel E

    2016-01-01

    Mitochondrial dysfunction is a hallmark of aging, and underlies the development of many diseases. Cells maintain mitochondrial homeostasis through a number of pathways that remodel the mitochondrial proteome or alter mitochondrial content during times of stress or metabolic adaptation. Here, using yeast as a model system, we identify a new mitochondrial degradation system that remodels the mitochondrial proteome of aged cells. Unlike many common mitochondrial degradation pathways, this system selectively removes a subset of membrane proteins from the mitochondrial inner and outer membranes, while leaving the remainder of the organelle intact. Selective removal of preexisting proteins is achieved by sorting into a mitochondrial-derived compartment, or MDC, followed by release through mitochondrial fission and elimination by autophagy. Formation of MDCs requires the import receptors Tom70/71, and failure to form these structures exacerbates preexisting mitochondrial dysfunction, suggesting that the MDC pathway provides protection to mitochondria in times of stress. DOI: http://dx.doi.org/10.7554/eLife.13943.001 PMID:27097106

  10. Rhes, a striatal-selective protein implicated in Huntington disease, binds beclin-1 and activates autophagy.

    PubMed

    Mealer, Robert G; Murray, Alexandra J; Shahani, Neelam; Subramaniam, Srinivasa; Snyder, Solomon H

    2014-02-07

    The protein mutated in Huntington disease (HD), mutant huntingtin (mHtt), is expressed throughout the brain and body. However, the pathology of HD is characterized by early and dramatic destruction selectively of the striatum. We previously reported that the striatal-specific protein Rhes binds mHtt and enhances its cytotoxicity. Moreover, Rhes-deleted mice are dramatically protected from neurodegeneration and motor dysfunction in mouse models of HD. We now report a function of Rhes in autophagy, a lysosomal degradation pathway implicated in aging and HD neurodegeneration. In PC12 cells, deletion of endogenous Rhes decreases autophagy, whereas Rhes overexpression activates autophagy. These effects are independent of mTOR and opposite in the direction predicted by the known activation of mTOR by Rhes. Rhes robustly binds the autophagy regulator Beclin-1, decreasing its inhibitory interaction with Bcl-2 independent of JNK-1 signaling. Finally, co-expression of mHtt blocks Rhes-induced autophagy activation. Thus, the isolated pathology and delayed onset of HD may reflect the striatal-selective expression and changes in autophagic activity of Rhes.

  11. Monoclonal antibody selection for interleukin-4 quantification using suspension arrays and forward-phase protein microarrays.

    PubMed

    Wang, L; Cole, K D; Peterson, A; He, Hua-Jun; Gaigalas, A K; Zong, Y

    2007-12-01

    A recombinant mouse interleukin-4 (IL-4) and three different purified rat antimouse IL-4 monoclonal antibodies (Mab) with different clonalities were employed as a model system. This system was used to examine monoclonal antibody effectiveness using both conventional and high-throughput measurement techniques to select antibodies for attaining the most sensitive detection of the recombinant IL-4 through the "sandwich-type" immunoassays. Surface plasmon resonance (SPR) measurements and two high-throughput methods, suspension arrays (also called multiplexed bead arrays) and forward-phase protein microarrays, predicted the same capture (BVD4-1D11) and detection (BVD6-24G2) antibody pair for the most sensitive detection of the recombinant cytokine. By using this antibody pair, we were able to detect as low as 2 pg/mL of IL-4 in buffer solution and 13.5 pg/mL of IL-4 spiked in 100% normal mouse serum with the multiplexed bead arrays. Due to the large amount of material required for SPR measurements, the study suggests that the multiplexed bead arrays and protein microarrays are both suited for the selection of numerous antibodies against the same analyte of interest to meet the need in the areas of systems biology and reproducible clinical diagnostics for better patient care.

  12. Light scattering evidence of selective protein fouling on biocompatible block copolymer micelles

    NASA Astrophysics Data System (ADS)

    Giacomelli, Fernando C.; Stepánek, Petr; Schmidt, Vanessa; Jäger, Eliézer; Jäger, Alessandro; Giacomelli, Cristiano

    2012-07-01

    Selective protein fouling on block copolymer micelles with well-known potential for tumour-targeting drug delivery was evidenced by using dynamic light scattering measurements. The stability and interaction of block copolymer micelles with model proteins (BSA, IgG, lysozyme and CytC) is reported for systems featuring a hydrophobic (poly[2-(diisopropylamino)-ethyl methacrylate]) (PDPA) core and hydrophilic coronas comprising poly(ethylene oxide)/poly(glycerol monomethacrylate) (PEO-b-PG2MA) or poly[2-(methacryloyloxy)ethyl phosphorylcholine] (PMPC). The results revealed that protein size and hydrophilic chain density play important roles in the observed interactions. The PEO113-b-PG2MA30-b-PDPA50 nanoparticles are stable and protein adsorption is prevented at all investigated protein environments. The successful protein-repellent characteristic of these nanoparticles is attributed to a high hydrophilic surface chain density (>0.1 chains per nm2) and to the length of the hydrophilic chains. On the other hand, although PMPC also has protein-repellent characteristics, the low surface chain density of the hydrophilic shell is supposed to enable interactions with small proteins. The PMPC40-b-PDPA70 micelles are stable in BSA and IgG environments due to weak repulsion forces between PMPC and the proteins, to the hydration layer, and particularly to a size-effect where the large BSA (RH = 4.2 nm) and IgG (RH = 7.0 nm) do not easily diffuse within the PMPC shell. Conversely, a clear interaction was observed with the 2.1 nm radius lysozyme. The lysozyme protein can diffuse within the PMPC micellar shell towards the PDPA hydrophobic core in a process favored by its smaller size and the low hydrophilic PMPC surface chain density (~0.049 chains per nm2) as compared to PEO-b-PG2MA (~0.110 chains per nm2). The same behavior was not evidenced with the 2.3 nm radius positively charged CytC, probably due to its higher surface hydrophilicity and the consequent chemical

  13. Responsive Photonic Crystal Carbohydrate Hydrogel Sensor Materials for Selective and Sensitive Lectin Protein Detection.

    PubMed

    Cai, Zhongyu; Sasmal, Aniruddha; Liu, Xinyu; Asher, Sanford A

    2017-10-04

    Lectin proteins, such as the highly toxic lectin protein, ricin, and the immunochemically important lectin, jacalin, play significant roles in many biological functions. It is highly desirable to develop a simple but efficient method to selectively detect lectin proteins. Here we report the development of carbohydrate containing responsive hydrogel sensing materials for the selective detection of lectin proteins. The copolymerization of a vinyl linked carbohydrate monomer with acrylamide and acrylic acid forms a carbohydrate hydrogel that shows specific "multivalent" binding to lectin proteins. The resulting carbohydrate hydrogels are attached to 2-D photonic crystals (PCs) that brightly diffract visible light. This diffraction provides an optical readout that sensitively monitors the hydrogel volume. We utilize lactose, galactose, and mannose containing hydrogels to fabricate a series of 2-D PC sensors that show strong selective binding to the lectin proteins ricin, jacalin, and concanavalin A (Con A). This binding causes a carbohydrate hydrogel shrinkage which significantly shifts the diffraction wavelength. The resulting 2-D PC sensors can selectively detect the lectin proteins ricin, jacalin, and Con A. These unoptimized 2-D PC hydrogel sensors show a limit of detection (LoD) of 7.5 × 10(-8) M for ricin, a LoD of 2.3 × 10(-7) M for jacalin, and a LoD of 3.8 × 10(-8) M for Con A, respectively. This sensor fabrication approach may enable numerous sensors for the selective detection of numerous lectin proteins.

  14. Balanced intraintestinal nutrition: digestion, absorption and biological value of selected preparations of milk proteins.

    PubMed

    Ziemlański, S; Cieślakowa, D; Kunachowicz, H; Pałaszewska, M

    1978-01-01

    The absorption of an enzymatic hydrolysate of whey proteins, hydrolysates of milk proteins and casein, 7% hydrolysate of bovine blood produced by Polfa under the trade name "Aminokwasy", and a standard mixture of amino acids from isolated small intestine loop of dogs and rats was compared. The composition of amino acids of the initial proteins and hydrolysates obtained from these proteins was determined. The biological value of selected proteins and hydrolysates was assessed determining the mean weight gain, nitrogen content of the body, urinary excretion of urea and creatinine, blood urea level and NPU. No significant differences were found in the absorption of nitrogen from the hydrolysate of milk and whey proteins. The nutritional value of whey protein hydrolysate was, however, slightly higher than that of an analogous hydrolysate of milk proteins. It seems that of all tested proteins whey hydrolysate (free of lactose) is the most suitable source for obtaining preparations for no-residue intraintestinal feeding.

  15. Evolution of sparsity and modularity in a model of protein allostery

    NASA Astrophysics Data System (ADS)

    Hemery, Mathieu; Rivoire, Olivier

    2015-04-01

    The sequence of a protein is not only constrained by its physical and biochemical properties under current selection, but also by features of its past evolutionary history. Understanding the extent and the form that these evolutionary constraints may take is important to interpret the information in protein sequences. To study this problem, we introduce a simple but physical model of protein evolution where selection targets allostery, the functional coupling of distal sites on protein surfaces. This model shows how the geometrical organization of couplings between amino acids within a protein structure can depend crucially on its evolutionary history. In particular, two scenarios are found to generate a spatial concentration of functional constraints: high mutation rates and fluctuating selective pressures. This second scenario offers a plausible explanation for the high tolerance of natural proteins to mutations and for the spatial organization of their least tolerant amino acids, as revealed by sequence analysis and mutagenesis experiments. It also implies a faculty to adapt to new selective pressures that is consistent with observations. The model illustrates how several independent functional modules may emerge within the same protein structure, depending on the nature of past environmental fluctuations. Our model thus relates the evolutionary history of proteins to the geometry of their functional constraints, with implications for decoding and engineering protein sequences.

  16. Selection of Temporal Lags When Modeling Economic and Financial Processes.

    PubMed

    Matilla-Garcia, Mariano; Ojeda, Rina B; Marin, Manuel Ruiz

    2016-10-01

    This paper suggests new nonparametric statistical tools and procedures for modeling linear and nonlinear univariate economic and financial processes. In particular, the tools presented help in selecting relevant lags in the model description of a general linear or nonlinear time series; that is, nonlinear models are not a restriction. The tests seem to be robust to the selection of free parameters. We also show that the test can be used as a diagnostic tool for well-defined models.

  17. The use of experimental structures to model protein dynamics.

    PubMed

    Katebi, Ataur R; Sankar, Kannan; Jia, Kejue; Jernigan, Robert L

    2015-01-01

    The number of solved protein structures submitted in the Protein Data Bank (PDB) has increased dramatically in recent years. For some specific proteins, this number is very high-for example, there are over 550 solved structures for HIV-1 protease, one protein that is essential for the life cycle of human immunodeficiency virus (HIV) which causes acquired immunodeficiency syndrome (AIDS) in humans. The large number of structures for the same protein and its variants include a sample of different conformational states of the protein. A rich set of structures solved experimentally for the same protein has information buried within the dataset that can explain the functional dynamics and structural mechanism of the protein. To extract the dynamics information and functional mechanism from the experimental structures, this chapter focuses on two methods-Principal Component Analysis (PCA) and Elastic Network Models (ENM). PCA is a widely used statistical dimensionality reduction technique to classify and visualize high-dimensional data. On the other hand, ENMs are well-established simple biophysical method for modeling the functionally important global motions of proteins. This chapter covers the basics of these two. Moreover, an improved ENM version that utilizes the variations found within a given set of structures for a protein is described. As a practical example, we have extracted the functional dynamics and mechanism of HIV-1 protease dimeric structure by using a set of 329 PDB structures of this protein. We have described, step by step, how to select a set of protein structures, how to extract the needed information from the PDB files for PCA, how to extract the dynamics information using PCA, how to calculate ENM modes, how to measure the congruency between the dynamics computed from the principal components (PCs) and the ENM modes, and how to compute entropies using the PCs. We provide the computer programs or references to software tools to accomplish each step

  18. The Use of Experimental Structures to Model Protein Dynamics

    PubMed Central

    Katebi, Ataur R.; Sankar, Kannan; Jia, Kejue; Jernigan, Robert L.

    2014-01-01

    Summary The number of solved protein structures submitted in the Protein Data Bank (PDB) has increased dramatically in recent years. For some specific proteins, this number is very high – for example, there are over 550 solved structures for HIV-1 protease, one protein that is essential for the life cycle of human immunodeficiency virus (HIV) which causes acquired immunodeficiency syndrome (AIDS) in humans. The large number of structures for the same protein and its variants include a sample of different conformational states of the protein. A rich set of structures solved experimentally for the same protein has information buried within the dataset that can explain the functional dynamics and structural mechanism of the protein. To extract the dynamics information and functional mechanism from the experimental structures, this chapter focuses on two methods – Principal Component Analysis (PCA) and Elastic Network Models (ENM). PCA is a widely used statistical dimensionality reduction technique to classify and visualize high-dimensional data. On the other hand, ENMs are well-established simple biophysical method for modeling the functionally important global motions of proteins. This chapter covers the basics of these two. Moreover, an improved ENM version that utilizes the variations found within a given set of structures for a protein is described. As a practical example, we have extracted the functional dynamics and mechanism of HIV-1 protease dimeric structure by using a set of 329 PDB structures of this protein. We have described, step by step, how to select a set of protein structures, how to extract the needed information from the PDB files for PCA, how to extract the dynamics information using PCA, how to calculate ENM modes, how to measure the congruency between the dynamics computed from the principal components (PCs) and the ENM modes, and how to compute entropies using the PCs. We provide the computer programs or references to software tools to

  19. The Multilingual Lexicon: Modelling Selection and Control

    ERIC Educational Resources Information Center

    de Bot, Kees

    2004-01-01

    In this paper an overview of research on the multilingual lexicon is presented as the basis for a model for processing multiple languages. With respect to specific issues relating to the processing of more than two languages, it is suggested that there is no need to develop a specific model for such multilingual processing, but at the same time we…

  20. The Multilingual Lexicon: Modelling Selection and Control

    ERIC Educational Resources Information Center

    de Bot, Kees

    2004-01-01

    In this paper an overview of research on the multilingual lexicon is presented as the basis for a model for processing multiple languages. With respect to specific issues relating to the processing of more than two languages, it is suggested that there is no need to develop a specific model for such multilingual processing, but at the same time we…

  1. Optimizing the selective recognition of protein isoforms through tuning of nanoparticle hydrophobicity†

    PubMed Central

    Moyano, Daniel F.; Xu, Yisheng; Rotello, Vincent M.

    2014-01-01

    We demonstrate that ligand hydrophobicity can be used to increase affinity and selectivity of binding between monolayer-protected cationic gold nanoparticles and β– lactoglobulin protein isoforms containing two amino acid mutations. PMID:24838611

  2. Model Selection for Monitoring CO2 Plume during Sequestration

    SciTech Connect

    2014-12-31

    The model selection method developed as part of this project mainly includes four steps: (1) assessing the connectivity/dynamic characteristics of a large prior ensemble of models, (2) model clustering using multidimensional scaling coupled with k-mean clustering, (3) model selection using the Bayes' rule in the reduced model space, (4) model expansion using iterative resampling of the posterior models. The fourth step expresses one of the advantages of the method: it provides a built-in means of quantifying the uncertainty in predictions made with the selected models. In our application to plume monitoring, by expanding the posterior space of models, the final ensemble of representations of geological model can be used to assess the uncertainty in predicting the future displacement of the CO2 plume. The software implementation of this approach is attached here.

  3. Protein Simulation Data in the Relational Model

    PubMed Central

    Simms, Andrew M.; Daggett, Valerie

    2011-01-01

    High performance computing is leading to unprecedented volumes of data. Relational databases offer a robust and scalable model for storing and analyzing scientific data. However, these features do not come without a cost—significant design effort is required to build a functional and efficient repository. Modeling protein simulation data in a relational database presents several challenges: the data captured from individual simulations are large, multi-dimensional, and must integrate with both simulation software and external data sites. Here we present the dimensional design and relational implementation of a comprehensive data warehouse for storing and analyzing molecular dynamics simulations using SQL Server. PMID:23204646

  4. Simple Model of Protein Folding Kinetics

    NASA Astrophysics Data System (ADS)

    Zwanzig, Robert

    1995-10-01

    A simple model of the kinetics of protein folding is presented. The reaction coordinate is the "correctness" of a configuration compared with the native state. The model has a gap in the energy spectrum, a large configurational entropy, a free energy barrier between folded and partially folded states, and a good thermodynamic folding transition. Folding kinetics is described by a master equation. The folding time is estimated by means of a local thermodynamic equilibrium assumption and then is calculated both numerically and analytically by solving the master equation. The folding time has a maximum near the folding transition temperature and can have a minimum at a lower temperature.

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

    PubMed Central

    Pietribiasi, Mauro; Waniewski, Jacek; Załuska, Alicja; Załuska, Wojciech; Lindholm, Bengt

    2016-01-01

    Background 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. Methods 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. Results 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). Conclusions 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

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

    PubMed

    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. A rapid and robust method for selective isotope labeling of proteins

    PubMed Central

    Lin, Myat T.; Sperling, Lindsay J.; Frericks Schmidt, Heather L.; Tang, Ming; Samoilova, Rimma I.; Kumasaka, Takashi; Iwasaki, Toshio; Dikanov, Sergei A.; Rienstra, Chad M.; Gennis, Robert B.

    2011-01-01

    Amino-acid selective isotope labeling of proteins offers numerous advantages in mechanistic studies by revealing structural and functional information unattainable from a crystallographic approach. However, efficient labeling of proteins with selected amino acids necessitates auxotrophic hosts, which are often not available. We have constructed a set of auxotrophs in a commonly used Escherichia coli expression strain C43(DE3), a derivative of E. coli BL21(DE3), which can be used for isotopic labeling of individual amino acids or sets of amino acids. These strains have general applicability to either soluble or membrane proteins that can be expressed in E. coli. We present examples in which proteins are selectively labeled with 13C- and 15N-amino acids and studied using magic-angle spinning solid-state NMR and pulsed EPR, demonstrating the utility of these strains for biophysical characterization of membrane proteins, radical-generating enzymes and metalloproteins. PMID:21925267

  8. A rapid and robust method for selective isotope labeling of proteins.

    PubMed

    Lin, Myat T; Sperling, Lindsay J; Frericks Schmidt, Heather L; Tang, Ming; Samoilova, Rimma I; Kumasaka, Takashi; Iwasaki, Toshio; Dikanov, Sergei A; Rienstra, Chad M; Gennis, Robert B

    2011-12-01

    Amino-acid selective isotope labeling of proteins offers numerous advantages in mechanistic studies by revealing structural and functional information unattainable from a crystallographic approach. However, efficient labeling of proteins with selected amino acids necessitates auxotrophic hosts, which are often not available. We have constructed a set of auxotrophs in a commonly used Escherichia coli expression strain C43(DE3), a derivative of E. coli BL21(DE3), which can be used for isotopic labeling of individual amino acids or sets of amino acids. These strains have general applicability to either soluble or membrane proteins that can be expressed in E. coli. We present examples in which proteins are selectively labeled with (13)C- and (15)N-amino acids and studied using magic-angle spinning solid-state NMR and pulsed EPR, demonstrating the utility of these strains for biophysical characterization of membrane proteins, radical-generating enzymes and metalloproteins. Copyright © 2011 Elsevier Inc. All rights reserved.

  9. Protein Folding Activity of Ribosomal RNA Is a Selective Target of Two Unrelated Antiprion Drugs

    PubMed Central

    Tribouillard-Tanvier, Déborah; Dos Reis, Suzana; Gug, Fabienne; Voisset, Cécile; Béringue, Vincent; Sabate, Raimon; Kikovska, Ema; Talarek, Nicolas; Bach, Stéphane; Huang, Chenhui; Desban, Nathalie; Saupe, Sven J.; Supattapone, Surachai; Thuret, Jean-Yves; Chédin, Stéphane; Vilette, Didier; Galons, Hervé; Sanyal, Suparna; Blondel, Marc

    2008-01-01

    Background 6-Aminophenanthridine (6AP) and Guanabenz (GA, a drug currently in use for the treatment of hypertension) were isolated as antiprion drugs using a yeast-based assay. These structurally unrelated molecules are also active against mammalian prion in several cell-based assays and in vivo in a mouse model for prion-based diseases. Methodology/Principal Findings Here we report the identification of cellular targets of these drugs. Using affinity chromatography matrices for both drugs, we demonstrate an RNA-dependent interaction of 6AP and GA with the ribosome. These specific interactions have no effect on the peptidyl transferase activity of the ribosome or on global translation. In contrast, 6AP and GA specifically inhibit the ribosomal RNA-mediated protein folding activity of the ribosome. Conclusion/Significance 6AP and GA are therefore the first compounds to selectively inhibit the protein folding activity of the ribosome. They thus constitute precious tools to study the yet largely unexplored biological role of this protein folding activity. PMID:18478094

  10. Astrophysical Model Selection in Gravitational Wave Astronomy

    NASA Technical Reports Server (NTRS)

    Adams, Matthew R.; Cornish, Neil J.; Littenberg, Tyson B.

    2012-01-01

    Theoretical studies in gravitational wave astronomy have mostly focused on the information that can be extracted from individual detections, such as the mass of a binary system and its location in space. Here we consider how the information from multiple detections can be used to constrain astrophysical population models. This seemingly simple problem is made challenging by the high dimensionality and high degree of correlation in the parameter spaces that describe the signals, and by the complexity of the astrophysical models, which can also depend on a large number of parameters, some of which might not be directly constrained by the observations. We present a method for constraining population models using a hierarchical Bayesian modeling approach which simultaneously infers the source parameters and population model and provides the joint probability distributions for both. We illustrate this approach by considering the constraints that can be placed on population models for galactic white dwarf binaries using a future space-based gravitational wave detector. We find that a mission that is able to resolve approximately 5000 of the shortest period binaries will be able to constrain the population model parameters, including the chirp mass distribution and a characteristic galaxy disk radius to within a few percent. This compares favorably to existing bounds, where electromagnetic observations of stars in the galaxy constrain disk radii to within 20%.

  11. Bayesian model selection for LISA pathfinder

    NASA Astrophysics Data System (ADS)

    Karnesis, Nikolaos; Nofrarias, Miquel; Sopuerta, Carlos F.; Gibert, Ferran; Armano, Michele; Audley, Heather; Congedo, Giuseppe; Diepholz, Ingo; Ferraioli, Luigi; Hewitson, Martin; Hueller, Mauro; Korsakova, Natalia; McNamara, Paul W.; Plagnol, Eric; Vitale, Stefano

    2014-03-01

    The main goal of the LISA Pathfinder (LPF) mission is to fully characterize the acceleration noise models and to test key technologies for future space-based gravitational-wave observatories similar to the eLISA concept. The data analysis team has developed complex three-dimensional models of the LISA Technology Package (LTP) experiment onboard the LPF. These models are used for simulations, but, more importantly, they will be used for parameter estimation purposes during flight operations. One of the tasks of the data analysis team is to identify the physical effects that contribute significantly to the properties of the instrument noise. A way of approaching this problem is to recover the essential parameters of a LTP model fitting the data. Thus, we want to define the simplest model that efficiently explains the observations. To do so, adopting a Bayesian framework, one has to estimate the so-called Bayes factor between two competing models. In our analysis, we use three main different methods to estimate it: the reversible jump Markov chain Monte Carlo method, the Schwarz criterion, and the Laplace approximation. They are applied to simulated LPF experiments in which the most probable LTP model that explains the observations is recovered. The same type of analysis presented in this paper is expected to be followed during flight operations. Moreover, the correlation of the output of the aforementioned methods with the design of the experiment is explored.

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

    PubMed

    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/ .

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

    SciTech Connect

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

    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.

  14. Coupling between protein level selection and codon usage optimization in the evolution of bacteria and archaea.

    PubMed

    Ran, Wenqi; Kristensen, David M; Koonin, Eugene V

    2014-03-25

    The relationship between the selection affecting codon usage and selection on protein sequences of orthologous genes in diverse groups of bacteria and archaea was examined by using the Alignable Tight Genome Clusters database of prokaryote genomes. The codon usage bias is generally low, with 57.5% of the gene-specific optimal codon frequencies (Fopt) being below 0.55. This apparent weak selection on codon usage contrasts with the strong purifying selection on amino acid sequences, with 65.8% of the gene-specific dN/dS ratios being below 0.1. For most of the genomes compared, a limited but statistically significant negative correlation between Fopt and dN/dS was observed, which is indicative of a link between selection on protein sequence and selection on codon usage. The strength of the coupling between the protein level selection and codon usage bias showed a strong positive correlation with the genomic GC content. Combined with previous observations on the selection for GC-rich codons in bacteria and archaea with GC-rich genomes, these findings suggest that selection for translational fine-tuning could be an important factor in microbial evolution that drives the evolution of genome GC content away from mutational equilibrium. This type of selection is particularly pronounced in slowly evolving, "high-status" genes. A significantly stronger link between the two aspects of selection is observed in free-living bacteria than in parasitic bacteria and in genes encoding metabolic enzymes and transporters than in informational genes. These differences might reflect the special importance of translational fine-tuning for the adaptability of gene expression to environmental changes. The results of this work establish the coupling between protein level selection and selection for translational optimization as a distinct and potentially important factor in microbial evolution. IMPORTANCE Selection affects the evolution of microbial genomes at many levels, including both

  15. Simple model of membrane proteins including solvent.

    PubMed

    Pagan, D L; Shiryayev, A; Connor, T P; Gunton, J D

    2006-05-14

    We report a numerical simulation for the phase diagram of a simple two-dimensional model, similar to the one proposed by Noro and Frenkel [J. Chem. Phys. 114, 2477 (2001)] for membrane proteins, but one that includes the role of the solvent. We first use Gibbs ensemble Monte Carlo simulations to determine the phase behavior of particles interacting via a square-well potential in two dimensions for various values of the interaction range. A phenomenological model for the solute-solvent interactions is then studied to understand how the fluid-fluid coexistence curve is modified by solute-solvent interactions. It is shown that such a model can yield systems with liquid-liquid phase separation curves that have both upper and lower critical points, as well as closed loop phase diagrams, as is the case with the corresponding three-dimensional model.

  16. Large-Scale Identification of Putative Exported Proteins in Candida albicans by Genetic Selection

    PubMed Central

    Monteoliva, L.; López Matas, M.; Gil, C.; Nombela, C.; Pla, J.

    2002-01-01

    In all living organisms, secreted proteins play essential roles in different processes. Of special interest is the construction of the fungal cell wall, since this structure is absent from mammalian cells. The identification of the proteins involved in its biogenesis is therefore a primary goal in antifungal research. To perform a systematic identification of such proteins in Candida albicans, we carried out a genetic screening in which in-frame fusions with an intracellular allele of invertase gene SUC2 of Saccharomyces cerevisiae can be used to select and identify putatively exported proteins in the heterologous host S. cerevisiae. Eighty-three clones were selected, including 11 previously identified genes from C. albicans as well as 41 C. albicans genes that encode proteins homologous to already described proteins from related organisms. They include enzymes involved in cell wall synthesis and protein secretion. We also found membrane receptors and transporters presumably related to the interaction of C. albicans with the environment as well as extracellular enzymes and proteins involved in different morphological transitions. In addition, 11 C. albicans open reading frames (ORFs) identified in this screening encode proteins homologous to unknown or putative proteins, while 5 ORFs encode novel secreted proteins without known homologues in other organisms. This screening procedure therefore not only identifies a set of targets of interest in antifungal research but also provides new clues for understanding the topological locations of many proteins involved in processes relevant to the pathogenicity of this microorganism. PMID:12456000

  17. Large-scale identification of putative exported proteins in Candida albicans by genetic selection.

    PubMed

    Monteoliva, L; Matas, M López; Gil, C; Nombela, C; Pla, J

    2002-08-01

    In all living organisms, secreted proteins play essential roles in different processes. Of special interest is the construction of the fungal cell wall, since this structure is absent from mammalian cells. The identification of the proteins involved in its biogenesis is therefore a primary goal in antifungal research. To perform a systematic identification of such proteins in Candida albicans, we carried out a genetic screening in which in-frame fusions with an intracellular allele of invertase gene SUC2 of Saccharomyces cerevisiae can be used to select and identify putatively exported proteins in the heterologous host S. cerevisiae. Eighty-three clones were selected, including 11 previously identified genes from C. albicans as well as 41 C. albicans genes that encode proteins homologous to already described proteins from related organisms. They include enzymes involved in cell wall synthesis and protein secretion. We also found membrane receptors and transporters presumably related to the interaction of C. albicans with the environment as well as extracellular enzymes and proteins involved in different morphological transitions. In addition, 11 C. albicans open reading frames (ORFs) identified in this screening encode proteins homologous to unknown or putative proteins, while 5 ORFs encode novel secreted proteins without known homologues in other organisms. This screening procedure therefore not only identifies a set of targets of interest in antifungal research but also provides new clues for understanding the topological locations of many proteins involved in processes relevant to the pathogenicity of this microorganism.

  18. Comparison of descriptors for predicting selectivity of protein-imprinted polymers.

    PubMed

    Raim, Vladimir; Zadok, Israel; Srebnik, Simcha

    2016-08-01

    Molecular imprinting is a technique that is used to create artificial receptors by the formation of a polymer network around a template molecule, creating a molecularly imprinted polymer. These artificial receptors may be used in applications that require molecular recognition, such as enantioseparations, biosensors, artificial catalysis, drug delivery and others. Small molecules, such as drugs, have been imprinted with high efficiency and, combined with the low cost of preparation, molecularly imprinted polymers have acquired commercial usage. While attempts at imprinting proteins have been significantly less successful, the great potential of protein-imprinted polymers (PIPs) in medicine and industry attracted much research. Multifunctionality, conformational flexibility, large size of the proteins, and aqueous polymerization environment are some of the obstacles faced by protein imprinting. We explore the relation between PIP selectivity and the properties of the template and competitor proteins. A comprehensive statistical analysis of published studies reveals a statistically significant correlation between four protein descriptors and the corresponding selectivity of PIPs. Namely, a PIP will generally be more selective against large competitor proteins with a smooth surface, whose isoelectric point and aspect ratio are significantly different than those of the template protein. The size of the protein, as measured by its molecular weight, appears to be independent of the template protein characteristics. Copyright © 2016 John Wiley & Sons, Ltd.

  19. Using multilevel models to quantify heterogeneity in resource selection

    USGS Publications Warehouse

    Wagner, Tyler; Diefenbach, Duane R.; Christensen, Sonja; Norton, Andrew S.

    2011-01-01

    Models of resource selection are being used increasingly to predict or model the effects of management actions rather than simply quantifying habitat selection. Multilevel, or hierarchical, models are an increasingly popular method to analyze animal resource selection because they impose a relatively weak stochastic constraint to model heterogeneity in habitat use and also account for unequal sample sizes among individuals. However, few studies have used multilevel models to model coefficients as a function of predictors that may influence habitat use at different scales or quantify differences in resource selection among groups. We used an example with white-tailed deer (Odocoileus virginianus) to illustrate how to model resource use as a function of distance to road that varies among deer by road density at the home range scale. We found that deer avoidance of roads decreased as road density increased. Also, we used multilevel models with sika deer (Cervus nippon) and white-tailed deer to examine whether resource selection differed between species. We failed to detect differences in resource use between these two species and showed how information-theoretic and graphical measures can be used to assess how resource use may have differed. Multilevel models can improve our understanding of how resource selection varies among individuals and provides an objective, quantifiable approach to assess differences or changes in resource selection.

  20. Using multilevel models to quantify heterogeneity in resource selection

    USGS Publications Warehouse

    Wagner, T.; Diefenbach, D.R.; Christensen, S.A.; Norton, A.S.

    2011-01-01

    Models of resource selection are being used increasingly to predict or model the effects of management actions rather than simply quantifying habitat selection. Multilevel, or hierarchical, models are an increasingly popular method to analyze animal resource selection because they impose a relatively weak stochastic constraint to model heterogeneity in habitat use and also account for unequal sample sizes among individuals. However, few studies have used multilevel models to model coefficients as a function of predictors that may influence habitat use at different scales or quantify differences in resource selection among groups. We used an example with white-tailed deer (Odocoileus virginianus) to illustrate how to model resource use as a function of distance to road that varies among deer by road density at the home range scale. We found that deer avoidance of roads decreased as road density increased. Also, we used multilevel models with sika deer (Cervus nippon) and white-tailed deer to examine whether resource selection differed between species. We failed to detect differences in resource use between these two species and showed how information-theoretic and graphical measures can be used to assess how resource use may have differed. Multilevel models can improve our understanding of how resource selection varies among individuals and provides an objective, quantifiable approach to assess differences or changes in resource selection. ?? The Wildlife Society, 2011.

  1. Mining Proteins with Non-Experimental Annotations Based on an Active Sample Selection Strategy for Predicting Protein Subcellular Localization.

    PubMed

    Cao, Junzhe; Liu, Wenqi; He, Jianjun; Gu, Hong

    2013-01-01

    Subcellular localization of a protein is important to understand proteins' functions and interactions. There are many techniques based on computational methods to predict protein subcellular locations, but it has been shown that many prediction tasks have a training data shortage problem. This paper introduces a new method to mine proteins with non-experimental annotations, which are labeled by non-experimental evidences of protein databases to overcome the training data shortage problem. A novel active sample selection strategy is designed, taking advantage of active learning technology, to actively find useful samples from the entire data pool of candidate proteins with non-experimental annotations. This approach can adequately estimate the "value" of each sample, automatically select the most valuable samples and add them into the original training set, to help to retrain the classifiers. Numerical experiments with for four popular multi-label classifiers on three benchmark datasets show that the proposed method can effectively select the valuable samples to supplement the original training set and significantly improve the performances of predicting classifiers.

  2. Pathophysiological Progression Model for Selected Toxicological Endpoints

    EPA Science Inventory

    The existing continuum paradigms are effective models to organize toxicological data associated with endpoints used in human health assessments. A compendium of endpoints characterized along a pathophysiological continuum would serve to: weigh the relative importance of effects o...

  3. Protein interactions central to stabilizing the K+ channel selectivity filter in a four-sited configuration for selective K+ permeation

    PubMed Central

    Sauer, David B.; Zeng, Weizhong; Raghunathan, Srinivasan; Jiang, Youxing

    2011-01-01

    The structural and functional conversion of the nonselective NaK channel to a K+ selective channel (NaK2K) allows us to identify two key residues, Tyr and Asp in the filter sequence of TVGYGD, that participate in interactions central to stabilizing the K+ channel selectivity filter. By using protein crystallography and channel electrophysiology, we demonstrate that the K+ channel filter exists as an energetically strained structure and requires these key protein interactions working in concert to hold the filter in the precisely defined four-sited configuration that is essential for selective K+ permeation. Disruption of either interaction, as tested on both the NaK2K and eukaryotic Kv1.6 channels, can reduce or completely abolish K+ selectivity and in some cases may also lead to channel inactivation due to conformational changes at the filter. Additionally, on the scaffold of NaK we recapitulate the protein interactions found in the filter of the Kir channel family, which uses a distinct interaction network to achieve similar stabilization of the filter. PMID:21933962

  4. Homology Modeling of Class A G Protein-Coupled Receptors

    PubMed Central

    Costanzi, Stefano

    2012-01-01

    G protein-coupled receptors (GPCRs) are a large superfamily of membrane bound signaling proteins that hold great pharmaceutical interest. Since experimentally elucidated structures are available only for a very limited number of receptors, homology modeling has become a widespread technique for the construction of GPCR models intended to study the structure-function relationships of the receptors and aid the discovery and development of ligands capable of modulating their activity. Through this chapter, various aspects involved in the constructions of homology models of the serpentine domain of the largest class of GPCRs, known as class A or rhodopsin family, are illustrated. In particular, the chapter provides suggestions, guidelines and critical thoughts on some of the most crucial aspect of GPCR modeling, including: collection of candidate templates and a structure-based alignment of their sequences; identification and alignment of the transmembrane helices of the query receptor to the corresponding domains of the candidate templates; selection of one or more templates receptor; election of homology or de novo modeling for the construction of specific extracellular and intracellular domains; construction of the three-dimensional models, with special consideration to extracellular regions, disulfide bridges, and interhelical cavity; validation of the models through controlled virtual screening experiments. PMID:22323225

  5. Fabrication of nanometer-sized protein patterns using atomic force microscopy and selective immobilization.

    PubMed Central

    Wadu-Mesthrige, K; Amro, N A; Garno, J C; Xu, S; Liu , G

    2001-01-01

    A new methodology is introduced to produce nanometer-sized protein patterns. The approach includes two main steps, nanopatterning of self-assembled monolayers using atomic force microscopy (AFM)-based nanolithography and subsequent selective immobilization of proteins on the patterned monolayers. The resulting templates and protein patterns are characterized in situ using AFM. Compared with conventional protein fabrication methods, this approach is able to produce smaller patterns with higher spatial precision. In addition, fabrication and characterization are completed in near physiological conditions. The adsorption configuration and bioreactivity of the proteins within the nanopatterns are also studied in situ. PMID:11259301

  6. Animal models of protein allergenicity: potential benefits, pitfalls and challenges.

    PubMed

    Dearman, R J; Kimber, I

    2009-04-01

    Food allergy is an important health issue. With an increasing interest in novel foods derived from transgenic crop plants, there is a growing need for the development of approaches suitable for the characterization of the allergenic potential of proteins. There are methods available currently (such as homology searches and serological testing) that are very effective at identifying proteins that are likely to cross-react with known allergens. However, animal models may play a role in the identification of truly novel proteins, such as bacterial or fungal proteins, that have not been experienced previously in the diet. We consider here the potential benefits, pitfalls and challenges of the selection of various animal models, including the mouse, the rat, the dog and the neonatal swine. The advantages and disadvantages of various experimental end-points are discussed, including the measurement of specific IgE by ELISA, Western blotting or functional tests such as the passive cutaneous anaphylaxis assay, and the assessment of challenge-induced clinical symptoms in previously sensitized animals. The experimental variables of route of exposure to test proteins and the incorporation of adjuvant to increase the sensitivity of the responses are considered also. It is important to emphasize that currently none of these approaches has been validated for the purposes of hazard identification in the context of a safety assessment. However, the available evidence suggests that the judicious use of an accurate and robust animal model could provide important additional data that would contribute significantly to the assessment of the potential allergenicity of novel proteins.

  7. Positive Evolutionary Selection of an HD Motif on Alzheimer Precursor Protein Orthologues Suggests a Functional Role

    PubMed Central

    Miklós, István; Zádori, Zoltán

    2012-01-01

    HD amino acid duplex has been found in the active center of many different enzymes. The dyad plays remarkably different roles in their catalytic processes that usually involve metal coordination. An HD motif is positioned directly on the amyloid beta fragment (Aβ) and on the carboxy-terminal region of the extracellular domain (CAED) of the human amyloid precursor protein (APP) and a taxonomically well defined group of APP orthologues (APPOs). In human Aβ HD is part of a presumed, RGD-like integrin-binding motif RHD; however, neither RHD nor RXD demonstrates reasonable conservation in APPOs. The sequences of CAEDs and the position of the HD are not particularly conserved either, yet we show with a novel statistical method using evolutionary modeling that the presence of HD on CAEDs cannot be the result of neutral evolutionary forces (p<0.0001). The motif is positively selected along the evolutionary process in the majority of APPOs, despite the fact that HD motif is underrepresented in the proteomes of all species of the animal kingdom. Position migration can be explained by high probability occurrence of multiple copies of HD on intermediate sequences, from which only one is kept by selective evolutionary forces, in a similar way as in the case of the “transcription binding site turnover.” CAED of all APP orthologues and homologues are predicted to bind metal ions including Amyloid-like protein 1 (APLP1) and Amyloid-like protein 2 (APLP2). Our results suggest that HDs on the CAEDs are most probably key components of metal-binding domains, which facilitate and/or regulate inter- or intra-molecular interactions in a metal ion-dependent or metal ion concentration-dependent manner. The involvement of naturally occurring mutations of HD (Tottori (D7N) and English (H6R) mutations) in early onset Alzheimer's disease gives additional support to our finding that HD has an evolutionary preserved function on APPOs. PMID:22319430

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

    SciTech Connect

    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

  9. Amyloid precursor protein selective gamma-secretase inhibitors for treatment of Alzheimer's disease

    PubMed Central

    2010-01-01

    Introduction Inhibition of gamma-secretase presents a direct target for lowering Aβ production in the brain as a therapy for Alzheimer's disease (AD). However, gamma-secretase is known to process multiple substrates in addition to amyloid precursor protein (APP), most notably Notch, which has limited clinical development of inhibitors targeting this enzyme. It has been postulated that APP substrate selective inhibitors of gamma-secretase would be preferable to non-selective inhibitors from a safety perspective for AD therapy. Methods In vitro assays monitoring inhibitor potencies at APP γ-site cleavage (equivalent to Aβ40), and Notch ε-site cleavage, in conjunction with a single cell assay to simultaneously monitor selectivity for inhibition of Aβ production vs. Notch signaling were developed to discover APP selective gamma-secretase inhibitors. In vivo efficacy for acute reduction of brain Aβ was determined in the PDAPP transgene model of AD, as well as in wild-type FVB strain mice. In vivo selectivity was determined following seven days x twice per day (b.i.d.) treatment with 15 mg/kg/dose to 1,000 mg/kg/dose ELN475516, and monitoring brain Aβ reduction vs. Notch signaling endpoints in periphery. Results The APP selective gamma-secretase inhibitors ELN318463 and ELN475516 reported here behave as classic gamma-secretase inhibitors, demonstrate 75- to 120-fold selectivity for inhibiting Aβ production compared with Notch signaling in cells, and displace an active site directed inhibitor at very high concentrations only in the presence of substrate. ELN318463 demonstrated discordant efficacy for reduction of brain Aβ in the PDAPP compared with wild-type FVB, not observed with ELN475516. Improved in vivo safety of ELN475516 was demonstrated in the 7d repeat dose study in wild-type mice, where a 33% reduction of brain Aβ was observed in mice terminated three hours post last dose at the lowest dose of inhibitor tested. No overt in-life or post

  10. Amyloid precursor protein selective gamma-secretase inhibitors for treatment of Alzheimer's disease.

    PubMed

    Basi, Guriqbal S; Hemphill, Susanna; Brigham, Elizabeth F; Liao, Anna; Aubele, Danielle L; Baker, Jeanne; Barbour, Robin; Bova, Michael; Chen, Xiao-Hua; Dappen, Michael S; Eichenbaum, Tovah; Goldbach, Erich; Hawkinson, Jon; Lawler-Herbold, Rose; Hu, Kang; Hui, Terence; Jagodzinski, Jacek J; Keim, Pamela S; Kholodenko, Dora; Latimer, Lee H; Lee, Mike; Marugg, Jennifer; Mattson, Matthew N; McCauley, Scott; Miller, James L; Motter, Ruth; Mutter, Linda; Neitzel, Martin L; Ni, Huifang; Nguyen, Lan; Quinn, Kevin; Ruslim, Lany; Semko, Christopher M; Shapiro, Paul; Smith, Jenifer; Soriano, Ferdie; Szoke, Balazs; Tanaka, Kevin; Tang, Pearl; Tucker, John A; Ye, Xiacong Michael; Yu, Mei; Wu, Jing; Xu, Ying-Zi; Garofalo, Albert W; Sauer, John Michael; Konradi, Andrei W; Ness, Daniel; Shopp, George; Pleiss, Michael A; Freedman, Stephen B; Schenk, Dale

    2010-12-29

    Inhibition of gamma-secretase presents a direct target for lowering Aβ production in the brain as a therapy for Alzheimer's disease (AD). However, gamma-secretase is known to process multiple substrates in addition to amyloid precursor protein (APP), most notably Notch, which has limited clinical development of inhibitors targeting this enzyme. It has been postulated that APP substrate selective inhibitors of gamma-secretase would be preferable to non-selective inhibitors from a safety perspective for AD therapy. In vitro assays monitoring inhibitor potencies at APP γ-site cleavage (equivalent to Aβ40), and Notch ε-site cleavage, in conjunction with a single cell assay to simultaneously monitor selectivity for inhibition of Aβ production vs. Notch signaling were developed to discover APP selective gamma-secretase inhibitors. In vivo efficacy for acute reduction of brain Aβ was determined in the PDAPP transgene model of AD, as well as in wild-type FVB strain mice. In vivo selectivity was determined following seven days x twice per day (b.i.d.) treatment with 15 mg/kg/dose to 1,000 mg/kg/dose ELN475516, and monitoring brain Aβ reduction vs. Notch signaling endpoints in periphery. The APP selective gamma-secretase inhibitors ELN318463 and ELN475516 reported here behave as classic gamma-secretase inhibitors, demonstrate 75- to 120-fold selectivity for inhibiting Aβ production compared with Notch signaling in cells, and displace an active site directed inhibitor at very high concentrations only in the presence of substrate. ELN318463 demonstrated discordant efficacy for reduction of brain Aβ in the PDAPP compared with wild-type FVB, not observed with ELN475516. Improved in vivo safety of ELN475516 was demonstrated in the 7d repeat dose study in wild-type mice, where a 33% reduction of brain Aβ was observed in mice terminated three hours post last dose at the lowest dose of inhibitor tested. No overt in-life or post-mortem indications of systemic toxicity, nor

  11. Discovery of Sulfonamidebenzamides as Selective Apoptotic CHOP Pathway Activators of the Unfolded Protein Response

    PubMed Central

    2014-01-01

    Cellular proteins that fail to fold properly result in inactive or disfunctional proteins that can have toxic functions. The unfolded protein response (UPR) is a two-tiered cellular mechanism initiated by eukaryotic cells that have accumulated misfolded proteins within the endoplasmic reticulum (ER). An adaptive pathway facilitates the clearance of the undesired proteins; however, if overwhelmed, cells trigger apoptosis by upregulating transcription factors such as C/EBP-homologous protein (CHOP). A high throughput screen was performed directed at identifying compounds that selectively upregulate the apoptotic CHOP pathway while avoiding adaptive signaling cascades, resulting in a sulfonamidebenzamide chemotype that was optimized. These efforts produced a potent and selective CHOP inducer (AC50 = 0.8 μM; XBP1 > 80 μM), which was efficacious in both mouse embryonic fibroblast cells and a human oral squamous cell cancer cell line, and demonstrated antiproliferative effects for multiple cancer cell lines in the NCI-60 panel. PMID:25530830

  12. Deviance statistics in model fit and selection in ROC studies

    NASA Astrophysics Data System (ADS)

    Lei, Tianhu; Bae, K. Ty

    2013-03-01

    A general non-linear regression model-based Bayesian inference approach is used in our ROC (Receiver Operating Characteristics) study. In the sampling of posterior distribution, two prior models - continuous Gaussian and discrete categorical - are used for the scale parameter. How to judge Goodness-of-Fit (GOF) of each model and how to criticize these two models, Deviance statistics and Deviance information criterion (DIC) are adopted to address these problems. Model fit and model selection focus on the adequacy of models. Judging model adequacy is essentially measuring agreement of model and observations. Deviance statistics and DIC provide overall measures on model fit and selection. In order to investigate model fit at each category of observations, we find that the cumulative, exponential contributions from individual observations to Deviance statistics are good estimates of FPF (false positive fraction) and TPF (true positive fraction) on which the ROC curve is based. This finding further leads to a new measure for model fit, called FPF-TPF distance, which is an Euclidean distance defined on FPF-TPF space. It combines both local and global fitting. Deviance statistics and FPFTPF distance are shown to be consistent and in good agreement. Theoretical derivation and numerical simulations for this new method for model fit and model selection of ROC data analysis are included. Keywords: General non-linear regression model, Bayesian Inference, Markov Chain Monte Carlo (MCMC) method, Goodness-of-Fit (GOF), Model selection, Deviance statistics, Deviance information criterion (DIC), Continuous conjugate prior, Discrete categorical prior. ∗

  13. Nonmathematical Models for Evolution of Altruism, and for Group Selection

    PubMed Central

    Darlington, P. J.

    1972-01-01

    Mathematical biologists have failed to produce a satisfactory general model for evolution of altruism, i.e., of behaviors by which “altruists” benefit other individuals but not themselves; kin selection does not seem to be a sufficient explanation of nonreciprocal altruism. Nonmathematical (but mathematically acceptable) models are now proposed for evolution of negative altruism in dual-determinant and of positive altruism in tri-determinant systems. Peck orders, territorial systems, and an ant society are analyzed as examples. In all models, evolution is primarily by individual selection, probably supplemented by group selection. Group selection is differential extinction of populations. It can act only on populations preformed by selection at the individual level, but can either cancel individual selective trends (effecting evolutionary homeostasis) or supplement them; its supplementary effect is probably increasingly important in the evolution of increasingly organized populations. PMID:4501113

  14. Structural requirements to obtain highly potent and selective 18 kDa Translocator Protein (TSPO) Ligands.

    PubMed

    Taliani, Sabrina; Pugliesi, Isabella; Da Settimo, Federico

    2011-01-01

    The (18 kDa) Translocator Protein (TSPO), was initially identified in 1977 as peripheral binding site for the benzodiazepine diazepam and named "Peripheral-type benzodiazepine receptor (PBR)". It is an evolutionarily well-conserved protein particularly located at the outer/inner mitochondrial membrane contact sites, in closely association with the 32 kDa voltage-dependent anion channel (VDAC) and the 30 kDa adenine nucleotide translocase (ANT), thus forming the mitochondrial permeability transition pore (MPTP). TSPO is ubiquitary expressed in peripheral tissues (steroid producing tissues, liver, heart, kidney, lung, immune system) and in lower levels in the central nervous system, where it is mainly located in glial cells, and in neurons. TSPO is involved in a variety of biological processes such as cholesterol transport, steroidogenesis, calcium homeostasis, lipid metabolism, mitochondrial oxidation, cell growth and differentiation, apoptosis induction, and regulation of immune functions. In the last decade, many studies have reported that TSPO basal expression is altered in a number of human pathologies, such as cancer and neurodegenerative disorders (Huntington's and Alzheimer's diseases), as well as in various forms of brain injury and inflammation and anxiety. Consequently, TSPO has not only been suggested as a promising drug target for a number of therapeutic applications (anticonvulsant, anxiolytic, immunomodulating, etc.), but also as valid diagnostic marker for related-disease state and progression, prompting the development of specific labelled ligands as powerful tools for imaging techniques. A number of structurally different classes of ligands have been reported, showing high affinity and selectivity towards TSPO. Indeed, most of these ligands have been designed starting from selective CBR ligands which were structurally modified in order to shift their affinity towards TSPO. Extensive structure-activity relationship studies were performed allowing to

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

    PubMed

    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.

  16. Escherichia coli auxotroph host strains for amino acid-selective isotope labeling of recombinant proteins.

    PubMed

    Lin, Myat T; Fukazawa, Risako; Miyajima-Nakano, Yoshiharu; Matsushita, Shinichi; Choi, Sylvia K; Iwasaki, Toshio; Gennis, Robert B

    2015-01-01

    Enrichment of proteins with isotopes such as (2)H, (15)N, and (13)C is commonly carried out in magnetic resonance and vibrational spectroscopic characterization of protein structures, mechanisms, and dynamics. Although uniform isotopic labeling of proteins is straightforward, efficient labeling of proteins with only a selected set of amino acid types is often challenging. A number of approaches have been described in the literature for amino acid-selective isotope labeling of proteins, each with its own limitations. Since Escherichia coli represents the most cost-effective and widely used host for heterologous production of foreign proteins, an efficient method to express proteins selectively labeled with isotopes would be highly valuable for these studies. However, an obvious drawback is misincorporation and dilution of input isotope labels to unwanted amino acid types due to metabolic scrambling in vivo. To overcome this problem, we have generated E. coli auxotroph strains that are compatible with the widely used T7 RNA polymerase overexpression systems and that minimize metabolic scrambling. We present several examples of selective amino acid isotope labeling of simple and complex proteins with bound cofactors, as an initial guide for practical applications of these E. coli strains. © 2015 Elsevier Inc. All rights reserved.

  17. Truly Absorbed Microbial Protein Synthesis, Rumen Bypass Protein, Endogenous Protein, and Total Metabolizable Protein from Starchy and Protein-Rich Raw Materials: Model Comparison and Predictions.

    PubMed

    Parand, Ehsan; Vakili, Alireza; Mesgaran, Mohsen Danesh; van Duinkerken, Gert; Yu, Peiqiang

    2015-07-29

    This study was carried out to measure truly absorbed microbial protein synthesis, rumen bypass protein, and endogenous protein loss, as well as total metabolizable protein, from starchy and protein-rich raw feed materials with model comparisons. Predictions by the DVE2010 system as a more mechanistic model were compared with those of two other models, DVE1994 and NRC-2001, that are frequently used in common international feeding practice. DVE1994 predictions for intestinally digestible rumen undegradable protein (ARUP) for starchy concentrates were higher (27 vs 18 g/kg DM, p < 0.05, SEM = 1.2) than predictions by the NRC-2001, whereas there was no difference in predictions for ARUP from protein concentrates among the three models. DVE2010 and NRC-2001 had highest estimations of intestinally digestible microbial protein for starchy (92 g/kg DM in DVE2010 vs 46 g/kg DM in NRC-2001 and 67 g/kg DM in DVE1994, p < 0.05 SEM = 4) and protein concentrates (69 g/kg DM in NRC-2001 vs 31 g/kg DM in DVE1994 and 49 g/kg DM in DVE2010, p < 0.05 SEM = 4), respectively. Potential protein supplies predicted by tested models from starchy and protein concentrates are widely different, and comparable direct measurements are needed to evaluate the actual ability of different models to predict the potential protein supply to dairy cows from different feedstuffs.

  18. Modeling Selective Intergranular Oxidation of Binary Alloys

    SciTech Connect

    Xu, Zhijie; Li, Dongsheng; Schreiber, Daniel K.; Rosso, Kevin M.; Bruemmer, Stephen M.

    2015-01-07

    Intergranular attack of alloys under hydrothermal conditions is a complex problem that depends on metal and oxygen transport kinetics via solid-state and channel-like pathways to an advancing oxidation front. Experiments reveal very different rates of intergranular attack and minor element depletion distances ahead of the oxidation front for nickel-based binary alloys depending on the minor element. For example, a significant Cr depletion up to 9 µm ahead of grain boundary crack tips were documented for Ni-5Cr binary alloy, in contrast to relatively moderate Al depletion for Ni-5Al (~100s of nm). We present a mathematical kinetics model that adapts Wagner’s model for thick film growth to intergranular attack of binary alloys. The transport coefficients of elements O, Ni, Cr, and Al in bulk alloys and along grain boundaries were estimated from the literature. For planar surface oxidation, a critical concentration of the minor element can be determined from the model where the oxide of minor element becomes dominant over the major element. This generic model for simple grain boundary oxidation can predict oxidation penetration velocities and minor element depletion distances ahead of the advancing front that are comparable to experimental data. The significant distance of depletion of Cr in Ni-5Cr in contrast to the localized Al depletion in Ni-5Al can be explained by the model due to the combination of the relatively faster diffusion of Cr along the grain boundary and slower diffusion in bulk grains, relative to Al.

  19. Targeted reengineering of protein geranylgeranyltransferase type I selectivity functionally implicates active-site residues in protein-substrate recognition.

    PubMed

    Gangopadhyay, Soumyashree A; Losito, Erica L; Hougland, James L

    2014-01-21

    Posttranslational modifications are vital for the function of many proteins. Prenylation is one such modification, wherein protein geranylgeranyltransferase type I (GGTase-I) or protein farnesyltransferase (FTase) modify proteins by attaching a 20- or 15-carbon isoprenoid group, respectively, to a cysteine residue near the C-terminus of a target protein. These enzymes require a C-terminal Ca1a2X sequence on their substrates, with the a1, a2, and X residues serving as substrate-recognition elements for FTase and/or GGTase-I. While crystallographic structures of rat GGTase-I show a tightly packed and hydrophobic a2 residue binding pocket, consistent with a preference for moderately sized a2 residues in GGTase-I substrates, the functional impact of enzyme-substrate contacts within this active site remains to be determined. Using site-directed mutagenesis and peptide substrate structure-activity studies, we have identified specific active-site residues within rat GGTase-I involved in substrate recognition and developed novel GGTase-I variants with expanded/altered substrate selectivity. The ability to drastically alter GGTase-I selectivity mirrors similar behavior observed in FTase but employs mutation of a distinct set of structurally homologous active-site residues. Our work demonstrates that tunable selectivity may be a general phenomenon among multispecific enzymes involved in posttranslational modification and raises the possibility of variable substrate selectivity among GGTase-I orthologues from different organisms. Furthermore, the GGTase-I variants developed herein can serve as tools for studying GGTase-I substrate selectivity and the effects of prenylation pathway modifications on specific proteins.

  20. Development of SPAWM: selection program for available watershed models.

    PubMed

    Cho, Yongdeok; Roesner, Larry A

    2014-01-01

    A selection program for available watershed models (also known as SPAWM) was developed. Thirty-three commonly used watershed models were analyzed in depth and classified in accordance to their attributes. These attributes consist of: (1) land use; (2) event or continuous; (3) time steps; (4) water quality; (5) distributed or lumped; (6) subsurface; (7) overland sediment; and (8) best management practices. Each of these attributes was further classified into sub-attributes. Based on user selected sub-attributes, the most appropriate watershed model is selected from the library of watershed models. SPAWM is implemented using Excel Visual Basic and is designed for use by novices as well as by experts on watershed modeling. It ensures that the necessary sub-attributes required by the user are captured and made available in the selected watershed model.

  1. Selection of soluble protein expression constructs: the experimental determination of protein domain boundaries.

    PubMed

    Dyson, Michael R

    2010-08-01

    Proteins can contain multiple domains each of which is capable of possessing a separate independent function and three-dimensional structure. It is often useful to clone and express individual protein domains to study their biochemical properties and for structure determination. However, the annotated domain boundaries in databases such as Pfam or SMART are not always accurate. The present review summarizes various strategies for the experimental determination of protein domain boundaries.

  2. Boosting model performance and interpretation by entangling preprocessing selection and variable selection.

    PubMed

    Gerretzen, Jan; Szymańska, Ewa; Bart, Jacob; Davies, Antony N; van Manen, Henk-Jan; van den Heuvel, Edwin R; Jansen, Jeroen J; Buydens, Lutgarde M C

    2016-09-28

    The aim of data preprocessing is to remove data artifacts-such as a baseline, scatter effects or noise-and to enhance the contextually relevant information. Many preprocessing methods exist to deliver one or more of these benefits, but which method or combination of methods should be used for the specific data being analyzed is difficult to select. Recently, we have shown that a preprocessing selection approach based on Design of Experiments (DoE) enables correct selection of highly appropriate preprocessing strategies within reasonable time frames. In that approach, the focus was solely on improving the predictive performance of the chemometric model. This is, however, only one of the two relevant criteria in modeling: interpretation of the model results can be just as important. Variable selection is often used to achieve such interpretation. Data artifacts, however, may hamper proper variable selection by masking the true relevant variables. The choice of preprocessing therefore has a huge impact on the outcome of variable selection methods and may thus hamper an objective interpretation of the final model. To enhance such objective interpretation, we here integrate variable selection into the preprocessing selection approach that is based on DoE. We show that the entanglement of preprocessing selection and variable selection not only improves the interpretation, but also the predictive performance of the model. This is achieved by analyzing several experimental data sets of which the true relevant variables are available as prior knowledge. We show that a selection of variables is provided that complies more with the true informative variables compared to individual optimization of both model aspects. Importantly, the approach presented in this work is generic. Different types of models (e.g. PCR, PLS, …) can be incorporated into it, as well as different variable selection methods and different preprocessing methods, according to the taste and experience of

  3. Probing heme protein conformational equilibration rates with kinetic selection.

    PubMed

    Tian, W D; Sage, J T; Champion, P M; Chien, E; Sligar, S G

    1996-03-19

    Double-pulse flash photolysis experiments on solutions of carbonmonoxymyoglobin (MbCO) are used to determine the time scale for protein conformational averaging. The interconversion times for transitions between the "open" and "closed" subpopulations of MbCO are found to be 10(-6)-10(-4)s, depending on solvent composition and temperature. In aqueous solution at 273 K, the interconversion rate is found to be 1.4 x 10(6)s. Since the interconversion rate is comparable to or slower than the geminate rebinding rate, we describe the geminate phase of the kinetics as a superposition of contributions from the open and closed states. Although geminate kinetics remain intrinsically nonexponential for both open and closed states near room temperature, we find that substates within these two subpopulations interconvert more rapidly than the geminate rebinding. These observations cannot be explained by a superposition of contributions from a quasicontinuous conformational distribution (Steinbach et al., 1991) and are probably due to the long-time tail of the relaxation of the protein (Tian et al., 1992). Bimolecular rebinding takes place at a statistically averaged rate, since the interconversion and relaxation rates are faster than the bimolecular kinetics. The geminate and bimolecular kinetics are analyzed quantitatively as a function of pH using this approach and the spectroscopically determined populations of the open and closed states. The analysis accounts for the observed kinetics and also successfully predicts the kinetic response observed in the double-pulse experiments. In aqueous solution at 273 K, the geminate amplitudes and rates are found to be I(0)g = 32% and k(0)g = 1.3 x 10(7)s(-1) for the open state and I(1)g = 9.3% and k(1)g = 1.4 x 10(6)s(-1) for the closed state. In 75% glycerol solution at 264 K, the dominant component of the geminate rebinding is characterized by I(0)g1 = 89% and k(0)g1 = 3.1 x 10(6)s(-1) for the open state and I(1)g1 = 26% and k(1)g1 = 3

  4. The Genealogy of Samples in Models with Selection

    PubMed Central

    Neuhauser, C.; Krone, S. M.

    1997-01-01

    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. PMID:9071604

  5. The genealogy of samples in models with selection.

    PubMed

    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.

  6. Novel approach for selecting the best predictor for identifying the binding sites in DNA binding proteins

    PubMed Central

    Nagarajan, R.; Ahmad, Shandar; Michael Gromiha, M.

    2013-01-01

    Protein–DNA complexes play vital roles in many cellular processes by the interactions of amino acids with DNA. Several computational methods have been developed for predicting the interacting residues in DNA-binding proteins using sequence and/or structural information. These methods showed different levels of accuracies, which may depend on the choice of data sets used in training, the feature sets selected for developing a predictive model, the ability of the models to capture information useful for prediction or a combination of these factors. In many cases, different methods are likely to produce similar results, whereas in others, the predictors may return contradictory predictions. In this situation, a priori estimates of prediction performance applicable to the system being investigated would be helpful for biologists to choose the best method for designing their experiments. In this work, we have constructed unbiased, stringent and diverse data sets for DNA-binding proteins based on various biologically relevant considerations: (i) seven structural classes, (ii) 86 folds, (iii) 106 superfamilies, (iv) 194 families, (v) 15 binding motifs, (vi) single/double-stranded DNA, (vii) DNA conformation (A, B, Z, etc.), (viii) three functions and (ix) disordered regions. These data sets were culled as non-redundant with sequence identities of 25 and 40% and used to evaluate the performance of 11 different methods in which online services or standalone programs are available. We observed that the best performing methods for each of the data sets showed significant biases toward the data sets selected for their benchmark. Our analysis revealed important data set features, which could be used to estimate these context-specific biases and hence suggest the best method to be used for a given problem. We have developed a web server, which considers these features on demand and displays the best method that the investigator should use. The web server is freely available at

  7. Determining a Retention Model for the Selected Marine Corps Reserve

    DTIC Science & Technology

    2016-03-01

    period of contractual drilling obligation and their MDSD falls within the current year. In this model , the OAP variable includes prior service Marines...RETENTION MODEL FOR THE SELECTED MARINE CORPS RESERVE by Andrew D. Dausman March 2016 Thesis Advisor: Marigee Bacolod Co-Advisor: Chad W...2016 3. REPORT TYPE AND DATES COVERED Master’s thesis 4. TITLE AND SUBTITLE DETERMINING A RETENTION MODEL FOR THE SELECTED MARINE CORPS RESERVE

  8. Markov state models of protein misfolding

    NASA Astrophysics Data System (ADS)

    Sirur, Anshul; De Sancho, David; Best, Robert B.

    2016-02-01

    Markov state models (MSMs) are an extremely useful tool for understanding the conformational dynamics of macromolecules and for analyzing MD simulations in a quantitative fashion. They have been extensively used for peptide and protein folding, for small molecule binding, and for the study of native ensemble dynamics. Here, we adapt the MSM methodology to gain insight into the dynamics of misfolded states. To overcome possible flaws in root-mean-square deviation (RMSD)-based metrics, we introduce a novel discretization approach, based on coarse-grained contact maps. In addition, we extend the MSM methodology to include "sink" states in order to account for the irreversibility (on simulation time scales) of processes like protein misfolding. We apply this method to analyze the mechanism of misfolding of tandem repeats of titin domains, and how it is influenced by confinement in a chaperonin-like cavity.

  9. Floodflow frequency model selection in Australia

    NASA Astrophysics Data System (ADS)

    Vogel, Richard M.; McMahon, Thomas A.; Chiew, Francis H. S.

    1993-06-01

    Uniform flood frequency guidelines in Australia and the United States recommend the use of the log Pearson type 3 (LP3) distribution in flood frequency investigations. Many investigators have suggested alternate models such as the Generalized Extreme Value (GEV) distribution as an improvement over the LP3 distribution. Using floodflow data at 61 sites across Australia, we explore the suitability of various flood frequency models using L-moment diagrams. We also repeat the experiment performed in the original US Water Resource Council report (Bulletin 17B) which led to the LP3 mandate in the United States. Our evaluations reveal that among the models tested, the GEV and Wakeby distributions provide the best approximation to floodflow data in the regions of Australia that are dominated by rainfall during the winter months, such as southwest Western Australia and Tasmania. For the remainder of the continent, the Generalized Pareto (GPA) and Wakeby distributions provide the best approximation to floodflow data. The two- and three-parameter log-normal models and the LP3 distribution performed satisfactorily, yet not as well as either the GEV or GPA distributions. Other models such as the Gumbel, log-normal, normal, Pearson, exponential, and uniform distributions are shown to perform poorly. Recent research indicates that regional index-flood type procedures should be more accurate and more robust than the type of at-site procedures evaluated here. Nevertheless, this study reveals that index-flood procedures should not be restricted to a si