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Sample records for protein concentration predicts

  1. An Improved Method of Predicting Extinction Coefficients for the Determination of Protein Concentration.

    Science.gov (United States)

    Hilario, Eric C; Stern, Alan; Wang, Charlie H; Vargas, Yenny W; Morgan, Charles J; Swartz, Trevor E; Patapoff, Thomas W

    2017-01-01

    Concentration determination is an important method of protein characterization required in the development of protein therapeutics. There are many known methods for determining the concentration of a protein solution, but the easiest to implement in a manufacturing setting is absorption spectroscopy in the ultraviolet region. For typical proteins composed of the standard amino acids, absorption at wavelengths near 280 nm is due to the three amino acid chromophores tryptophan, tyrosine, and phenylalanine in addition to a contribution from disulfide bonds. According to the Beer-Lambert law, absorbance is proportional to concentration and path length, with the proportionality constant being the extinction coefficient. Typically the extinction coefficient of proteins is experimentally determined by measuring a solution absorbance then experimentally determining the concentration, a measurement with some inherent variability depending on the method used. In this study, extinction coefficients were calculated based on the measured absorbance of model compounds of the four amino acid chromophores. These calculated values for an unfolded protein were then compared with an experimental concentration determination based on enzymatic digestion of proteins. The experimentally determined extinction coefficient for the native proteins was consistently found to be 1.05 times the calculated value for the unfolded proteins for a wide range of proteins with good accuracy and precision under well-controlled experimental conditions. The value of 1.05 times the calculated value was termed the predicted extinction coefficient. Statistical analysis shows that the differences between predicted and experimentally determined coefficients are scattered randomly, indicating no systematic bias between the values among the proteins measured. The predicted extinction coefficient was found to be accurate and not subject to the inherent variability of experimental methods. We propose the use of a

  2. Predicting the activity coefficients of free-solvent for concentrated globular protein solutions using independently determined physical parameters.

    Directory of Open Access Journals (Sweden)

    Devin W McBride

    Full Text Available The activity coefficient is largely considered an empirical parameter that was traditionally introduced to correct the non-ideality observed in thermodynamic systems such as osmotic pressure. Here, the activity coefficient of free-solvent is related to physically realistic parameters and a mathematical expression is developed to directly predict the activity coefficients of free-solvent, for aqueous protein solutions up to near-saturation concentrations. The model is based on the free-solvent model, which has previously been shown to provide excellent prediction of the osmotic pressure of concentrated and crowded globular proteins in aqueous solutions up to near-saturation concentrations. Thus, this model uses only the independently determined, physically realizable quantities: mole fraction, solvent accessible surface area, and ion binding, in its prediction. Predictions are presented for the activity coefficients of free-solvent for near-saturated protein solutions containing either bovine serum albumin or hemoglobin. As a verification step, the predictability of the model for the activity coefficient of sucrose solutions was evaluated. The predicted activity coefficients of free-solvent are compared to the calculated activity coefficients of free-solvent based on osmotic pressure data. It is observed that the predicted activity coefficients are increasingly dependent on the solute-solvent parameters as the protein concentration increases to near-saturation concentrations.

  3. The use of crude protein content to predict concentrations of lysine ...

    African Journals Online (AJOL)

    Correlations were determined between the crude protein (CP) and lysine or methionine concentrations of grain from wheat (cultivar: palmiet), barley (cultivar: clipper) and triticale (cultivar: usgen 19) grown in the Western Cape region of South Africa. Twenty samples of varying CP content were collected for each grain type ...

  4. Evaluation of the use of serum C-reactive protein concentration to predict outcome in puppies infected with canine parvovirus

    DEFF Research Database (Denmark)

    McClure, Vanessa; van Schoor, Mirinda; Thompson, Peter N.

    2013-01-01

    Objective-To evaluate associations of serum C-reactive protein (CRP) concentration with duration of hospitalization and with outcome in puppies with canine parvoviral enteritis. Design-Prospective observational study. Animals-79 client-owned puppies with naturally acquired canine parvovirus...... infection. Procedures-All puppies received supportive care. Serum CRP concentration was measured at the time of admission, approximately every 10 to 12 hours for the first 48 hours, and then every 24 hours until discharge from the hospital or death. Associations between outcome and CRP concentration...... at various time points or changes in CRP concentration over time were assessed via multiple logistic regression. Associations of CRP concentration with survival time and duration of hospitalization among survivors were estimated with Cox proportional hazards regression. Use of CRP concentration to predict...

  5. CMAQ predicted concentration files

    Data.gov (United States)

    U.S. Environmental Protection Agency — model predicted concentrations. This dataset is associated with the following publication: Muñiz-Unamunzaga, M., R. Borge, G. Sarwar, B. Gantt, D. de la Paz, C....

  6. CMAQ predicted concentration files

    Data.gov (United States)

    U.S. Environmental Protection Agency — CMAQ predicted ozone. This dataset is associated with the following publication: Gantt, B., G. Sarwar, J. Xing, H. Simon, D. Schwede, B. Hutzell, R. Mathur, and A....

  7. Protein docking prediction using predicted protein-protein interface

    Directory of Open Access Journals (Sweden)

    Li Bin

    2012-01-01

    Full Text Available Abstract Background Many important cellular processes are carried out by protein complexes. To provide physical pictures of interacting proteins, many computational protein-protein prediction methods have been developed in the past. However, it is still difficult to identify the correct docking complex structure within top ranks among alternative conformations. Results We present a novel protein docking algorithm that utilizes imperfect protein-protein binding interface prediction for guiding protein docking. Since the accuracy of protein binding site prediction varies depending on cases, the challenge is to develop a method which does not deteriorate but improves docking results by using a binding site prediction which may not be 100% accurate. The algorithm, named PI-LZerD (using Predicted Interface with Local 3D Zernike descriptor-based Docking algorithm, is based on a pair wise protein docking prediction algorithm, LZerD, which we have developed earlier. PI-LZerD starts from performing docking prediction using the provided protein-protein binding interface prediction as constraints, which is followed by the second round of docking with updated docking interface information to further improve docking conformation. Benchmark results on bound and unbound cases show that PI-LZerD consistently improves the docking prediction accuracy as compared with docking without using binding site prediction or using the binding site prediction as post-filtering. Conclusion We have developed PI-LZerD, a pairwise docking algorithm, which uses imperfect protein-protein binding interface prediction to improve docking accuracy. PI-LZerD consistently showed better prediction accuracy over alternative methods in the series of benchmark experiments including docking using actual docking interface site predictions as well as unbound docking cases.

  8. Protein docking prediction using predicted protein-protein interface.

    Science.gov (United States)

    Li, Bin; Kihara, Daisuke

    2012-01-10

    Many important cellular processes are carried out by protein complexes. To provide physical pictures of interacting proteins, many computational protein-protein prediction methods have been developed in the past. However, it is still difficult to identify the correct docking complex structure within top ranks among alternative conformations. We present a novel protein docking algorithm that utilizes imperfect protein-protein binding interface prediction for guiding protein docking. Since the accuracy of protein binding site prediction varies depending on cases, the challenge is to develop a method which does not deteriorate but improves docking results by using a binding site prediction which may not be 100% accurate. The algorithm, named PI-LZerD (using Predicted Interface with Local 3D Zernike descriptor-based Docking algorithm), is based on a pair wise protein docking prediction algorithm, LZerD, which we have developed earlier. PI-LZerD starts from performing docking prediction using the provided protein-protein binding interface prediction as constraints, which is followed by the second round of docking with updated docking interface information to further improve docking conformation. Benchmark results on bound and unbound cases show that PI-LZerD consistently improves the docking prediction accuracy as compared with docking without using binding site prediction or using the binding site prediction as post-filtering. We have developed PI-LZerD, a pairwise docking algorithm, which uses imperfect protein-protein binding interface prediction to improve docking accuracy. PI-LZerD consistently showed better prediction accuracy over alternative methods in the series of benchmark experiments including docking using actual docking interface site predictions as well as unbound docking cases.

  9. Short-term effects of medetomidine on photosynthesis and protein synthesis in periphyton, epipsammon and plankton communities in relation to predicted environmental concentrations.

    Science.gov (United States)

    Ohlauson, Cecilia; Eriksson, Karl Martin; Blanck, Hans

    2012-01-01

    Medetomidine is a new antifouling substance, highly effective against barnacles. As part of a thorough ecotoxicological evaluation of medetomidine, its short-term effects on algal and bacterial communities were investigated and environmental concentrations were predicted with the MAMPEC model. Photosynthesis and bacterial protein synthesis for three marine communities, viz. periphyton, epipsammon and plankton were used as effect indicators, and compared with the predicted environmental concentrations (PECs). The plankton community showed a significant decrease in photosynthetic activity of 16% at 2 mg l⁻¹ of medetomidine, which was the only significant effect observed. PECs were estimated for a harbor, shipping lane and marina environment using three different model scenarios (MAMPEC default, Baltic and OECD scenarios). The highest PEC of 57 ng l⁻¹, generated for a marina with the Baltic scenario, was at least 10,000-fold lower than the concentration that significantly decreased photosynthetic activity. It is concluded that medetomidine does not cause any acute toxic effects on bacterial protein synthesis and only small acute effects on photosynthesis at high concentrations in marine microbial communities. It is also concluded that the hazard from medetomidine on these processes is low since the effect levels are much lower than the highest PEC.

  10. Prediction of milk, fat and protein yields in first lactation from serum ß-lactoglobulin concentrations during gestation in Italian Brown heifers

    Directory of Open Access Journals (Sweden)

    Paola Superchi

    2010-01-01

    Full Text Available The Authors report the results of a study carried out on 23 pregnant Italian Brown heifers, with the aim to determine the relationships between blood serum ß-lactoglobulin (ß-LG concentrations during first gestation and subsequent milk production and quality in first lactation, in order to obtain an improved selection method for replacement heifers. At weeks 20, 26 and 32 of gestation, ß-LG concentrations (±SE were 706±78, 753±66 and 772±63 ng/ml, respectively (P>0.05. High and significant (P≤0.05 correlation coefficients were observed only between ß-LG content at week 32 and total milk and protein yields in first lactation. Prediction equations of milk, fat and protein production in first lactation from log10 ß-LG content at week 32 of gestation, from parent average genetic indexes and from both were calculated by means of multiple regression analysis. When the contribution of both ß-LG content and predicted genetic indexes were considered, the regression equations gave generally a better estimate of the production parameters in first lactation (higher R2, lower SE of estimate than the above mentioned parameters alone. These results suggest that it is valuable to pre-estimate milk, fat and protein production in Italian Brown first lactating cows by means of the analysis of serum ß-LG content during gestation.

  11. Protein Sorting Prediction

    DEFF Research Database (Denmark)

    Nielsen, Henrik

    2017-01-01

    and drawbacks of each of these approaches is described through many examples of methods that predict secretion, integration into membranes, or subcellular locations in general. The aim of this chapter is to provide a user-level introduction to the field with a minimum of computational theory.......Many computational methods are available for predicting protein sorting in bacteria. When comparing them, it is important to know that they can be grouped into three fundamentally different approaches: signal-based, global-property-based and homology-based prediction. In this chapter, the strengths...

  12. Does low protein concentration of tissue-type plasminogen activator predict a low risk of spontaneous deep vein thrombosis?

    DEFF Research Database (Denmark)

    Gram, J; Sidelmann, Johannes Jakobsen; Jespersen, J

    1995-01-01

    activity (p Reasonable separation could be obtained for t-PA with a cut-off point of 5...... interval 70%-94%). We observed a significantly negative association between concentration of t-PA and fibrinolytic activity (rs = -0.47; p WORDS)...

  13. Protein Structure Prediction by Protein Threading

    Science.gov (United States)

    Xu, Ying; Liu, Zhijie; Cai, Liming; Xu, Dong

    The seminal work of Bowie, Lüthy, and Eisenberg (Bowie et al., 1991) on "the inverse protein folding problem" laid the foundation of protein structure prediction by protein threading. By using simple measures for fitness of different amino acid types to local structural environments defined in terms of solvent accessibility and protein secondary structure, the authors derived a simple and yet profoundly novel approach to assessing if a protein sequence fits well with a given protein structural fold. Their follow-up work (Elofsson et al., 1996; Fischer and Eisenberg, 1996; Fischer et al., 1996a,b) and the work by Jones, Taylor, and Thornton (Jones et al., 1992) on protein fold recognition led to the development of a new brand of powerful tools for protein structure prediction, which we now term "protein threading." These computational tools have played a key role in extending the utility of all the experimentally solved structures by X-ray crystallography and nuclear magnetic resonance (NMR), providing structural models and functional predictions for many of the proteins encoded in the hundreds of genomes that have been sequenced up to now.

  14. Viscosity of high concentration protein formulations of monoclonal antibodies of the IgG1 and IgG4 subclass - Prediction of viscosity through protein-protein interaction measurements

    DEFF Research Database (Denmark)

    Neergaard, Martin S; Kalonia, Devendra S; Parshad, Henrik

    2013-01-01

    The purpose of this work was to explore the relation between protein-protein interactions (PPIs) and solution viscosity at high protein concentration using three monoclonal antibodies (mAbs), two of the IgG4 subclass and one of the IgG1 subclass. A range of methods was used to quantify the PPI...... low or high protein concentration determined using DLS. The PPI measurements were correlated with solution viscosity (measured by DLS using polystyrene nanospheres and ultrasonic shear rheology) as a function of pH (4-9) and ionic strength (10, 50 and 150mM). Our measurements showed that the highest...... solution viscosity was observed under conditions with the most negative kD, the highest apparent radius and the lowest net charge. An increase in ionic strength resulted in a change in the nature of the PPI at low pH from repulsive to attractive. In the neutral to alkaline pH region the mAbs behaved...

  15. Prediction Methods for Blood Glucose Concentration

    DEFF Research Database (Denmark)

    “Recent Results on Glucose–Insulin Predictions by Means of a State Observer for Time-Delay Systems” by Pasquale Palumbo et al. introduces a prediction model which in real time predicts the insulin concentration in blood which in turn is used in a control system. The method is tested in simulation...... EEG signals to predict upcoming hypoglycemic situations in real-time by employing artificial neural networks. The results of a 30-day long clinical study with the implanted device and the developed algorithm are presented. The chapter “Meta-Learning Based Blood Glucose Predictor for Diabetic......, but the insulin amount is chosen using factors that account for this expectation. The increasing availability of more accurate continuous blood glucose measurement (CGM) systems is attracting much interest to the possibilities of explicit prediction of future BG values. Against this background, in 2014 a two...

  16. RHEOLOGY OF CHICKPEA PROTEIN CONCENTRATE DISPERSIONS

    Directory of Open Access Journals (Sweden)

    Aurelia Ionescu

    2011-12-01

    Full Text Available Chickpea proteins are used as ingredients in comminuted sausage products and many oriental textured foods. Rheological behaviour of chickpea protein concentrate was studied using a controlled stress rheometer. The protein dispersion prepared with phosphate buffer at pH 7.0 presented non-Newtonian shear thinning behaviour and rheological data well fitted to the Sisko, Carreau and Cross models. The viscoelastic properties of the chickpea protein suspensions were estimated by measuring the storage and loss moduli in oscillatory frequency conditions (0.1-10 Hz at 20°C. Moreover, thermally induced gelation of the chickpea proteins (16, 24 and 36% was studied at pH 7.0 and 4.5 in the temperature range 50 to 100oC and salt concentration ranging from 0 to 1 M. Gelling behaviour was quantified by means of dynamic rheological measurements. Gels formation was preceded by the decrease of storage modulus and loss moduli, coupled with the increase of the phase angle (delta. The beginning of thermal gelation was influenced by protein concentration, pH and salt level. In all studied cases, storage modulus increased rapidly in the temperature range 70-90°C. All rheological parameters measured at 90°C were significantly higher at pH 4.5 compared to pH 7.0.

  17. Neural Networks for protein Structure Prediction

    DEFF Research Database (Denmark)

    Bohr, Henrik

    1998-01-01

    This is a review about neural network applications in bioinformatics. Especially the applications to protein structure prediction, e.g. prediction of secondary structures, prediction of surface structure, fold class recognition and prediction of the 3-dimensional structure of protein backbones...

  18. Prediction of Protein-Protein Interactions Related to Protein Complexes Based on Protein Interaction Networks

    Directory of Open Access Journals (Sweden)

    Peng Liu

    2015-01-01

    Full Text Available A method for predicting protein-protein interactions based on detected protein complexes is proposed to repair deficient interactions derived from high-throughput biological experiments. Protein complexes are pruned and decomposed into small parts based on the adaptive k-cores method to predict protein-protein interactions associated with the complexes. The proposed method is adaptive to protein complexes with different structure, number, and size of nodes in a protein-protein interaction network. Based on different complex sets detected by various algorithms, we can obtain different prediction sets of protein-protein interactions. The reliability of the predicted interaction sets is proved by using estimations with statistical tests and direct confirmation of the biological data. In comparison with the approaches which predict the interactions based on the cliques, the overlap of the predictions is small. Similarly, the overlaps among the predicted sets of interactions derived from various complex sets are also small. Thus, every predicted set of interactions may complement and improve the quality of the original network data. Meanwhile, the predictions from the proposed method replenish protein-protein interactions associated with protein complexes using only the network topology.

  19. Prediction Methods for Blood Glucose Concentration

    DEFF Research Database (Denmark)

    -day workshop on the design, use and evaluation of prediction methods for blood glucose concentration was held at the Johannes Kepler University Linz, Austria. One intention of the workshop was to bring together experts working in various fields on the same topic, in order to shed light from different angles...... discussions which allowed to receive direct feedback from the point of view of different disciplines. This book is based on the contributions of that workshop and is intended to convey an overview of the different aspects involved in the prediction. The individual chapters are based on the presentations given...... in the process of writing this book: All authors for their individual contributions, all reviewers of the book chapters, Daniela Hummer for the entire organization of the workshop, Boris Tasevski for helping with the typesetting, Florian Reiterer for his help editing the book, as well as Oliver Jackson and Karin...

  20. Protein Concentrate Production from Thin Stillage.

    Science.gov (United States)

    Ratanapariyanuch, Kornsulee; Shim, Youn Young; Emami, Shahram; Reaney, Martin J T

    2016-12-21

    Two-stage fermentation (TSF) of saccharified wheat with a consortium of endemic lactobacilli produced CO 2 and induced colloid separation of fermented solution to produce a protein concentrate (PC). Protein-rich slurry (50%, db) was obtained by decanting solution or skimming floating material during or after TSF. Washing and drying processes were explored to improve protein content, extend storage life of slurry, and yield converted stillage for compound recovery. Centrifuging and washing slurry afforded a PC and clarified solution. PC protein content increased to 60% (w/w, db). The PC was dried in a spray dryer or drum dryer or tray dryer. Dried PC water activity ranged 0.23-0.30. The dried PC lysine content was low, but lysine availability (95%) was excellent. Liquid from TSF and washing was readily microfiltered. Mass recovery of protein, glycerol, 1,3-propanediol, lactic acid, acetic acid, and glycerylphosphorylcholine from combined TSF, washing, and filtration were 66, 76, 72, 77, 74, and 84%, respectively.

  1. Computational prediction of protein hot spot residues.

    Science.gov (United States)

    Morrow, John Kenneth; Zhang, Shuxing

    2012-01-01

    Most biological processes involve multiple proteins interacting with each other. It has been recently discovered that certain residues in these protein-protein interactions, which are called hot spots, contribute more significantly to binding affinity than others. Hot spot residues have unique and diverse energetic properties that make them challenging yet important targets in the modulation of protein-protein complexes. Design of therapeutic agents that interact with hot spot residues has proven to be a valid methodology in disrupting unwanted protein-protein interactions. Using biological methods to determine which residues are hot spots can be costly and time consuming. Recent advances in computational approaches to predict hot spots have incorporated a myriad of features, and have shown increasing predictive successes. Here we review the state of knowledge around protein-protein interactions, hot spots, and give an overview of multiple in silico prediction techniques of hot spot residues.

  2. Influence of physical and chemical properties of HTSXT-FTIR samples on the quality of prediction models developed to determine absolute concentrations of total proteins, carbohydrates and triglycerides: a preliminary study on the determination of their absolute concentrations in fresh microalgal biomass.

    Science.gov (United States)

    Serrano León, Esteban; Coat, Rémy; Moutel, Benjamin; Pruvost, Jérémy; Legrand, Jack; Gonçalves, Olivier

    2014-11-01

    Absolute concentrations of total macromolecules (triglycerides, proteins and carbohydrates) in microorganisms can be rapidly measured by FTIR spectroscopy, but caution is needed to avoid non-specific experimental bias. Here, we assess the limits within which this approach can be used on model solutions of macromolecules of interest. We used the Bruker HTSXT-FTIR system. Our results show that the solid deposits obtained after the sampling procedure present physical and chemical properties that influence the quality of the absolute concentration prediction models (univariate and multivariate). The accuracy of the models was degraded by a factor of 2 or 3 outside the recommended concentration interval of 0.5-35 µg spot(-1). Change occurred notably in the sample hydrogen bond network, which could, however, be controlled using an internal probe (pseudohalide anion). We also demonstrate that for aqueous solutions, accurate prediction of total carbohydrate quantities (in glucose equivalent) could not be made unless a constant amount of protein was added to the model solution (BSA). The results of the prediction model for more complex solutions, here with two components: glucose and BSA, were very encouraging, suggesting that this FTIR approach could be used as a rapid quantification method for mixtures of molecules of interest, provided the limits of use of the HTSXT-FTIR method are precisely known and respected. This last finding opens the way to direct quantification of total molecules of interest in more complex matrices.

  3. Prediction of protein loop geometries in solution

    NARCIS (Netherlands)

    Rapp, Chaya S.; Strauss, Temima; Nederveen, Aart; Fuentes, Gloria

    2007-01-01

    The ability to determine the structure of a protein in solution is a critical tool for structural biology, as proteins in their native state are found in aqueous environments. Using a physical chemistry based prediction protocol, we demonstrate the ability to reproduce protein loop geometries in

  4. Information assessment on predicting protein-protein interactions

    Directory of Open Access Journals (Sweden)

    Gerstein Mark

    2004-10-01

    Full Text Available Abstract Background Identifying protein-protein interactions is fundamental for understanding the molecular machinery of the cell. Proteome-wide studies of protein-protein interactions are of significant value, but the high-throughput experimental technologies suffer from high rates of both false positive and false negative predictions. In addition to high-throughput experimental data, many diverse types of genomic data can help predict protein-protein interactions, such as mRNA expression, localization, essentiality, and functional annotation. Evaluations of the information contributions from different evidences help to establish more parsimonious models with comparable or better prediction accuracy, and to obtain biological insights of the relationships between protein-protein interactions and other genomic information. Results Our assessment is based on the genomic features used in a Bayesian network approach to predict protein-protein interactions genome-wide in yeast. In the special case, when one does not have any missing information about any of the features, our analysis shows that there is a larger information contribution from the functional-classification than from expression correlations or essentiality. We also show that in this case alternative models, such as logistic regression and random forest, may be more effective than Bayesian networks for predicting interactions. Conclusions In the restricted problem posed by the complete-information subset, we identified that the MIPS and Gene Ontology (GO functional similarity datasets as the dominating information contributors for predicting the protein-protein interactions under the framework proposed by Jansen et al. Random forests based on the MIPS and GO information alone can give highly accurate classifications. In this particular subset of complete information, adding other genomic data does little for improving predictions. We also found that the data discretizations used in the

  5. The effects of dietary energy and protein concentrations on ostrich ...

    African Journals Online (AJOL)

    The effects were investigated of energy and protein concentrations (with associated amino acid concentrations) in ostrich diets on leather quality of the skins of 50 ostriches. Energy concentrations were 9.0, 10.5 and 12.0 MJ ME/kg diet and protein concentrations were 130, 150 and 170 g/kg diet. The physical leather ...

  6. Prediction of Protein Configurational Entropy (Popcoen).

    Science.gov (United States)

    Goethe, Martin; Gleixner, Jan; Fita, Ignacio; Rubi, J Miguel

    2018-03-13

    A knowledge-based method for configurational entropy prediction of proteins is presented; this methodology is extremely fast, compared to previous approaches, because it does not involve any type of configurational sampling. Instead, the configurational entropy of a query fold is estimated by evaluating an artificial neural network, which was trained on molecular-dynamics simulations of ∼1000 proteins. The predicted entropy can be incorporated into a large class of protein software based on cost-function minimization/evaluation, in which configurational entropy is currently neglected for performance reasons. Software of this type is used for all major protein tasks such as structure predictions, proteins design, NMR and X-ray refinement, docking, and mutation effect predictions. Integrating the predicted entropy can yield a significant accuracy increase as we show exemplarily for native-state identification with the prominent protein software FoldX. The method has been termed Popcoen for Prediction of Protein Configurational Entropy. An implementation is freely available at http://fmc.ub.edu/popcoen/ .

  7. Refractometric total protein concentrations in icteric serum from dogs.

    Science.gov (United States)

    Gupta, Aradhana; Stockham, Steven L

    2014-01-01

    To determine whether high serum bilirubin concentrations interfere with the measurement of serum total protein concentration by refractometry and to assess potential biases among refractometer measurements. Evaluation study. Sera from 2 healthy Greyhounds. Bilirubin was dissolved in 0.1M NaOH, and the resulting solution was mixed with sera from 2 dogs from which food had been withheld to achieve various bilirubin concentrations up to 40 mg/dL. Refractometric total protein concentrations were estimated with 3 clinical refractometers. A biochemical analyzer was used to measure biuret assay-based total protein and bilirubin concentrations with spectrophotometric assays. No interference with refractometric measurement of total protein concentrations was detected with bilirubin concentrations up to 41.5 mg/dL. Biases in refractometric total protein concentrations were detected and were related to the conversion of refractive index values to total protein concentrations. Hyperbilirubinemia did not interfere with the refractometric estimation of serum total protein concentration. The agreement among total protein concentrations estimated by 3 refractometers was dependent on the method of conversion of refractive index to total protein concentration and was independent of hyperbilirubinemia.

  8. Algorithms for Protein Structure Prediction

    DEFF Research Database (Denmark)

    Paluszewski, Martin

    -trace. Here we present three different approaches for reconstruction of C-traces from predictable measures. In our first approach [63, 62], the C-trace is positioned on a lattice and a tabu-search algorithm is applied to find minimum energy structures. The energy function is based on half-sphere-exposure (HSE......) is more robust than standard Monte Carlo search. In the second approach for reconstruction of C-traces, an exact branch and bound algorithm has been developed [67, 65]. The model is discrete and makes use of secondary structure predictions, HSE, CN and radius of gyration. We show how to compute good lower...... bounds for partial structures very fast. Using these lower bounds, we are able to find global minimum structures in a huge conformational space in reasonable time. We show that many of these global minimum structures are of good quality compared to the native structure. Our branch and bound algorithm...

  9. Protein secondary structure: category assignment and predictability

    DEFF Research Database (Denmark)

    Andersen, Claus A.; Bohr, Henrik; Brunak, Søren

    2001-01-01

    In the last decade, the prediction of protein secondary structure has been optimized using essentially one and the same assignment scheme known as DSSP. We present here a different scheme, which is more predictable. This scheme predicts directly the hydrogen bonds, which stabilize the secondary......-forward neural network with one hidden layer on a data set identical to the one used in earlier work....

  10. PREDICTION OF STRESS CONCENTRATION FACTORS IN

    African Journals Online (AJOL)

    ES OBE

    consider the effect of brace spacing on strengths of tubular K joints without consideration of same effect on square section K Joints. This lack of studies design strengths of gapped square section K joints makes availability of information on stress concentration factors in same joints scarce. However, information on 'Hot Spot' ...

  11. Production of protein concentrate and isolate from cashew ...

    African Journals Online (AJOL)

    The protein isolates were obtained by an alkaline extraction-isoelectric precipitation method, which involved aqueous alkaline extraction of the proteins at low temperature, and isoelectric precipitation of the protein fractions; the protein concentrates were obtained using an alkaline extraction-methanol precipitation method, ...

  12. Nutritional and functional properties of whey proteins concentrate and isolate

    Directory of Open Access Journals (Sweden)

    Zoran Herceg

    2006-12-01

    Full Text Available Whey protein fractions represent 18 - 20 % of total milk nitrogen content. Nutritional value in addition to diverse physico - chemical and functional properties make whey proteins highly suitable for application in foodstuffs. In the most cases, whey proteins are used because of their functional properties. Whey proteins possess favourable functional characteristics such as gelling, water binding, emulsification and foaming ability. Due to application of new process techniques (membrane fractionation techniques, it is possible to produce various whey - protein based products. The most important products based on the whey proteins are whey protein concentrates (WPC and whey protein isolates (WPI. The aim of this paper was to give comprehensive review of nutritional and functional properties of the most common used whey proteins (whey protein concentrate - WPC and whey protein isolate - WPI in the food industry.

  13. Bioinformatic Prediction of WSSV-Host Protein-Protein Interaction

    Directory of Open Access Journals (Sweden)

    Zheng Sun

    2014-01-01

    Full Text Available WSSV is one of the most dangerous pathogens in shrimp aquaculture. However, the molecular mechanism of how WSSV interacts with shrimp is still not very clear. In the present study, bioinformatic approaches were used to predict interactions between proteins from WSSV and shrimp. The genome data of WSSV (NC_003225.1 and the constructed transcriptome data of F. chinensis were used to screen potentially interacting proteins by searching in protein interaction databases, including STRING, Reactome, and DIP. Forty-four pairs of proteins were suggested to have interactions between WSSV and the shrimp. Gene ontology analysis revealed that 6 pairs of these interacting proteins were classified into “extracellular region” or “receptor complex” GO-terms. KEGG pathway analysis showed that they were involved in the “ECM-receptor interaction pathway.” In the 6 pairs of interacting proteins, an envelope protein called “collagen-like protein” (WSSV-CLP encoded by an early virus gene “wsv001” in WSSV interacted with 6 deduced proteins from the shrimp, including three integrin alpha (ITGA, two integrin beta (ITGB, and one syndecan (SDC. Sequence analysis on WSSV-CLP, ITGA, ITGB, and SDC revealed that they possessed the sequence features for protein-protein interactions. This study might provide new insights into the interaction mechanisms between WSSV and shrimp.

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

    Science.gov (United States)

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

    2013-01-01

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

  15. Urine protein concentration estimation for biomarker discovery

    OpenAIRE

    Mistry, Hiten D.; Bramham, Kate; Weston, Andrew; Ward, Malcolm; Thompson, Andrew; Chappell, Lucy C.

    2013-01-01

    Recent advances have been made in the study of urinary proteomics as a diagnostic tool for renal disease and pre-eclampsia which requires accurate measurement of urinary protein. We compared different protein assays (Bicinchoninic acid (BCA), Lowry and Bradford) against the ‘gold standard’ amino-acid assay in urine from 43 women (8 non-pregnant, 34 pregnant, including 8 with pre-eclampsia. BCA assay was superior to both Lowry and Bradford assays (Bland Altman bias: 0.08) compared to amino-aci...

  16. A Kernel for Protein Secondary Structure Prediction

    OpenAIRE

    Guermeur , Yann; Lifchitz , Alain; Vert , Régis

    2004-01-01

    http://mitpress.mit.edu/catalog/item/default.asp?ttype=2&tid=10338&mode=toc; International audience; Multi-class support vector machines have already proved efficient in protein secondary structure prediction as ensemble methods, to combine the outputs of sets of classifiers based on different principles. In this chapter, their implementation as basic prediction methods, processing the primary structure or the profile of multiple alignments, is investigated. A kernel devoted to the task is in...

  17. Mapping monomeric threading to protein-protein structure prediction.

    Science.gov (United States)

    Guerler, Aysam; Govindarajoo, Brandon; Zhang, Yang

    2013-03-25

    The key step of template-based protein-protein structure prediction is the recognition of complexes from experimental structure libraries that have similar quaternary fold. Maintaining two monomer and dimer structure libraries is however laborious, and inappropriate library construction can degrade template recognition coverage. We propose a novel strategy SPRING to identify complexes by mapping monomeric threading alignments to protein-protein interactions based on the original oligomer entries in the PDB, which does not rely on library construction and increases the efficiency and quality of complex template recognitions. SPRING is tested on 1838 nonhomologous protein complexes which can recognize correct quaternary template structures with a TM score >0.5 in 1115 cases after excluding homologous proteins. The average TM score of the first model is 60% and 17% higher than that by HHsearch and COTH, respectively, while the number of targets with an interface RMSD benchmark proteins. Although the relative performance of SPRING and ZDOCK depends on the level of homology filters, a combination of the two methods can result in a significantly higher model quality than ZDOCK at all homology thresholds. These data demonstrate a new efficient approach to quaternary structure recognition that is ready to use for genome-scale modeling of protein-protein interactions due to the high speed and accuracy.

  18. 21 CFR 172.385 - Whole fish protein concentrate.

    Science.gov (United States)

    2010-04-01

    ... CONSUMPTION Special Dietary and Nutritional Additives § 172.385 Whole fish protein concentrate. The food additive whole fish protein concentrate may be safely used as a food supplement in accordance with the... fish that are used in other forms for human food. (b) The additive consists essentially of a dried fish...

  19. Total protein and cholesterol concentrations in brain regions of male ...

    African Journals Online (AJOL)

    The results showed similarities (P>0.05) between the treatments in total protein concentrations in the cerebral cortex, medulla, hypothalamus, amygdala, mesencephalon and hippocampus. Total protein concentrations however differed significantly between diets (P<0.05) in the cerebellum and pons varoli with the lowest ...

  20. Predicting Secretory Proteins with SignalP

    DEFF Research Database (Denmark)

    Nielsen, Henrik

    2017-01-01

    SignalP is the currently most widely used program for prediction of signal peptides from amino acid sequences. Proteins with signal peptides are targeted to the secretory pathway, but are not necessarily secreted. After a brief introduction to the biology of signal peptides and the history...

  1. Radionuclides and selected trace elements in marine protein concentrates

    Energy Technology Data Exchange (ETDEWEB)

    Beasley, T M; Jokela, T A; Eagle, R J

    1971-12-01

    The concentrations of various trace elements and radionuclides have been measured in marine protein concentrates prepared from surface feeding fishes. As with concentrates prepared from benthic fishes, the /sup 210/Pb-/sup 210/Po pair are the most significant radionuclides present. Concentrations of stable Pb, Co and Ag in certain concentrates are sufficiently high to contribute substantially to estimated current intakes of these elements.

  2. Computational prediction of protein-protein interactions in Leishmania predicted proteomes.

    Directory of Open Access Journals (Sweden)

    Antonio M Rezende

    Full Text Available The Trypanosomatids parasites Leishmania braziliensis, Leishmania major and Leishmania infantum are important human pathogens. Despite of years of study and genome availability, effective vaccine has not been developed yet, and the chemotherapy is highly toxic. Therefore, it is clear just interdisciplinary integrated studies will have success in trying to search new targets for developing of vaccines and drugs. An essential part of this rationale is related to protein-protein interaction network (PPI study which can provide a better understanding of complex protein interactions in biological system. Thus, we modeled PPIs for Trypanosomatids through computational methods using sequence comparison against public database of protein or domain interaction for interaction prediction (Interolog Mapping and developed a dedicated combined system score to address the predictions robustness. The confidence evaluation of network prediction approach was addressed using gold standard positive and negative datasets and the AUC value obtained was 0.94. As result, 39,420, 43,531 and 45,235 interactions were predicted for L. braziliensis, L. major and L. infantum respectively. For each predicted network the top 20 proteins were ranked by MCC topological index. In addition, information related with immunological potential, degree of protein sequence conservation among orthologs and degree of identity compared to proteins of potential parasite hosts was integrated. This information integration provides a better understanding and usefulness of the predicted networks that can be valuable to select new potential biological targets for drug and vaccine development. Network modularity which is a key when one is interested in destabilizing the PPIs for drug or vaccine purposes along with multiple alignments of the predicted PPIs were performed revealing patterns associated with protein turnover. In addition, around 50% of hypothetical protein present in the networks

  3. Influence of Bleaching on Flavor of 34% Whey Protein Concentrate and Residual Benzoic Acid Concentration in Dried Whey Proteins

    Science.gov (United States)

    Previous studies have shown that bleaching negatively affects the flavor of 70% whey protein concentrate (WPC70), but bleaching effects on lower-protein products have not been established. Benzoyl peroxide (BP), a whey bleaching agent, degrades to benzoic acid (BA) and may elevate BA concentrations...

  4. Predictive and comparative analysis of Ebolavirus proteins

    Science.gov (United States)

    Cong, Qian; Pei, Jimin; Grishin, Nick V

    2015-01-01

    Ebolavirus is the pathogen for Ebola Hemorrhagic Fever (EHF). This disease exhibits a high fatality rate and has recently reached a historically epidemic proportion in West Africa. Out of the 5 known Ebolavirus species, only Reston ebolavirus has lost human pathogenicity, while retaining the ability to cause EHF in long-tailed macaque. Significant efforts have been spent to determine the three-dimensional (3D) structures of Ebolavirus proteins, to study their interaction with host proteins, and to identify the functional motifs in these viral proteins. Here, in light of these experimental results, we apply computational analysis to predict the 3D structures and functional sites for Ebolavirus protein domains with unknown structure, including a zinc-finger domain of VP30, the RNA-dependent RNA polymerase catalytic domain and a methyltransferase domain of protein L. In addition, we compare sequences of proteins that interact with Ebolavirus proteins from RESTV-resistant primates with those from RESTV-susceptible monkeys. The host proteins that interact with GP and VP35 show an elevated level of sequence divergence between the RESTV-resistant and RESTV-susceptible species, suggesting that they may be responsible for host specificity. Meanwhile, we detect variable positions in protein sequences that are likely associated with the loss of human pathogenicity in RESTV, map them onto the 3D structures and compare their positions to known functional sites. VP35 and VP30 are significantly enriched in these potential pathogenicity determinants and the clustering of such positions on the surfaces of VP35 and GP suggests possible uncharacterized interaction sites with host proteins that contribute to the virulence of Ebolavirus. PMID:26158395

  5. Predictive and comparative analysis of Ebolavirus proteins.

    Science.gov (United States)

    Cong, Qian; Pei, Jimin; Grishin, Nick V

    2015-01-01

    Ebolavirus is the pathogen for Ebola Hemorrhagic Fever (EHF). This disease exhibits a high fatality rate and has recently reached a historically epidemic proportion in West Africa. Out of the 5 known Ebolavirus species, only Reston ebolavirus has lost human pathogenicity, while retaining the ability to cause EHF in long-tailed macaque. Significant efforts have been spent to determine the three-dimensional (3D) structures of Ebolavirus proteins, to study their interaction with host proteins, and to identify the functional motifs in these viral proteins. Here, in light of these experimental results, we apply computational analysis to predict the 3D structures and functional sites for Ebolavirus protein domains with unknown structure, including a zinc-finger domain of VP30, the RNA-dependent RNA polymerase catalytic domain and a methyltransferase domain of protein L. In addition, we compare sequences of proteins that interact with Ebolavirus proteins from RESTV-resistant primates with those from RESTV-susceptible monkeys. The host proteins that interact with GP and VP35 show an elevated level of sequence divergence between the RESTV-resistant and RESTV-susceptible species, suggesting that they may be responsible for host specificity. Meanwhile, we detect variable positions in protein sequences that are likely associated with the loss of human pathogenicity in RESTV, map them onto the 3D structures and compare their positions to known functional sites. VP35 and VP30 are significantly enriched in these potential pathogenicity determinants and the clustering of such positions on the surfaces of VP35 and GP suggests possible uncharacterized interaction sites with host proteins that contribute to the virulence of Ebolavirus.

  6. Quantification of protein concentration using UV absorbance and Coomassie dyes.

    Science.gov (United States)

    Noble, James E

    2014-01-01

    The measurement of a solubilized protein concentration in solution is an important assay in biochemistry research and development labs for applications ranging from enzymatic studies to providing data for biopharmaceutical lot release. Spectrophotometric protein quantification assays are methods that use UV and visible spectroscopy to rapidly determine the concentration of protein, relative to a standard, or using an assigned extinction coefficient. Where multiple samples need measurement, and/or the sample volume and concentration is limited, preparations of the Coomassie dye commonly known as the Bradford assay can be used. © 2014 Elsevier Inc. All rights reserved.

  7. Predicting Protein Secondary Structure with Markov Models

    DEFF Research Database (Denmark)

    Fischer, Paul; Larsen, Simon; Thomsen, Claus

    2004-01-01

    we are considering here, is to predict the secondary structure from the primary one. To this end we train a Markov model on training data and then use it to classify parts of unknown protein sequences as sheets, helices or coils. We show how to exploit the directional information contained...... in the Markov model for this task. Classifications that are purely based on statistical models might not always be biologically meaningful. We present combinatorial methods to incorporate biological background knowledge to enhance the prediction performance....

  8. Protein complex prediction in large ontology attributed protein-protein interaction networks.

    Science.gov (United States)

    Zhang, Yijia; Lin, Hongfei; Yang, Zhihao; Wang, Jian; Li, Yanpeng; Xu, Bo

    2013-01-01

    Protein complexes are important for unraveling the secrets of cellular organization and function. Many computational approaches have been developed to predict protein complexes in protein-protein interaction (PPI) networks. However, most existing approaches focus mainly on the topological structure of PPI networks, and largely ignore the gene ontology (GO) annotation information. In this paper, we constructed ontology attributed PPI networks with PPI data and GO resource. After constructing ontology attributed networks, we proposed a novel approach called CSO (clustering based on network structure and ontology attribute similarity). Structural information and GO attribute information are complementary in ontology attributed networks. CSO can effectively take advantage of the correlation between frequent GO annotation sets and the dense subgraph for protein complex prediction. Our proposed CSO approach was applied to four different yeast PPI data sets and predicted many well-known protein complexes. The experimental results showed that CSO was valuable in predicting protein complexes and achieved state-of-the-art performance.

  9. Inactive Doses and Protein Concentration of Gamma Irradiated Yersinia Enterocolitica

    International Nuclear Information System (INIS)

    Irawan Sugoro; Sandra Hermanto

    2009-01-01

    Yersinia enterocolitica is one of bacteria which cause coliform mastitis in dairy cows. The bacteria could be inactivated by gamma irradiation as inactivated vaccine candidate. The experiment has been conducted to determine the inactive doses and the protein concentration of Yersinia enterocolitica Y3 which has been irradiated by gamma rays. The cells cultures were irradiated by gamma rays with doses of 0, 100, 200, 400, 600, 800, 1.000 and 1.500 Gy (doses rate was 1089,59 Gy/hours). The inactive dose was determined by the drop test method and the protein concentration of cells were determined by Lowry method. The results showed that the inactive doses occurred on 800 – 1500 Gy. The different irradiation doses of cell cultures showed the effect of gamma irradiation on the protein concentration that was random and has a significant effect on the protein concentration. (author)

  10. Effect of Limited Hydrolysis on Traditional Soy Protein Concentrate

    Directory of Open Access Journals (Sweden)

    Mirjana B. Pesic

    2006-09-01

    Full Text Available The influence of limited proteolysis of soy protein concentrate on proteinextractability, the composition of the extractable proteins, their emulsifying properties andsome nutritional properties were investigated. Traditional concentrate (alcohol leachedconcentrate was hydrolyzed using trypsin and pepsin as hydrolytic agents. Significantdifferences in extractable protein composition between traditional concentrate and theirhydrolysates were observed by polyacrylamide gel electrophoresis (PAGE and by SDSPAGE.All hydrolysates showed better extractability than the original protein concentrate,whereas significantly better emulsifying properties were noticed at modified concentratesobtained by trypsin induced hydrolysis. These improved properties are the result of twosimultaneous processes, dissociation and degradation of insoluble alcohol-induced proteinaggregates. Enzyme induced hydrolysis had no influence on trypsin-inibitor activity, andsignificantly reduced phytic acid content.

  11. Behavior of whey protein concentrates under extreme storage conditions

    Science.gov (United States)

    The overseas demand for whey protein concentrates (WPC) has increased steadily in recent years. Emergency aid foods often include WPC, but shelf-life studies of whey proteins under different shipment and storage conditions have not been conducted in the last 50 yr. Microbial quality, compound form...

  12. Nutritive evaluation of Telfairia occidentalis leaf protein concentrate ...

    African Journals Online (AJOL)

    Leaf meal (LM), leaf proteins concentrate (LPC) and LPC residues from Telfairia occidentalis were produced, chemically characterized and the protein quality of the LPC evaluated using rats. Five infant weaning foods were formulated using varying combinations of T. occidentalis LPC and soybean meal. These foods were ...

  13. CHEMICAL COMPOSITION AND FUNCTIONAL PROPERTIES OF RICE PROTEIN CONCENTRATES

    Directory of Open Access Journals (Sweden)

    V. V. Kolpakova

    2015-01-01

    Full Text Available Traditionally rice and products of its processing are used to cook porridge, pilaf, lettuce, confectionery, fish, dairy and meat products. At the same time new ways of its processing with releasing of protein products for more effective using, including the use of a glutenfree diet, are developing. The task of this study was a comparative research of nutrition and biological value and functional properties of protein and protein-calcium concentrates produced from rice flour milled from white and brown rice. The traditional and special methods were used. Concentrates were isolated with enzyme preparations of xylanase and amylolytic activity with the next dissolution of protein in diluted hydrochloric acid. Concentrates differed in the content of mineral substances (calcium, zinc, iron and other elements, amino acids and functional properties. The values of the functional properties and indicators of the nutritional value of concentrates from white rice show the advisability of their using in food products, including gluten-free products prepared on the basis of the emulsion and foam systems, and concentrates from brown rice in food products prepared on the basis of using of the emulsion systems. Protein concentrates of brown rice have a low foaming capacity and there is no foam stability at all.

  14. Comparison of observed and predicted Kr-85 air concentrations

    International Nuclear Information System (INIS)

    Yildiran, M.; Miller, C.W.

    1984-01-01

    A computer code, ANEMOS has been written to estimate concentrations in air and ground deposition rates for Atmospheric Nuclides Emitted from Multiple Operation Sources. This code uses a modified Gaussian plume equation. Output from ANEMOS includes annual-average air concentrations and ground deposition rates of dispersed radionuclides and daughters. To use the environmental transport model properly, some estimate of the models predictive accuracy must be obtained. To validate the ANEMOS model, one year of weekly average Kr-85 concentrations observed at 13 stations located 28 to 144 km distant from continuous point source at the Savannah River Plant have been used. There was a general tendency for the model to underpredict the observed air concentrations slightly. Pearson's correlation between pairs of logarithms of observed and predicted annual-average values was r=0.84. The monthly results tend to show more scatter than do either the seasonal or the annual comparisons. (orig.)

  15. Comparison of observed and predicted Kr-85 air concentrations

    International Nuclear Information System (INIS)

    Yildiran, M.; Miller, C.W.

    1984-01-01

    A computer code, ANEMOS has been written to estimate concentrations in air and ground deposition rates for Atmospheric Nuclides Emitted from Multiple Operation Sources. This code uses a modified Gaussian plum equation. Output from ANEMOS includes annual-average air concentrations and ground deposition rates of dispersed radionuclides and daughters. To use the environmental transport model properly, some estimate of the models predictive accuracy must be obtained. To validate the ANEMOS model, one year of weekly average Kr-85 concentrations observed at 13 stations located 28 to 144 km distant from continuous point source at the Savannah River Plant (SRP), Aiken, South Carolina, have been used. There was a general tendency for the model to underpredict the observed air concentrations slightly. Pearsons's correlation between pairs of logarithms of observed and predicted annual-average values was r = 0.84. The monthly results tend to show more scatter than do either the seasonal or the annual comparisons. 18 references, 3 figures, 3 tables

  16. Predicting radon/radon daughter concentrations in underground mines

    International Nuclear Information System (INIS)

    Leach, V.A.

    1984-01-01

    A detailed description of a computer programme is outlined for the calculation of radon/radon daughter concentrations in air. This computer model is used to predict the radon/radon daughter concentrations in Working Level (WL) at the workplace and at the various junctions at either end of the branches in a typical ventilation network proposed for the Jabiluka mine in the Northern Territory

  17. Development of system on predicting uranium concentration from pregnant solution

    International Nuclear Information System (INIS)

    Yi Weiping

    2004-01-01

    Uranium concentration from pregnant solution is primary index of process for in-situ leaching of uranium, and the suitable method with which to predicate this index and effective means to solve it with were continuously studied hard. SPUC-system on predicting uranium concentration based on GM model of gray system theory is developed, and the mathematical model, constitution, function and theory foundation of this system are introduced. (authors)

  18. Protein complex prediction based on k-connected subgraphs in protein interaction network

    OpenAIRE

    Habibi, Mahnaz; Eslahchi, Changiz; Wong, Limsoon

    2010-01-01

    Abstract Background Protein complexes play an important role in cellular mechanisms. Recently, several methods have been presented to predict protein complexes in a protein interaction network. In these methods, a protein complex is predicted as a dense subgraph of protein interactions. However, interactions data are incomplete and a protein complex does not have to be a complete or dense subgraph. Results We propose a more appropriate protein complex prediction method, CFA, that is based on ...

  19. Use of refractometry for determination of psittacine plasma protein concentration.

    Science.gov (United States)

    Cray, Carolyn; Rodriguez, Marilyn; Arheart, Kristopher L

    2008-12-01

    Previous studies have demonstrated both poor and good correlation of total protein concentrations in various avian species using refractometry and biuret methodologies. The purpose of the current study was to compare these 2 techniques of total protein determination using plasma samples from several psittacine species and to determine the effect of cholesterol and other solutes on refractometry results. Total protein concentration in heparinized plasma samples without visible lipemia was analyzed by refractometry and an automated biuret method on a dry reagent analyzer (Ortho 250). Cholesterol, glucose, and uric acid concentrations were measured using the same analyzer. Results were compared using Deming regression analysis, Bland-Altman bias plots, and Spearman's rank correlation. Correlation coefficients (r) for total protein results by refractometry and biuret methods were 0.49 in African grey parrots (n=28), 0.77 in Amazon parrots (20), 0.57 in cockatiels (20), 0.73 in cockatoos (36), 0.86 in conures (20), and 0.93 in macaws (38) (Prefractometry in Amazon parrots, conures, and macaws (n=25 each, PRefractometry can be used to accurately measure total protein concentration in nonlipemic plasma samples from some psittacine species. Method and species-specific reference intervals should be used in the interpretation of total protein values.

  20. Validation of predicted exponential concentration profiles of chemicals in soils

    International Nuclear Information System (INIS)

    Hollander, Anne; Baijens, Iris; Ragas, Ad; Huijbregts, Mark; Meent, Dik van de

    2007-01-01

    Multimedia mass balance models assume well-mixed homogeneous compartments. Particularly for soils, this does not correspond to reality, which results in potentially large uncertainties in estimates of transport fluxes from soils. A theoretically expected exponential decrease model of chemical concentrations with depth has been proposed, but hardly tested against empirical data. In this paper, we explored the correspondence between theoretically predicted soil concentration profiles and 84 field measured profiles. In most cases, chemical concentrations in soils appear to decline exponentially with depth, and values for the chemical specific soil penetration depth (d p ) are predicted within one order of magnitude. Over all, the reliability of multimedia models will improve when they account for depth-dependent soil concentrations, so we recommend to take into account the described theoretical exponential decrease model of chemical concentrations with depth in chemical fate studies. In this model the d p -values should estimated be either based on local conditions or on a fixed d p -value, which we recommend to be 10 cm for chemicals with a log K ow > 3. - Multimedia mass model predictions will improve when taking into account depth dependent soil concentrations

  1. Comparison of predicted and measured variations of indoor radon concentration

    International Nuclear Information System (INIS)

    Arvela, H.; Voutilainen, A.; Maekelaeinen, I.; Castren, O.; Winqvist, K.

    1988-01-01

    Prediction of the variations of indoor radon concentration were calculated using a model relating indoor radon concentration to radon entry rate, air infiltration and meteorological factors. These calculated variations have been compared with seasonal variations of 33 houses during 1-4 years, with winter-summer concentration ratios of 300 houses and the measured diurnal variation. In houses with a slab in ground contact the measured seasonal variations are quite often in agreement with variations predicted for nearly pure pressure difference driven flow. The contribution of a diffusion source is significant in houses with large porous concrete walls against the ground. Air flow due to seasonally variable thermal convection within eskers strongly affects the seasonal variations within houses located thereon. Measured and predicted winter-summer concentration ratios demonstrate that, on average, the ratio is a function of radon concentration. The ratio increases with increasing winter concentration. According to the model the diurnal maximum caused by a pressure difference driven flow occurs in the morning, a finding which is in agreement with the measurements. The model presented can be used for differentiating between factors affecting radon entry into houses. (author)

  2. Blood harmane concentrations and dietary protein consumption in essential tremor.

    Science.gov (United States)

    Louis, E D; Zheng, W; Applegate, L; Shi, L; Factor-Litvak, P

    2005-08-09

    Beta-carboline alkaloids (e.g., harmane) are highly tremorogenic chemicals. Animal protein (meat) is the major dietary source of these alkaloids. The authors previously demonstrated that blood harmane concentrations were elevated in patients with essential tremor (ET) vs controls. Whether this difference is due to greater animal protein consumption by patients or their failure to metabolize harmane is unknown. The aim of this study was to determine whether patients with ET and controls differ with regard to 1) daily animal protein consumption and 2) the correlation between animal protein consumption and blood harmane concentration. Data on current diet were collected with a semiquantitative food frequency questionnaire and daily calories and consumption of animal protein and other food types was calculated. Blood harmane concentrations were log-transformed (logHA). The mean logHA was higher in 106 patients than 161 controls (0.61 +/- 0.67 vs 0.43 +/- 0.72 g(-10)/mL, p = 0.035). Patients and controls consumed similar amounts of animal protein (50.2 +/- 19.6 vs 49.4 +/- 19.1 g/day, p = 0.74) and other food types (animal fat, carbohydrates, vegetable fat) and had similar caloric intakes. In controls, logHA was correlated with daily consumption of animal protein (r = 0.24, p = 0.003); in patients, there was no such correlation (r = -0.003, p = 0.98). The similarity between patients and controls in daily animal protein consumption and the absence of the normal correlation between daily animal protein consumption and logHA in patients suggests that another factor (e.g., a metabolic defect) may be increasing blood harmane concentration in patients.

  3. Barrier, mechanical and optical properties of whey protein concentrate films

    Directory of Open Access Journals (Sweden)

    Viviane Machado Azevedo

    2014-08-01

    Full Text Available Whey is recognized as a valuable source of high quality protein and, when processed as protein concentrate, may be used in the production of biodegradable films. The objective of the study was to develop films of whey protein concentrate 80% (WPC at concentrations of 6, 8, 10 and 12% and evaluate the influence of this factor in the barrier, mechanical and optical properties of the films. Treatments showed moisture content with a mean value of 22.10% ± 0.76and high solubility values between 56.67 to 62.42%. Thus, there is little or no influence of varying the concentration of WPC in these properties and high hydrophilicity of the films. With increasing concentration of WPC, increases the water vapor permeability of the films (7.42 x 10-13 to 3.49 x 10-12 g.m-1.s-1.Pa-1. The treatment at the concentration of 6% of WPC showed a higher modulus of elasticity (287.90 ± 41.79 MPa. Thegreater rigidity in films with higher concentrations is possibly due to the greater number of bonds between molecules of the polymeric matrix. The films have the same puncture resistance. The increased concentration of WPC promotes resistance to the action of a localized force. In general, films of whey protein concentrate in the tested concentrations exhibited slightly yellowish color and transparency, and can be used in food packaging that requiring intermediate permeability to water vapor, to keep moisture and texture desired.

  4. Romanian plant produces protein concentrate from paraffin-nourished yeasts

    Energy Technology Data Exchange (ETDEWEB)

    1985-01-01

    One of the world's few factories in which proteins are produced by continuous biotechnology is located in Romania. Here, at the bioproteins plant, microorganisms are converted into a flour which contains a protein concentrate that is so essential to the fattening of swine, cattle, sheep, fowl, and fish. These microorganisms are Candida type yeasts. The culture medium in which they are grown contains sulfates and phosphates. Paraffin, a petroleum product, supplies the carbon that is essential to the microorganisms viability.

  5. Pea protein concentrate as a substitute for fish meal protein in sea bass diet

    Directory of Open Access Journals (Sweden)

    E. Badini

    2010-01-01

    Full Text Available Pea seeds, even if lower in protein than oilseed meals, have been shown to successfully replace moderate amounts of fish meal protein in diets for carnivorous fish species (Kaushik et al., 1993, Gouveia and Davies, 2000. A further processing of such pulses provides concentrated protein products which look very promising as fish meal substitutes in aquafeeds (Thiessen et al., 2003. The aim of the present study was to evaluate nutrient digestibility, growth response, nutrient and energy retention efficiencies and whole body composition of sea bass (Dicentrarchus labrax, L. fed complete diets in which a pea protein concentrate (PPC was used to replace graded levels of fish meal protein.

  6. Prediction of toxic metals concentration using artificial intelligence techniques

    Science.gov (United States)

    Gholami, R.; Kamkar-Rouhani, A.; Doulati Ardejani, F.; Maleki, Sh.

    2011-12-01

    Groundwater and soil pollution are noted to be the worst environmental problem related to the mining industry because of the pyrite oxidation, and hence acid mine drainage generation, release and transport of the toxic metals. The aim of this paper is to predict the concentration of Ni and Fe using a robust algorithm named support vector machine (SVM). Comparison of the obtained results of SVM with those of the back-propagation neural network (BPNN) indicates that the SVM can be regarded as a proper algorithm for the prediction of toxic metals concentration due to its relative high correlation coefficient and the associated running time. As a matter of fact, the SVM method has provided a better prediction of the toxic metals Fe and Ni and resulted the running time faster compared with that of the BPNN.

  7. Flow-covariate prediction of stream pesticide concentrations.

    Science.gov (United States)

    Mosquin, Paul L; Aldworth, Jeremy; Chen, Wenlin

    2018-01-01

    Potential peak functions (e.g., maximum rolling averages over a given duration) of annual pesticide concentrations in the aquatic environment are important exposure parameters (or target quantities) for ecological risk assessments. These target quantities require accurate concentration estimates on nonsampled days in a monitoring program. We examined stream flow as a covariate via universal kriging to improve predictions of maximum m-day (m = 1, 7, 14, 30, 60) rolling averages and the 95th percentiles of atrazine concentration in streams where data were collected every 7 or 14 d. The universal kriging predictions were evaluated against the target quantities calculated directly from the daily (or near daily) measured atrazine concentration at 32 sites (89 site-yr) as part of the Atrazine Ecological Monitoring Program in the US corn belt region (2008-2013) and 4 sites (62 site-yr) in Ohio by the National Center for Water Quality Research (1993-2008). Because stream flow data are strongly skewed to the right, 3 transformations of the flow covariate were considered: log transformation, short-term flow anomaly, and normalized Box-Cox transformation. The normalized Box-Cox transformation resulted in predictions of the target quantities that were comparable to those obtained from log-linear interpolation (i.e., linear interpolation on the log scale) for 7-d sampling. However, the predictions appeared to be negatively affected by variability in regression coefficient estimates across different sample realizations of the concentration time series. Therefore, revised models incorporating seasonal covariates and partially or fully constrained regression parameters were investigated, and they were found to provide much improved predictions in comparison with those from log-linear interpolation for all rolling average measures. Environ Toxicol Chem 2018;37:260-273. © 2017 SETAC. © 2017 SETAC.

  8. Different protein-protein interface patterns predicted by different machine learning methods.

    Science.gov (United States)

    Wang, Wei; Yang, Yongxiao; Yin, Jianxin; Gong, Xinqi

    2017-11-22

    Different types of protein-protein interactions make different protein-protein interface patterns. Different machine learning methods are suitable to deal with different types of data. Then, is it the same situation that different interface patterns are preferred for prediction by different machine learning methods? Here, four different machine learning methods were employed to predict protein-protein interface residue pairs on different interface patterns. The performances of the methods for different types of proteins are different, which suggest that different machine learning methods tend to predict different protein-protein interface patterns. We made use of ANOVA and variable selection to prove our result. Our proposed methods taking advantages of different single methods also got a good prediction result compared to single methods. In addition to the prediction of protein-protein interactions, this idea can be extended to other research areas such as protein structure prediction and design.

  9. Protein function prediction using neighbor relativity in protein-protein interaction network.

    Science.gov (United States)

    Moosavi, Sobhan; Rahgozar, Masoud; Rahimi, Amir

    2013-04-01

    There is a large gap between the number of discovered proteins and the number of functionally annotated ones. Due to the high cost of determining protein function by wet-lab research, function prediction has become a major task for computational biology and bioinformatics. Some researches utilize the proteins interaction information to predict function for un-annotated proteins. In this paper, we propose a novel approach called "Neighbor Relativity Coefficient" (NRC) based on interaction network topology which estimates the functional similarity between two proteins. NRC is calculated for each pair of proteins based on their graph-based features including distance, common neighbors and the number of paths between them. In order to ascribe function to an un-annotated protein, NRC estimates a weight for each neighbor to transfer its annotation to the unknown protein. Finally, the unknown protein will be annotated by the top score transferred functions. We also investigate the effect of using different coefficients for various types of functions. The proposed method has been evaluated on Saccharomyces cerevisiae and Homo sapiens interaction networks. The performance analysis demonstrates that NRC yields better results in comparison with previous protein function prediction approaches that utilize interaction network. Copyright © 2012 Elsevier Ltd. All rights reserved.

  10. Protein structure based prediction of catalytic residues.

    Science.gov (United States)

    Fajardo, J Eduardo; Fiser, Andras

    2013-02-22

    Worldwide structural genomics projects continue to release new protein structures at an unprecedented pace, so far nearly 6000, but only about 60% of these proteins have any sort of functional annotation. We explored a range of features that can be used for the prediction of functional residues given a known three-dimensional structure. These features include various centrality measures of nodes in graphs of interacting residues: closeness, betweenness and page-rank centrality. We also analyzed the distance of functional amino acids to the general center of mass (GCM) of the structure, relative solvent accessibility (RSA), and the use of relative entropy as a measure of sequence conservation. From the selected features, neural networks were trained to identify catalytic residues. We found that using distance to the GCM together with amino acid type provide a good discriminant function, when combined independently with sequence conservation. Using an independent test set of 29 annotated protein structures, the method returned 411 of the initial 9262 residues as the most likely to be involved in function. The output 411 residues contain 70 of the annotated 111 catalytic residues. This represents an approximately 14-fold enrichment of catalytic residues on the entire input set (corresponding to a sensitivity of 63% and a precision of 17%), a performance competitive with that of other state-of-the-art methods. We found that several of the graph based measures utilize the same underlying feature of protein structures, which can be simply and more effectively captured with the distance to GCM definition. This also has the added the advantage of simplicity and easy implementation. Meanwhile sequence conservation remains by far the most influential feature in identifying functional residues. We also found that due the rapid changes in size and composition of sequence databases, conservation calculations must be recalibrated for specific reference databases.

  11. Low plasma adiponectin concentrations do not predict weight gain in humans

    DEFF Research Database (Denmark)

    Vozarova, Barbora; Stefan, Norbert; Lindsay, Robert S

    2002-01-01

    Low concentrations of plasma adiponectin, the most abundant adipose-specific protein, are observed in obese individuals and predict the development of type 2 diabetes. Administration of adiponectin to rodents prevented diet-induced weight gain, suggesting a potential etiologic role of hypoadipone......Low concentrations of plasma adiponectin, the most abundant adipose-specific protein, are observed in obese individuals and predict the development of type 2 diabetes. Administration of adiponectin to rodents prevented diet-induced weight gain, suggesting a potential etiologic role...... of hypoadiponectinemia in the development of obesity. Our aim was to prospectively examine whether low plasma adiponectin concentrations predict future weight gain in Pima Indians, explaining the predictive effect of adiponectin on the development of type 2 diabetes. We measured plasma adiponectin concentrations in 219...... nondiabetic Pima Indians (112 M/107 F, age 31 +/- 9 years, body weight 96 +/- 20 kg [mean +/- SD]) in whom body weight and height were measured and BMI calculated at baseline and follow-up. Cross-sectionally, plasma adiponectin concentrations were negatively associated with body weight (r = -0.28, P = 0...

  12. Predictability Analysis of PM10 Concentrations in Budapest

    Science.gov (United States)

    Ferenczi, Zita

    2013-04-01

    Climate, weather and air quality may have harmful effects on human health and environment. Over the past few hundred years we had to face the changes in climate in parallel with the changes in air quality. These observed changes in climate, weather and air quality continuously interact with each other: pollutants are changing the climate, thus changing the weather, but climate also has impacts on air quality. The increasing number of extreme weather situations may be a result of climate change, which could create favourable conditions for rising of pollutant concentrations. Air quality in Budapest is determined by domestic and traffic emissions combined with the meteorological conditions. In some cases, the effect of long-range transport could also be essential. While the time variability of the industrial and traffic emissions is not significant, the domestic emissions increase in winter season. In recent years, PM10 episodes have caused the most critical air quality problems in Budapest, especially in winter. In Budapest, an air quality network of 11 stations detects the concentration values of different pollutants hourly. The Hungarian Meteorological Service has developed an air quality prediction model system for the area of Budapest. The system forecasts the concentration of air pollutants (PM10, NO2, SO2 and O3) for two days in advance. In this work we used meteorological parameters and PM10 data detected by the stations of the air quality network, as well as the forecasted PM10 values of the air quality prediction model system. In this work we present the evaluation of PM10 predictions in the last two years and the most important meteorological parameters affecting PM10 concentration. The results of this analysis determine the effect of the meteorological parameters and the emission of aerosol particles on the PM10 concentration values as well as the limits of this prediction system.

  13. Alpha complexes in protein structure prediction

    DEFF Research Database (Denmark)

    Winter, Pawel; Fonseca, Rasmus

    2015-01-01

    Reducing the computational effort and increasing the accuracy of potential energy functions is of utmost importance in modeling biological systems, for instance in protein structure prediction, docking or design. Evaluating interactions between nonbonded atoms is the bottleneck of such computations......-complexes from scratch for every configuration encountered during the search for the native structure would make this approach hopelessly slow. However, it is argued that kinetic a-complexes can be used to reduce the computational effort of determining the potential energy when "moving" from one configuration...... to a neighboring one. As a consequence, relatively expensive (initial) construction of an a-complex is expected to be compensated by subsequent fast kinetic updates during the search process. Computational results presented in this paper are limited. However, they suggest that the applicability of a...

  14. Effect of Crude Protein Levels in Concentrate and Concentrate Levels in Diet on Fermentation

    Directory of Open Access Journals (Sweden)

    Dinh Van Dung

    2014-06-01

    Full Text Available The effect of concentrate mixtures with crude protein (CP levels 10%, 13%, 16%, and 19% and diets with roughage to concentrate ratios 80:20, 60:40, 40:60, and 20:80 (w/w were determined on dry matter (DM and organic matter (OM digestibility, and fermentation metabolites using an in vitro fermentation technique. In vitro fermented attributes were measured after 4, 24, and 48 h of incubation respectively. The digestibility of DM and OM, and total volatile fatty acid (VFA increased whereas pH decreased with the increased amount of concentrate in the diet (p<0.001, however CP levels of concentrate did not have any influence on these attributes. Gas production reduced with increased CP levels, while it increased with increasing concentrate levels. Ammonia nitrogen (NH3-N concentration and microbial CP production increased significantly (p<0.05 by increasing CP levels and with increasing concentrate levels in diet as well, however, no significant difference was found between 16% and 19% CP levels. Therefore, 16% CP in concentrate and increasing proportion of concentrate up to 80% in diet all had improved digestibility of DM and organic matter, and higher microbial protein production, with improved fermentation characteristics.

  15. Whey protein concentration by ultrafiltration and study of functional properties

    Directory of Open Access Journals (Sweden)

    Sidiane Iltchenco

    2018-06-01

    Full Text Available ABSTRACT: This paper aim to evaluate the ultrafiltration (UF process for constituents recovery from whey. Sequences of factorial designs were performed by varying temperature (5 to 40°C and pressure (1 to 3 bar, to maximize the proteins concentration using membrane of 100kDa in dead end system. Based on the best result new experiments were performed with membrane of 50kDa and 10kDa. With the membrane of 50 the protein retention was about 3 times higher than the membrane of 100kDa. The concentrated obtained by UF membrane of 10kDa, 10°C and 2 bar in laboratory scale showed a mean protein retention of 80 %, greater protein solubility, emulsion stability and the identification of β-lactoglobulins (18.3 kDa and α-lactalbumin fractions (14.2kDa. Therefore, the use of membrane of 100 and 50kDa are became a industrially recommendable alternatives to concentration of whey proteins, and/or as a previous step to the fractionation of whey constituents using membrane ≤10kDa, aiming at future applications in different areas (food, pharmaceutical, chemical, etc..

  16. Whey protein concentrate storage at elevated temperature and humidity

    Science.gov (United States)

    Dairy processors are finding new export markets for whey protein concentrate (WPC), a byproduct of cheesemaking, but they need to know if full-sized bags of this powder will withstand high temperature and relative humidity (RH) levels during unrefrigerated storage under tropical conditions. To answ...

  17. Raapzaadeiwitconcentraat en erwteneiwitconcentraat in biologisch biggenvoer = Canola protein concentrate and pea protein concentratrate in diets for organically housed piglets

    NARCIS (Netherlands)

    Peet-Schwering, van der C.M.C.; Binnendijk, G.P.; Diepen, van J.T.M.

    2011-01-01

    At the Experimental Farm Raalte it was investigated whether canola protein concentrate and pea protein concentrate are suitable protein-rich feedstuffs for organically housed piglets. It is concluded that both protein concentrates are suitable protein-rich feedstuffs for piglets. Feed intake and

  18. Development of Indoor Air Pollution Concentration Prediction by Geospatial Analysis

    Directory of Open Access Journals (Sweden)

    Adyati Pradini Yudison

    2015-07-01

    Full Text Available People living near busy roads are potentially exposed to traffic-induced air pollutants. The pollutants may intrude into the indoor environment, causing health risks to the occupants. Prediction of pollutant exposure therefore is of great importance for impact assessment and policy making related to environmentally sustainable transport. This study involved the selection of spatial interpolation methods that can be used for prediction of indoor air quality based on outdoor pollutant mapping without indoor measurement data. The research was undertaken in the densely populated area of Karees, Bandung, Indonesia. The air pollutant NO2 was monitored in this area as a preliminary study. Nitrogen dioxide concentrations were measured by passive diffusion tube. Outdoor NO2 concentrations were measured at 94 locations, consisting of 30 roadside and 64 outdoor locations. Residential indoor NO2 concentrations were measured at 64 locations. To obtain a spatially continuous air quality map, the spatial interpolation methods of inverse distance weighting (IDW and Kriging were applied. Selection of interpolation method was done based on the smallest root mean square error (RMSE and standard deviation (SD. The most appropriate interpolation method for outdoor NO2 concentration mapping was Kriging with an SD value of 5.45 µg/m3 and an RMSE value of 5.45 µg/m3, while for indoor NO2 concentration mapping the IDW was best fitted with an RMSE value of 5.92 µg/m3 and an SD value of 5.92 µg/m3.

  19. HKC: An Algorithm to Predict Protein Complexes in Protein-Protein Interaction Networks

    Directory of Open Access Journals (Sweden)

    Xiaomin Wang

    2011-01-01

    Full Text Available With the availability of more and more genome-scale protein-protein interaction (PPI networks, research interests gradually shift to Systematic Analysis on these large data sets. A key topic is to predict protein complexes in PPI networks by identifying clusters that are densely connected within themselves but sparsely connected with the rest of the network. In this paper, we present a new topology-based algorithm, HKC, to detect protein complexes in genome-scale PPI networks. HKC mainly uses the concepts of highest k-core and cohesion to predict protein complexes by identifying overlapping clusters. The experiments on two data sets and two benchmarks show that our algorithm has relatively high F-measure and exhibits better performance compared with some other methods.

  20. Comparing predicted estrogen concentrations with measurements in US waters

    International Nuclear Information System (INIS)

    Kostich, Mitch; Flick, Robert; Martinson, John

    2013-01-01

    The range of exposure rates to the steroidal estrogens estrone (E1), beta-estradiol (E2), estriol (E3), and ethinyl estradiol (EE2) in the aquatic environment was investigated by modeling estrogen introduction via municipal wastewater from sewage plants across the US. Model predictions were compared to published measured concentrations. Predictions were congruent with most of the measurements, but a few measurements of E2 and EE2 exceed those that would be expected from the model, despite very conservative model assumptions of no degradation or in-stream dilution. Although some extreme measurements for EE2 may reflect analytical artifacts, remaining data suggest concentrations of E2 and EE2 may reach twice the 99th percentile predicted from the model. The model and bulk of the measurement data both suggest that cumulative exposure rates to humans are consistently low relative to effect levels, but also suggest that fish exposures to E1, E2, and EE2 sometimes substantially exceed chronic no-effect levels. -- Highlights: •Conservatively modeled steroidal estrogen concentrations in ambient water. •Found reasonable agreement between model and published measurements. •Model and measurements agree that risks to humans are remote. •Model and measurements agree significant questions remain about risk to fish. •Need better understanding of temporal variations and their impact on fish. -- Our model and published measurements for estrogens suggest aquatic exposure rates for humans are below potential effect levels, but fish exposure sometimes exceeds published no-effect levels

  1. Paracetamol (acetaminophen) protein adduct concentrations during therapeutic dosing.

    Science.gov (United States)

    Heard, Kennon; Green, Jody L; Anderson, Victoria; Bucher-Bartelson, Becki; Dart, Richard C

    2016-03-01

    Paracetamol protein adducts (PPA) are a biomarker of paracetamol exposure. PPA are quantified as paracetamol-cysteine (APAP-CYS), and concentrations above 1.1 μmol l(-1) have been suggested as a marker of paracetamol-induced hepatotoxicity. However, there is little information on the range of concentrations observed during prolonged therapeutic dosing. The aim of the present study was to describe the concentration of PPA in the serum of subjects taking therapeutic doses of paracetamol for at least 16 days. Preplanned secondary aim of a prospective randomized controlled (placebo vs. 4g day(-1) paracetamol) trial. We measured subjects' serum PPA concentrations every 3 days for a minimum of 16 days. We also measured concentrations on study days 1-3 and 16-25 in subsets of patients. PPA were quantified as APAP-CYS after gel filtration and protein digestion using liquid chromatography/mass spectrometry. Ninety per cent of subjects had detectable PPA after five doses. Median APAP-CYS concentrations in paracetamol-treated subjects increased to a plateau of 0.1 μmol l(-1) on day 7, where they remained. The highest concentration measured was 1.1 μmol l(-1) and two subjects never had detectable PPA levels. PPA were detected in the serum of 78% of subjects 9 days after their final dose. PPA are detectable in the vast majority of subjects taking therapeutic doses of paracetamol. While most have concentrations well below the threshold associated with hepatotoxicity, concentrations may approach 1.1 μmol l(-1) in rare cases. Adducts are detectable after a few doses and can persist for over a week after dosing is stopped. © 2015 The British Pharmacological Society.

  2. Diagnostic Accuracy of Urine Protein/Creatinine Ratio Is Influenced by Urine Concentration

    Science.gov (United States)

    Yang, Chih-Yu; Chen, Fu-An; Chen, Chun-Fan; Liu, Wen-Sheng; Shih, Chia-Jen; Ou, Shuo-Ming; Yang, Wu-Chang; Lin, Chih-Ching; Yang, An-Hang

    2015-01-01

    Background The usage of urine protein/creatinine ratio to estimate daily urine protein excretion is prevalent, but relatively little attention has been paid to the influence of urine concentration and its impact on test accuracy. We took advantage of 24-hour urine collection to examine both urine protein/creatinine ratio (UPCR) and daily urine protein excretion, with the latter as the reference standard. Specific gravity from a concomitant urinalysis of the same urine sample was used to indicate the urine concentration. Methods During 2010 to 2014, there were 540 adequately collected 24h urine samples with protein concentration, creatinine concentration, total volume, and a concomitant urinalysis of the same sample. Variables associated with an accurate UPCR estimation were determined by multivariate linear regression analysis. Receiver operating characteristic (ROC) curves were generated to determine the discriminant cut-off values of urine creatinine concentration for predicting an accurate UPCR estimation in either dilute or concentrated urine samples. Results Our findings indicated that for dilute urine, as indicated by a low urine specific gravity, UPCR is more likely to overestimate the actual daily urine protein excretion. On the contrary, UPCR of concentrated urine is more likely to result in an underestimation. By ROC curve analysis, the best cut-off value of urine creatinine concentration for predicting overestimation by UPCR of dilute urine (specific gravity ≦ 1.005) was ≦ 38.8 mg/dL, whereas the best cut-off values of urine creatinine for predicting underestimation by UPCR of thick urine were ≧ 63.6 mg/dL (specific gravity ≧ 1.015), ≧ 62.1 mg/dL (specific gravity ≧ 1.020), ≧ 61.5 mg/dL (specific gravity ≧ 1.025), respectively. We also compared distribution patterns of urine creatinine concentration of 24h urine cohort with a concurrent spot urine cohort and found that the underestimation might be more profound in single voided samples

  3. Diagnostic Accuracy of Urine Protein/Creatinine Ratio Is Influenced by Urine Concentration.

    Science.gov (United States)

    Yang, Chih-Yu; Chen, Fu-An; Chen, Chun-Fan; Liu, Wen-Sheng; Shih, Chia-Jen; Ou, Shuo-Ming; Yang, Wu-Chang; Lin, Chih-Ching; Yang, An-Hang

    2015-01-01

    The usage of urine protein/creatinine ratio to estimate daily urine protein excretion is prevalent, but relatively little attention has been paid to the influence of urine concentration and its impact on test accuracy. We took advantage of 24-hour urine collection to examine both urine protein/creatinine ratio (UPCR) and daily urine protein excretion, with the latter as the reference standard. Specific gravity from a concomitant urinalysis of the same urine sample was used to indicate the urine concentration. During 2010 to 2014, there were 540 adequately collected 24h urine samples with protein concentration, creatinine concentration, total volume, and a concomitant urinalysis of the same sample. Variables associated with an accurate UPCR estimation were determined by multivariate linear regression analysis. Receiver operating characteristic (ROC) curves were generated to determine the discriminant cut-off values of urine creatinine concentration for predicting an accurate UPCR estimation in either dilute or concentrated urine samples. Our findings indicated that for dilute urine, as indicated by a low urine specific gravity, UPCR is more likely to overestimate the actual daily urine protein excretion. On the contrary, UPCR of concentrated urine is more likely to result in an underestimation. By ROC curve analysis, the best cut-off value of urine creatinine concentration for predicting overestimation by UPCR of dilute urine (specific gravity ≦ 1.005) was ≦ 38.8 mg/dL, whereas the best cut-off values of urine creatinine for predicting underestimation by UPCR of thick urine were ≧ 63.6 mg/dL (specific gravity ≧ 1.015), ≧ 62.1 mg/dL (specific gravity ≧ 1.020), ≧ 61.5 mg/dL (specific gravity ≧ 1.025), respectively. We also compared distribution patterns of urine creatinine concentration of 24h urine cohort with a concurrent spot urine cohort and found that the underestimation might be more profound in single voided samples. The UPCR in samples with low

  4. Construction of ontology augmented networks for protein complex prediction.

    Science.gov (United States)

    Zhang, Yijia; Lin, Hongfei; Yang, Zhihao; Wang, Jian

    2013-01-01

    Protein complexes are of great importance in understanding the principles of cellular organization and function. The increase in available protein-protein interaction data, gene ontology and other resources make it possible to develop computational methods for protein complex prediction. Most existing methods focus mainly on the topological structure of protein-protein interaction networks, and largely ignore the gene ontology annotation information. In this article, we constructed ontology augmented networks with protein-protein interaction data and gene ontology, which effectively unified the topological structure of protein-protein interaction networks and the similarity of gene ontology annotations into unified distance measures. After constructing ontology augmented networks, a novel method (clustering based on ontology augmented networks) was proposed to predict protein complexes, which was capable of taking into account the topological structure of the protein-protein interaction network, as well as the similarity of gene ontology annotations. Our method was applied to two different yeast protein-protein interaction datasets and predicted many well-known complexes. The experimental results showed that (i) ontology augmented networks and the unified distance measure can effectively combine the structure closeness and gene ontology annotation similarity; (ii) our method is valuable in predicting protein complexes and has higher F1 and accuracy compared to other competing methods.

  5. Protein-Based Urine Test Predicts Kidney Transplant Outcomes

    Science.gov (United States)

    ... News Releases News Release Thursday, August 22, 2013 Protein-based urine test predicts kidney transplant outcomes NIH- ... supporting development of noninvasive tests. Levels of a protein in the urine of kidney transplant recipients can ...

  6. Continuous protein concentration via free-flow moving reaction boundary electrophoresis.

    Science.gov (United States)

    Kong, Fanzhi; Zhang, Min; Chen, Jingjing; Fan, Liuyin; Xiao, Hua; Liu, Shaorong; Cao, Chengxi

    2017-07-28

    In this work, we developed the model and theory of free-flow moving reaction boundary electrophoresis (FFMRB) for continuous protein concentration for the first time. The theoretical results indicated that (i) the moving reaction boundary (MRB) can be quantitatively designed in free-flow electrophoresis (FFE) system; (ii) charge-to-mass ratio (Z/M) analysis could provide guidance for protein concentration optimization; and (iii) the maximum processing capacity could be predicted. To demonstrate the model and theory, three model proteins of hemoglobin (Hb), cytochrome C (Cyt C) and C-phycocyanin (C-PC) were chosen for the experiments. The experimental results verified that (i) stable MRBs with different velocities could be established in FFE apparatus with weak acid/weak base neutralization reaction system; (ii) proteins of Hb, Cyt C and C-PC were well concentrated with FFMRB; and (iii) a maximum processing capacity and recovery ratio of Cyt C enrichment were 126mL/h and 95.5% respectively, and a maximum enrichment factor was achieved 12.6 times for Hb. All of the experiments demonstrated the protein concentration model and theory. In contrast to other methods, the continuous processing ability enables FFMRB to efficiently enrich diluted protein or peptide in large volume solution. Copyright © 2017 Elsevier B.V. All rights reserved.

  7. Semen quality and concentration of soluble proteins in the seminal plasma of Alpine bucks Semen quality and concentration of soluble proteins in the seminal plasma of Alpine bucks

    Directory of Open Access Journals (Sweden)

    Simone Eliza Facione Guimarães

    2010-06-01

    Full Text Available It was aimed to study the in vitro seminal quality analyzed by complementary tests and to compare them with physical, morphological and biochemical aspects of male goat semen of the Alpine breed. This experiment took place at the Federal University of Viçosa, situated at 20º45’ S latitude and 42º51’ W longitude, Southwest of Brazil. It was done during the summer months of January and February, and three adult male goats of the Alpine breed were used in intensive conditions. The semen was collected by artificial vagina method. In all semen samples (45 ejaculates, after the physical and morphological analysis, the hiposmotic test was done. In 24 ejaculates, it were done thermo-resistance test, and in 21 ejaculates it were determined the concentration of total soluble proteins in seminal plasma. The male goats presented difference in the semen physical and morphological aspects, in the hiposmotic test and thermo-resistance test, but they did not presented difference in total soluble proteins concentration in seminal plasma. Results of the slow thermo-resistance test and hiposmotic test were positively correlated (r = 0.60. It was concluded, according to our results, that the concentration of total soluble proteins in seminal plasma can not be used as a parameter to predict the seminal quality of Alpine bucks.It was aimed to study the in vitro seminal quality analyzed by complementary tests and to compare them with physical, morphological and biochemical aspects of male goat semen of the Alpine breed. This experiment took place at the Federal University of Viçosa, situated at 20º45’ S latitude and 42º51’ W longitude, Southwest of Brazil. It was done during the summer months of January and February, and three adult male goats of the Alpine breed were used in intensive conditions. The semen was collected by artificial vagina method. In all semen samples (45 ejaculates, after the physical and morphological analysis, the hiposmotic test

  8. Protein concentrate production from the biomass contaminated with radionuclides

    International Nuclear Information System (INIS)

    Nizhko, V.F.; Shinkarenko, M.P.; Polozhaj, V.V.; Krivchik, O.V.

    1992-01-01

    Coefficients of radionuclides accumulation are determined for traditional and rare forage crops grown on contaminated soils. It is shown that with low concentration of radionuclides in soil minimal level of contamination were found in the biomass of lupine (Lupinus luteus L.) and sainfoin (Onobrychis hybridus L.). Relatively high levels of contamination were found in comfrey (Symphytum asperum Lepech.) and bistort (Polygonum divaricatum L.). Comparatively low accumulation coefficients in case of higher density of soil contamination were observed for white and yellow sweetclovers (Melilotus albus Medik. and M. officinalis (L.) Desr.), while higher values of coefficients were found for bird's-foot trefoil (Lotus corniculatus L.), white clover (Trifolium repens L.) and alsike clover (t. hybridum L.). Biomass of white sweet-clover and alsike clover has been processed to produce leaf protein concentrate. It is shown that with biomass contamination of 1 kBq/kg and above conventional technology based on thermal precipitation of the protein does not provide production of pure product. More purified protein concentrates are obtained after two-stage processing of the biomass

  9. A prediction model for assessing residential radon concentration in Switzerland

    International Nuclear Information System (INIS)

    Hauri, Dimitri D.; Huss, Anke; Zimmermann, Frank; Kuehni, Claudia E.; Röösli, Martin

    2012-01-01

    Indoor radon is regularly measured in Switzerland. However, a nationwide model to predict residential radon levels has not been developed. The aim of this study was to develop a prediction model to assess indoor radon concentrations in Switzerland. The model was based on 44,631 measurements from the nationwide Swiss radon database collected between 1994 and 2004. Of these, 80% randomly selected measurements were used for model development and the remaining 20% for an independent model validation. A multivariable log-linear regression model was fitted and relevant predictors selected according to evidence from the literature, the adjusted R², the Akaike's information criterion (AIC), and the Bayesian information criterion (BIC). The prediction model was evaluated by calculating Spearman rank correlation between measured and predicted values. Additionally, the predicted values were categorised into three categories (50th, 50th–90th and 90th percentile) and compared with measured categories using a weighted Kappa statistic. The most relevant predictors for indoor radon levels were tectonic units and year of construction of the building, followed by soil texture, degree of urbanisation, floor of the building where the measurement was taken and housing type (P-values <0.001 for all). Mean predicted radon values (geometric mean) were 66 Bq/m³ (interquartile range 40–111 Bq/m³) in the lowest exposure category, 126 Bq/m³ (69–215 Bq/m³) in the medium category, and 219 Bq/m³ (108–427 Bq/m³) in the highest category. Spearman correlation between predictions and measurements was 0.45 (95%-CI: 0.44; 0.46) for the development dataset and 0.44 (95%-CI: 0.42; 0.46) for the validation dataset. Kappa coefficients were 0.31 for the development and 0.30 for the validation dataset, respectively. The model explained 20% overall variability (adjusted R²). In conclusion, this residential radon prediction model, based on a large number of measurements, was demonstrated to be

  10. Data Based Prediction of Blood Glucose Concentrations Using Evolutionary Methods.

    Science.gov (United States)

    Hidalgo, J Ignacio; Colmenar, J Manuel; Kronberger, Gabriel; Winkler, Stephan M; Garnica, Oscar; Lanchares, Juan

    2017-08-08

    Predicting glucose values on the basis of insulin and food intakes is a difficult task that people with diabetes need to do daily. This is necessary as it is important to maintain glucose levels at appropriate values to avoid not only short-term, but also long-term complications of the illness. Artificial intelligence in general and machine learning techniques in particular have already lead to promising results in modeling and predicting glucose concentrations. In this work, several machine learning techniques are used for the modeling and prediction of glucose concentrations using as inputs the values measured by a continuous monitoring glucose system as well as also previous and estimated future carbohydrate intakes and insulin injections. In particular, we use the following four techniques: genetic programming, random forests, k-nearest neighbors, and grammatical evolution. We propose two new enhanced modeling algorithms for glucose prediction, namely (i) a variant of grammatical evolution which uses an optimized grammar, and (ii) a variant of tree-based genetic programming which uses a three-compartment model for carbohydrate and insulin dynamics. The predictors were trained and tested using data of ten patients from a public hospital in Spain. We analyze our experimental results using the Clarke error grid metric and see that 90% of the forecasts are correct (i.e., Clarke error categories A and B), but still even the best methods produce 5 to 10% of serious errors (category D) and approximately 0.5% of very serious errors (category E). We also propose an enhanced genetic programming algorithm that incorporates a three-compartment model into symbolic regression models to create smoothed time series of the original carbohydrate and insulin time series.

  11. Protein-lipid interactions in concentrated infant formula

    International Nuclear Information System (INIS)

    Rowley, B.O.; Richardson, T.

    1985-01-01

    Radiolabeled milk proteins ([carbon-14] β-lactoglobulin or [carbon-14] kappa-casein) were added to raw skim milk used to prepare concentrated humanized infant formula. Ultracentrifugation of the sterilized product allowed separation of three fractions: lipids and the proteins associated with them; free casein micelles and other dense particles; and the fluid phase. Distribution of radiolabeled tracer proteins or of protein measured by chemical methods among these three phases varied significantly with differences in processing conditions (time and temperature of sterilization) or amount of certain additives (potassium hydroxide or urea). In the range of 0 to 8 meq/L of potassium hydroxide added to the formula after homogenization but before sterilization, the lipid layer content of carbon-14 from [carbon-14] kappa-casein in the sterilized product decreased by 4.7% for each 1 meq/L of added potassium hydroxide. Lipid layer content of protein decreased by 2 g/L ( of a total of 32 g/L) for each 1 meq/L potassium hydroxide

  12. Molecular Effects of Concentrated Solutes on Protein Hydration, Dynamics, and Electrostatics.

    Science.gov (United States)

    Abriata, Luciano A; Spiga, Enrico; Peraro, Matteo Dal

    2016-08-23

    Most studies of protein structure and function are performed in dilute conditions, but proteins typically experience high solute concentrations in their physiological scenarios and biotechnological applications. High solute concentrations have well-known effects on coarse protein traits like stability, diffusion, and shape, but likely also perturb other traits through finer effects pertinent at the residue and atomic levels. Here, NMR and molecular dynamics investigations on ubiquitin disclose variable interactions with concentrated solutes that lead to localized perturbations of the protein's surface, hydration, electrostatics, and dynamics, all dependent on solute size and chemical properties. Most strikingly, small polar uncharged molecules are sticky on the protein surface, whereas charged small molecules are not, but the latter still perturb the internal protein electrostatics as they diffuse nearby. Meanwhile, interactions with macromolecular crowders are favored mainly through hydrophobic, but not through polar, surface patches. All the tested small solutes strongly slow down water exchange at the protein surface, whereas macromolecular crowders do not exert such strong perturbation. Finally, molecular dynamics simulations predict that unspecific interactions slow down microsecond- to millisecond-timescale protein dynamics despite having only mild effects on pico- to nanosecond fluctuations as corroborated by NMR. We discuss our results in the light of recent advances in understanding proteins inside living cells, focusing on the physical chemistry of quinary structure and cellular organization, and we reinforce the idea that proteins should be studied in native-like media to achieve a faithful description of their function. Copyright © 2016 Biophysical Society. Published by Elsevier Inc. All rights reserved.

  13. C-reactive protein, fibrinogen, and cardiovascular disease prediction

    DEFF Research Database (Denmark)

    Kaptoge, Stephen; Di Angelantonio, Emanuele; Pennells, Lisa

    2012-01-01

    There is debate about the value of assessing levels of C-reactive protein (CRP) and other biomarkers of inflammation for the prediction of first cardiovascular events.......There is debate about the value of assessing levels of C-reactive protein (CRP) and other biomarkers of inflammation for the prediction of first cardiovascular events....

  14. Airborne Precursors Predict Maternal Serum Perfluoroalkyl Acid Concentrations.

    Science.gov (United States)

    Makey, Colleen M; Webster, Thomas F; Martin, Jonathan W; Shoeib, Mahiba; Harner, Tom; Dix-Cooper, Linda; Webster, Glenys M

    2017-07-05

    Human exposure to persistent perfluoroalkyl acids (PFAAs), including perfluorooctanoic acid (PFOA), perfluorononanoic acid (PFNA), and perfluorooctanesulfonate (PFOS), can occur directly from contaminated food, water, air, and dust. However, precursors to PFAAs (PreFAAs), such as dipolyfluoroalkyl phosphates (diPAPs), fluorotelomer alcohols (FTOHs), perfluorooctyl sulfonamides (FOSAs), and sulfonamidoethanols (FOSEs), which can be biotransformed to PFAAs, may also be a source of exposure. PFAAs were analyzed in 50 maternal sera samples collected in 2007-2008 from participants in Vancouver, Canada, while PFAAs and PreFAAs were measured in matching samples of residential bedroom air collected by passive sampler and in sieved vacuum dust (<150 μm). Concentrations of PreFAAs were higher than for PFAAs in air and dust. Positive associations were discovered between airborne 10:2 FTOH and serum PFOA and PFNA and between airborne MeFOSE and serum PFOS. On average, serum PFOS concentrations were 2.3 ng/mL (95%CI: 0.40, 4.3) higher in participants with airborne MeFOSE concentrations in the highest tertile relative to the lowest tertile. Among all PFAAs, only PFNA in air and vacuum dust predicted serum PFNA. Results suggest that airborne PFAA precursors were a source of PFOA, PFNA, and PFOS exposure in this population.

  15. Refining intra-protein contact prediction by graph analysis

    Directory of Open Access Journals (Sweden)

    Eyal Eran

    2007-05-01

    Full Text Available Abstract Background Accurate prediction of intra-protein residue contacts from sequence information will allow the prediction of protein structures. Basic predictions of such specific contacts can be further refined by jointly analyzing predicted contacts, and by adding information on the relative positions of contacts in the protein primary sequence. Results We introduce a method for graph analysis refinement of intra-protein contacts, termed GARP. Our previously presented intra-contact prediction method by means of pair-to-pair substitution matrix (P2PConPred was used to test the GARP method. In our approach, the top contact predictions obtained by a basic prediction method were used as edges to create a weighted graph. The edges were scored by a mutual clustering coefficient that identifies highly connected graph regions, and by the density of edges between the sequence regions of the edge nodes. A test set of 57 proteins with known structures was used to determine contacts. GARP improves the accuracy of the P2PConPred basic prediction method in whole proteins from 12% to 18%. Conclusion Using a simple approach we increased the contact prediction accuracy of a basic method by 1.5 times. Our graph approach is simple to implement, can be used with various basic prediction methods, and can provide input for further downstream analyses.

  16. An Overview of Practical Applications of Protein Disorder Prediction and Drive for Faster, More Accurate Predictions.

    Science.gov (United States)

    Deng, Xin; Gumm, Jordan; Karki, Suman; Eickholt, Jesse; Cheng, Jianlin

    2015-07-07

    Protein disordered regions are segments of a protein chain that do not adopt a stable structure. Thus far, a variety of protein disorder prediction methods have been developed and have been widely used, not only in traditional bioinformatics domains, including protein structure prediction, protein structure determination and function annotation, but also in many other biomedical fields. The relationship between intrinsically-disordered proteins and some human diseases has played a significant role in disorder prediction in disease identification and epidemiological investigations. Disordered proteins can also serve as potential targets for drug discovery with an emphasis on the disordered-to-ordered transition in the disordered binding regions, and this has led to substantial research in drug discovery or design based on protein disordered region prediction. Furthermore, protein disorder prediction has also been applied to healthcare by predicting the disease risk of mutations in patients and studying the mechanistic basis of diseases. As the applications of disorder prediction increase, so too does the need to make quick and accurate predictions. To fill this need, we also present a new approach to predict protein residue disorder using wide sequence windows that is applicable on the genomic scale.

  17. An Overview of Practical Applications of Protein Disorder Prediction and Drive for Faster, More Accurate Predictions

    Directory of Open Access Journals (Sweden)

    Xin Deng

    2015-07-01

    Full Text Available Protein disordered regions are segments of a protein chain that do not adopt a stable structure. Thus far, a variety of protein disorder prediction methods have been developed and have been widely used, not only in traditional bioinformatics domains, including protein structure prediction, protein structure determination and function annotation, but also in many other biomedical fields. The relationship between intrinsically-disordered proteins and some human diseases has played a significant role in disorder prediction in disease identification and epidemiological investigations. Disordered proteins can also serve as potential targets for drug discovery with an emphasis on the disordered-to-ordered transition in the disordered binding regions, and this has led to substantial research in drug discovery or design based on protein disordered region prediction. Furthermore, protein disorder prediction has also been applied to healthcare by predicting the disease risk of mutations in patients and studying the mechanistic basis of diseases. As the applications of disorder prediction increase, so too does the need to make quick and accurate predictions. To fill this need, we also present a new approach to predict protein residue disorder using wide sequence windows that is applicable on the genomic scale.

  18. Assessment of nutritional quality of water hyacinth leaf protein concentrate

    Directory of Open Access Journals (Sweden)

    Oyeyemi Adeyemi

    2016-09-01

    Full Text Available This study was embarked upon to convert water hyacinth, an environmental nuisance, to a natural resource for economic development. Water hyacinth leaf protein concentrate (WHLPC was extracted in edible form and determination of its physicochemical characteristics, total alkaloids and phenolic compounds was done. Analysis of proximate composition and amino acid profile of the WHLPC was also done. The level of heavy metals (mg/kg in WHLPC was found to be Cd (0.02 ± 0.001, Cr (0.13 ± 0.001, Pd (0.003 ± 0.001 and Hg (0.02 ± 0.001 while concentrations of Pb, Pt, Sn, Fe, Cu, Zn, Ni and Co were found to be 0.001 ± 0.00. Level of all heavy metals was found to be within safe limit. Proximate analysis revealed that protein in WHLPC accounted for 50% of its nutrients, carbohydrate accounted for 33% of its nutrients while fat, ash and fibre made up the remaining nutrients. Amino acid analysis showed that WHLPC contained 17 out of 20 common amino acids, particularly, Phe (3.67%, Leu (5.01%. Level of total alkaloids and phenolic compounds was 16.6 mg/kg and 6.0 mg/kg respectively. Evidence from this study suggests that WHLPC is a good source of leaf protein concentrate (LPC; it is nutritious and acutely non toxic.

  19. CNNcon: improved protein contact maps prediction using cascaded neural networks.

    Directory of Open Access Journals (Sweden)

    Wang Ding

    Full Text Available BACKGROUNDS: Despite continuing progress in X-ray crystallography and high-field NMR spectroscopy for determination of three-dimensional protein structures, the number of unsolved and newly discovered sequences grows much faster than that of determined structures. Protein modeling methods can possibly bridge this huge sequence-structure gap with the development of computational science. A grand challenging problem is to predict three-dimensional protein structure from its primary structure (residues sequence alone. However, predicting residue contact maps is a crucial and promising intermediate step towards final three-dimensional structure prediction. Better predictions of local and non-local contacts between residues can transform protein sequence alignment to structure alignment, which can finally improve template based three-dimensional protein structure predictors greatly. METHODS: CNNcon, an improved multiple neural networks based contact map predictor using six sub-networks and one final cascade-network, was developed in this paper. Both the sub-networks and the final cascade-network were trained and tested with their corresponding data sets. While for testing, the target protein was first coded and then input to its corresponding sub-networks for prediction. After that, the intermediate results were input to the cascade-network to finish the final prediction. RESULTS: The CNNcon can accurately predict 58.86% in average of contacts at a distance cutoff of 8 Å for proteins with lengths ranging from 51 to 450. The comparison results show that the present method performs better than the compared state-of-the-art predictors. Particularly, the prediction accuracy keeps steady with the increase of protein sequence length. It indicates that the CNNcon overcomes the thin density problem, with which other current predictors have trouble. This advantage makes the method valuable to the prediction of long length proteins. As a result, the effective

  20. Predictable tuning of protein expression in bacteria

    DEFF Research Database (Denmark)

    Bonde, Mads; Pedersen, Margit; Klausen, Michael Schantz

    2016-01-01

    We comprehensively assessed the contribution of the Shine-Dalgarno sequence to protein expression and used the data to develop EMOPEC (Empirical Model and Oligos for Protein Expression Changes; http://emopec.biosustain.dtu.dk). EMOPEC is a free tool that makes it possible to modulate the expressi...

  1. Signal peptides and protein localization prediction

    DEFF Research Database (Denmark)

    Nielsen, Henrik

    2005-01-01

    In 1999, the Nobel prize in Physiology or Medicine was awarded to Gunther Blobel “for the discovery that proteins have intrinsic signals that govern their transport and localization in the cell”. Since the subcellular localization of a protein is an important clue to its function, the characteriz...

  2. Absence of diurnal variation of C-reactive protein concentrations in healthy human subjects

    Science.gov (United States)

    Meier-Ewert, H. K.; Ridker, P. M.; Rifai, N.; Price, N.; Dinges, D. F.; Mullington, J. M.

    2001-01-01

    BACKGROUND: The concentration of C-reactive protein (CRP) in otherwise healthy subjects has been shown to predict future risk of myocardial infarction and stroke. CRP is synthesized by the liver in response to interleukin-6, the serum concentration of which is subject to diurnal variation. METHODS: To examine the existence of a time-of-day effect for baseline CRP values, we determined CRP concentrations in hourly blood samples drawn from healthy subjects (10 males, 3 females; age range, 21-35 years) during a baseline day in a controlled environment (8 h of nighttime sleep). RESULTS: Overall CRP concentrations were low, with only three subjects having CRP concentrations >2 mg/L. Comparison of raw data showed stability of CRP concentrations throughout the 24 h studied. When compared with cutoff values of CRP quintile derived from population-based studies, misclassification of greater than one quintile did not occur as a result of diurnal variation in any of the subjects studied. Nonparametric ANOVA comparing different time points showed no significant differences for both raw and z-transformed data. Analysis for rhythmic diurnal variation using a method fitting a cosine curve to the group data was negative. CONCLUSIONS: Our data show that baseline CRP concentrations are not subject to time-of-day variation and thus help to explain why CRP concentrations are a better predictor of vascular risk than interleukin-6. Determination of CRP for cardiovascular risk prediction may be performed without concern for diurnal variation.

  3. Limitations of polyethylene glycol-induced precipitation as predictive tool for protein solubility during formulation development.

    Science.gov (United States)

    Hofmann, Melanie; Winzer, Matthias; Weber, Christian; Gieseler, Henning

    2018-05-01

    Polyethylene glycol (PEG)-induced protein precipitation is often used to extrapolate apparent protein solubility at specific formulation compositions. The procedure was used for several fields of application such as protein crystal growth but also protein formulation development. Nevertheless, most studies focused on applicability in protein crystal growth. In contrast, this study focuses on applicability of PEG-induced precipitation during high-concentration protein formulation development. In this study, solubility of three different model proteins was investigated over a broad range of pH. Solubility values predicted by PEG-induced precipitation were compared to real solubility behaviour determined by either turbidity or content measurements. Predicted solubility by PEG-induced precipitation was confirmed for an Fc fusion protein and a monoclonal antibody. In contrast, PEG-induced precipitation failed to predict solubility of a single-domain antibody construct. Applicability of PEG-induced precipitation as indicator of protein solubility during formulation development was found to be not valid for one of three model molecules. Under certain conditions, PEG-induced protein precipitation is not valid for prediction of real protein solubility behaviour. The procedure should be used carefully as tool for formulation development, and the results obtained should be validated by additional investigations. © 2017 Royal Pharmaceutical Society.

  4. The 82-plex plasma protein signature that predicts increasing inflammation

    DEFF Research Database (Denmark)

    Tepel, Martin; Beck, Hans C; Tan, Qihua

    2015-01-01

    The objective of the study was to define the specific plasma protein signature that predicts the increase of the inflammation marker C-reactive protein from index day to next-day using proteome analysis and novel bioinformatics tools. We performed a prospective study of 91 incident kidney....... The prediction model selected and validated 82 plasma proteins which determined increased next-day C-reactive protein (area under receiver-operator-characteristics curve, 0.772; 95% confidence interval, 0.669 to 0.876; P signature (P ....001) was associated with observed increased next-day C-reactive protein. The 82-plex protein signature outperformed routine clinical procedures. The category-free net reclassification index improved with 82-plex plasma protein signature (total net reclassification index, 88.3%). Using the 82-plex plasma protein...

  5. Text mining improves prediction of protein functional sites.

    Directory of Open Access Journals (Sweden)

    Karin M Verspoor

    Full Text Available We present an approach that integrates protein structure analysis and text mining for protein functional site prediction, called LEAP-FS (Literature Enhanced Automated Prediction of Functional Sites. The structure analysis was carried out using Dynamics Perturbation Analysis (DPA, which predicts functional sites at control points where interactions greatly perturb protein vibrations. The text mining extracts mentions of residues in the literature, and predicts that residues mentioned are functionally important. We assessed the significance of each of these methods by analyzing their performance in finding known functional sites (specifically, small-molecule binding sites and catalytic sites in about 100,000 publicly available protein structures. The DPA predictions recapitulated many of the functional site annotations and preferentially recovered binding sites annotated as biologically relevant vs. those annotated as potentially spurious. The text-based predictions were also substantially supported by the functional site annotations: compared to other residues, residues mentioned in text were roughly six times more likely to be found in a functional site. The overlap of predictions with annotations improved when the text-based and structure-based methods agreed. Our analysis also yielded new high-quality predictions of many functional site residues that were not catalogued in the curated data sources we inspected. We conclude that both DPA and text mining independently provide valuable high-throughput protein functional site predictions, and that integrating the two methods using LEAP-FS further improves the quality of these predictions.

  6. Text Mining Improves Prediction of Protein Functional Sites

    Science.gov (United States)

    Cohn, Judith D.; Ravikumar, Komandur E.

    2012-01-01

    We present an approach that integrates protein structure analysis and text mining for protein functional site prediction, called LEAP-FS (Literature Enhanced Automated Prediction of Functional Sites). The structure analysis was carried out using Dynamics Perturbation Analysis (DPA), which predicts functional sites at control points where interactions greatly perturb protein vibrations. The text mining extracts mentions of residues in the literature, and predicts that residues mentioned are functionally important. We assessed the significance of each of these methods by analyzing their performance in finding known functional sites (specifically, small-molecule binding sites and catalytic sites) in about 100,000 publicly available protein structures. The DPA predictions recapitulated many of the functional site annotations and preferentially recovered binding sites annotated as biologically relevant vs. those annotated as potentially spurious. The text-based predictions were also substantially supported by the functional site annotations: compared to other residues, residues mentioned in text were roughly six times more likely to be found in a functional site. The overlap of predictions with annotations improved when the text-based and structure-based methods agreed. Our analysis also yielded new high-quality predictions of many functional site residues that were not catalogued in the curated data sources we inspected. We conclude that both DPA and text mining independently provide valuable high-throughput protein functional site predictions, and that integrating the two methods using LEAP-FS further improves the quality of these predictions. PMID:22393388

  7. Is protein structure prediction still an enigma?

    African Journals Online (AJOL)

    STORAGESEVER

    2008-12-29

    Dec 29, 2008 ... Computer methods for protein analysis address this problem since they study the .... neighbor methods, molecular dynamic simulation, and approaches .... fuzzy clustering, neural net works, logistic regression, decision tree ...

  8. Predictive modelling of fatigue failure in concentrated lubricated contacts.

    Science.gov (United States)

    Evans, H P; Snidle, R W; Sharif, K J; Bryant, M J

    2012-01-01

    Reducing frictional losses in response to the energy agenda will require use of less viscous lubricants causing hydrodynamically-lubricated bearings to operate with thinner films leading to "mixed lubrication" conditions in which a degree of direct interaction occurs between surfaces protected only by boundary tribofilms. The paper considers the consequences of thinner films and mixed lubrication for concentrated contacts such as those occurring between the teeth of power transmission gears and in rolling element bearings. Surface fatigue in gears remains a serious problem in demanding applications, and its solution will become more pressing with the tendency towards thinner oils. The particular form of failure examined here is micropitting, which is identified as a fatigue phenomenon occurring at the scale of the surface roughness asperities. It has emerged recently as a systemic difficulty in the operation of large scale wind turbines where it occurs in both power transmission gears and their support bearings. Predictive physical modelling of these contacts requires a transient mixed lubrication analysis for conditions in which the predicted lubricant film thickness is of the same order or significantly less than the height of surface roughness features. Numerical solvers have therefore been developed which are able to deal with situations in which transient solid contacts occur between surface asperity features under realistic engineering conditions. Results of the analysis, which reveal the detailed time-varying behaviour of pressure and film clearance, have been used to predict fatigue and damage accumulation at the scale of surface asperity features with the aim of improving understanding of the micropitting phenomenon. The possible consequences on fatigue of residual stress fields resulting from plastic deformation of surface asperities is also considered.

  9. Predicting and validating protein interactions using network structure.

    Directory of Open Access Journals (Sweden)

    Pao-Yang Chen

    2008-07-01

    Full Text Available Protein interactions play a vital part in the function of a cell. As experimental techniques for detection and validation of protein interactions are time consuming, there is a need for computational methods for this task. Protein interactions appear to form a network with a relatively high degree of local clustering. In this paper we exploit this clustering by suggesting a score based on triplets of observed protein interactions. The score utilises both protein characteristics and network properties. Our score based on triplets is shown to complement existing techniques for predicting protein interactions, outperforming them on data sets which display a high degree of clustering. The predicted interactions score highly against test measures for accuracy. Compared to a similar score derived from pairwise interactions only, the triplet score displays higher sensitivity and specificity. By looking at specific examples, we show how an experimental set of interactions can be enriched and validated. As part of this work we also examine the effect of different prior databases upon the accuracy of prediction and find that the interactions from the same kingdom give better results than from across kingdoms, suggesting that there may be fundamental differences between the networks. These results all emphasize that network structure is important and helps in the accurate prediction of protein interactions. The protein interaction data set and the program used in our analysis, and a list of predictions and validations, are available at http://www.stats.ox.ac.uk/bioinfo/resources/PredictingInteractions.

  10. Protein function prediction involved on radio-resistant bacteria

    International Nuclear Information System (INIS)

    Mezhoud, Karim; Mankai, Houda; Sghaier, Haitham; Barkallah, Insaf

    2009-01-01

    Previously, we identified 58 proteins under positive selection in ionizing-radiation-resistant bacteria (IRRB) but absent in all ionizing-radiation-sensitive bacteria (IRSB). These are good reasons to believe these 58 proteins with their interactions with other proteins (interactomes) are a part of the answer to the question as to how IRRB resist to radiation, because our knowledge of interactomes of positively selected orphan proteins in IRRB might allow us to define cellular pathways important to ionizing-radiation resistance. Using the Database of Interacting Proteins and the PSIbase, we have predicted interactions of orthologs of the 58 proteins under positive selection in IRRB but absent in all IRSB. We used integrate experimental data sets with molecular interaction networks and protein structure prediction from databases. Among these, 18 proteins with their interactomes were identified in Deinococcus radiodurans R1. DNA checkpoint and repair, kinases pathways, energetic and nucleotide metabolisms were the important biological process that found. We predicted the interactomes of 58 proteins under positive selection in IRRB. It is hoped our data will provide new clues as to the cellular pathways that are important for ionizing-radiation resistance. We have identified news proteins involved on DNA management which were not previously mentioned. It is an important input in addition to protein that studied. It does still work to deepen our study on these new proteins

  11. Dimethylglycine accumulates in uremia and predicts elevated plasma homocysteine concentrations.

    Science.gov (United States)

    McGregor, D O; Dellow, W J; Lever, M; George, P M; Robson, R A; Chambers, S T

    2001-06-01

    Hyperhomocysteinemia is a risk factor for atherosclerosis that is common in chronic renal failure (CRF), but its cause is unknown. Homocysteine metabolism is linked to betaine-homocysteine methyl transferase (BHMT), a zinc metalloenzyme that converts glycine betaine (GB) to N,N dimethylglycine (DMG). DMG is a known feedback inhibitor of BHMT. We postulated that DMG might accumulate in CRF and contribute to hyperhomocysteinemia by inhibiting BHMT activity. Plasma and urine concentrations of GB and DMG were measured in 33 dialysis patients (15 continuous ambulatory peritoneal dialysis and 18 hemodialysis), 33 patients with CRF, and 33 age-matched controls. Concentrations of fasting plasma total homocysteine (tHcy), red cell and serum folate, vitamins B(6) and B(12), serum zinc, and routine biochemistry were also measured. Groups were compared, and determinants of plasma tHcy were identified by correlations and stepwise linear regression. Plasma DMG increased as renal function declined and was twofold to threefold elevated in dialysis patients. Plasma GB did not differ between groups. The fractional excretion of GB (FE(GB)) was increased tenfold, and FED(MG) was doubled in CRF patients compared with controls. Plasma tHcy correlated positively with plasma DMG, the plasma DMG:GB ratio, plasma creatinine, and FE(GB) and negatively with serum folate, zinc, and plasma GB. In the multiple regression model, only plasma creatinine, plasma DMG, or the DMG:GB ratio was independent predictors of tHcy. DMG accumulates in CRF and independently predicts plasma tHcy concentrations. These findings suggest that reduced BHMT activity is important in the pathogenesis of hyperhomocysteinemia in CRF.

  12. EVA: continuous automatic evaluation of protein structure prediction servers.

    Science.gov (United States)

    Eyrich, V A; Martí-Renom, M A; Przybylski, D; Madhusudhan, M S; Fiser, A; Pazos, F; Valencia, A; Sali, A; Rost, B

    2001-12-01

    Evaluation of protein structure prediction methods is difficult and time-consuming. Here, we describe EVA, a web server for assessing protein structure prediction methods, in an automated, continuous and large-scale fashion. Currently, EVA evaluates the performance of a variety of prediction methods available through the internet. Every week, the sequences of the latest experimentally determined protein structures are sent to prediction servers, results are collected, performance is evaluated, and a summary is published on the web. EVA has so far collected data for more than 3000 protein chains. These results may provide valuable insight to both developers and users of prediction methods. http://cubic.bioc.columbia.edu/eva. eva@cubic.bioc.columbia.edu

  13. Evaluation of serum biochemical marker concentrations and survival time in dogs with protein-losing enteropathy.

    Science.gov (United States)

    Equilino, Mirjam; Théodoloz, Vincent; Gorgas, Daniela; Doherr, Marcus G; Heilmann, Romy M; Suchodolski, Jan S; Steiner, Jörg M; Burgener Dvm, Iwan A

    2015-01-01

    To evaluate serum concentrations of biochemical markers and survival time in dogs with protein-losing enteropathy (PLE). Prospective study. 29 dogs with PLE and 18 dogs with food-responsive diarrhea (FRD). Data regarding serum concentrations of various biochemical markers at the initial evaluation were available for 18 of the 29 dogs with PLE and compared with findings for dogs with FRD. Correlations between biochemical marker concentrations and survival time (interval between time of initial evaluation and death or euthanasia) for dogs with PLE were evaluated. Serum C-reactive protein concentration was high in 13 of 18 dogs with PLE and in 2 of 18 dogs with FRD. Serum concentration of canine pancreatic lipase immunoreactivity was high in 3 dogs with PLE but within the reference interval in all dogs with FRD. Serum α1-proteinase inhibitor concentration was less than the lower reference limit in 9 dogs with PLE and 1 dog with FRD. Compared with findings in dogs with FRD, values of those 3 variables in dogs with PLE were significantly different. Serum calprotectin (measured by radioimmunoassay and ELISA) and S100A12 concentrations were high but did not differ significantly between groups. Seventeen of the 29 dogs with PLE were euthanized owing to this disease; median survival time was 67 days (range, 2 to 2,551 days). Serum C-reactive protein, canine pancreatic lipase immunoreactivity, and α1-proteinase inhibitor concentrations differed significantly between dogs with PLE and FRD. Most initial biomarker concentrations were not predictive of survival time in dogs with PLE.

  14. Topology of membrane proteins-predictions, limitations and variations.

    Science.gov (United States)

    Tsirigos, Konstantinos D; Govindarajan, Sudha; Bassot, Claudio; Västermark, Åke; Lamb, John; Shu, Nanjiang; Elofsson, Arne

    2017-10-26

    Transmembrane proteins perform a variety of important biological functions necessary for the survival and growth of the cells. Membrane proteins are built up by transmembrane segments that span the lipid bilayer. The segments can either be in the form of hydrophobic alpha-helices or beta-sheets which create a barrel. A fundamental aspect of the structure of transmembrane proteins is the membrane topology, that is, the number of transmembrane segments, their position in the protein sequence and their orientation in the membrane. Along these lines, many predictive algorithms for the prediction of the topology of alpha-helical and beta-barrel transmembrane proteins exist. The newest algorithms obtain an accuracy close to 80% both for alpha-helical and beta-barrel transmembrane proteins. However, lately it has been shown that the simplified picture presented when describing a protein family by its topology is limited. To demonstrate this, we highlight examples where the topology is either not conserved in a protein superfamily or where the structure cannot be described solely by the topology of a protein. The prediction of these non-standard features from sequence alone was not successful until the recent revolutionary progress in 3D-structure prediction of proteins. Copyright © 2017 Elsevier Ltd. All rights reserved.

  15. HomPPI: a class of sequence homology based protein-protein interface prediction methods

    Directory of Open Access Journals (Sweden)

    Dobbs Drena

    2011-06-01

    Full Text Available Abstract Background Although homology-based methods are among the most widely used methods for predicting the structure and function of proteins, the question as to whether interface sequence conservation can be effectively exploited in predicting protein-protein interfaces has been a subject of debate. Results We studied more than 300,000 pair-wise alignments of protein sequences from structurally characterized protein complexes, including both obligate and transient complexes. We identified sequence similarity criteria required for accurate homology-based inference of interface residues in a query protein sequence. Based on these analyses, we developed HomPPI, a class of sequence homology-based methods for predicting protein-protein interface residues. We present two variants of HomPPI: (i NPS-HomPPI (Non partner-specific HomPPI, which can be used to predict interface residues of a query protein in the absence of knowledge of the interaction partner; and (ii PS-HomPPI (Partner-specific HomPPI, which can be used to predict the interface residues of a query protein with a specific target protein. Our experiments on a benchmark dataset of obligate homodimeric complexes show that NPS-HomPPI can reliably predict protein-protein interface residues in a given protein, with an average correlation coefficient (CC of 0.76, sensitivity of 0.83, and specificity of 0.78, when sequence homologs of the query protein can be reliably identified. NPS-HomPPI also reliably predicts the interface residues of intrinsically disordered proteins. Our experiments suggest that NPS-HomPPI is competitive with several state-of-the-art interface prediction servers including those that exploit the structure of the query proteins. The partner-specific classifier, PS-HomPPI can, on a large dataset of transient complexes, predict the interface residues of a query protein with a specific target, with a CC of 0.65, sensitivity of 0.69, and specificity of 0.70, when homologs of

  16. Deep learning methods for protein torsion angle prediction.

    Science.gov (United States)

    Li, Haiou; Hou, Jie; Adhikari, Badri; Lyu, Qiang; Cheng, Jianlin

    2017-09-18

    Deep learning is one of the most powerful machine learning methods that has achieved the state-of-the-art performance in many domains. Since deep learning was introduced to the field of bioinformatics in 2012, it has achieved success in a number of areas such as protein residue-residue contact prediction, secondary structure prediction, and fold recognition. In this work, we developed deep learning methods to improve the prediction of torsion (dihedral) angles of proteins. We design four different deep learning architectures to predict protein torsion angles. The architectures including deep neural network (DNN) and deep restricted Boltzmann machine (DRBN), deep recurrent neural network (DRNN) and deep recurrent restricted Boltzmann machine (DReRBM) since the protein torsion angle prediction is a sequence related problem. In addition to existing protein features, two new features (predicted residue contact number and the error distribution of torsion angles extracted from sequence fragments) are used as input to each of the four deep learning architectures to predict phi and psi angles of protein backbone. The mean absolute error (MAE) of phi and psi angles predicted by DRNN, DReRBM, DRBM and DNN is about 20-21° and 29-30° on an independent dataset. The MAE of phi angle is comparable to the existing methods, but the MAE of psi angle is 29°, 2° lower than the existing methods. On the latest CASP12 targets, our methods also achieved the performance better than or comparable to a state-of-the art method. Our experiment demonstrates that deep learning is a valuable method for predicting protein torsion angles. The deep recurrent network architecture performs slightly better than deep feed-forward architecture, and the predicted residue contact number and the error distribution of torsion angles extracted from sequence fragments are useful features for improving prediction accuracy.

  17. Effect of ceramic membrane channel diameter on limiting retentate protein concentration during skim milk microfiltration.

    Science.gov (United States)

    Adams, Michael C; Barbano, David M

    2016-01-01

    Our objective was to determine the effect of retentate flow channel diameter (4 or 6mm) of nongraded permeability 100-nm pore size ceramic membranes operated in nonuniform transmembrane pressure mode on the limiting retentate protein concentration (LRPC) while microfiltering (MF) skim milk at a temperature of 50°C, a flux of 55 kg · m(-2) · h(-1), and an average cross-flow velocity of 7 m · s(-1). At the above conditions, the retentate true protein concentration was incrementally increased from 7 to 11.5%. When temperature, flux, and average cross-flow velocity were controlled, ceramic membrane retentate flow channel diameter did not affect the LRPC. This indicates that LRPC is not a function of the Reynolds number. Computational fluid dynamics data, which indicated that both membranes had similar radial velocity profiles within their retentate flow channels, supported this finding. Membranes with 6-mm flow channels can be operated at a lower pressure decrease from membrane inlet to membrane outlet (ΔP) or at a higher cross-flow velocity, depending on which is controlled, than membranes with 4-mm flow channels. This implies that 6-mm membranes could achieve a higher LRPC than 4-mm membranes at the same ΔP due to an increase in cross-flow velocity. In theory, the higher LRPC of the 6-mm membranes could facilitate 95% serum protein removal in 2 MF stages with diafiltration between stages if no serum protein were rejected by the membrane. At the same flux, retentate protein concentration, and average cross-flow velocity, 4-mm membranes require 21% more energy to remove a given amount of permeate than 6-mm membranes, despite the lower surface area of the 6-mm membranes. Equations to predict skim milk MF retentate viscosity as a function of protein concentration and temperature are provided. Retentate viscosity, retentate recirculation pump frequency required to maintain a given cross-flow velocity at a given retentate viscosity, and retentate protein

  18. PSPP: a protein structure prediction pipeline for computing clusters.

    Directory of Open Access Journals (Sweden)

    Michael S Lee

    2009-07-01

    Full Text Available Protein structures are critical for understanding the mechanisms of biological systems and, subsequently, for drug and vaccine design. Unfortunately, protein sequence data exceed structural data by a factor of more than 200 to 1. This gap can be partially filled by using computational protein structure prediction. While structure prediction Web servers are a notable option, they often restrict the number of sequence queries and/or provide a limited set of prediction methodologies. Therefore, we present a standalone protein structure prediction software package suitable for high-throughput structural genomic applications that performs all three classes of prediction methodologies: comparative modeling, fold recognition, and ab initio. This software can be deployed on a user's own high-performance computing cluster.The pipeline consists of a Perl core that integrates more than 20 individual software packages and databases, most of which are freely available from other research laboratories. The query protein sequences are first divided into domains either by domain boundary recognition or Bayesian statistics. The structures of the individual domains are then predicted using template-based modeling or ab initio modeling. The predicted models are scored with a statistical potential and an all-atom force field. The top-scoring ab initio models are annotated by structural comparison against the Structural Classification of Proteins (SCOP fold database. Furthermore, secondary structure, solvent accessibility, transmembrane helices, and structural disorder are predicted. The results are generated in text, tab-delimited, and hypertext markup language (HTML formats. So far, the pipeline has been used to study viral and bacterial proteomes.The standalone pipeline that we introduce here, unlike protein structure prediction Web servers, allows users to devote their own computing assets to process a potentially unlimited number of queries as well as perform

  19. Influence of bleaching on flavor of 34% whey protein concentrate and residual benzoic acid concentration in dried whey products

    Science.gov (United States)

    Previous studies have shown that bleaching negatively affects the flavor of 70% whey protein concentrate (WPC70), but bleaching effects on lower-protein products have not been established. Benzoyl peroxide (BP), a whey bleaching agent, degrades to benzoic acid (BA) and may elevate BA concentrations...

  20. Toxicological relationships between proteins obtained from protein target predictions of large toxicity databases

    International Nuclear Information System (INIS)

    Nigsch, Florian; Mitchell, John B.O.

    2008-01-01

    The combination of models for protein target prediction with large databases containing toxicological information for individual molecules allows the derivation of 'toxiclogical' profiles, i.e., to what extent are molecules of known toxicity predicted to interact with a set of protein targets. To predict protein targets of drug-like and toxic molecules, we built a computational multiclass model using the Winnow algorithm based on a dataset of protein targets derived from the MDL Drug Data Report. A 15-fold Monte Carlo cross-validation using 50% of each class for training, and the remaining 50% for testing, provided an assessment of the accuracy of that model. We retained the 3 top-ranking predictions and found that in 82% of all cases the correct target was predicted within these three predictions. The first prediction was the correct one in almost 70% of cases. A model built on the whole protein target dataset was then used to predict the protein targets for 150 000 molecules from the MDL Toxicity Database. We analysed the frequency of the predictions across the panel of protein targets for experimentally determined toxicity classes of all molecules. This allowed us to identify clusters of proteins related by their toxicological profiles, as well as toxicities that are related. Literature-based evidence is provided for some specific clusters to show the relevance of the relationships identified

  1. Detection of dysregulated protein-association networks by high-throughput proteomics predicts cancer vulnerabilities.

    Science.gov (United States)

    Lapek, John D; Greninger, Patricia; Morris, Robert; Amzallag, Arnaud; Pruteanu-Malinici, Iulian; Benes, Cyril H; Haas, Wilhelm

    2017-10-01

    The formation of protein complexes and the co-regulation of the cellular concentrations of proteins are essential mechanisms for cellular signaling and for maintaining homeostasis. Here we use isobaric-labeling multiplexed proteomics to analyze protein co-regulation and show that this allows the identification of protein-protein associations with high accuracy. We apply this 'interactome mapping by high-throughput quantitative proteome analysis' (IMAHP) method to a panel of 41 breast cancer cell lines and show that deviations of the observed protein co-regulations in specific cell lines from the consensus network affects cellular fitness. Furthermore, these aberrant interactions serve as biomarkers that predict the drug sensitivity of cell lines in screens across 195 drugs. We expect that IMAHP can be broadly used to gain insight into how changing landscapes of protein-protein associations affect the phenotype of biological systems.

  2. Protein complex prediction based on k-connected subgraphs in protein interaction network

    Directory of Open Access Journals (Sweden)

    Habibi Mahnaz

    2010-09-01

    Full Text Available Abstract Background Protein complexes play an important role in cellular mechanisms. Recently, several methods have been presented to predict protein complexes in a protein interaction network. In these methods, a protein complex is predicted as a dense subgraph of protein interactions. However, interactions data are incomplete and a protein complex does not have to be a complete or dense subgraph. Results We propose a more appropriate protein complex prediction method, CFA, that is based on connectivity number on subgraphs. We evaluate CFA using several protein interaction networks on reference protein complexes in two benchmark data sets (MIPS and Aloy, containing 1142 and 61 known complexes respectively. We compare CFA to some existing protein complex prediction methods (CMC, MCL, PCP and RNSC in terms of recall and precision. We show that CFA predicts more complexes correctly at a competitive level of precision. Conclusions Many real complexes with different connectivity level in protein interaction network can be predicted based on connectivity number. Our CFA program and results are freely available from http://www.bioinf.cs.ipm.ir/softwares/cfa/CFA.rar.

  3. Protein denaturation and functional properties of Lenient Steam Injection heat treated whey protein concentrate

    DEFF Research Database (Denmark)

    Dickow, Jonatan Ahrens; Kaufmann, Niels; Wiking, Lars

    2012-01-01

    Whey protein concentrate (WPC) was heat treated by use of the novel heat treatment method of Lenient Steam Injection (LSI) to elucidate new functional properties in relation to heat-induced gelation of heat treated WPC. Denaturation was measured by both DSC and FPLC, and the results of the two...... methods were highly correlated. Temperatures of up to 90 °C were applicable using LSI, whereas only 68 °C could be reached by plate heat exchange before coagulation/fouling. Denaturation of whey proteins increased with increasing heat treatment temperature up to a degree of 30–35% denaturation at 90 °C...

  4. Prediction of human protein function according to Gene Ontology categories

    DEFF Research Database (Denmark)

    Jensen, Lars Juhl; Gupta, Ramneek; Stærfeldt, Hans Henrik

    2003-01-01

    developed a method for prediction of protein function for a subset of classes from the Gene Ontology classification scheme. This subset includes several pharmaceutically interesting categories-transcription factors, receptors, ion channels, stress and immune response proteins, hormones and growth factors...

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

    Science.gov (United States)

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

    2012-02-01

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

  6. A large-scale evaluation of computational protein function prediction

    NARCIS (Netherlands)

    Radivojac, P.; Clark, W.T.; Oron, T.R.; Schnoes, A.M.; Wittkop, T.; Kourmpetis, Y.A.I.; Dijk, van A.D.J.; Friedberg, I.

    2013-01-01

    Automated annotation of protein function is challenging. As the number of sequenced genomes rapidly grows, the overwhelming majority of protein products can only be annotated computationally. If computational predictions are to be relied upon, it is crucial that the accuracy of these methods be

  7. Protein-Protein Interactions Prediction Based on Iterative Clique Extension with Gene Ontology Filtering

    Directory of Open Access Journals (Sweden)

    Lei Yang

    2014-01-01

    Full Text Available Cliques (maximal complete subnets in protein-protein interaction (PPI network are an important resource used to analyze protein complexes and functional modules. Clique-based methods of predicting PPI complement the data defection from biological experiments. However, clique-based predicting methods only depend on the topology of network. The false-positive and false-negative interactions in a network usually interfere with prediction. Therefore, we propose a method combining clique-based method of prediction and gene ontology (GO annotations to overcome the shortcoming and improve the accuracy of predictions. According to different GO correcting rules, we generate two predicted interaction sets which guarantee the quality and quantity of predicted protein interactions. The proposed method is applied to the PPI network from the Database of Interacting Proteins (DIP and most of the predicted interactions are verified by another biological database, BioGRID. The predicted protein interactions are appended to the original protein network, which leads to clique extension and shows the significance of biological meaning.

  8. Limited hydrolysis of soybean protein concentrate and isolate with ...

    African Journals Online (AJOL)

    STORAGESEVER

    2009-07-20

    Jul 20, 2009 ... world, since its proteins have high biological value while its cost is ... literatures that limited proteolysis of soybean protein pro- ducts offered a ..... hydrolysis of soluble protein present in waste liquors from soy processing.

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

    Directory of Open Access Journals (Sweden)

    Cui Guangyu

    2012-05-01

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

  10. Determination of protein concentration in raw milk by mid-infrared fourier transform infrared/attenuated total reflectance spectroscopy.

    Science.gov (United States)

    Etzion, Y; Linker, R; Cogan, U; Shmulevich, I

    2004-09-01

    This study investigates the potential use of attenuated total reflectance spectroscopy in the mid-infrared range for determining protein concentration in raw cow milk. The determination of protein concentration is based on the characteristic absorbance of milk proteins, which includes 2 absorbance bands in the 1500 to 1700 cm(-1) range, known as the amide I and amide II bands, and absorbance in the 1060 to 1100 cm(-1) range, which is associated with phosphate groups covalently bound to casein proteins. To minimize the influence of the strong water band (centered around 1640 cm(-1)) that overlaps with the amide I and amide II bands, an optimized automatic procedure for accurate water subtraction was applied. Following water subtraction, the spectra were analyzed by 3 methods, namely simple band integration, partial least squares (PLS) and neural networks. For the neural network models, the spectra were first decomposed by principal component analysis (PCA), and the neural network inputs were the spectra principal components scores. In addition, the concentrations of 2 constituents expected to interact with the protein (i.e., fat and lactose) were also used as inputs. These approaches were tested with 235 spectra of standardized raw milk samples, corresponding to 26 protein concentrations in the 2.47 to 3.90% (weight per volume) range. The simple integration method led to very poor results, whereas PLS resulted in prediction errors of about 0.22% protein. The neural network approach led to prediction errors of 0.20% protein when based on PCA scores only, and 0.08% protein when lactose and fat concentrations were also included in the model. These results indicate the potential usefulness of Fourier transform infrared/attenuated total reflectance spectroscopy for rapid, possibly online, determination of protein concentration in raw milk.

  11. Improving accuracy of protein-protein interaction prediction by considering the converse problem for sequence representation

    Directory of Open Access Journals (Sweden)

    Wang Yong

    2011-10-01

    Full Text Available Abstract Background With the development of genome-sequencing technologies, protein sequences are readily obtained by translating the measured mRNAs. Therefore predicting protein-protein interactions from the sequences is of great demand. The reason lies in the fact that identifying protein-protein interactions is becoming a bottleneck for eventually understanding the functions of proteins, especially for those organisms barely characterized. Although a few methods have been proposed, the converse problem, if the features used extract sufficient and unbiased information from protein sequences, is almost untouched. Results In this study, we interrogate this problem theoretically by an optimization scheme. Motivated by the theoretical investigation, we find novel encoding methods for both protein sequences and protein pairs. Our new methods exploit sufficiently the information of protein sequences and reduce artificial bias and computational cost. Thus, it significantly outperforms the available methods regarding sensitivity, specificity, precision, and recall with cross-validation evaluation and reaches ~80% and ~90% accuracy in Escherichia coli and Saccharomyces cerevisiae respectively. Our findings here hold important implication for other sequence-based prediction tasks because representation of biological sequence is always the first step in computational biology. Conclusions By considering the converse problem, we propose new representation methods for both protein sequences and protein pairs. The results show that our method significantly improves the accuracy of protein-protein interaction predictions.

  12. Improving protein function prediction methods with integrated literature data

    Directory of Open Access Journals (Sweden)

    Gabow Aaron P

    2008-04-01

    Full Text Available Abstract Background Determining the function of uncharacterized proteins is a major challenge in the post-genomic era due to the problem's complexity and scale. Identifying a protein's function contributes to an understanding of its role in the involved pathways, its suitability as a drug target, and its potential for protein modifications. Several graph-theoretic approaches predict unidentified functions of proteins by using the functional annotations of better-characterized proteins in protein-protein interaction networks. We systematically consider the use of literature co-occurrence data, introduce a new method for quantifying the reliability of co-occurrence and test how performance differs across species. We also quantify changes in performance as the prediction algorithms annotate with increased specificity. Results We find that including information on the co-occurrence of proteins within an abstract greatly boosts performance in the Functional Flow graph-theoretic function prediction algorithm in yeast, fly and worm. This increase in performance is not simply due to the presence of additional edges since supplementing protein-protein interactions with co-occurrence data outperforms supplementing with a comparably-sized genetic interaction dataset. Through the combination of protein-protein interactions and co-occurrence data, the neighborhood around unknown proteins is quickly connected to well-characterized nodes which global prediction algorithms can exploit. Our method for quantifying co-occurrence reliability shows superior performance to the other methods, particularly at threshold values around 10% which yield the best trade off between coverage and accuracy. In contrast, the traditional way of asserting co-occurrence when at least one abstract mentions both proteins proves to be the worst method for generating co-occurrence data, introducing too many false positives. Annotating the functions with greater specificity is harder

  13. Protein complex prediction via dense subgraphs and false positive analysis.

    Directory of Open Access Journals (Sweden)

    Cecilia Hernandez

    Full Text Available Many proteins work together with others in groups called complexes in order to achieve a specific function. Discovering protein complexes is important for understanding biological processes and predict protein functions in living organisms. Large-scale and throughput techniques have made possible to compile protein-protein interaction networks (PPI networks, which have been used in several computational approaches for detecting protein complexes. Those predictions might guide future biologic experimental research. Some approaches are topology-based, where highly connected proteins are predicted to be complexes; some propose different clustering algorithms using partitioning, overlaps among clusters for networks modeled with unweighted or weighted graphs; and others use density of clusters and information based on protein functionality. However, some schemes still require much processing time or the quality of their results can be improved. Furthermore, most of the results obtained with computational tools are not accompanied by an analysis of false positives. We propose an effective and efficient mining algorithm for discovering highly connected subgraphs, which is our base for defining protein complexes. Our representation is based on transforming the PPI network into a directed acyclic graph that reduces the number of represented edges and the search space for discovering subgraphs. Our approach considers weighted and unweighted PPI networks. We compare our best alternative using PPI networks from Saccharomyces cerevisiae (yeast and Homo sapiens (human with state-of-the-art approaches in terms of clustering, biological metrics and execution times, as well as three gold standards for yeast and two for human. Furthermore, we analyze false positive predicted complexes searching the PDBe (Protein Data Bank in Europe database in order to identify matching protein complexes that have been purified and structurally characterized. Our analysis shows

  14. Estimation of daily protein intake based on spot urine urea nitrogen concentration in chronic kidney disease patients.

    Science.gov (United States)

    Kanno, Hiroko; Kanda, Eiichiro; Sato, Asako; Sakamoto, Kaori; Kanno, Yoshihiko

    2016-04-01

    Determination of daily protein intake in the management of chronic kidney disease (CKD) requires precision. Inaccuracies in recording dietary intake occur, and estimation from total urea excretion presents hurdles owing to the difficulty of collecting whole urine for 24 h. Spot urine has been used for measuring daily sodium intake and urinary protein excretion. In this cross-sectional study, we investigated whether urea nitrogen (UN) concentration in spot urine can be used to predict daily protein intake instead of the 24-h urine collection in 193 Japanese CKD patients (Stages G1-G5). After patient randomization into 2 datasets for the development and validation of models, bootstrapping was used to develop protein intake estimation models. The parameters for the candidate multivariate regression models were male gender, age, body mass index (BMI), diabetes mellitus, dyslipidemia, proteinuria, estimated glomerular filtration rate, serum albumin level, spot urinary UN and creatinine level, and spot urinary UN/creatinine levels. The final model contained BMI and spot urinary UN level. The final model was selected because of the higher correlation between the predicted and measured protein intakes r = 0.558 (95 % confidence interval 0.400, 0.683), and the smaller distribution of the difference between the measured and predicted protein intakes than those of the other models. The results suggest that UN concentration in spot urine may be used to estimate daily protein intake and that a prediction formula would be useful for nutritional control in CKD patients.

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

    Science.gov (United States)

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

    2009-01-01

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

  16. Drying and hydration of proteins at high concentration

    NARCIS (Netherlands)

    Bouman, J.

    2015-01-01

    Proteins are the building blocks of life and serve a wide range of essential functions in organisms. Many metabolic reactions in organisms are catalysed by enzymes, DNA is replicated by proteins and in cells proteins often facilitate active transport of e.g. glucose or ions. Proteins also serve

  17. Rationally designed synthetic protein hydrogels with predictable mechanical properties.

    Science.gov (United States)

    Wu, Junhua; Li, Pengfei; Dong, Chenling; Jiang, Heting; Bin Xue; Gao, Xiang; Qin, Meng; Wang, Wei; Bin Chen; Cao, Yi

    2018-02-12

    Designing synthetic protein hydrogels with tailored mechanical properties similar to naturally occurring tissues is an eternal pursuit in tissue engineering and stem cell and cancer research. However, it remains challenging to correlate the mechanical properties of protein hydrogels with the nanomechanics of individual building blocks. Here we use single-molecule force spectroscopy, protein engineering and theoretical modeling to prove that the mechanical properties of protein hydrogels are predictable based on the mechanical hierarchy of the cross-linkers and the load-bearing modules at the molecular level. These findings provide a framework for rationally designing protein hydrogels with independently tunable elasticity, extensibility, toughness and self-healing. Using this principle, we demonstrate the engineering of self-healable muscle-mimicking hydrogels that can significantly dissipate energy through protein unfolding. We expect that this principle can be generalized for the construction of protein hydrogels with customized mechanical properties for biomedical applications.

  18. Roles for text mining in protein function prediction.

    Science.gov (United States)

    Verspoor, Karin M

    2014-01-01

    The Human Genome Project has provided science with a hugely valuable resource: the blueprints for life; the specification of all of the genes that make up a human. While the genes have all been identified and deciphered, it is proteins that are the workhorses of the human body: they are essential to virtually all cell functions and are the primary mechanism through which biological function is carried out. Hence in order to fully understand what happens at a molecular level in biological organisms, and eventually to enable development of treatments for diseases where some aspect of a biological system goes awry, we must understand the functions of proteins. However, experimental characterization of protein function cannot scale to the vast amount of DNA sequence data now available. Computational protein function prediction has therefore emerged as a problem at the forefront of modern biology (Radivojac et al., Nat Methods 10(13):221-227, 2013).Within the varied approaches to computational protein function prediction that have been explored, there are several that make use of biomedical literature mining. These methods take advantage of information in the published literature to associate specific proteins with specific protein functions. In this chapter, we introduce two main strategies for doing this: association of function terms, represented as Gene Ontology terms (Ashburner et al., Nat Genet 25(1):25-29, 2000), to proteins based on information in published articles, and a paradigm called LEAP-FS (Literature-Enhanced Automated Prediction of Functional Sites) in which literature mining is used to validate the predictions of an orthogonal computational protein function prediction method.

  19. Study of the proteins in the defatted flour and protein concentrate of baru nuts (Dipteryx alata Vog

    Directory of Open Access Journals (Sweden)

    Rita de Cássia Avellaneda Guimarães

    2012-09-01

    Full Text Available Baru (Dipteryx alata Vog. is an abundant legume in the Brazilian Savanna. Its nuts can be exploited sustainably using its protein and lipid fractions. This study aimed to analyze the proteins of the nuts present in the defatted flour and protein concentrate in terms of their functional properties, the profile of their fractions, and the in vitro digestibility. The flour was defatted with hexane and extracted at the pH of higher protein solubility to obtain the protein concentrate. The electrophoretic profile of the protein fractions was evaluated in SDS-PAGE gel. The functional properties of the proteins indicate the possibility of their use in various foods, like soybeans providing water absorption capacity, oil absorption capacity, emulsifying properties, and foamability. Globulins, followed by the albumins, are the major fractions of the flour and protein concentrate, respectively. Digestibility was greater for the concentrate than for the defatted flour.

  20. Application of Machine Learning Approaches for Protein-protein Interactions Prediction.

    Science.gov (United States)

    Zhang, Mengying; Su, Qiang; Lu, Yi; Zhao, Manman; Niu, Bing

    2017-01-01

    Proteomics endeavors to study the structures, functions and interactions of proteins. Information of the protein-protein interactions (PPIs) helps to improve our knowledge of the functions and the 3D structures of proteins. Thus determining the PPIs is essential for the study of the proteomics. In this review, in order to study the application of machine learning in predicting PPI, some machine learning approaches such as support vector machine (SVM), artificial neural networks (ANNs) and random forest (RF) were selected, and the examples of its applications in PPIs were listed. SVM and RF are two commonly used methods. Nowadays, more researchers predict PPIs by combining more than two methods. This review presents the application of machine learning approaches in predicting PPI. Many examples of success in identification and prediction in the area of PPI prediction have been discussed, and the PPIs research is still in progress. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

  1. Exploiting protein flexibility to predict the location of allosteric sites

    Directory of Open Access Journals (Sweden)

    Panjkovich Alejandro

    2012-10-01

    Full Text Available Abstract Background Allostery is one of the most powerful and common ways of regulation of protein activity. However, for most allosteric proteins identified to date the mechanistic details of allosteric modulation are not yet well understood. Uncovering common mechanistic patterns underlying allostery would allow not only a better academic understanding of the phenomena, but it would also streamline the design of novel therapeutic solutions. This relatively unexplored therapeutic potential and the putative advantages of allosteric drugs over classical active-site inhibitors fuel the attention allosteric-drug research is receiving at present. A first step to harness the regulatory potential and versatility of allosteric sites, in the context of drug-discovery and design, would be to detect or predict their presence and location. In this article, we describe a simple computational approach, based on the effect allosteric ligands exert on protein flexibility upon binding, to predict the existence and position of allosteric sites on a given protein structure. Results By querying the literature and a recently available database of allosteric sites, we gathered 213 allosteric proteins with structural information that we further filtered into a non-redundant set of 91 proteins. We performed normal-mode analysis and observed significant changes in protein flexibility upon allosteric-ligand binding in 70% of the cases. These results agree with the current view that allosteric mechanisms are in many cases governed by changes in protein dynamics caused by ligand binding. Furthermore, we implemented an approach that achieves 65% positive predictive value in identifying allosteric sites within the set of predicted cavities of a protein (stricter parameters set, 0.22 sensitivity, by combining the current analysis on dynamics with previous results on structural conservation of allosteric sites. We also analyzed four biological examples in detail, revealing

  2. Prediction of Protein Thermostability by an Efficient Neural Network Approach

    Directory of Open Access Journals (Sweden)

    Jalal Rezaeenour

    2016-10-01

    Full Text Available Introduction: Manipulation of protein stability is important for understanding the principles that govern protein thermostability, both in basic research and industrial applications. Various data mining techniques exist for prediction of thermostable proteins. Furthermore, ANN methods have attracted significant attention for prediction of thermostability, because they constitute an appropriate approach to mapping the non-linear input-output relationships and massive parallel computing. Method: An Extreme Learning Machine (ELM was applied to estimate thermal behavior of 1289 proteins. In the proposed algorithm, the parameters of ELM were optimized using a Genetic Algorithm (GA, which tuned a set of input variables, hidden layer biases, and input weights, to and enhance the prediction performance. The method was executed on a set of amino acids, yielding a total of 613 protein features. A number of feature selection algorithms were used to build subsets of the features. A total of 1289 protein samples and 613 protein features were calculated from UniProt database to understand features contributing to the enzymes’ thermostability and find out the main features that influence this valuable characteristic. Results:At the primary structure level, Gln, Glu and polar were the features that mostly contributed to protein thermostability. At the secondary structure level, Helix_S, Coil, and charged_Coil were the most important features affecting protein thermostability. These results suggest that the thermostability of proteins is mainly associated with primary structural features of the protein. According to the results, the influence of primary structure on the thermostabilty of a protein was more important than that of the secondary structure. It is shown that prediction accuracy of ELM (mean square error can improve dramatically using GA with error rates RMSE=0.004 and MAPE=0.1003. Conclusion: The proposed approach for forecasting problem

  3. Comparison of marine dispersion model predictions with environmental radionuclide concentrations

    International Nuclear Information System (INIS)

    Johnson, C.E.; McKay, W.A.

    1988-01-01

    The comparison of marine dispersion model results with measurements is an essential part of model development and testing. The results from two residual flow models are compared with seawater concentrations, and in one case with concentrations measured in marine molluscs. For areas with short turnover times, seawater concentrations respond rapidly to variations in discharge rate and marine currents. These variations are difficult to model, and comparison with concentrations in marine animals provides an alternative and complementary technique for model validation with the advantages that the measurements reflect the mean conditions and frequently form a useful time series. (author)

  4. False positive reduction in protein-protein interaction predictions using gene ontology annotations

    Directory of Open Access Journals (Sweden)

    Lin Yen-Han

    2007-07-01

    Full Text Available Abstract Background Many crucial cellular operations such as metabolism, signalling, and regulations are based on protein-protein interactions. However, the lack of robust protein-protein interaction information is a challenge. One reason for the lack of solid protein-protein interaction information is poor agreement between experimental findings and computational sets that, in turn, comes from huge false positive predictions in computational approaches. Reduction of false positive predictions and enhancing true positive fraction of computationally predicted protein-protein interaction datasets based on highly confident experimental results has not been adequately investigated. Results Gene Ontology (GO annotations were used to reduce false positive protein-protein interactions (PPI pairs resulting from computational predictions. Using experimentally obtained PPI pairs as a training dataset, eight top-ranking keywords were extracted from GO molecular function annotations. The sensitivity of these keywords is 64.21% in the yeast experimental dataset and 80.83% in the worm experimental dataset. The specificities, a measure of recovery power, of these keywords applied to four predicted PPI datasets for each studied organisms, are 48.32% and 46.49% (by average of four datasets in yeast and worm, respectively. Based on eight top-ranking keywords and co-localization of interacting proteins a set of two knowledge rules were deduced and applied to remove false positive protein pairs. The 'strength', a measure of improvement provided by the rules was defined based on the signal-to-noise ratio and implemented to measure the applicability of knowledge rules applying to the predicted PPI datasets. Depending on the employed PPI-predicting methods, the strength varies between two and ten-fold of randomly removing protein pairs from the datasets. Conclusion Gene Ontology annotations along with the deduced knowledge rules could be implemented to partially

  5. High Concentration Protein Ultrafiltration: a Comparative Fouling Assessment

    Science.gov (United States)

    Lim, Y. P.; Mohammad, A. W.

    2018-05-01

    In this paper, the predominant fouling mechanism via pH manipulation in gelatin ultrafiltration (UF) at constant operating pressure was studied. Two 30 kDa molecular weight cut off (MWCO) UF membranes with different hydrophilic/hydrophobic properties were tested at solution pH near gelatin isoelectric point (IEP), pH below and above gelatin’s IEP. The resistance-in-series model was used to determine quantitatively the contribution of each filtration resistance occurred during gelatin UF. The governing fouling mechanisms were investigated using classical blocking laws. The results demonstrated that concentration polarization remain as dominant fouling resistance in gelatin UF, but exceptional case was observed at pH away from gelatin’s IEP, showing that combined reversible and irreversible fouling resistances contributed around 57% and 37%, respectively to the overall fouling resistances. Under all experimental condition tested, permeate flux decline was accurately predicted by all the models studied. Fouling profile was fitted well with “Standard Blocking”, “Intermediate Blocking” and “Cake Filtration” model for regenerated cellulose acetate (RCA) membrane and “Cake Filtration” model for polyethersulphone (PES) membrane.

  6. Nutritional and functional properties of whey proteins concentrate and isolate

    OpenAIRE

    Zoran Herceg; Anet Režek

    2006-01-01

    Whey protein fractions represent 18 - 20 % of total milk nitrogen content. Nutritional value in addition to diverse physico - chemical and functional properties make whey proteins highly suitable for application in foodstuffs. In the most cases, whey proteins are used because of their functional properties. Whey proteins possess favourable functional characteristics such as gelling, water binding, emulsification and foaming ability. Due to application of new process techniques (membrane fract...

  7. Protein subcellular localization prediction using artificial intelligence technology.

    Science.gov (United States)

    Nair, Rajesh; Rost, Burkhard

    2008-01-01

    Proteins perform many important tasks in living organisms, such as catalysis of biochemical reactions, transport of nutrients, and recognition and transmission of signals. The plethora of aspects of the role of any particular protein is referred to as its "function." One aspect of protein function that has been the target of intensive research by computational biologists is its subcellular localization. Proteins must be localized in the same subcellular compartment to cooperate toward a common physiological function. Aberrant subcellular localization of proteins can result in several diseases, including kidney stones, cancer, and Alzheimer's disease. To date, sequence homology remains the most widely used method for inferring the function of a protein. However, the application of advanced artificial intelligence (AI)-based techniques in recent years has resulted in significant improvements in our ability to predict the subcellular localization of a protein. The prediction accuracy has risen steadily over the years, in large part due to the application of AI-based methods such as hidden Markov models (HMMs), neural networks (NNs), and support vector machines (SVMs), although the availability of larger experimental datasets has also played a role. Automatic methods that mine textual information from the biological literature and molecular biology databases have considerably sped up the process of annotation for proteins for which some information regarding function is available in the literature. State-of-the-art methods based on NNs and HMMs can predict the presence of N-terminal sorting signals extremely accurately. Ab initio methods that predict subcellular localization for any protein sequence using only the native amino acid sequence and features predicted from the native sequence have shown the most remarkable improvements. The prediction accuracy of these methods has increased by over 30% in the past decade. The accuracy of these methods is now on par with

  8. Protein-protein interaction site predictions with three-dimensional probability distributions of interacting atoms on protein surfaces.

    Directory of Open Access Journals (Sweden)

    Ching-Tai Chen

    Full Text Available Protein-protein interactions are key to many biological processes. Computational methodologies devised to predict protein-protein interaction (PPI sites on protein surfaces are important tools in providing insights into the biological functions of proteins and in developing therapeutics targeting the protein-protein interaction sites. One of the general features of PPI sites is that the core regions from the two interacting protein surfaces are complementary to each other, similar to the interior of proteins in packing density and in the physicochemical nature of the amino acid composition. In this work, we simulated the physicochemical complementarities by constructing three-dimensional probability density maps of non-covalent interacting atoms on the protein surfaces. The interacting probabilities were derived from the interior of known structures. Machine learning algorithms were applied to learn the characteristic patterns of the probability density maps specific to the PPI sites. The trained predictors for PPI sites were cross-validated with the training cases (consisting of 432 proteins and were tested on an independent dataset (consisting of 142 proteins. The residue-based Matthews correlation coefficient for the independent test set was 0.423; the accuracy, precision, sensitivity, specificity were 0.753, 0.519, 0.677, and 0.779 respectively. The benchmark results indicate that the optimized machine learning models are among the best predictors in identifying PPI sites on protein surfaces. In particular, the PPI site prediction accuracy increases with increasing size of the PPI site and with increasing hydrophobicity in amino acid composition of the PPI interface; the core interface regions are more likely to be recognized with high prediction confidence. The results indicate that the physicochemical complementarity patterns on protein surfaces are important determinants in PPIs, and a substantial portion of the PPI sites can be predicted

  9. Protein-Protein Interaction Site Predictions with Three-Dimensional Probability Distributions of Interacting Atoms on Protein Surfaces

    Science.gov (United States)

    Chen, Ching-Tai; Peng, Hung-Pin; Jian, Jhih-Wei; Tsai, Keng-Chang; Chang, Jeng-Yih; Yang, Ei-Wen; Chen, Jun-Bo; Ho, Shinn-Ying; Hsu, Wen-Lian; Yang, An-Suei

    2012-01-01

    Protein-protein interactions are key to many biological processes. Computational methodologies devised to predict protein-protein interaction (PPI) sites on protein surfaces are important tools in providing insights into the biological functions of proteins and in developing therapeutics targeting the protein-protein interaction sites. One of the general features of PPI sites is that the core regions from the two interacting protein surfaces are complementary to each other, similar to the interior of proteins in packing density and in the physicochemical nature of the amino acid composition. In this work, we simulated the physicochemical complementarities by constructing three-dimensional probability density maps of non-covalent interacting atoms on the protein surfaces. The interacting probabilities were derived from the interior of known structures. Machine learning algorithms were applied to learn the characteristic patterns of the probability density maps specific to the PPI sites. The trained predictors for PPI sites were cross-validated with the training cases (consisting of 432 proteins) and were tested on an independent dataset (consisting of 142 proteins). The residue-based Matthews correlation coefficient for the independent test set was 0.423; the accuracy, precision, sensitivity, specificity were 0.753, 0.519, 0.677, and 0.779 respectively. The benchmark results indicate that the optimized machine learning models are among the best predictors in identifying PPI sites on protein surfaces. In particular, the PPI site prediction accuracy increases with increasing size of the PPI site and with increasing hydrophobicity in amino acid composition of the PPI interface; the core interface regions are more likely to be recognized with high prediction confidence. The results indicate that the physicochemical complementarity patterns on protein surfaces are important determinants in PPIs, and a substantial portion of the PPI sites can be predicted correctly with

  10. Prediction of Protein–Protein Interactions by Evidence Combining Methods

    Directory of Open Access Journals (Sweden)

    Ji-Wei Chang

    2016-11-01

    Full Text Available Most cellular functions involve proteins’ features based on their physical interactions with other partner proteins. Sketching a map of protein–protein interactions (PPIs is therefore an important inception step towards understanding the basics of cell functions. Several experimental techniques operating in vivo or in vitro have made significant contributions to screening a large number of protein interaction partners, especially high-throughput experimental methods. However, computational approaches for PPI predication supported by rapid accumulation of data generated from experimental techniques, 3D structure definitions, and genome sequencing have boosted the map sketching of PPIs. In this review, we shed light on in silico PPI prediction methods that integrate evidence from multiple sources, including evolutionary relationship, function annotation, sequence/structure features, network topology and text mining. These methods are developed for integration of multi-dimensional evidence, for designing the strategies to predict novel interactions, and for making the results consistent with the increase of prediction coverage and accuracy.

  11. Protein structure prediction using bee colony optimization metaheuristic

    DEFF Research Database (Denmark)

    Fonseca, Rasmus; Paluszewski, Martin; Winter, Pawel

    2010-01-01

    of the proteins structure, an energy potential and some optimization algorithm that ¿nds the structure with minimal energy. Bee Colony Optimization (BCO) is a relatively new approach to solving opti- mization problems based on the foraging behaviour of bees. Several variants of BCO have been suggested......Predicting the native structure of proteins is one of the most challenging problems in molecular biology. The goal is to determine the three-dimensional struc- ture from the one-dimensional amino acid sequence. De novo prediction algorithms seek to do this by developing a representation...... our BCO method to generate good solutions to the protein structure prediction problem. The results show that BCO generally ¿nds better solutions than simulated annealing which so far has been the metaheuristic of choice for this problem....

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

    Science.gov (United States)

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

    2013-07-01

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

  13. Plasma proteins predict conversion to dementia from prodromal disease.

    Science.gov (United States)

    Hye, Abdul; Riddoch-Contreras, Joanna; Baird, Alison L; Ashton, Nicholas J; Bazenet, Chantal; Leung, Rufina; Westman, Eric; Simmons, Andrew; Dobson, Richard; Sattlecker, Martina; Lupton, Michelle; Lunnon, Katie; Keohane, Aoife; Ward, Malcolm; Pike, Ian; Zucht, Hans Dieter; Pepin, Danielle; Zheng, Wei; Tunnicliffe, Alan; Richardson, Jill; Gauthier, Serge; Soininen, Hilkka; Kłoszewska, Iwona; Mecocci, Patrizia; Tsolaki, Magda; Vellas, Bruno; Lovestone, Simon

    2014-11-01

    The study aimed to validate previously discovered plasma biomarkers associated with AD, using a design based on imaging measures as surrogate for disease severity and assess their prognostic value in predicting conversion to dementia. Three multicenter cohorts of cognitively healthy elderly, mild cognitive impairment (MCI), and AD participants with standardized clinical assessments and structural neuroimaging measures were used. Twenty-six candidate proteins were quantified in 1148 subjects using multiplex (xMAP) assays. Sixteen proteins correlated with disease severity and cognitive decline. Strongest associations were in the MCI group with a panel of 10 proteins predicting progression to AD (accuracy 87%, sensitivity 85%, and specificity 88%). We have identified 10 plasma proteins strongly associated with disease severity and disease progression. Such markers may be useful for patient selection for clinical trials and assessment of patients with predisease subjective memory complaints. Copyright © 2014 The Authors. Published by Elsevier Inc. All rights reserved.

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

    DEFF Research Database (Denmark)

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

    2012-01-01

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

  15. Delineation of concentration ranges and longitudinal changes of human plasma protein variants.

    Directory of Open Access Journals (Sweden)

    Olgica Trenchevska

    Full Text Available Human protein diversity arises as a result of alternative splicing, single nucleotide polymorphisms (SNPs and posttranslational modifications. Because of these processes, each protein can exists as multiple variants in vivo. Tailored strategies are needed to study these protein variants and understand their role in health and disease. In this work we utilized quantitative mass spectrometric immunoassays to determine the protein variants concentration of beta-2-microglobulin, cystatin C, retinol binding protein, and transthyretin, in a population of 500 healthy individuals. Additionally, we determined the longitudinal concentration changes for the protein variants from four individuals over a 6 month period. Along with the native forms of the four proteins, 13 posttranslationally modified variants and 7 SNP-derived variants were detected and their concentration determined. Correlations of the variants concentration with geographical origin, gender, and age of the individuals were also examined. This work represents an important step toward building a catalog of protein variants concentrations and examining their longitudinal changes.

  16. Fast dynamics perturbation analysis for prediction of protein functional sites

    Directory of Open Access Journals (Sweden)

    Cohn Judith D

    2008-01-01

    Full Text Available Abstract Background We present a fast version of the dynamics perturbation analysis (DPA algorithm to predict functional sites in protein structures. The original DPA algorithm finds regions in proteins where interactions cause a large change in the protein conformational distribution, as measured using the relative entropy Dx. Such regions are associated with functional sites. Results The Fast DPA algorithm, which accelerates DPA calculations, is motivated by an empirical observation that Dx in a normal-modes model is highly correlated with an entropic term that only depends on the eigenvalues of the normal modes. The eigenvalues are accurately estimated using first-order perturbation theory, resulting in a N-fold reduction in the overall computational requirements of the algorithm, where N is the number of residues in the protein. The performance of the original and Fast DPA algorithms was compared using protein structures from a standard small-molecule docking test set. For nominal implementations of each algorithm, top-ranked Fast DPA predictions overlapped the true binding site 94% of the time, compared to 87% of the time for original DPA. In addition, per-protein recall statistics (fraction of binding-site residues that are among predicted residues were slightly better for Fast DPA. On the other hand, per-protein precision statistics (fraction of predicted residues that are among binding-site residues were slightly better using original DPA. Overall, the performance of Fast DPA in predicting ligand-binding-site residues was comparable to that of the original DPA algorithm. Conclusion Compared to the original DPA algorithm, the decreased run time with comparable performance makes Fast DPA well-suited for implementation on a web server and for high-throughput analysis.

  17. Predicting protein-protein interactions from multimodal biological data sources via nonnegative matrix tri-factorization.

    Science.gov (United States)

    Wang, Hua; Huang, Heng; Ding, Chris; Nie, Feiping

    2013-04-01

    Protein interactions are central to all the biological processes and structural scaffolds in living organisms, because they orchestrate a number of cellular processes such as metabolic pathways and immunological recognition. Several high-throughput methods, for example, yeast two-hybrid system and mass spectrometry method, can help determine protein interactions, which, however, suffer from high false-positive rates. Moreover, many protein interactions predicted by one method are not supported by another. Therefore, computational methods are necessary and crucial to complete the interactome expeditiously. In this work, we formulate the problem of predicting protein interactions from a new mathematical perspective--sparse matrix completion, and propose a novel nonnegative matrix factorization (NMF)-based matrix completion approach to predict new protein interactions from existing protein interaction networks. Through using manifold regularization, we further develop our method to integrate different biological data sources, such as protein sequences, gene expressions, protein structure information, etc. Extensive experimental results on four species, Saccharomyces cerevisiae, Drosophila melanogaster, Homo sapiens, and Caenorhabditis elegans, have shown that our new methods outperform related state-of-the-art protein interaction prediction methods.

  18. Predicting protein structures with a multiplayer online game

    OpenAIRE

    Cooper, Seth; Khatib, Firas; Treuille, Adrien; Barbero, Janos; Lee, Jeehyung; Beenen, Michael; Leaver-Fay, Andrew; Baker, David; Popović, Zoran

    2010-01-01

    People exert significant amounts of problem solving effort playing computer games. Simple image- and text-recognition tasks have been successfully crowd-sourced through gamesi, ii, iii, but it is not clear if more complex scientific problems can be similarly solved with human-directed computing. Protein structure prediction is one such problem: locating the biologically relevant native conformation of a protein is a formidable computational challenge given the very large size of the search sp...

  19. Prediction of protein-protein interaction sites in sequences and 3D structures by random forests.

    Directory of Open Access Journals (Sweden)

    Mile Sikić

    2009-01-01

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

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

    Directory of Open Access Journals (Sweden)

    Mu Gao

    2009-03-01

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

  1. Prediction of Hydrophobic Cores of Proteins Using Wavelet Analysis.

    Science.gov (United States)

    Hirakawa; Kuhara

    1997-01-01

    Information concerning the secondary structures, flexibility, epitope and hydrophobic regions of amino acid sequences can be extracted by assigning physicochemical indices to each amino acid residue, and information on structure can be derived using the sliding window averaging technique, which is in wide use for smoothing out raw functions. Wavelet analysis has shown great potential and applicability in many fields, such as astronomy, radar, earthquake prediction, and signal or image processing. This approach is efficient for removing noise from various functions. Here we employed wavelet analysis to smooth out a plot assigned to a hydrophobicity index for amino acid sequences. We then used the resulting function to predict hydrophobic cores in globular proteins. We calculated the prediction accuracy for the hydrophobic cores of 88 representative set of proteins. Use of wavelet analysis made feasible the prediction of hydrophobic cores at 6.13% greater accuracy than the sliding window averaging technique.

  2. Cascaded bidirectional recurrent neural networks for protein secondary structure prediction.

    Science.gov (United States)

    Chen, Jinmiao; Chaudhari, Narendra

    2007-01-01

    Protein secondary structure (PSS) prediction is an important topic in bioinformatics. Our study on a large set of non-homologous proteins shows that long-range interactions commonly exist and negatively affect PSS prediction. Besides, we also reveal strong correlations between secondary structure (SS) elements. In order to take into account the long-range interactions and SS-SS correlations, we propose a novel prediction system based on cascaded bidirectional recurrent neural network (BRNN). We compare the cascaded BRNN against another two BRNN architectures, namely the original BRNN architecture used for speech recognition as well as Pollastri's BRNN that was proposed for PSS prediction. Our cascaded BRNN achieves an overall three state accuracy Q3 of 74.38\\%, and reaches a high Segment OVerlap (SOV) of 66.0455. It outperforms the original BRNN and Pollastri's BRNN in both Q3 and SOV. Specifically, it improves the SOV score by 4-6%.

  3. Predicting Protein Function via Semantic Integration of Multiple Networks.

    Science.gov (United States)

    Yu, Guoxian; Fu, Guangyuan; Wang, Jun; Zhu, Hailong

    2016-01-01

    Determining the biological functions of proteins is one of the key challenges in the post-genomic era. The rapidly accumulated large volumes of proteomic and genomic data drives to develop computational models for automatically predicting protein function in large scale. Recent approaches focus on integrating multiple heterogeneous data sources and they often get better results than methods that use single data source alone. In this paper, we investigate how to integrate multiple biological data sources with the biological knowledge, i.e., Gene Ontology (GO), for protein function prediction. We propose a method, called SimNet, to Semantically integrate multiple functional association Networks derived from heterogenous data sources. SimNet firstly utilizes GO annotations of proteins to capture the semantic similarity between proteins and introduces a semantic kernel based on the similarity. Next, SimNet constructs a composite network, obtained as a weighted summation of individual networks, and aligns the network with the kernel to get the weights assigned to individual networks. Then, it applies a network-based classifier on the composite network to predict protein function. Experiment results on heterogenous proteomic data sources of Yeast, Human, Mouse, and Fly show that, SimNet not only achieves better (or comparable) results than other related competitive approaches, but also takes much less time. The Matlab codes of SimNet are available at https://sites.google.com/site/guoxian85/simnet.

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

    Science.gov (United States)

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

    2017-01-01

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

  5. Prediction of protein–protein interactions: unifying evolution and structure at protein interfaces

    International Nuclear Information System (INIS)

    Tuncbag, Nurcan; Gursoy, Attila; Keskin, Ozlem

    2011-01-01

    The vast majority of the chores in the living cell involve protein–protein interactions. Providing details of protein interactions at the residue level and incorporating them into protein interaction networks are crucial toward the elucidation of a dynamic picture of cells. Despite the rapid increase in the number of structurally known protein complexes, we are still far away from a complete network. Given experimental limitations, computational modeling of protein interactions is a prerequisite to proceed on the way to complete structural networks. In this work, we focus on the question 'how do proteins interact?' rather than 'which proteins interact?' and we review structure-based protein–protein interaction prediction approaches. As a sample approach for modeling protein interactions, PRISM is detailed which combines structural similarity and evolutionary conservation in protein interfaces to infer structures of complexes in the protein interaction network. This will ultimately help us to understand the role of protein interfaces in predicting bound conformations

  6. Predicting the Concentration Characteristics of Itakpe Iron Ore for cut ...

    African Journals Online (AJOL)

    MICHAEL HORSFALL

    ABSTRACT: Concentration characteristics of an ore are very critical to the estimation of cut-off grade of a ... enormous financial cost of laboratory analysis and time required for such .... Arua A.I. (1997) Fundamentals of statistics, Publisher,.

  7. Correlation for predicting aerosol concentration in sodium spray fires

    International Nuclear Information System (INIS)

    Marimuthu, K.

    2001-01-01

    Aerosol behaviour computer codes are reported for the study of time-dependent airborne aerosol concentration in a containment. The use of available computer codes requires a thorough knowledge of the various rate processes employed to describe the aerosol behaviour. The present work describes a simple empirical equation to calculate sodium fire aerosol concentration with respect to time in a containment and is applicable to sodium spray fire conditions. Sodium spray fire aerosol concentration values obtained using this simplified approach agree reasonably well with experimental results. The empirical equation described in the present work is incorporated in the spray fire code NACOM and the code calculated values of aerosol concentration agreement with the sodium spray fire experimental results is reasonably good. (author)

  8. Predicting human epidermal melanin concentrations for different skin tones

    CSIR Research Space (South Africa)

    Smit, Jacoba E

    2011-07-01

    Full Text Available epidermal melanin concentrations for different skin tones JE Smit 1 , AE Karsten 2 , RW Sparrow 1 1 CSIR Biosciences, Pretoria, South Africa 2 CSIR National Laser Centre, Pretoria, South Africa Author e-mail address: KSmit...

  9. Variation in predicted internal concentrations in relation to PBPK model complexity for rainbow trout

    Energy Technology Data Exchange (ETDEWEB)

    Salmina, E.S.; Wondrousch, D. [UFZ Department of Ecological Chemistry, Helmholtz Centre for Environmental Research, Permoserstr. 15, 04318 Leipzig (Germany); Institute for Organic Chemistry, Technical University Bergakademie Freiberg, Leipziger Str. 29, 09596 Freiberg (Germany); Kühne, R. [UFZ Department of Ecological Chemistry, Helmholtz Centre for Environmental Research, Permoserstr. 15, 04318 Leipzig (Germany); Potemkin, V.A. [Department of Chemistry, South Ural State Medical University, Vorovskogo 64, 454048, Chelyabinsk (Russian Federation); Schüürmann, G. [UFZ Department of Ecological Chemistry, Helmholtz Centre for Environmental Research, Permoserstr. 15, 04318 Leipzig (Germany); Institute for Organic Chemistry, Technical University Bergakademie Freiberg, Leipziger Str. 29, 09596 Freiberg (Germany)

    2016-04-15

    The present study is motivated by the increasing demand to consider internal partitioning into tissues instead of exposure concentrations for the environmental toxicity assessment. To this end, physiologically based pharmacokinetic (PBPK) models can be applied. We evaluated the variation in accuracy of PBPK model outcomes depending on tissue constituents modeled as sorptive phases and chemical distribution tendencies addressed by molecular descriptors. The model performance was examined using data from 150 experiments for 28 chemicals collected from US EPA databases. The simplest PBPK model is based on the “K{sub ow}-lipid content” approach as being traditional for environmental toxicology. The most elaborated one considers five biological sorptive phases (polar and non-polar lipids, water, albumin and the remaining proteins) and makes use of LSER (linear solvation energy relationship) parameters to describe the compound partitioning behavior. The “K{sub ow}-lipid content”-based PBPK model shows more than one order of magnitude difference in predicted and measured values for 37% of the studied exposure experiments while for the most elaborated model this happens only for 7%. It is shown that further improvements could be achieved by introducing corrections for metabolic biotransformation and compound transmission hindrance through a cellular membrane. The analysis of the interface distribution tendencies shows that polar tissue constituents, namely water, polar lipids and proteins, play an important role in the accumulation behavior of polar compounds with H-bond donating functional groups. For compounds without H-bond donating fragments preferable accumulation phases are storage lipids and water depending on compound polarity. - Highlights: • For reliable predictions, models of a certain complexity should be compared. • For reliable predictions non-lipid fish tissue constituents should be considered. • H-donor compounds preferably accumulate in water

  10. Variation in predicted internal concentrations in relation to PBPK model complexity for rainbow trout

    International Nuclear Information System (INIS)

    Salmina, E.S.; Wondrousch, D.; Kühne, R.; Potemkin, V.A.; Schüürmann, G.

    2016-01-01

    The present study is motivated by the increasing demand to consider internal partitioning into tissues instead of exposure concentrations for the environmental toxicity assessment. To this end, physiologically based pharmacokinetic (PBPK) models can be applied. We evaluated the variation in accuracy of PBPK model outcomes depending on tissue constituents modeled as sorptive phases and chemical distribution tendencies addressed by molecular descriptors. The model performance was examined using data from 150 experiments for 28 chemicals collected from US EPA databases. The simplest PBPK model is based on the “K_o_w-lipid content” approach as being traditional for environmental toxicology. The most elaborated one considers five biological sorptive phases (polar and non-polar lipids, water, albumin and the remaining proteins) and makes use of LSER (linear solvation energy relationship) parameters to describe the compound partitioning behavior. The “K_o_w-lipid content”-based PBPK model shows more than one order of magnitude difference in predicted and measured values for 37% of the studied exposure experiments while for the most elaborated model this happens only for 7%. It is shown that further improvements could be achieved by introducing corrections for metabolic biotransformation and compound transmission hindrance through a cellular membrane. The analysis of the interface distribution tendencies shows that polar tissue constituents, namely water, polar lipids and proteins, play an important role in the accumulation behavior of polar compounds with H-bond donating functional groups. For compounds without H-bond donating fragments preferable accumulation phases are storage lipids and water depending on compound polarity. - Highlights: • For reliable predictions, models of a certain complexity should be compared. • For reliable predictions non-lipid fish tissue constituents should be considered. • H-donor compounds preferably accumulate in water, polar

  11. Predicting turns in proteins with a unified model.

    Directory of Open Access Journals (Sweden)

    Qi Song

    Full Text Available MOTIVATION: Turns are a critical element of the structure of a protein; turns play a crucial role in loops, folds, and interactions. Current prediction methods are well developed for the prediction of individual turn types, including α-turn, β-turn, and γ-turn, etc. However, for further protein structure and function prediction it is necessary to develop a uniform model that can accurately predict all types of turns simultaneously. RESULTS: In this study, we present a novel approach, TurnP, which offers the ability to investigate all the turns in a protein based on a unified model. The main characteristics of TurnP are: (i using newly exploited features of structural evolution information (secondary structure and shape string of protein based on structure homologies, (ii considering all types of turns in a unified model, and (iii practical capability of accurate prediction of all turns simultaneously for a query. TurnP utilizes predicted secondary structures and predicted shape strings, both of which have greater accuracy, based on innovative technologies which were both developed by our group. Then, sequence and structural evolution features, which are profile of sequence, profile of secondary structures and profile of shape strings are generated by sequence and structure alignment. When TurnP was validated on a non-redundant dataset (4,107 entries by five-fold cross-validation, we achieved an accuracy of 88.8% and a sensitivity of 71.8%, which exceeded the most state-of-the-art predictors of certain type of turn. Newly determined sequences, the EVA and CASP9 datasets were used as independent tests and the results we achieved were outstanding for turn predictions and confirmed the good performance of TurnP for practical applications.

  12. Prediction of heterodimeric protein complexes from weighted protein-protein interaction networks using novel features and kernel functions.

    Directory of Open Access Journals (Sweden)

    Peiying Ruan

    Full Text Available Since many proteins express their functional activity by interacting with other proteins and forming protein complexes, it is very useful to identify sets of proteins that form complexes. For that purpose, many prediction methods for protein complexes from protein-protein interactions have been developed such as MCL, MCODE, RNSC, PCP, RRW, and NWE. These methods have dealt with only complexes with size of more than three because the methods often are based on some density of subgraphs. However, heterodimeric protein complexes that consist of two distinct proteins occupy a large part according to several comprehensive databases of known complexes. In this paper, we propose several feature space mappings from protein-protein interaction data, in which each interaction is weighted based on reliability. Furthermore, we make use of prior knowledge on protein domains to develop feature space mappings, domain composition kernel and its combination kernel with our proposed features. We perform ten-fold cross-validation computational experiments. These results suggest that our proposed kernel considerably outperforms the naive Bayes-based method, which is the best existing method for predicting heterodimeric protein complexes.

  13. Protein Function Prediction Based on Sequence and Structure Information

    KAUST Repository

    Smaili, Fatima Z.

    2016-05-25

    The number of available protein sequences in public databases is increasing exponentially. However, a significant fraction of these sequences lack functional annotation which is essential to our understanding of how biological systems and processes operate. In this master thesis project, we worked on inferring protein functions based on the primary protein sequence. In the approach we follow, 3D models are first constructed using I-TASSER. Functions are then deduced by structurally matching these predicted models, using global and local similarities, through three independent enzyme commission (EC) and gene ontology (GO) function libraries. The method was tested on 250 “hard” proteins, which lack homologous templates in both structure and function libraries. The results show that this method outperforms the conventional prediction methods based on sequence similarity or threading. Additionally, our method could be improved even further by incorporating protein-protein interaction information. Overall, the method we use provides an efficient approach for automated functional annotation of non-homologous proteins, starting from their sequence.

  14. Which clustering algorithm is better for predicting protein complexes?

    Directory of Open Access Journals (Sweden)

    Moschopoulos Charalampos N

    2011-12-01

    Full Text Available Abstract Background Protein-Protein interactions (PPI play a key role in determining the outcome of most cellular processes. The correct identification and characterization of protein interactions and the networks, which they comprise, is critical for understanding the molecular mechanisms within the cell. Large-scale techniques such as pull down assays and tandem affinity purification are used in order to detect protein interactions in an organism. Today, relatively new high-throughput methods like yeast two hybrid, mass spectrometry, microarrays, and phage display are also used to reveal protein interaction networks. Results In this paper we evaluated four different clustering algorithms using six different interaction datasets. We parameterized the MCL, Spectral, RNSC and Affinity Propagation algorithms and applied them to six PPI datasets produced experimentally by Yeast 2 Hybrid (Y2H and Tandem Affinity Purification (TAP methods. The predicted clusters, so called protein complexes, were then compared and benchmarked with already known complexes stored in published databases. Conclusions While results may differ upon parameterization, the MCL and RNSC algorithms seem to be more promising and more accurate at predicting PPI complexes. Moreover, they predict more complexes than other reviewed algorithms in absolute numbers. On the other hand the spectral clustering algorithm achieves the highest valid prediction rate in our experiments. However, it is nearly always outperformed by both RNSC and MCL in terms of the geometrical accuracy while it generates the fewest valid clusters than any other reviewed algorithm. This article demonstrates various metrics to evaluate the accuracy of such predictions as they are presented in the text below. Supplementary material can be found at: http://www.bioacademy.gr/bioinformatics/projects/ppireview.htm

  15. PRODIGY : a web server for predicting the binding affinity of protein-protein complexes

    NARCIS (Netherlands)

    Xue, Li; Garcia Lopes Maia Rodrigues, João; Kastritis, Panagiotis L; Bonvin, Alexandre Mjj; Vangone, Anna

    2016-01-01

    Gaining insights into the structural determinants of protein-protein interactions holds the key for a deeper understanding of biological functions, diseases and development of therapeutics. An important aspect of this is the ability to accurately predict the binding strength for a given

  16. Predicting membrane protein types by fusing composite protein sequence features into pseudo amino acid composition.

    Science.gov (United States)

    Hayat, Maqsood; Khan, Asifullah

    2011-02-21

    Membrane proteins are vital type of proteins that serve as channels, receptors, and energy transducers in a cell. Prediction of membrane protein types is an important research area in bioinformatics. Knowledge of membrane protein types provides some valuable information for predicting novel example of the membrane protein types. However, classification of membrane protein types can be both time consuming and susceptible to errors due to the inherent similarity of membrane protein types. In this paper, neural networks based membrane protein type prediction system is proposed. Composite protein sequence representation (CPSR) is used to extract the features of a protein sequence, which includes seven feature sets; amino acid composition, sequence length, 2 gram exchange group frequency, hydrophobic group, electronic group, sum of hydrophobicity, and R-group. Principal component analysis is then employed to reduce the dimensionality of the feature vector. The probabilistic neural network (PNN), generalized regression neural network, and support vector machine (SVM) are used as classifiers. A high success rate of 86.01% is obtained using SVM for the jackknife test. In case of independent dataset test, PNN yields the highest accuracy of 95.73%. These classifiers exhibit improved performance using other performance measures such as sensitivity, specificity, Mathew's correlation coefficient, and F-measure. The experimental results show that the prediction performance of the proposed scheme for classifying membrane protein types is the best reported, so far. This performance improvement may largely be credited to the learning capabilities of neural networks and the composite feature extraction strategy, which exploits seven different properties of protein sequences. The proposed Mem-Predictor can be accessed at http://111.68.99.218/Mem-Predictor. Copyright © 2010 Elsevier Ltd. All rights reserved.

  17. On the quantitative Amido Black B staining of protein spots in agar gel at low local protein concentrations

    NARCIS (Netherlands)

    Jansen, M.T.

    1962-01-01

    Protein spots in agar gel of identical protein content but different in surface area are found to bind different amounts of dye upon staining with Amido Black B. The lower the protein concentration within the agar gel, the more the Amido Black B content of the spot falls short of the value expected

  18. Predicting the binding patterns of hub proteins: a study using yeast protein interaction networks.

    Directory of Open Access Journals (Sweden)

    Carson M Andorf

    Full Text Available Protein-protein interactions are critical to elucidating the role played by individual proteins in important biological pathways. Of particular interest are hub proteins that can interact with large numbers of partners and often play essential roles in cellular control. Depending on the number of binding sites, protein hubs can be classified at a structural level as singlish-interface hubs (SIH with one or two binding sites, or multiple-interface hubs (MIH with three or more binding sites. In terms of kinetics, hub proteins can be classified as date hubs (i.e., interact with different partners at different times or locations or party hubs (i.e., simultaneously interact with multiple partners.Our approach works in 3 phases: Phase I classifies if a protein is likely to bind with another protein. Phase II determines if a protein-binding (PB protein is a hub. Phase III classifies PB proteins as singlish-interface versus multiple-interface hubs and date versus party hubs. At each stage, we use sequence-based predictors trained using several standard machine learning techniques.Our method is able to predict whether a protein is a protein-binding protein with an accuracy of 94% and a correlation coefficient of 0.87; identify hubs from non-hubs with 100% accuracy for 30% of the data; distinguish date hubs/party hubs with 69% accuracy and area under ROC curve of 0.68; and SIH/MIH with 89% accuracy and area under ROC curve of 0.84. Because our method is based on sequence information alone, it can be used even in settings where reliable protein-protein interaction data or structures of protein-protein complexes are unavailable to obtain useful insights into the functional and evolutionary characteristics of proteins and their interactions.We provide a web server for our three-phase approach: http://hybsvm.gdcb.iastate.edu.

  19. Dry fractionation for production of functional pea protein concentrates

    NARCIS (Netherlands)

    Pelgrom, P.J.M.; Vissers, A.M.; Boom, R.M.; Schutyser, M.A.I.

    2013-01-01

    Dry milling in combination with air classification was evaluated as an alternative to conventional wet extraction of protein from yellow field peas (Pisum sativum). Major advantages of dry fractionation are retention of native functionality of proteins and its lower energy and water use. Peas were

  20. Dry fractionation for sustainable production of plant protein concentrates

    NARCIS (Netherlands)

    Pelgrom, P.J.M.

    2015-01-01

    The global demand for protein-rich foods is expected to double in the coming decades due to the increasing prosperity and world population. To keep up with the demand, the transition from an animal to a plant-based protein supply is desirable from long-term economic and environmental

  1. Dry fractionation for sustainable production of functional legume protein concentrates

    NARCIS (Netherlands)

    Schutyser, M.A.I.; Pelgrom, P.J.M.; Goot, van der A.J.; Boom, R.M.

    2015-01-01

    Plant proteins gain increasing interest as part of a sustainable diet. Because plant materials not only contain protein, they are generally isolated via an energy intensive wet fractionation. This review discusses dry fractionation as an alternative and more sustainable route for producing

  2. USING TURBIDITY DATA TO PREDICT SUSPENDED SEDIMENT CONCENTRATIONS: POSSIBILITIES, LIMITATIONS, AND PITFALLS

    Science.gov (United States)

    This talk will look at the relationships between turbidity and suspended sediment concentrations in a variety of geographic areas, geomorphic river types, and river sizes; and attempt to give guidance on using existing turbidity data to predict suspended sediment concentrations.

  3. SitesIdentify: a protein functional site prediction tool

    Directory of Open Access Journals (Sweden)

    Doig Andrew J

    2009-11-01

    Full Text Available Abstract Background The rate of protein structures being deposited in the Protein Data Bank surpasses the capacity to experimentally characterise them and therefore computational methods to analyse these structures have become increasingly important. Identifying the region of the protein most likely to be involved in function is useful in order to gain information about its potential role. There are many available approaches to predict functional site, but many are not made available via a publicly-accessible application. Results Here we present a functional site prediction tool (SitesIdentify, based on combining sequence conservation information with geometry-based cleft identification, that is freely available via a web-server. We have shown that SitesIdentify compares favourably to other functional site prediction tools in a comparison of seven methods on a non-redundant set of 237 enzymes with annotated active sites. Conclusion SitesIdentify is able to produce comparable accuracy in predicting functional sites to its closest available counterpart, but in addition achieves improved accuracy for proteins with few characterised homologues. SitesIdentify is available via a webserver at http://www.manchester.ac.uk/bioinformatics/sitesidentify/

  4. C-Reactive Protein, Fibrinogen, and Cardiovascular Disease Prediction

    NARCIS (Netherlands)

    Kaptoge, Stephen; Di Angelantonio, Emanuele; Pennells, Lisa; Wood, Angela M.; White, Ian R.; Gao, Pei; Walker, Matthew; Thompson, Alexander; Sarwar, Nadeem; Caslake, Muriel; Butterworth, Adam S.; Amouyel, Philippe; Assmann, Gerd; Bakker, Stephan J. L.; Barr, Elizabeth L. M.; Barrett-Connor, Elizabeth; Benjamin, Emelia J.; Bjorkelund, Cecilia; Brenner, Hermann; Brunner, Eric; Clarke, Robert; Cooper, Jackie A.; Cremer, Peter; Cushman, Mary; Dagenais, Gilles R.; D'Agostino, Ralph B.; Dankner, Rachel; Davey-Smith, George; Deeg, Dorly; Dekker, Jacqueline M.; Engstrom, Gunnar; Folsom, Aaron R.; Fowkes, F. Gerry R.; Gallacher, John; Gaziano, J. Michael; Giampaoli, Simona; Gillum, Richard F.; Hofman, Albert; Howard, Barbara V.; Ingelsson, Erik; Iso, Hiroyasu; Jorgensen, Torben; Kiechl, Stefan; Kitamura, Akihiko; Kiyohara, Yutaka; Koenig, Wolfgang; Kromhout, Daan; Kuller, Lewis H.; Lawlor, Debbie A.; Meade, Tom W.

    2012-01-01

    Background There is debate about the value of assessing levels of C-reactive protein (CRP) and other biomarkers of inflammation for the prediction of first cardiovascular events. Methods We analyzed data from 52 prospective studies that included 246,669 participants without a history of

  5. Sequence-based feature prediction and annotation of proteins

    DEFF Research Database (Denmark)

    Juncker, Agnieszka; Jensen, Lars J.; Pierleoni, Andrea

    2009-01-01

    A recent trend in computational methods for annotation of protein function is that many prediction tools are combined in complex workflows and pipelines to facilitate the analysis of feature combinations, for example, the entire repertoire of kinase-binding motifs in the human proteome....

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

    Science.gov (United States)

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

    2014-07-01

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

  7. Boosting compound-protein interaction prediction by deep learning.

    Science.gov (United States)

    Tian, Kai; Shao, Mingyu; Wang, Yang; Guan, Jihong; Zhou, Shuigeng

    2016-11-01

    The identification of interactions between compounds and proteins plays an important role in network pharmacology and drug discovery. However, experimentally identifying compound-protein interactions (CPIs) is generally expensive and time-consuming, computational approaches are thus introduced. Among these, machine-learning based methods have achieved a considerable success. However, due to the nonlinear and imbalanced nature of biological data, many machine learning approaches have their own limitations. Recently, deep learning techniques show advantages over many state-of-the-art machine learning methods in some applications. In this study, we aim at improving the performance of CPI prediction based on deep learning, and propose a method called DL-CPI (the abbreviation of Deep Learning for Compound-Protein Interactions prediction), which employs deep neural network (DNN) to effectively learn the representations of compound-protein pairs. Extensive experiments show that DL-CPI can learn useful features of compound-protein pairs by a layerwise abstraction, and thus achieves better prediction performance than existing methods on both balanced and imbalanced datasets. Copyright © 2016 Elsevier Inc. All rights reserved.

  8. Protein Secondary Structure Prediction Using Deep Convolutional Neural Fields.

    Science.gov (United States)

    Wang, Sheng; Peng, Jian; Ma, Jianzhu; Xu, Jinbo

    2016-01-11

    Protein secondary structure (SS) prediction is important for studying protein structure and function. When only the sequence (profile) information is used as input feature, currently the best predictors can obtain ~80% Q3 accuracy, which has not been improved in the past decade. Here we present DeepCNF (Deep Convolutional Neural Fields) for protein SS prediction. DeepCNF is a Deep Learning extension of Conditional Neural Fields (CNF), which is an integration of Conditional Random Fields (CRF) and shallow neural networks. DeepCNF can model not only complex sequence-structure relationship by a deep hierarchical architecture, but also interdependency between adjacent SS labels, so it is much more powerful than CNF. Experimental results show that DeepCNF can obtain ~84% Q3 accuracy, ~85% SOV score, and ~72% Q8 accuracy, respectively, on the CASP and CAMEO test proteins, greatly outperforming currently popular predictors. As a general framework, DeepCNF can be used to predict other protein structure properties such as contact number, disorder regions, and solvent accessibility.

  9. Prediction of antigenic epitopes on protein surfaces by consensus scoring

    Directory of Open Access Journals (Sweden)

    Zhang Chi

    2009-09-01

    Full Text Available Abstract Background Prediction of antigenic epitopes on protein surfaces is important for vaccine design. Most existing epitope prediction methods focus on protein sequences to predict continuous epitopes linear in sequence. Only a few structure-based epitope prediction algorithms are available and they have not yet shown satisfying performance. Results We present a new antigen Epitope Prediction method, which uses ConsEnsus Scoring (EPCES from six different scoring functions - residue epitope propensity, conservation score, side-chain energy score, contact number, surface planarity score, and secondary structure composition. Applied to unbounded antigen structures from an independent test set, EPCES was able to predict antigenic eptitopes with 47.8% sensitivity, 69.5% specificity and an AUC value of 0.632. The performance of the method is statistically similar to other published methods. The AUC value of EPCES is slightly higher compared to the best results of existing algorithms by about 0.034. Conclusion Our work shows consensus scoring of multiple features has a better performance than any single term. The successful prediction is also due to the new score of residue epitope propensity based on atomic solvent accessibility.

  10. Effect of initial protein concentration and pH on in vitro gastric digestion of heated whey proteins.

    Science.gov (United States)

    Zhang, Sha; Vardhanabhuti, Bongkosh

    2014-02-15

    The in vitro digestion of heated whey protein aggregates having different structure and physicochemical properties was evaluated under simulated gastric conditions. Aggregates were formed by heating whey protein isolates (WPI) at 3-9% w/w initial protein concentration and pH 3.0-7.0. Results showed that high protein concentration led to formation of larger WPI aggregates with fewer remaining monomers. Aggregates formed at high protein concentrations showed slower degradation rate compared to those formed at low protein concentration. The effect of initial protein concentration on peptide release pattern was not apparent. Heating pH was a significant factor affecting digestion pattern. At pH above the isoelectric point, the majority of the proteins involved in the aggregation, and aggregates formed at pH 6.0 were more susceptible to pepsin digestion than at pH 7.0. At acidic conditions, only small amount of proteins was involved in the aggregation and heated aggregates were easily digested by pepsin, while the remaining unaggregated proteins were very resistant to gastric digestion. The potential physiological implication of these results on satiety was discussed. Copyright © 2013 Elsevier Ltd. All rights reserved.

  11. Prenatal maternal cortisol concentrations predict neurodevelopment in middle childhood.

    Science.gov (United States)

    Davis, Elysia Poggi; Head, Kevin; Buss, Claudia; Sandman, Curt A

    2017-01-01

    Glucocorticoids (cortisol in humans) are the end product of the hypothalamic-pituitary-adrenocortical (HPA) axis and are proposed as a key mechanism for programming fetal brain development. The present prospective longitudinal study evaluates the association between prenatal maternal cortisol concentrations and child neurodevelopment. Participants included a low risk sample of 91 mother-child pairs. Prenatal maternal plasma cortisol concentrations were measured at 19 and 31 gestational weeks. Brain development and cognitive functioning were assessed when children were 6-9 years of age. Structural magnetic resonance imaging scans were acquired and cortical thickness was determined. Child cognitive functioning was evaluated using standardized measures (Wechsler Intelligence Scale for Children IV and Expressive Vocabulary Test, Second Edition). Higher maternal cortisol concentrations during the third trimester were associated with greater child cortical thickness primarily in frontal regions. No significant associations were observed between prenatal maternal cortisol concentrations and child cortical thinning. Elevated third trimester maternal cortisol additionally was associated with enhanced child cognitive performance. Findings in this normative sample of typically developing children suggest that elevated maternal cortisol during late gestation exert lasting benefits for brain development and cognitive functioning 6-9 years later. The benefits of fetal exposure to higher maternal cortisol during the third trimester for child neurodevelopment are consistent with the role cortisol plays in maturation of the human fetus. It is plausible that more extreme elevations in maternal cortisol concentrations late in gestation, as well as exposure to pharmacological levels of synthetic glucocorticoids, may have neurotoxic effects on the developing fetal brain. Copyright © 2016. Published by Elsevier Ltd.

  12. Nanoparticles-cell association predicted by protein corona fingerprints

    Science.gov (United States)

    Palchetti, S.; Digiacomo, L.; Pozzi, D.; Peruzzi, G.; Micarelli, E.; Mahmoudi, M.; Caracciolo, G.

    2016-06-01

    In a physiological environment (e.g., blood and interstitial fluids) nanoparticles (NPs) will bind proteins shaping a ``protein corona'' layer. The long-lived protein layer tightly bound to the NP surface is referred to as the hard corona (HC) and encodes information that controls NP bioactivity (e.g. cellular association, cellular signaling pathways, biodistribution, and toxicity). Decrypting this complex code has become a priority to predict the NP biological outcomes. Here, we use a library of 16 lipid NPs of varying size (Ø ~ 100-250 nm) and surface chemistry (unmodified and PEGylated) to investigate the relationships between NP physicochemical properties (nanoparticle size, aggregation state and surface charge), protein corona fingerprints (PCFs), and NP-cell association. We found out that none of the NPs' physicochemical properties alone was exclusively able to account for association with human cervical cancer cell line (HeLa). For the entire library of NPs, a total of 436 distinct serum proteins were detected. We developed a predictive-validation modeling that provides a means of assessing the relative significance of the identified corona proteins. Interestingly, a minor fraction of the HC, which consists of only 8 PCFs were identified as main promoters of NP association with HeLa cells. Remarkably, identified PCFs have several receptors with high level of expression on the plasma membrane of HeLa cells.In a physiological environment (e.g., blood and interstitial fluids) nanoparticles (NPs) will bind proteins shaping a ``protein corona'' layer. The long-lived protein layer tightly bound to the NP surface is referred to as the hard corona (HC) and encodes information that controls NP bioactivity (e.g. cellular association, cellular signaling pathways, biodistribution, and toxicity). Decrypting this complex code has become a priority to predict the NP biological outcomes. Here, we use a library of 16 lipid NPs of varying size (Ø ~ 100-250 nm) and surface

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

    Science.gov (United States)

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

    2014-10-12

    A long standing problem in structural bioinformatics is to determine the three-dimensional (3-D) structure of a protein when only a sequence of amino acid residues is given. Many computational methodologies and algorithms have been proposed as a solution to the 3-D Protein Structure Prediction (3-D-PSP) problem. These methods can be divided in four main classes: (a) first principle methods without database information; (b) first principle methods with database information; (c) fold recognition and threading methods; and (d) comparative modeling methods and sequence alignment strategies. Deterministic computational techniques, optimization techniques, data mining and machine learning approaches are typically used in the construction of computational solutions for the PSP problem. Our main goal with this work is to review the methods and computational strategies that are currently used in 3-D protein prediction. Copyright © 2014 Elsevier Ltd. All rights reserved.

  14. Parallel protein secondary structure prediction based on neural networks.

    Science.gov (United States)

    Zhong, Wei; Altun, Gulsah; Tian, Xinmin; Harrison, Robert; Tai, Phang C; Pan, Yi

    2004-01-01

    Protein secondary structure prediction has a fundamental influence on today's bioinformatics research. In this work, binary and tertiary classifiers of protein secondary structure prediction are implemented on Denoeux belief neural network (DBNN) architecture. Hydrophobicity matrix, orthogonal matrix, BLOSUM62 and PSSM (position specific scoring matrix) are experimented separately as the encoding schemes for DBNN. The experimental results contribute to the design of new encoding schemes. New binary classifier for Helix versus not Helix ( approximately H) for DBNN produces prediction accuracy of 87% when PSSM is used for the input profile. The performance of DBNN binary classifier is comparable to other best prediction methods. The good test results for binary classifiers open a new approach for protein structure prediction with neural networks. Due to the time consuming task of training the neural networks, Pthread and OpenMP are employed to parallelize DBNN in the hyperthreading enabled Intel architecture. Speedup for 16 Pthreads is 4.9 and speedup for 16 OpenMP threads is 4 in the 4 processors shared memory architecture. Both speedup performance of OpenMP and Pthread is superior to that of other research. With the new parallel training algorithm, thousands of amino acids can be processed in reasonable amount of time. Our research also shows that hyperthreading technology for Intel architecture is efficient for parallel biological algorithms.

  15. Protein 8-class secondary structure prediction using conditional neural fields.

    Science.gov (United States)

    Wang, Zhiyong; Zhao, Feng; Peng, Jian; Xu, Jinbo

    2011-10-01

    Compared with the protein 3-class secondary structure (SS) prediction, the 8-class prediction gains less attention and is also much more challenging, especially for proteins with few sequence homologs. This paper presents a new probabilistic method for 8-class SS prediction using conditional neural fields (CNFs), a recently invented probabilistic graphical model. This CNF method not only models the complex relationship between sequence features and SS, but also exploits the interdependency among SS types of adjacent residues. In addition to sequence profiles, our method also makes use of non-evolutionary information for SS prediction. Tested on the CB513 and RS126 data sets, our method achieves Q8 accuracy of 64.9 and 64.7%, respectively, which are much better than the SSpro8 web server (51.0 and 48.0%, respectively). Our method can also be used to predict other structure properties (e.g. solvent accessibility) of a protein or the SS of RNA. Copyright © 2011 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  16. A dual small-molecule rheostat for precise control of protein concentration in Mammalian cells.

    Science.gov (United States)

    Lin, Yu Hsuan; Pratt, Matthew R

    2014-04-14

    One of the most successful strategies for controlling protein concentrations in living cells relies on protein destabilization domains (DD). Under normal conditions, a DD will be rapidly degraded by the proteasome. However, the same DD can be stabilized or "shielded" in a stoichiometric complex with a small molecule, enabling dose-dependent control of its concentration. This process has been exploited by several labs to post-translationally control the expression levels of proteins in vitro as well as in vivo, although the previous technologies resulted in permanent fusion of the protein of interest to the DD, which can affect biological activity and complicate results. We previously reported a complementary strategy, termed traceless shielding (TShld), in which the protein of interest is released in its native form. Here, we describe an optimized protein concentration control system, TTShld, which retains the traceless features of TShld but utilizes two tiers of small molecule control to set protein concentrations in living cells. These experiments provide the first protein concentration control system that results in both a wide range of protein concentrations and proteins free from engineered fusion constructs. The TTShld system has a greatly improved dynamic range compared to our previously reported system, and the traceless feature is attractive for elucidation of the consequences of protein concentration in cell biology. © 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  17. Prediction of localization and interactions of apoptotic proteins

    Directory of Open Access Journals (Sweden)

    Matula Pavel

    2009-07-01

    Full Text Available Abstract During apoptosis several mitochondrial proteins are released. Some of them participate in caspase-independent nuclear DNA degradation, especially apoptosis-inducing factor (AIF and endonuclease G (endoG. Another interesting protein, which was expected to act similarly as AIF due to the high sequence homology with AIF is AIF-homologous mitochondrion-associated inducer of death (AMID. We studied the structure, cellular localization, and interactions of several proteins in silico and also in cells using fluorescent microscopy. We found the AMID protein to be cytoplasmic, most probably incorporated into the cytoplasmic side of the lipid membranes. Bioinformatic predictions were conducted to analyze the interactions of the studied proteins with each other and with other possible partners. We conducted molecular modeling of proteins with unknown 3D structures. These models were then refined by MolProbity server and employed in molecular docking simulations of interactions. Our results show data acquired using a combination of modern in silico methods and image analysis to understand the localization, interactions and functions of proteins AMID, AIF, endonuclease G, and other apoptosis-related proteins.

  18. Combining modularity, conservation, and interactions of proteins significantly increases precision and coverage of protein function prediction

    Directory of Open Access Journals (Sweden)

    Sers Christine T

    2010-12-01

    Full Text Available Abstract Background While the number of newly sequenced genomes and genes is constantly increasing, elucidation of their function still is a laborious and time-consuming task. This has led to the development of a wide range of methods for predicting protein functions in silico. We report on a new method that predicts function based on a combination of information about protein interactions, orthology, and the conservation of protein networks in different species. Results We show that aggregation of these independent sources of evidence leads to a drastic increase in number and quality of predictions when compared to baselines and other methods reported in the literature. For instance, our method generates more than 12,000 novel protein functions for human with an estimated precision of ~76%, among which are 7,500 new functional annotations for 1,973 human proteins that previously had zero or only one function annotated. We also verified our predictions on a set of genes that play an important role in colorectal cancer (MLH1, PMS2, EPHB4 and could confirm more than 73% of them based on evidence in the literature. Conclusions The combination of different methods into a single, comprehensive prediction method infers thousands of protein functions for every species included in the analysis at varying, yet always high levels of precision and very good coverage.

  19. Efficient prediction of human protein-protein interactions at a global scale.

    Science.gov (United States)

    Schoenrock, Andrew; Samanfar, Bahram; Pitre, Sylvain; Hooshyar, Mohsen; Jin, Ke; Phillips, Charles A; Wang, Hui; Phanse, Sadhna; Omidi, Katayoun; Gui, Yuan; Alamgir, Md; Wong, Alex; Barrenäs, Fredrik; Babu, Mohan; Benson, Mikael; Langston, Michael A; Green, James R; Dehne, Frank; Golshani, Ashkan

    2014-12-10

    Our knowledge of global protein-protein interaction (PPI) networks in complex organisms such as humans is hindered by technical limitations of current methods. On the basis of short co-occurring polypeptide regions, we developed a tool called MP-PIPE capable of predicting a global human PPI network within 3 months. With a recall of 23% at a precision of 82.1%, we predicted 172,132 putative PPIs. We demonstrate the usefulness of these predictions through a range of experiments. The speed and accuracy associated with MP-PIPE can make this a potential tool to study individual human PPI networks (from genomic sequences alone) for personalized medicine.

  20. Exploring the potential of 3D Zernike descriptors and SVM for protein-protein interface prediction.

    Science.gov (United States)

    Daberdaku, Sebastian; Ferrari, Carlo

    2018-02-06

    The correct determination of protein-protein interaction interfaces is important for understanding disease mechanisms and for rational drug design. To date, several computational methods for the prediction of protein interfaces have been developed, but the interface prediction problem is still not fully understood. Experimental evidence suggests that the location of binding sites is imprinted in the protein structure, but there are major differences among the interfaces of the various protein types: the characterising properties can vary a lot depending on the interaction type and function. The selection of an optimal set of features characterising the protein interface and the development of an effective method to represent and capture the complex protein recognition patterns are of paramount importance for this task. In this work we investigate the potential of a novel local surface descriptor based on 3D Zernike moments for the interface prediction task. Descriptors invariant to roto-translations are extracted from circular patches of the protein surface enriched with physico-chemical properties from the HQI8 amino acid index set, and are used as samples for a binary classification problem. Support Vector Machines are used as a classifier to distinguish interface local surface patches from non-interface ones. The proposed method was validated on 16 classes of proteins extracted from the Protein-Protein Docking Benchmark 5.0 and compared to other state-of-the-art protein interface predictors (SPPIDER, PrISE and NPS-HomPPI). The 3D Zernike descriptors are able to capture the similarity among patterns of physico-chemical and biochemical properties mapped on the protein surface arising from the various spatial arrangements of the underlying residues, and their usage can be easily extended to other sets of amino acid properties. The results suggest that the choice of a proper set of features characterising the protein interface is crucial for the interface prediction

  1. Calculating flux to predict future cave radon concentrations

    Czech Academy of Sciences Publication Activity Database

    Rowberry, Matthew David; Martí, Xavier; Frontera, C.; Van De Wiel, M.J.; Briestenský, Miloš

    2016-01-01

    Roč. 157, JUN (2016), 16-26 ISSN 0265-931X R&D Projects: GA MŠk LM2010008 Institutional support: RVO:67985891 ; RVO:68378271 Keywords : cave radon concentration * cave radon flux * cave ventilation * radioactive decay * fault slip * numerical modelling Subject RIV: DC - Siesmology, Volcanology, Earth Structure; BG - Nuclear, Atomic and Molecular Physics, Colliders (FZU-D) Impact factor: 2.310, year: 2016

  2. A probabilistic fragment-based protein structure prediction algorithm.

    Directory of Open Access Journals (Sweden)

    David Simoncini

    Full Text Available Conformational sampling is one of the bottlenecks in fragment-based protein structure prediction approaches. They generally start with a coarse-grained optimization where mainchain atoms and centroids of side chains are considered, followed by a fine-grained optimization with an all-atom representation of proteins. It is during this coarse-grained phase that fragment-based methods sample intensely the conformational space. If the native-like region is sampled more, the accuracy of the final all-atom predictions may be improved accordingly. In this work we present EdaFold, a new method for fragment-based protein structure prediction based on an Estimation of Distribution Algorithm. Fragment-based approaches build protein models by assembling short fragments from known protein structures. Whereas the probability mass functions over the fragment libraries are uniform in the usual case, we propose an algorithm that learns from previously generated decoys and steers the search toward native-like regions. A comparison with Rosetta AbInitio protocol shows that EdaFold is able to generate models with lower energies and to enhance the percentage of near-native coarse-grained decoys on a benchmark of [Formula: see text] proteins. The best coarse-grained models produced by both methods were refined into all-atom models and used in molecular replacement. All atom decoys produced out of EdaFold's decoy set reach high enough accuracy to solve the crystallographic phase problem by molecular replacement for some test proteins. EdaFold showed a higher success rate in molecular replacement when compared to Rosetta. Our study suggests that improving low resolution coarse-grained decoys allows computational methods to avoid subsequent sampling issues during all-atom refinement and to produce better all-atom models. EdaFold can be downloaded from http://www.riken.jp/zhangiru/software.html [corrected].

  3. Constraint Logic Programming approach to protein structure prediction

    Directory of Open Access Journals (Sweden)

    Fogolari Federico

    2004-11-01

    Full Text Available Abstract Background The protein structure prediction problem is one of the most challenging problems in biological sciences. Many approaches have been proposed using database information and/or simplified protein models. The protein structure prediction problem can be cast in the form of an optimization problem. Notwithstanding its importance, the problem has very seldom been tackled by Constraint Logic Programming, a declarative programming paradigm suitable for solving combinatorial optimization problems. Results Constraint Logic Programming techniques have been applied to the protein structure prediction problem on the face-centered cube lattice model. Molecular dynamics techniques, endowed with the notion of constraint, have been also exploited. Even using a very simplified model, Constraint Logic Programming on the face-centered cube lattice model allowed us to obtain acceptable results for a few small proteins. As a test implementation their (known secondary structure and the presence of disulfide bridges are used as constraints. Simplified structures obtained in this way have been converted to all atom models with plausible structure. Results have been compared with a similar approach using a well-established technique as molecular dynamics. Conclusions The results obtained on small proteins show that Constraint Logic Programming techniques can be employed for studying protein simplified models, which can be converted into realistic all atom models. The advantage of Constraint Logic Programming over other, much more explored, methodologies, resides in the rapid software prototyping, in the easy way of encoding heuristics, and in exploiting all the advances made in this research area, e.g. in constraint propagation and its use for pruning the huge search space.

  4. Constraint Logic Programming approach to protein structure prediction.

    Science.gov (United States)

    Dal Palù, Alessandro; Dovier, Agostino; Fogolari, Federico

    2004-11-30

    The protein structure prediction problem is one of the most challenging problems in biological sciences. Many approaches have been proposed using database information and/or simplified protein models. The protein structure prediction problem can be cast in the form of an optimization problem. Notwithstanding its importance, the problem has very seldom been tackled by Constraint Logic Programming, a declarative programming paradigm suitable for solving combinatorial optimization problems. Constraint Logic Programming techniques have been applied to the protein structure prediction problem on the face-centered cube lattice model. Molecular dynamics techniques, endowed with the notion of constraint, have been also exploited. Even using a very simplified model, Constraint Logic Programming on the face-centered cube lattice model allowed us to obtain acceptable results for a few small proteins. As a test implementation their (known) secondary structure and the presence of disulfide bridges are used as constraints. Simplified structures obtained in this way have been converted to all atom models with plausible structure. Results have been compared with a similar approach using a well-established technique as molecular dynamics. The results obtained on small proteins show that Constraint Logic Programming techniques can be employed for studying protein simplified models, which can be converted into realistic all atom models. The advantage of Constraint Logic Programming over other, much more explored, methodologies, resides in the rapid software prototyping, in the easy way of encoding heuristics, and in exploiting all the advances made in this research area, e.g. in constraint propagation and its use for pruning the huge search space.

  5. Aggregation in concentrated protein solutions: Insights from rheology, neutron scattering and molecular simulations

    Science.gov (United States)

    Castellanos, Maria Monica

    Aggregation of therapeutic proteins is currently one of the major challenges in the bio-pharmaceutical industry, because aggregates could induce immunogenic responses and compromise the quality of the product. Current scientific efforts, both in industry and academia, are focused on developing rational approaches to screen different drug candidates and predict their stability under different conditions. Moreover, aggregation is promoted in highly concentrated protein solutions, which are typically required for subcutaneous injection. In order to gain further understanding about the mechanisms that lead to aggregation, an approach that combined rheology, neutron scattering, and molecular simulations was undertaken. Two model systems were studied in this work: Bovine Serum Albumin in surfactant-free Phosphate Buffered Saline at pH = 7.4 at concentrations from 11 mg/mL up to ˜519 mg/mL, and a monoclonal antibody in 20 mM Histidine/Histidine Hydrochloride at pH = 6.0 with 60 mg/mL trehalose and 0.2 mg/mL polysorbate-80 at concentrations from 53 mg/mL up to ˜220 mg/mL. The antibody used here has three mutations in the CH2 domain, which result in lower stability upon incubation at 40 °C with respect to the wild-type protein, based on size-exclusion chromatography assays. This temperature is below 49 °C, where unfolding of the least stable, CH2 domain occurs, according to differential scanning calorimetry. This dissertation focuses on identifying the role of aggregation on the viscosity of protein solutions. The protein solutions of this work show an increase in the low shear viscosity in the absence of surfactants, because proteins adsorb at the air/water interface forming a viscoelastic film that affects the measured rheology. Stable surfactant-laden protein solutions behave as simple Newtonian fluids. However, the surfactant-laden antibody solution also shows an increase in the low shear viscosity from bulk aggregation, after prolonged incubation at 40 °C. Small

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

    Science.gov (United States)

    Clyde, M A; Parmigiani, G

    1998-07-01

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

  7. Incorporating functional inter-relationships into protein function prediction algorithms

    Directory of Open Access Journals (Sweden)

    Kumar Vipin

    2009-05-01

    Full Text Available Abstract Background Functional classification schemes (e.g. the Gene Ontology that serve as the basis for annotation efforts in several organisms are often the source of gold standard information for computational efforts at supervised protein function prediction. While successful function prediction algorithms have been developed, few previous efforts have utilized more than the protein-to-functional class label information provided by such knowledge bases. For instance, the Gene Ontology not only captures protein annotations to a set of functional classes, but it also arranges these classes in a DAG-based hierarchy that captures rich inter-relationships between different classes. These inter-relationships present both opportunities, such as the potential for additional training examples for small classes from larger related classes, and challenges, such as a harder to learn distinction between similar GO terms, for standard classification-based approaches. Results We propose a method to enhance the performance of classification-based protein function prediction algorithms by addressing the issue of using these interrelationships between functional classes constituting functional classification schemes. Using a standard measure for evaluating the semantic similarity between nodes in an ontology, we quantify and incorporate these inter-relationships into the k-nearest neighbor classifier. We present experiments on several large genomic data sets, each of which is used for the modeling and prediction of over hundred classes from the GO Biological Process ontology. The results show that this incorporation produces more accurate predictions for a large number of the functional classes considered, and also that the classes benefitted most by this approach are those containing the fewest members. In addition, we show how our proposed framework can be used for integrating information from the entire GO hierarchy for improving the accuracy of

  8. Predicting protein complexes from weighted protein-protein interaction graphs with a novel unsupervised methodology: Evolutionary enhanced Markov clustering.

    Science.gov (United States)

    Theofilatos, Konstantinos; Pavlopoulou, Niki; Papasavvas, Christoforos; Likothanassis, Spiros; Dimitrakopoulos, Christos; Georgopoulos, Efstratios; Moschopoulos, Charalampos; Mavroudi, Seferina

    2015-03-01

    Proteins are considered to be the most important individual components of biological systems and they combine to form physical protein complexes which are responsible for certain molecular functions. Despite the large availability of protein-protein interaction (PPI) information, not much information is available about protein complexes. Experimental methods are limited in terms of time, efficiency, cost and performance constraints. Existing computational methods have provided encouraging preliminary results, but they phase certain disadvantages as they require parameter tuning, some of them cannot handle weighted PPI data and others do not allow a protein to participate in more than one protein complex. In the present paper, we propose a new fully unsupervised methodology for predicting protein complexes from weighted PPI graphs. The proposed methodology is called evolutionary enhanced Markov clustering (EE-MC) and it is a hybrid combination of an adaptive evolutionary algorithm and a state-of-the-art clustering algorithm named enhanced Markov clustering. EE-MC was compared with state-of-the-art methodologies when applied to datasets from the human and the yeast Saccharomyces cerevisiae organisms. Using public available datasets, EE-MC outperformed existing methodologies (in some datasets the separation metric was increased by 10-20%). Moreover, when applied to new human datasets its performance was encouraging in the prediction of protein complexes which consist of proteins with high functional similarity. In specific, 5737 protein complexes were predicted and 72.58% of them are enriched for at least one gene ontology (GO) function term. EE-MC is by design able to overcome intrinsic limitations of existing methodologies such as their inability to handle weighted PPI networks, their constraint to assign every protein in exactly one cluster and the difficulties they face concerning the parameter tuning. This fact was experimentally validated and moreover, new

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

    Science.gov (United States)

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

    2015-07-01

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

  10. Transthyretin Concentrations in Acute Stroke Patients Predict Convalescent Rehabilitation.

    Science.gov (United States)

    Isono, Naofumi; Imamura, Yuki; Ohmura, Keiko; Ueda, Norihide; Kawabata, Shinji; Furuse, Motomasa; Kuroiwa, Toshihiko

    2017-06-01

    For stroke patients, intensive nutritional management is an important and effective component of inpatient rehabilitation. Accordingly, acute care hospitals must detect and prevent malnutrition at an early stage. Blood transthyretin levels are widely used as a nutritional monitoring index in critically ill patients. Here, we had analyzed the relationship between the transthyretin levels during the acute phase and Functional Independence Measure in stroke patients undergoing convalescent rehabilitation. We investigated 117 patients who were admitted to our hospital with acute ischemic or hemorrhagic stroke from February 2013 to October 2015 and subsequently transferred to convalescent hospitals after receiving acute treatment. Transthyretin concentrations were evaluated at 3 time points as follows: at admission, and 5 and 10 days after admission. After categorizing patients into 3 groups according to the minimum transthyretin level, we analyzed the association between transthyretin and Functional Independence Measure. In our patients, transthyretin levels decreased during the first 5 days after admission and recovered slightly during the subsequent 5 days. Notably, Functional Independence Measure efficiency was significantly associated with the decrease in transthyretin levels during the 5 days after admission. Patients with lower transthyretin levels had poorer Functional Independence Measure outcomes and tended not to be discharged to their own homes. A minimal transthyretin concentration (stroke patients undergoing convalescent rehabilitation. In particular, an early decrease in transthyretin levels suggests restricted rehabilitation efficiency. Accordingly, transthyretin levels should be monitored in acute stroke patients to indicate mid-term rehabilitation prospects. Copyright © 2017 National Stroke Association. Published by Elsevier Inc. All rights reserved.

  11. Prediction of methyl-side Chain Dynamics in Proteins

    International Nuclear Information System (INIS)

    Ming Dengming; Brueschweiler, Rafael

    2004-01-01

    A simple analytical model is presented for the prediction of methyl-side chain dynamics in comparison with S 2 order parameters obtained by NMR relaxation spectroscopy. The model, which is an extension of the local contact model for backbone order parameter prediction, uses a static 3D protein structure as input. It expresses the methyl-group S 2 order parameters as a function of local contacts of the methyl carbon with respect to the neighboring atoms in combination with the number of consecutive mobile dihedral angles between the methyl group and the protein backbone. For six out of seven proteins the prediction results are good when compared with experimentally determined methyl-group S 2 values with an average correlation coefficient r-bar=0.65±0.14. For the unusually rigid cytochrome c 2 no significant correlation between prediction and experiment is found. The presented model provides independent support for the reliability of current side-chain relaxation methods along with their interpretation by the model-free formalism

  12. Predicting arsenic concentrations in porewaters of buried uranium mill tailings

    Energy Technology Data Exchange (ETDEWEB)

    Langmuir, D.; Mahoney, J.; MacDonald, A.; Rowson, J.

    1999-10-01

    The proposed JEB Tailings Management Facility (TMF) to be emplaced below the groundwater table in northern Saskatchewan, Canada, will contain uranium mill tailings from McClean Lake, Midwest and Cigar Lake ore bodies, which are high in arsenic (up to 10%) and nickel (up to 5%). A serious concern is the possibility that high arsenic and nickel concentrations may be released from the buried tailings, contaminating adjacent groundwaters and a nearby lake. Laboratory tests and geochemical modeling were performed to examine ways to reduce the arsenic and nickel concentrations in TMF porewaters so as to minimize such contamination from tailings buried for 50 years and longer. The tests were designed to mimic conditions in the mill neutralization circuit (3 hr tests at 25 C), and in the TMF after burial (5--49 day aging tests). The aging tests were run at 50, 25 and 4 C (the temperature in the TMF). In order to optimize the removal of arsenic by adsorption and precipitation, ferric sulfate was added to tailings raffinates having Fe/As ratios of less than 3--5. The acid raffinates were then neutralized by addition of slaked lime to nominal pH values of 7, 8, or 9. Analysis and modeling of the test results showed that with slaked lime addition to acid tailings raffinates, relatively amorphous scorodite (ferric arsenate) precipitates near pH 1, and is the dominant form of arsenate in slake limed tailings solids except those high in Ni and As and low in Fe, in which cabrerite-annabergite (Ni, Mg, Fe(II) arsenate) may also precipitate near pH 5--6. In addition to the arsenate precipitates, smaller amounts of arsenate are also adsorbed onto tailings solids. The aging tests showed that after burial of the tailings, arsenic concentrations may increase with time from the breakdown of the arsenate phases (chiefly scorodite). However, the tests indicate that the rate of change decreases and approaches zero after 72 hrs at 25 C, and may equal zero at all times in the TMF at 4 C

  13. Prediction on long-term mean and mean square pollutant concentrations in an urban atmosphere

    Energy Technology Data Exchange (ETDEWEB)

    Kumar, S; Lamb, R G; Seinfeld, J H

    1976-01-01

    The general problem of predicting long-term average (say yearly) pollutant concentrations in an urban atmosphere is formulated. The pollutant concentration can be viewed as a random process, the complete description of which requires knowledge of its probability density function, which is unknown. The mean concentration is the first moment of the concentration distribution, and at present there exist a number of models for predicting the long-term mean concentration of an inert pollutant. The second moment, or mean square concentration, indicates additional features of the distribution, such as the level of fluctuations about the mean. In the paper a model proposed by Lamb for the long-term mean concentration is reviewed, and a new model for prediction of the long-term mean square concentration of an inert air pollutant is derived. The properties and uses of the model are discussed, and the equations defining the model are presented in a form for direct application to an urban area.

  14. Application of infrared portable sensor technology for predicting perceived astringency of acidic whey protein beverages.

    Science.gov (United States)

    Wang, Ting; Tan, Siow-Ying; Mutilangi, William; Plans, Marcal; Rodriguez-Saona, Luis

    2016-12-01

    Formulating whey protein beverages at acidic pH provides better clarity but the beverages typically develop an unpleasant and astringent flavor. Our aim was to evaluate the application of infrared spectroscopy and chemometrics in predicting astringency of acidic whey protein beverages. Whey protein isolate (WPI), whey protein concentrate (WPC), and whey protein hydrolysate (WPH) from different manufacturers were used to formulate beverages at pH ranging from 2.2 to 3.9. Trained panelists using the spectrum method of descriptive analysis tested the beverages providing astringency scores. A portable Fourier transform infrared spectroscopy attenuated total reflectance spectrometer was used for spectra collection that was analyzed by multivariate regression analysis (partial least squares regression) to build calibration models with the sensory astringency scores. Beverage astringency scores fluctuated from 1.9 to 5.2 units and were explained by pH, protein type (WPC, WPI, or WPH), source (manufacturer), and their interactions, revealing the complexity of astringency development in acidic whey protein beverages. The WPC and WPH beverages showed an increase in astringency as the pH of the solution was lowered, but no relationship was found for WPI beverages. The partial least squares regression analysis showed strong relationship between the reference astringency scores and the infrared predicted values (correlation coefficient >0.94), giving standard error of cross-validation ranging from 0.08 to 0.12 units, depending on whey protein type. Major absorption bands explaining astringency scores were associated with carboxylic groups and amide regions of proteins. The portable infrared technique allowed rapid prediction of astringency of acidic whey protein beverages, providing the industry a novel tool for monitoring sensory characteristics of whey-containing beverages. Copyright © 2016 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

  15. Protein-protein interaction site predictions with minimum covariance determinant and Mahalanobis distance.

    Science.gov (United States)

    Qiu, Zhijun; Zhou, Bo; Yuan, Jiangfeng

    2017-11-21

    Protein-protein interaction site (PPIS) prediction must deal with the diversity of interaction sites that limits their prediction accuracy. Use of proteins with unknown or unidentified interactions can also lead to missing interfaces. Such data errors are often brought into the training dataset. In response to these two problems, we used the minimum covariance determinant (MCD) method to refine the training data to build a predictor with better performance, utilizing its ability of removing outliers. In order to predict test data in practice, a method based on Mahalanobis distance was devised to select proper test data as input for the predictor. With leave-one-validation and independent test, after the Mahalanobis distance screening, our method achieved higher performance according to Matthews correlation coefficient (MCC), although only a part of test data could be predicted. These results indicate that data refinement is an efficient approach to improve protein-protein interaction site prediction. By further optimizing our method, it is hopeful to develop predictors of better performance and wide range of application. Copyright © 2017 Elsevier Ltd. All rights reserved.

  16. Characterization and Prediction of Protein Phosphorylation Hotspots in Arabidopsis thaliana.

    Science.gov (United States)

    Christian, Jan-Ole; Braginets, Rostyslav; Schulze, Waltraud X; Walther, Dirk

    2012-01-01

    The regulation of protein function by modulating the surface charge status via sequence-locally enriched phosphorylation sites (P-sites) in so called phosphorylation "hotspots" has gained increased attention in recent years. We set out to identify P-hotspots in the model plant Arabidopsis thaliana. We analyzed the spacing of experimentally detected P-sites within peptide-covered regions along Arabidopsis protein sequences as available from the PhosPhAt database. Confirming earlier reports (Schweiger and Linial, 2010), we found that, indeed, P-sites tend to cluster and that distributions between serine and threonine P-sites to their respected closest next P-site differ significantly from those for tyrosine P-sites. The ability to predict P-hotspots by applying available computational P-site prediction programs that focus on identifying single P-sites was observed to be severely compromised by the inevitable interference of nearby P-sites. We devised a new approach, named HotSPotter, for the prediction of phosphorylation hotspots. HotSPotter is based primarily on local amino acid compositional preferences rather than sequence position-specific motifs and uses support vector machines as the underlying classification engine. HotSPotter correctly identified experimentally determined phosphorylation hotspots in A. thaliana with high accuracy. Applied to the Arabidopsis proteome, HotSPotter-predicted 13,677 candidate P-hotspots in 9,599 proteins corresponding to 7,847 unique genes. Hotspot containing proteins are involved predominantly in signaling processes confirming the surmised modulating role of hotspots in signaling and interaction events. Our study provides new bioinformatics means to identify phosphorylation hotspots and lays the basis for further investigating novel candidate P-hotspots. All phosphorylation hotspot annotations and predictions have been made available as part of the PhosPhAt database at http://phosphat.mpimp-golm.mpg.de.

  17. Prediction of RNA-Binding Proteins by Voting Systems

    Directory of Open Access Journals (Sweden)

    C. R. Peng

    2011-01-01

    Full Text Available It is important to identify which proteins can interact with RNA for the purpose of protein annotation, since interactions between RNA and proteins influence the structure of the ribosome and play important roles in gene expression. This paper tries to identify proteins that can interact with RNA using voting systems. Firstly through Weka, 34 learning algorithms are chosen for investigation. Then simple majority voting system (SMVS is used for the prediction of RNA-binding proteins, achieving average ACC (overall prediction accuracy value of 79.72% and MCC (Matthew’s correlation coefficient value of 59.77% for the independent testing dataset. Then mRMR (minimum redundancy maximum relevance strategy is used, which is transferred into algorithm selection. In addition, the MCC value of each classifier is assigned to be the weight of the classifier’s vote. As a result, best average MCC values are attained when 22 algorithms are selected and integrated through weighted votes, which are 64.70% for the independent testing dataset, and ACC value is 82.04% at this moment.

  18. Gas Concentration Prediction Based on the Measured Data of a Coal Mine Rescue Robot

    Directory of Open Access Journals (Sweden)

    Xiliang Ma

    2016-01-01

    Full Text Available The coal mine environment is complex and dangerous after gas accident; then a timely and effective rescue and relief work is necessary. Hence prediction of gas concentration in front of coal mine rescue robot is an important significance to ensure that the coal mine rescue robot carries out the exploration and search and rescue mission. In this paper, a gray neural network is proposed to predict the gas concentration 10 meters in front of the coal mine rescue robot based on the gas concentration, temperature, and wind speed of the current position and 1 meter in front. Subsequently the quantum genetic algorithm optimization gray neural network parameters of the gas concentration prediction method are proposed to get more accurate prediction of the gas concentration in the roadway. Experimental results show that a gray neural network optimized by the quantum genetic algorithm is more accurate for predicting the gas concentration. The overall prediction error is 9.12%, and the largest forecasting error is 11.36%; compared with gray neural network, the gas concentration prediction error increases by 55.23%. This means that the proposed method can better allow the coal mine rescue robot to accurately predict the gas concentration in the coal mine roadway.

  19. Population PK modelling and simulation based on fluoxetine and norfluoxetine concentrations in milk: a milk concentration-based prediction model.

    Science.gov (United States)

    Tanoshima, Reo; Bournissen, Facundo Garcia; Tanigawara, Yusuke; Kristensen, Judith H; Taddio, Anna; Ilett, Kenneth F; Begg, Evan J; Wallach, Izhar; Ito, Shinya

    2014-10-01

    Population pharmacokinetic (pop PK) modelling can be used for PK assessment of drugs in breast milk. However, complex mechanistic modelling of a parent and an active metabolite using both blood and milk samples is challenging. We aimed to develop a simple predictive pop PK model for milk concentration-time profiles of a parent and a metabolite, using data on fluoxetine (FX) and its active metabolite, norfluoxetine (NFX), in milk. Using a previously published data set of drug concentrations in milk from 25 women treated with FX, a pop PK model predictive of milk concentration-time profiles of FX and NFX was developed. Simulation was performed with the model to generate FX and NFX concentration-time profiles in milk of 1000 mothers. This milk concentration-based pop PK model was compared with the previously validated plasma/milk concentration-based pop PK model of FX. Milk FX and NFX concentration-time profiles were described reasonably well by a one compartment model with a FX-to-NFX conversion coefficient. Median values of the simulated relative infant dose on a weight basis (sRID: weight-adjusted daily doses of FX and NFX through breastmilk to the infant, expressed as a fraction of therapeutic FX daily dose per body weight) were 0.028 for FX and 0.029 for NFX. The FX sRID estimates were consistent with those of the plasma/milk-based pop PK model. A predictive pop PK model based on only milk concentrations can be developed for simultaneous estimation of milk concentration-time profiles of a parent (FX) and an active metabolite (NFX). © 2014 The British Pharmacological Society.

  20. Amino Acid Composition of an Organic Brown Rice Protein Concentrate and Isolate Compared to Soy and Whey Concentrates and Isolates

    Directory of Open Access Journals (Sweden)

    Douglas S. Kalman

    2014-06-01

    Full Text Available A protein concentrate (Oryzatein-80™ and a protein isolate (Oryzatein-90™ from organic whole-grain brown rice were analyzed for their amino acid composition. Two samples from different batches of Oryzatein-90™ and one sample of Oryzatein-80™ were provided by Axiom Foods (Los Angeles, CA, USA. Preparation and analysis was carried out by Covance Laboratories (Madison, WI, USA. After hydrolysis in 6-N hydrochloric acid for 24 h at approximately 110 °C and further chemical stabilization, samples were analyzed by HPLC after pre-injection derivitization. Total amino acid content of both the isolate and the concentrate was approximately 78% by weight with 36% essential amino acids and 18% branched-chain amino acids. These results are similar to the profiles of raw and cooked brown rice except in the case of glutamic acid which was 3% lower in the isolate and concentrate. The amino acid content and profile of the Oryzatein-90™ isolate was similar to published values for soy protein isolate but the total, essential, and branched-chain amino acid content of whey protein isolate was 20%, 39% and 33% greater, respectively, than that of Oryzatein-90™. These results provide a valuable addition to the nutrient database of protein isolates and concentrates from cereal grains.

  1. Amino Acid Composition of an Organic Brown Rice Protein Concentrate and Isolate Compared to Soy and Whey Concentrates and Isolates.

    Science.gov (United States)

    Kalman, Douglas S

    2014-06-30

    A protein concentrate (Oryzatein-80™) and a protein isolate (Oryzatein-90™) from organic whole-grain brown rice were analyzed for their amino acid composition. Two samples from different batches of Oryzatein-90™ and one sample of Oryzatein-80™ were provided by Axiom Foods (Los Angeles, CA, USA). Preparation and analysis was carried out by Covance Laboratories (Madison, WI, USA). After hydrolysis in 6-N hydrochloric acid for 24 h at approximately 110 °C and further chemical stabilization, samples were analyzed by HPLC after pre-injection derivitization. Total amino acid content of both the isolate and the concentrate was approximately 78% by weight with 36% essential amino acids and 18% branched-chain amino acids. These results are similar to the profiles of raw and cooked brown rice except in the case of glutamic acid which was 3% lower in the isolate and concentrate. The amino acid content and profile of the Oryzatein-90™ isolate was similar to published values for soy protein isolate but the total, essential, and branched-chain amino acid content of whey protein isolate was 20%, 39% and 33% greater, respectively, than that of Oryzatein-90™. These results provide a valuable addition to the nutrient database of protein isolates and concentrates from cereal grains.

  2. MetaGO: Predicting Gene Ontology of Non-homologous Proteins Through Low-Resolution Protein Structure Prediction and Protein-Protein Network Mapping.

    Science.gov (United States)

    Zhang, Chengxin; Zheng, Wei; Freddolino, Peter L; Zhang, Yang

    2018-03-10

    Homology-based transferal remains the major approach to computational protein function annotations, but it becomes increasingly unreliable when the sequence identity between query and template decreases below 30%. We propose a novel pipeline, MetaGO, to deduce Gene Ontology attributes of proteins by combining sequence homology-based annotation with low-resolution structure prediction and comparison, and partner's homology-based protein-protein network mapping. The pipeline was tested on a large-scale set of 1000 non-redundant proteins from the CAFA3 experiment. Under the stringent benchmark conditions where templates with >30% sequence identity to the query are excluded, MetaGO achieves average F-measures of 0.487, 0.408, and 0.598, for Molecular Function, Biological Process, and Cellular Component, respectively, which are significantly higher than those achieved by other state-of-the-art function annotations methods. Detailed data analysis shows that the major advantage of the MetaGO lies in the new functional homolog detections from partner's homology-based network mapping and structure-based local and global structure alignments, the confidence scores of which can be optimally combined through logistic regression. These data demonstrate the power of using a hybrid model incorporating protein structure and interaction networks to deduce new functional insights beyond traditional sequence homology-based referrals, especially for proteins that lack homologous function templates. The MetaGO pipeline is available at http://zhanglab.ccmb.med.umich.edu/MetaGO/. Copyright © 2018. Published by Elsevier Ltd.

  3. Combining neural networks for protein secondary structure prediction

    DEFF Research Database (Denmark)

    Riis, Søren Kamaric

    1995-01-01

    In this paper structured neural networks are applied to the problem of predicting the secondary structure of proteins. A hierarchical approach is used where specialized neural networks are designed for each structural class and then combined using another neural network. The submodels are designed...... by using a priori knowledge of the mapping between protein building blocks and the secondary structure and by using weight sharing. Since none of the individual networks have more than 600 adjustable weights over-fitting is avoided. When ensembles of specialized experts are combined the performance...

  4. Application of XGBoost algorithm in hourly PM2.5 concentration prediction

    Science.gov (United States)

    Pan, Bingyue

    2018-02-01

    In view of prediction techniques of hourly PM2.5 concentration in China, this paper applied the XGBoost(Extreme Gradient Boosting) algorithm to predict hourly PM2.5 concentration. The monitoring data of air quality in Tianjin city was analyzed by using XGBoost algorithm. The prediction performance of the XGBoost method is evaluated by comparing observed and predicted PM2.5 concentration using three measures of forecast accuracy. The XGBoost method is also compared with the random forest algorithm, multiple linear regression, decision tree regression and support vector machines for regression models using computational results. The results demonstrate that the XGBoost algorithm outperforms other data mining methods.

  5. Usefulness of C1 Esterase Inhibitor Protein Concentrate in the ...

    African Journals Online (AJOL)

    2018-04-04

    Apr 4, 2018 ... 2018 Nigerian Journal of Clinical Practice | Published by Wolters Kluwer ‑ Medknow ... of this case report is to describe the lifesaving use of a novel C1‑INH protein ... edema of the upper lip, uvula, and tongue [Figure 1].

  6. Intestinal DNA concentration and protein synthesis in response to ...

    African Journals Online (AJOL)

    Performance, protein synthesis and mucosal DNA in small intestine of Leghorn hens may be affected by low quality feedstuff. An experiment was conducted in completely randomized design (CRD) in 2 × 2 factorial arrangement. Main factors included diets containing 20 and 40 % barley and black and blue strains of leghorn ...

  7. Intestinal DNA concentration and protein synthesis in response to ...

    African Journals Online (AJOL)

    Jane

    2011-10-05

    Oct 5, 2011 ... Full Length Research Paper. Intestinal ... transporters are membrane-bound proteins and operate ... sporters that are similar to those found on other plasma ... on fastDNA® kit (application manual revision 6540-400-4H01) and.

  8. Food chain model to predict westslope cutthroat trout ovary selenium concentrations from water concentrations in the Elk Valley, BC

    International Nuclear Information System (INIS)

    Orr, P.; Wiramanaden, C.; Franklin, W.; Fraser, C.

    2010-01-01

    The 5 coal mines operated by Teck Coal Ltd. in British Columbia's Elk River watershed release selenium during weathering of mine waste rock. Since 1966, several field studies have been conducted in which selenium concentrations in biota were measured. They revealed that tissue concentrations are higher in aquatic biota sampled in lentic compared to lotic habitats of the watershed with similar water selenium concentrations. Two food chain models were developed based on the available data. The models described dietary selenium accumulation in the ovaries of lotic versus lentic westslope cutthroat trout (WCT), a valued aquatic resource in the Elk River system. The following 3 trophic transfer relationships were characterized for each model: (1) water to base of the food web, (2) base of the food web to benthic invertebrates, and (3) benthic invertebrates to WCT ovaries. The lotic and lentic models combined the resulting equations for each trophic transfer relationships to predict WCT ovary concentrations from water concentrations. The models were in very good agreement with the available data, despite fish movement and the fact that composite benthic invertebrate sample data were only an approximation of the feeding preferences of individual fish. Based on the observed rates of increase in water selenium concentrations throughout the watershed, the models predicted very small/slow increases in WCT ovary concentrations with time.

  9. Age-dependent changes in the total protein concentrations in the ...

    African Journals Online (AJOL)

    related changes in total protein concentrations in ten regions of the pig brain and hypophyses from birth to 36 months of age. Age-related changes in protein concentrations in all the brain regions except the pons and cerebral cortex were not ...

  10. Prediction of Air Pollutants Concentration Based on an Extreme Learning Machine: The Case of Hong Kong

    OpenAIRE

    Zhang, Jiangshe; Ding, Weifu

    2017-01-01

    With the development of the economy and society all over the world, most metropolitan cities are experiencing elevated concentrations of ground-level air pollutants. It is urgent to predict and evaluate the concentration of air pollutants for some local environmental or health agencies. Feed-forward artificial neural networks have been widely used in the prediction of air pollutants concentration. However, there are some drawbacks, such as the low convergence rate and the local minimum. The e...

  11. Predicting pKa for proteins using COSMO-RS

    DEFF Research Database (Denmark)

    Andersson, Martin Peter; Jensen, Jan Halborg; Stipp, Susan Louise Svane

    2013-01-01

    We have used the COSMO-RS implicit solvation method to calculate the equilibrium constants, pKa, for deprotonation of the acidic residues of the ovomucoid inhibitor protein, OMTKY3. The root mean square error for comparison with experimental data is only 0.5 pH units and the maximum error 0.8 p......H units. The results show that the accuracy of pKa prediction using COSMO-RS is as good for large biomolecules as it is for smaller inorganic and organic acids and that the method compares very well to previous pKa predictions of the OMTKY3 protein using Quantum Mechanics/Molecular Mechanics. Our approach...

  12. Contingency Table Browser - prediction of early stage protein structure.

    Science.gov (United States)

    Kalinowska, Barbara; Krzykalski, Artur; Roterman, Irena

    2015-01-01

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

  13. PCI-SS: MISO dynamic nonlinear protein secondary structure prediction

    Directory of Open Access Journals (Sweden)

    Aboul-Magd Mohammed O

    2009-07-01

    Full Text Available Abstract Background Since the function of a protein is largely dictated by its three dimensional configuration, determining a protein's structure is of fundamental importance to biology. Here we report on a novel approach to determining the one dimensional secondary structure of proteins (distinguishing α-helices, β-strands, and non-regular structures from primary sequence data which makes use of Parallel Cascade Identification (PCI, a powerful technique from the field of nonlinear system identification. Results Using PSI-BLAST divergent evolutionary profiles as input data, dynamic nonlinear systems are built through a black-box approach to model the process of protein folding. Genetic algorithms (GAs are applied in order to optimize the architectural parameters of the PCI models. The three-state prediction problem is broken down into a combination of three binary sub-problems and protein structure classifiers are built using 2 layers of PCI classifiers. Careful construction of the optimization, training, and test datasets ensures that no homology exists between any training and testing data. A detailed comparison between PCI and 9 contemporary methods is provided over a set of 125 new protein chains guaranteed to be dissimilar to all training data. Unlike other secondary structure prediction methods, here a web service is developed to provide both human- and machine-readable interfaces to PCI-based protein secondary structure prediction. This server, called PCI-SS, is available at http://bioinf.sce.carleton.ca/PCISS. In addition to a dynamic PHP-generated web interface for humans, a Simple Object Access Protocol (SOAP interface is added to permit invocation of the PCI-SS service remotely. This machine-readable interface facilitates incorporation of PCI-SS into multi-faceted systems biology analysis pipelines requiring protein secondary structure information, and greatly simplifies high-throughput analyses. XML is used to represent the input

  14. Physical and chemical changes in whey protein concentrate stored at elevated temperature and humidity

    Science.gov (United States)

    The chemistry of whey protein concentrate (WPC) under adverse storage conditions was monitored to provide information on shelf life in hot, humid areas. WPC34 (34.9 g protein/100 g) and WPC80 (76.8 g protein/100 g) were stored for up to 18 mo under ambient conditions and at elevated temperature and...

  15. Utilizing knowledge base of amino acids structural neighborhoods to predict protein-protein interaction sites.

    Science.gov (United States)

    Jelínek, Jan; Škoda, Petr; Hoksza, David

    2017-12-06

    Protein-protein interactions (PPI) play a key role in an investigation of various biochemical processes, and their identification is thus of great importance. Although computational prediction of which amino acids take part in a PPI has been an active field of research for some time, the quality of in-silico methods is still far from perfect. We have developed a novel prediction method called INSPiRE which benefits from a knowledge base built from data available in Protein Data Bank. All proteins involved in PPIs were converted into labeled graphs with nodes corresponding to amino acids and edges to pairs of neighboring amino acids. A structural neighborhood of each node was then encoded into a bit string and stored in the knowledge base. When predicting PPIs, INSPiRE labels amino acids of unknown proteins as interface or non-interface based on how often their structural neighborhood appears as interface or non-interface in the knowledge base. We evaluated INSPiRE's behavior with respect to different types and sizes of the structural neighborhood. Furthermore, we examined the suitability of several different features for labeling the nodes. Our evaluations showed that INSPiRE clearly outperforms existing methods with respect to Matthews correlation coefficient. In this paper we introduce a new knowledge-based method for identification of protein-protein interaction sites called INSPiRE. Its knowledge base utilizes structural patterns of known interaction sites in the Protein Data Bank which are then used for PPI prediction. Extensive experiments on several well-established datasets show that INSPiRE significantly surpasses existing PPI approaches.

  16. Exploration of the dynamic properties of protein complexes predicted from spatially constrained protein-protein interaction networks.

    Directory of Open Access Journals (Sweden)

    Eric A Yen

    2014-05-01

    Full Text Available Protein complexes are not static, but rather highly dynamic with subunits that undergo 1-dimensional diffusion with respect to each other. Interactions within protein complexes are modulated through regulatory inputs that alter interactions and introduce new components and deplete existing components through exchange. While it is clear that the structure and function of any given protein complex is coupled to its dynamical properties, it remains a challenge to predict the possible conformations that complexes can adopt. Protein-fragment Complementation Assays detect physical interactions between protein pairs constrained to ≤8 nm from each other in living cells. This method has been used to build networks composed of 1000s of pair-wise interactions. Significantly, these networks contain a wealth of dynamic information, as the assay is fully reversible and the proteins are expressed in their natural context. In this study, we describe a method that extracts this valuable information in the form of predicted conformations, allowing the user to explore the conformational landscape, to search for structures that correlate with an activity state, and estimate the abundance of conformations in the living cell. The generator is based on a Markov Chain Monte Carlo simulation that uses the interaction dataset as input and is constrained by the physical resolution of the assay. We applied this method to an 18-member protein complex composed of the seven core proteins of the budding yeast Arp2/3 complex and 11 associated regulators and effector proteins. We generated 20,480 output structures and identified conformational states using principle component analysis. We interrogated the conformation landscape and found evidence of symmetry breaking, a mixture of likely active and inactive conformational states and dynamic exchange of the core protein Arc15 between core and regulatory components. Our method provides a novel tool for prediction and

  17. 21 CFR 184.1979c - Whey protein concentrate.

    Science.gov (United States)

    2010-04-01

    ... the methods prescribed in section 16.057 (liquid sample), entitled “Gravimetric Method—Official Final... concentrate, on a dry product basis, based on analytical methods in the referenced sections of “Official Methods of Analysis of the Association of Official Analytical Chemists,” 13th ed. (1980), which is...

  18. Predicting protein structures with a multiplayer online game.

    Science.gov (United States)

    Cooper, Seth; Khatib, Firas; Treuille, Adrien; Barbero, Janos; Lee, Jeehyung; Beenen, Michael; Leaver-Fay, Andrew; Baker, David; Popović, Zoran; Players, Foldit

    2010-08-05

    People exert large amounts of problem-solving effort playing computer games. Simple image- and text-recognition tasks have been successfully 'crowd-sourced' through games, but it is not clear if more complex scientific problems can be solved with human-directed computing. Protein structure prediction is one such problem: locating the biologically relevant native conformation of a protein is a formidable computational challenge given the very large size of the search space. Here we describe Foldit, a multiplayer online game that engages non-scientists in solving hard prediction problems. Foldit players interact with protein structures using direct manipulation tools and user-friendly versions of algorithms from the Rosetta structure prediction methodology, while they compete and collaborate to optimize the computed energy. We show that top-ranked Foldit players excel at solving challenging structure refinement problems in which substantial backbone rearrangements are necessary to achieve the burial of hydrophobic residues. Players working collaboratively develop a rich assortment of new strategies and algorithms; unlike computational approaches, they explore not only the conformational space but also the space of possible search strategies. The integration of human visual problem-solving and strategy development capabilities with traditional computational algorithms through interactive multiplayer games is a powerful new approach to solving computationally-limited scientific problems.

  19. I-TASSER server for protein 3D structure prediction

    Directory of Open Access Journals (Sweden)

    Zhang Yang

    2008-01-01

    Full Text Available Abstract Background Prediction of 3-dimensional protein structures from amino acid sequences represents one of the most important problems in computational structural biology. The community-wide Critical Assessment of Structure Prediction (CASP experiments have been designed to obtain an objective assessment of the state-of-the-art of the field, where I-TASSER was ranked as the best method in the server section of the recent 7th CASP experiment. Our laboratory has since then received numerous requests about the public availability of the I-TASSER algorithm and the usage of the I-TASSER predictions. Results An on-line version of I-TASSER is developed at the KU Center for Bioinformatics which has generated protein structure predictions for thousands of modeling requests from more than 35 countries. A scoring function (C-score based on the relative clustering structural density and the consensus significance score of multiple threading templates is introduced to estimate the accuracy of the I-TASSER predictions. A large-scale benchmark test demonstrates a strong correlation between the C-score and the TM-score (a structural similarity measurement with values in [0, 1] of the first models with a correlation coefficient of 0.91. Using a C-score cutoff > -1.5 for the models of correct topology, both false positive and false negative rates are below 0.1. Combining C-score and protein length, the accuracy of the I-TASSER models can be predicted with an average error of 0.08 for TM-score and 2 Å for RMSD. Conclusion The I-TASSER server has been developed to generate automated full-length 3D protein structural predictions where the benchmarked scoring system helps users to obtain quantitative assessments of the I-TASSER models. The output of the I-TASSER server for each query includes up to five full-length models, the confidence score, the estimated TM-score and RMSD, and the standard deviation of the estimations. The I-TASSER server is freely available

  20. Analysis of substructural variation in families of enzymatic proteins with applications to protein function prediction

    Directory of Open Access Journals (Sweden)

    Fofanov Viacheslav Y

    2010-05-01

    Full Text Available Abstract Background Structural variations caused by a wide range of physico-chemical and biological sources directly influence the function of a protein. For enzymatic proteins, the structure and chemistry of the catalytic binding site residues can be loosely defined as a substructure of the protein. Comparative analysis of drug-receptor substructures across and within species has been used for lead evaluation. Substructure-level similarity between the binding sites of functionally similar proteins has also been used to identify instances of convergent evolution among proteins. In functionally homologous protein families, shared chemistry and geometry at catalytic sites provide a common, local point of comparison among proteins that may differ significantly at the sequence, fold, or domain topology levels. Results This paper describes two key results that can be used separately or in combination for protein function analysis. The Family-wise Analysis of SubStructural Templates (FASST method uses all-against-all substructure comparison to determine Substructural Clusters (SCs. SCs characterize the binding site substructural variation within a protein family. In this paper we focus on examples of automatically determined SCs that can be linked to phylogenetic distance between family members, segregation by conformation, and organization by homology among convergent protein lineages. The Motif Ensemble Statistical Hypothesis (MESH framework constructs a representative motif for each protein cluster among the SCs determined by FASST to build motif ensembles that are shown through a series of function prediction experiments to improve the function prediction power of existing motifs. Conclusions FASST contributes a critical feedback and assessment step to existing binding site substructure identification methods and can be used for the thorough investigation of structure-function relationships. The application of MESH allows for an automated

  1. Evaluation of energy status of dairy cows using milk fat, protein and urea concentrations

    Directory of Open Access Journals (Sweden)

    Kirovski Danijela

    2011-11-01

    Full Text Available Energy status of dairy cows may be estimated using results for concentrations of fat, protein and urea (MUN in milk samples obtained from bulk tank or individual cows. Using individual cow milk samples is recommended on dairy farms in our geografical region due to the unhomogenity of cows in the herds in respect to their genetic potential for milk production. Depression of milk fat occurs as a consequence of heat stress, underfeeding of peripartal cows, overfeeding concentrate with reduced ration fiber levels or overfeeding with dietary fat. High milk fat content is usually combined with severe negative energy balance. Nutrition and feeding practices have great impact on milk protein level. A deficiency of crude protein in the ration may depress protein in milk. Feeding excessive dietary protein does not significantly increase milk protein. MUN analyses point out potential problems in feeding program on dairy farm. High MUN values may reflect excessive dietary crude protein and/or low rumen degradable non fiber carbohydrates intake. Also, MUN levels is impacted by heat stress since its value is increased during the summer season. Low MUNs indicate a possible dietary protein deficiency. Additionally, low MUNs concentration may indicate excess in dietary nonstructural carbohydrates. On the bases on the interrelationships between protein and urea concentrations, as well as protein and fat concentrations in individual milk sample, estimation of energy balance of dairy cows may be done more accurately.

  2. Sequence-based prediction of protein protein interaction using a deep-learning algorithm.

    Science.gov (United States)

    Sun, Tanlin; Zhou, Bo; Lai, Luhua; Pei, Jianfeng

    2017-05-25

    Protein-protein interactions (PPIs) are critical for many biological processes. It is therefore important to develop accurate high-throughput methods for identifying PPI to better understand protein function, disease occurrence, and therapy design. Though various computational methods for predicting PPI have been developed, their robustness for prediction with external datasets is unknown. Deep-learning algorithms have achieved successful results in diverse areas, but their effectiveness for PPI prediction has not been tested. We used a stacked autoencoder, a type of deep-learning algorithm, to study the sequence-based PPI prediction. The best model achieved an average accuracy of 97.19% with 10-fold cross-validation. The prediction accuracies for various external datasets ranged from 87.99% to 99.21%, which are superior to those achieved with previous methods. To our knowledge, this research is the first to apply a deep-learning algorithm to sequence-based PPI prediction, and the results demonstrate its potential in this field.

  3. Analysis of Low Frequency Protein Truncating Stop-Codon Variants and Fasting Concentration of Growth Hormone.

    Directory of Open Access Journals (Sweden)

    Erik Hallengren

    Full Text Available The genetic background of Growth Hormone (GH secretion is not well understood. Mutations giving rise to a stop codon have a high likelihood of affecting protein function.To analyze likely functional stop codon mutations that are associated with fasting plasma concentration of Growth Hormone.We analyzed stop codon mutations in 5451 individuals in the Malmö Diet and Cancer study by genotyping the Illumina Exome Chip. To enrich for stop codon mutations with likely functional effects on protein function, we focused on those disrupting >80% of the predicted amino acid sequence, which were carried by ≥ 10 individuals. Such mutations were related to GH concentration, measured with a high sensitivity assay (hs-GH and, if nominally significant, to GH related phenotypes, using linear regression analysis.Two stop codon mutations were associated with the fasting concentration of hs-GH. rs121909305 (NP_005370.1:p.R93* [Minor Allele Frequency (MAF = 0.8%] in the Myosin 1A gene (MYO1A was associated with a 0.36 (95%CI, 0.04 to 0.54; p=0.02 increment of the standardized value of the natural logarithm of hs-GH per 1 minor allele and rs35699176 (NP_067040.1:p.Q100* in the Zink Finger protein 77 gene (ZNF77 (MAF = 4.8% was associated with a 0.12 (95%CI, 0.02 to 0.22; p = 0.02 increase of hs-GH. The mutated high hs-GH associated allele of MYO1A was related to lower BMI (β-coefficient, -0.22; p = 0.05, waist (β-coefficient, -0.22; p = 0.04, body fat percentage (β-coefficient, -0.23; p = 0.03 and with higher HDL (β-coefficient, 0.23; p = 0.04. The ZNF77 stop codon was associated with height (β-coefficient, 0.11; p = 0.02 but not with cardiometabolic risk factors.We here suggest that a stop codon of MYO1A, disrupting 91% of the predicted amino acid sequence, is associated with higher hs-GH and GH-related traits suggesting that MYO1A is involved in GH metabolism and possibly body fat distribution. However, our results are preliminary and need replication in

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

    Science.gov (United States)

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

    2009-03-01

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

  5. A Physiologically Based Pharmacokinetic Model to Predict the Pharmacokinetics of Highly Protein-Bound Drugs and Impact of Errors in Plasma Protein Binding

    Science.gov (United States)

    Ye, Min; Nagar, Swati; Korzekwa, Ken

    2015-01-01

    Predicting the pharmacokinetics of highly protein-bound drugs is difficult. Also, since historical plasma protein binding data was often collected using unbuffered plasma, the resulting inaccurate binding data could contribute to incorrect predictions. This study uses a generic physiologically based pharmacokinetic (PBPK) model to predict human plasma concentration-time profiles for 22 highly protein-bound drugs. Tissue distribution was estimated from in vitro drug lipophilicity data, plasma protein binding, and blood: plasma ratio. Clearance was predicted with a well-stirred liver model. Underestimated hepatic clearance for acidic and neutral compounds was corrected by an empirical scaling factor. Predicted values (pharmacokinetic parameters, plasma concentration-time profile) were compared with observed data to evaluate model accuracy. Of the 22 drugs, less than a 2-fold error was obtained for terminal elimination half-life (t1/2, 100% of drugs), peak plasma concentration (Cmax, 100%), area under the plasma concentration-time curve (AUC0–t, 95.4%), clearance (CLh, 95.4%), mean retention time (MRT, 95.4%), and steady state volume (Vss, 90.9%). The impact of fup errors on CLh and Vss prediction was evaluated. Errors in fup resulted in proportional errors in clearance prediction for low-clearance compounds, and in Vss prediction for high-volume neutral drugs. For high-volume basic drugs, errors in fup did not propagate to errors in Vss prediction. This is due to the cancellation of errors in the calculations for tissue partitioning of basic drugs. Overall, plasma profiles were well simulated with the present PBPK model. PMID:26531057

  6. A physiologically based pharmacokinetic model to predict the pharmacokinetics of highly protein-bound drugs and the impact of errors in plasma protein binding.

    Science.gov (United States)

    Ye, Min; Nagar, Swati; Korzekwa, Ken

    2016-04-01

    Predicting the pharmacokinetics of highly protein-bound drugs is difficult. Also, since historical plasma protein binding data were often collected using unbuffered plasma, the resulting inaccurate binding data could contribute to incorrect predictions. This study uses a generic physiologically based pharmacokinetic (PBPK) model to predict human plasma concentration-time profiles for 22 highly protein-bound drugs. Tissue distribution was estimated from in vitro drug lipophilicity data, plasma protein binding and the blood: plasma ratio. Clearance was predicted with a well-stirred liver model. Underestimated hepatic clearance for acidic and neutral compounds was corrected by an empirical scaling factor. Predicted values (pharmacokinetic parameters, plasma concentration-time profile) were compared with observed data to evaluate the model accuracy. Of the 22 drugs, less than a 2-fold error was obtained for the terminal elimination half-life (t1/2 , 100% of drugs), peak plasma concentration (Cmax , 100%), area under the plasma concentration-time curve (AUC0-t , 95.4%), clearance (CLh , 95.4%), mean residence time (MRT, 95.4%) and steady state volume (Vss , 90.9%). The impact of fup errors on CLh and Vss prediction was evaluated. Errors in fup resulted in proportional errors in clearance prediction for low-clearance compounds, and in Vss prediction for high-volume neutral drugs. For high-volume basic drugs, errors in fup did not propagate to errors in Vss prediction. This is due to the cancellation of errors in the calculations for tissue partitioning of basic drugs. Overall, plasma profiles were well simulated with the present PBPK model. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

  7. A rapid method of predicting radiocaesium concentrations in sheep from activity levels in faeces

    International Nuclear Information System (INIS)

    McGee, E.J.; Synnott, H.J.; Colgan, P.A.; Keatinge, M.J.

    1994-01-01

    The use of faecal samples taken from sheep flocks as a means of predicting radiocaesium concentrations in live animals was studied. Radiocaesium levels in 1726 sheep from 29 flocks were measured using in vivo techniques and a single faecal sample taken from each flock was also analysed. A highly significant relationship was found to exist between mean flock activity and activity in the corresponding faecal samples. Least-square regression yielded a simple model for predicting mean flock radiocaesium concentrations based on activity levels in faecal samples. A similar analysis of flock maxima and activity levels in faeces provides an alternative model for predicting the expected within-flock maximum radiocaesium concentration. (Author)

  8. DomPep--a general method for predicting modular domain-mediated protein-protein interactions.

    Directory of Open Access Journals (Sweden)

    Lei Li

    Full Text Available Protein-protein interactions (PPIs are frequently mediated by the binding of a modular domain in one protein to a short, linear peptide motif in its partner. The advent of proteomic methods such as peptide and protein arrays has led to the accumulation of a wealth of interaction data for modular interaction domains. Although several computational programs have been developed to predict modular domain-mediated PPI events, they are often restricted to a given domain type. We describe DomPep, a method that can potentially be used to predict PPIs mediated by any modular domains. DomPep combines proteomic data with sequence information to achieve high accuracy and high coverage in PPI prediction. Proteomic binding data were employed to determine a simple yet novel parameter Ligand-Binding Similarity which, in turn, is used to calibrate Domain Sequence Identity and Position-Weighted-Matrix distance, two parameters that are used in constructing prediction models. Moreover, DomPep can be used to predict PPIs for both domains with experimental binding data and those without. Using the PDZ and SH2 domain families as test cases, we show that DomPep can predict PPIs with accuracies superior to existing methods. To evaluate DomPep as a discovery tool, we deployed DomPep to identify interactions mediated by three human PDZ domains. Subsequent in-solution binding assays validated the high accuracy of DomPep in predicting authentic PPIs at the proteome scale. Because DomPep makes use of only interaction data and the primary sequence of a domain, it can be readily expanded to include other types of modular domains.

  9. Biochemical studies on gamma irradiated male rats fed on whey protein concentrate

    International Nuclear Information System (INIS)

    Mohamed, N.E; Anwar, M.M.; El-bostany, N.A.

    2010-01-01

    This study carried out to investigate the possible role of whey protein protein concentrate in ameliorating some biochemical disorders induced in gamma irradiated male rats. Forty eight male albino rats were divided into four equal groups: Group 1 fed on normal diet during experimental period. Group 2 where the diet contain 15 % whey protein concentrate instead of soybean protein . Group 3 rats were exposed to whole body gamma radiation with single dose of 5 Gy and fed on the normal diet. Group 4 rate exposed to 5 Gy then fed on diet contain 15 % whey protein concentrate, the rats were decapitated after two and four weeks post irradiation. Exposure to whole body irradiation caused significant elevation of serum ALT, AST, glucose, urea, creatinine and total triiodothyronine with significant decrease in total protein, albumin and thyroxin. Irradiated rats fed on whey protein concentrate revealed significant improvement of some biochemical parameters. It could be conclude that whey protein concentrate may be considered as a useful protein source for reducing radiation injury via metabolic pathway.

  10. Composition, structure and functional properties of protein concentrates and isolates produced from walnut (Juglans regia L.).

    Science.gov (United States)

    Mao, Xiaoying; Hua, Yufei

    2012-01-01

    In this study, composition, structure and the functional properties of protein concentrate (WPC) and protein isolate (WPI) produced from defatted walnut flour (DFWF) were investigated. The results showed that the composition and structure of walnut protein concentrate (WPC) and walnut protein isolate (WPI) were significantly different. The molecular weight distribution of WPI was uniform and the protein composition of DFWF and WPC was complex with the protein aggregation. H(0) of WPC was significantly higher (p structure of WPI was similar to WPC. WPI showed big flaky plate like structures; whereas WPC appeared as a small flaky and more compact structure. The most functional properties of WPI were better than WPC. In comparing most functional properties of WPI and WPC with soybean protein concentrate and isolate, WPI and WPC showed higher fat absorption capacity (FAC). Emulsifying properties and foam properties of WPC and WPI in alkaline pH were comparable with that of soybean protein concentrate and isolate. Walnut protein concentrates and isolates can be considered as potential functional food ingredients.

  11. Exploration of the omics evidence landscape: adding qualitative labels to predicted protein-protein interactions

    NARCIS (Netherlands)

    Noort, V. van; Snel, B.; Huynen, M.A.

    2007-01-01

    ABSTRACT: BACKGROUND: In the post-genomic era various functional genomics, proteomics and computational techniques have been developed to elucidate the protein interaction network. While some of these techniques are specific for a certain type of interaction, most predict a mixture of interactions.

  12. Exploration of the omics evidence landscape: adding qualitative labels to predicted protein-protein interactions.

    NARCIS (Netherlands)

    Noort, V. van; Snel, B.; Huynen, M.A.

    2007-01-01

    BACKGROUND: In the post-genomic era various functional genomics, proteomics and computational techniques have been developed to elucidate the protein interaction network. While some of these techniques are specific for a certain type of interaction, most predict a mixture of interactions.

  13. Effect of Addition of Concentrated Proteins and Seminal Plasma Low Molecular Weight Proteins in Freezing and Thawing of Equine Semen

    Directory of Open Access Journals (Sweden)

    Fagundes, B.

    2011-07-01

    Full Text Available Difficulties in obtaining equine frozen semen with potential fertility are recognized. This study was designed to investigate the effect of seminal plasma on frozen/thawing of eight stallion semen from different breed using the following treatments: Seminal plasma with ten-fold concentrated proteins with molecular weight above 10 kDa on frozen extender; Part of seminal plasma with proteins under 10 kDa on frozen extender; Conventional freezing, using whole seminal plasma on frozen extender. Using the parameter of 30% of seminal motility post-thawing as index of good freezability, it was verified an increased percentage of stallions that presented good freezability when semen was frozen with seminal plasma containing ten-fold concentrated proteins with molecular weight above 10 kDa on frozen extender. These results, suggested the use of seminal plasma concentrated proteins from own stallion to freezing/thawing semen.

  14. Concentration of serum thyroid hormone binding proteins after 131I treatment of hyperthyroidism

    International Nuclear Information System (INIS)

    Harrop, J.S.; Hopton, M.R.; Lazarus, J.H.

    1981-01-01

    Serum concentrations of the thyroid hormone binding proteins, thyroxine binding globulin, prealbumin, and albumin were determined in 30 thyrotoxic patients before and after 131 I treatment. Each patient was placed into one of three groups according to response to treatment. The serum concentration of all three proteins rose significantly in 10 patients who became euthyroid, and a greater increase was seen in 10 patients who developed hypothyroidism. There was no significant change in thyroid hormone binding protein concentrations in 10 subjects who remained hyperthyroid. Changes in the concentration of thyroid hormone binding proteins should be borne in mind when total thyroid hormone concentrations are used to monitor the progress of patients receiving treatment for hyperthyroidism. (author)

  15. CMsearch: simultaneous exploration of protein sequence space and structure space improves not only protein homology detection but also protein structure prediction

    KAUST Repository

    Cui, Xuefeng; Lu, Zhiwu; Wang, Sheng; Jing-Yan Wang, Jim; Gao, Xin

    2016-01-01

    Motivation: Protein homology detection, a fundamental problem in computational biology, is an indispensable step toward predicting protein structures and understanding protein functions. Despite the advances in recent decades on sequence alignment

  16. Concentration addition, independent action and generalized concentration addition models for mixture effect prediction of sex hormone synthesis in vitro.

    Directory of Open Access Journals (Sweden)

    Niels Hadrup

    Full Text Available Humans are concomitantly exposed to numerous chemicals. An infinite number of combinations and doses thereof can be imagined. For toxicological risk assessment the mathematical prediction of mixture effects, using knowledge on single chemicals, is therefore desirable. We investigated pros and cons of the concentration addition (CA, independent action (IA and generalized concentration addition (GCA models. First we measured effects of single chemicals and mixtures thereof on steroid synthesis in H295R cells. Then single chemical data were applied to the models; predictions of mixture effects were calculated and compared to the experimental mixture data. Mixture 1 contained environmental chemicals adjusted in ratio according to human exposure levels. Mixture 2 was a potency adjusted mixture containing five pesticides. Prediction of testosterone effects coincided with the experimental Mixture 1 data. In contrast, antagonism was observed for effects of Mixture 2 on this hormone. The mixtures contained chemicals exerting only limited maximal effects. This hampered prediction by the CA and IA models, whereas the GCA model could be used to predict a full dose response curve. Regarding effects on progesterone and estradiol, some chemicals were having stimulatory effects whereas others had inhibitory effects. The three models were not applicable in this situation and no predictions could be performed. Finally, the expected contributions of single chemicals to the mixture effects were calculated. Prochloraz was the predominant but not sole driver of the mixtures, suggesting that one chemical alone was not responsible for the mixture effects. In conclusion, the GCA model seemed to be superior to the CA and IA models for the prediction of testosterone effects. A situation with chemicals exerting opposing effects, for which the models could not be applied, was identified. In addition, the data indicate that in non-potency adjusted mixtures the effects cannot

  17. Predicting the tolerated sequences for proteins and protein interfaces using RosettaBackrub flexible backbone design.

    Directory of Open Access Journals (Sweden)

    Colin A Smith

    Full Text Available Predicting the set of sequences that are tolerated by a protein or protein interface, while maintaining a desired function, is useful for characterizing protein interaction specificity and for computationally designing sequence libraries to engineer proteins with new functions. Here we provide a general method, a detailed set of protocols, and several benchmarks and analyses for estimating tolerated sequences using flexible backbone protein design implemented in the Rosetta molecular modeling software suite. The input to the method is at least one experimentally determined three-dimensional protein structure or high-quality model. The starting structure(s are expanded or refined into a conformational ensemble using Monte Carlo simulations consisting of backrub backbone and side chain moves in Rosetta. The method then uses a combination of simulated annealing and genetic algorithm optimization methods to enrich for low-energy sequences for the individual members of the ensemble. To emphasize certain functional requirements (e.g. forming a binding interface, interactions between and within parts of the structure (e.g. domains can be reweighted in the scoring function. Results from each backbone structure are merged together to create a single estimate for the tolerated sequence space. We provide an extensive description of the protocol and its parameters, all source code, example analysis scripts and three tests applying this method to finding sequences predicted to stabilize proteins or protein interfaces. The generality of this method makes many other applications possible, for example stabilizing interactions with small molecules, DNA, or RNA. Through the use of within-domain reweighting and/or multistate design, it may also be possible to use this method to find sequences that stabilize particular protein conformations or binding interactions over others.

  18. Prediction of Protein Hotspots from Whole Protein Sequences by a Random Projection Ensemble System

    Directory of Open Access Journals (Sweden)

    Jinjian Jiang

    2017-07-01

    Full Text Available Hotspot residues are important in the determination of protein-protein interactions, and they always perform specific functions in biological processes. The determination of hotspot residues is by the commonly-used method of alanine scanning mutagenesis experiments, which is always costly and time consuming. To address this issue, computational methods have been developed. Most of them are structure based, i.e., using the information of solved protein structures. However, the number of solved protein structures is extremely less than that of sequences. Moreover, almost all of the predictors identified hotspots from the interfaces of protein complexes, seldom from the whole protein sequences. Therefore, determining hotspots from whole protein sequences by sequence information alone is urgent. To address the issue of hotspot predictions from the whole sequences of proteins, we proposed an ensemble system with random projections using statistical physicochemical properties of amino acids. First, an encoding scheme involving sequence profiles of residues and physicochemical properties from the AAindex1 dataset is developed. Then, the random projection technique was adopted to project the encoding instances into a reduced space. Then, several better random projections were obtained by training an IBk classifier based on the training dataset, which were thus applied to the test dataset. The ensemble of random projection classifiers is therefore obtained. Experimental results showed that although the performance of our method is not good enough for real applications of hotspots, it is very promising in the determination of hotspot residues from whole sequences.

  19. Adaptive compressive learning for prediction of protein-protein interactions from primary sequence.

    Science.gov (United States)

    Zhang, Ya-Nan; Pan, Xiao-Yong; Huang, Yan; Shen, Hong-Bin

    2011-08-21

    Protein-protein interactions (PPIs) play an important role in biological processes. Although much effort has been devoted to the identification of novel PPIs by integrating experimental biological knowledge, there are still many difficulties because of lacking enough protein structural and functional information. It is highly desired to develop methods based only on amino acid sequences for predicting PPIs. However, sequence-based predictors are often struggling with the high-dimensionality causing over-fitting and high computational complexity problems, as well as the redundancy of sequential feature vectors. In this paper, a novel computational approach based on compressed sensing theory is proposed to predict yeast Saccharomyces cerevisiae PPIs from primary sequence and has achieved promising results. The key advantage of the proposed compressed sensing algorithm is that it can compress the original high-dimensional protein sequential feature vector into a much lower but more condensed space taking the sparsity property of the original signal into account. What makes compressed sensing much more attractive in protein sequence analysis is its compressed signal can be reconstructed from far fewer measurements than what is usually considered necessary in traditional Nyquist sampling theory. Experimental results demonstrate that proposed compressed sensing method is powerful for analyzing noisy biological data and reducing redundancy in feature vectors. The proposed method represents a new strategy of dealing with high-dimensional protein discrete model and has great potentiality to be extended to deal with many other complicated biological systems. Copyright © 2011 Elsevier Ltd. All rights reserved.

  20. PRmePRed: A protein arginine methylation prediction tool.

    Directory of Open Access Journals (Sweden)

    Pawan Kumar

    Full Text Available Protein methylation is an important Post-Translational Modification (PTMs of proteins. Arginine methylation carries out and regulates several important biological functions, including gene regulation and signal transduction. Experimental identification of arginine methylation site is a daunting task as it is costly as well as time and labour intensive. Hence reliable prediction tools play an important task in rapid screening and identification of possible methylation sites in proteomes. Our preliminary assessment using the available prediction methods on collected data yielded unimpressive results. This motivated us to perform a comprehensive data analysis and appraisal of features relevant in the context of biological significance, that led to the development of a prediction tool PRmePRed with better performance. The PRmePRed perform reasonably well with an accuracy of 84.10%, 82.38% sensitivity, 83.77% specificity, and Matthew's correlation coefficient of 66.20% in 10-fold cross-validation. PRmePRed is freely available at http://bioinfo.icgeb.res.in/PRmePRed/.

  1. Concentration-Induced Association in a Protein System Caused by a Highly Directional Patch Attraction.

    Science.gov (United States)

    Li, Weimin; Persson, Björn A; Lund, Mikael; Bergenholtz, Johan; Zackrisson Oskolkova, Malin

    2016-09-01

    Self-association of the protein lactoferrin is studied in solution using small-angle X-ray scattering techniques. Effective static structure factors have been shown to exhibit either a monotonic or a nonmonotonic dependence on protein concentration in the small wavevector limit, depending on salt concentration. The behavior correlates with a nonmonotonic dependence of the second virial coefficient on salt concentration, such that a maximum appears in the structure factor at a low protein concentration when the second virial coefficient is negative and close to a minimum. The results are interpreted in terms of an integral equation theory with explicit dimers, formulated by Wertheim, which provides a consistent framework able to explain the behavior in terms of a monomer-dimer equilibrium that appears because of a highly directional patch attraction. Short attraction ranges preclude trimer formation, which explains why the protein system behaves as if it were subject to a concentration-dependent isotropic protein-protein attraction. Superimposing an isotropic interaction, comprising screened Coulomb repulsion and van der Waals attraction, on the patch attraction allows for a semiquantitative modeling of the complete transition pathway from monomers in the dilute limit to monomer-dimer systems at somewhat higher protein concentrations.

  2. Baseline Plasma C-Reactive Protein Concentrations and Motor Prognosis in Parkinson Disease.

    Directory of Open Access Journals (Sweden)

    Atsushi Umemura

    Full Text Available C-reactive protein (CRP, a blood inflammatory biomarker, is associated with the development of Alzheimer disease. In animal models of Parkinson disease (PD, systemic inflammatory stimuli can promote neuroinflammation and accelerate dopaminergic neurodegeneration. However, the association between long-term systemic inflammations and neurodegeneration has not been assessed in PD patients.To investigate the longitudinal effects of baseline CRP concentrations on motor prognosis in PD.Retrospective analysis of 375 patients (mean age, 69.3 years; mean PD duration, 6.6 years. Plasma concentrations of high-sensitivity CRP were measured in the absence of infections, and the Unified Parkinson's Disease Rating Scale Part III (UPDRS-III scores were measured at five follow-up intervals (Days 1-90, 91-270, 271-450, 451-630, and 631-900.Change of UPDRS-III scores from baseline to each of the five follow-up periods.Change in UPDRS-III scores was significantly greater in PD patients with CRP concentrations ≥0.7 mg/L than in those with CRP concentrations <0.7 mg/L, as determined by a generalized estimation equation model (P = 0.021 for the entire follow-up period and by a generalized regression model (P = 0.030 for the last follow-up interval (Days 631-900. The regression coefficients of baseline CRP for the two periods were 1.41 (95% confidence interval [CI] 0.21-2.61 and 2.62 (95% CI 0.25-4.98, respectively, after adjusting for sex, age, baseline UPDRS-III score, dementia, and incremental L-dopa equivalent dose.Baseline plasma CRP levels were associated with motor deterioration and predicted motor prognosis in patients with PD. These associations were independent of sex, age, PD severity, dementia, and anti-Parkinsonian agents, suggesting that subclinical systemic inflammations could accelerate neurodegeneration in PD.

  3. Prediction and characterization of protein-protein interaction networks in swine

    Directory of Open Access Journals (Sweden)

    Wang Fen

    2012-01-01

    Full Text Available Abstract Background Studying the large-scale protein-protein interaction (PPI network is important in understanding biological processes. The current research presents the first PPI map of swine, which aims to give new insights into understanding their biological processes. Results We used three methods, Interolog-based prediction of porcine PPI network, domain-motif interactions from structural topology-based prediction of porcine PPI network and motif-motif interactions from structural topology-based prediction of porcine PPI network, to predict porcine protein interactions among 25,767 porcine proteins. We predicted 20,213, 331,484, and 218,705 porcine PPIs respectively, merged the three results into 567,441 PPIs, constructed four PPI networks, and analyzed the topological properties of the porcine PPI networks. Our predictions were validated with Pfam domain annotations and GO annotations. Averages of 70, 10,495, and 863 interactions were related to the Pfam domain-interacting pairs in iPfam database. For comparison, randomized networks were generated, and averages of only 4.24, 66.79, and 44.26 interactions were associated with Pfam domain-interacting pairs in iPfam database. In GO annotations, we found 52.68%, 75.54%, 27.20% of the predicted PPIs sharing GO terms respectively. However, the number of PPI pairs sharing GO terms in the 10,000 randomized networks reached 52.68%, 75.54%, 27.20% is 0. Finally, we determined the accuracy and precision of the methods. The methods yielded accuracies of 0.92, 0.53, and 0.50 at precisions of about 0.93, 0.74, and 0.75, respectively. Conclusion The results reveal that the predicted PPI networks are considerably reliable. The present research is an important pioneering work on protein function research. The porcine PPI data set, the confidence score of each interaction and a list of related data are available at (http://pppid.biositemap.com/.

  4. Deep recurrent conditional random field network for protein secondary prediction

    DEFF Research Database (Denmark)

    Johansen, Alexander Rosenberg; Sønderby, Søren Kaae; Sønderby, Casper Kaae

    2017-01-01

    Deep learning has become the state-of-the-art method for predicting protein secondary structure from only its amino acid residues and sequence profile. Building upon these results, we propose to combine a bi-directional recurrent neural network (biRNN) with a conditional random field (CRF), which...... of the labels for all time-steps. We condition the CRF on the output of biRNN, which learns a distributed representation based on the entire sequence. The biRNN-CRF is therefore close to ideally suited for the secondary structure task because a high degree of cross-talk between neighboring elements can...

  5. Prediction of thermodynamic instabilities of protein solutions from simple protein–protein interactions

    International Nuclear Information System (INIS)

    D’Agostino, Tommaso; Solana, José Ramón; Emanuele, Antonio

    2013-01-01

    Highlights: ► We propose a model of effective protein–protein interaction embedding solvent effects. ► A previous square-well model is enhanced by giving to the interaction a free energy character. ► The temperature dependence of the interaction is due to entropic effects of the solvent. ► The validity of the original SW model is extended to entropy driven phase transitions. ► We get good fits for lysozyme and haemoglobin spinodal data taken from literature. - Abstract: Statistical thermodynamics of protein solutions is often studied in terms of simple, microscopic models of particles interacting via pairwise potentials. Such modelling can reproduce the short range structure of protein solutions at equilibrium and predict thermodynamics instabilities of these systems. We introduce a square well model of effective protein–protein interaction that embeds the solvent’s action. We modify an existing model [45] by considering a well depth having an explicit dependence on temperature, i.e. an explicit free energy character, thus encompassing the statistically relevant configurations of solvent molecules around proteins. We choose protein solutions exhibiting demixing upon temperature decrease (lysozyme, enthalpy driven) and upon temperature increase (haemoglobin, entropy driven). We obtain satisfactory fits of spinodal curves for both the two proteins without adding any mean field term, thus extending the validity of the original model. Our results underline the solvent role in modulating or stretching the interaction potential

  6. Predicting Protein-Protein Interaction Sites with a Novel Membership Based Fuzzy SVM Classifier.

    Science.gov (United States)

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

    2015-01-01

    Predicting residues that participate in protein-protein interactions (PPI) helps to identify, which amino acids are located at the interface. In this paper, we show that the performance of the classical support vector machine (SVM) algorithm can further be improved with the use of a custom-designed fuzzy membership function, for the partner-specific PPI interface prediction problem. We evaluated the performances of both classical SVM and fuzzy SVM (F-SVM) on the PPI databases of three different model proteomes of Homo sapiens, Escherichia coli and Saccharomyces Cerevisiae and calculated the statistical significance of the developed F-SVM over classical SVM algorithm. We also compared our performance with the available state-of-the-art fuzzy methods in this domain and observed significant performance improvements. To predict interaction sites in protein complexes, local composition of amino acids together with their physico-chemical characteristics are used, where the F-SVM based prediction method exploits the membership function for each pair of sequence fragments. The average F-SVM performance (area under ROC curve) on the test samples in 10-fold cross validation experiment are measured as 77.07, 78.39, and 74.91 percent for the aforementioned organisms respectively. Performances on independent test sets are obtained as 72.09, 73.24 and 82.74 percent respectively. The software is available for free download from http://code.google.com/p/cmater-bioinfo.

  7. Improving protein-protein interaction prediction using evolutionary information from low-quality MSAs.

    Science.gov (United States)

    Várnai, Csilla; Burkoff, Nikolas S; Wild, David L

    2017-01-01

    Evolutionary information stored in multiple sequence alignments (MSAs) has been used to identify the interaction interface of protein complexes, by measuring either co-conservation or co-mutation of amino acid residues across the interface. Recently, maximum entropy related correlated mutation measures (CMMs) such as direct information, decoupling direct from indirect interactions, have been developed to identify residue pairs interacting across the protein complex interface. These studies have focussed on carefully selected protein complexes with large, good-quality MSAs. In this work, we study protein complexes with a more typical MSA consisting of fewer than 400 sequences, using a set of 79 intramolecular protein complexes. Using a maximum entropy based CMM at the residue level, we develop an interface level CMM score to be used in re-ranking docking decoys. We demonstrate that our interface level CMM score compares favourably to the complementarity trace score, an evolutionary information-based score measuring co-conservation, when combined with the number of interface residues, a knowledge-based potential and the variability score of individual amino acid sites. We also demonstrate, that, since co-mutation and co-complementarity in the MSA contain orthogonal information, the best prediction performance using evolutionary information can be achieved by combining the co-mutation information of the CMM with co-conservation information of a complementarity trace score, predicting a near-native structure as the top prediction for 41% of the dataset. The method presented is not restricted to small MSAs, and will likely improve interface prediction also for complexes with large and good-quality MSAs.

  8. Effect of membrane protein concentration on binding of 3H-imipramine in human platelets

    International Nuclear Information System (INIS)

    Barkai, A.I.; Kowalik, S.; Baron, M.

    1985-01-01

    Binding of 3 H-imipramine to platelet membranes has been implicated as a marker for depression. Comparing 3 H-IMI binding between depressed patients and normal subjects we observed an increase in the dissociation constant Kd with increasing membrane protein. This phenomenon was studied more rigorously in five normal subjects. Platelet membranes were prepared and adjusted to four concentrations of protein ranging from 100 to 800 micrograms/ml. The 3 H-IMI binding parameters of maximum binding sites number (Bmax) and Kd were obtained by Scatchard analysis at each membrane concentration. A positive linear relationship was found between K/sub d/ values and the concentration of membrane protein in the assay, but no change was observed in Bmax. The variability in Kd values reported in the literature may be accounted for in part by the different concentrations of membrane protein used in various studies

  9. Prediction of protein-protein interactions in dengue virus coat proteins guided by low resolution cryoEM structures

    Directory of Open Access Journals (Sweden)

    Srinivasan Narayanaswamy

    2010-06-01

    Full Text Available Abstract Background Dengue virus along with the other members of the flaviviridae family has reemerged as deadly human pathogens. Understanding the mechanistic details of these infections can be highly rewarding in developing effective antivirals. During maturation of the virus inside the host cell, the coat proteins E and M undergo conformational changes, altering the morphology of the viral coat. However, due to low resolution nature of the available 3-D structures of viral assemblies, the atomic details of these changes are still elusive. Results In the present analysis, starting from Cα positions of low resolution cryo electron microscopic structures the residue level details of protein-protein interaction interfaces of dengue virus coat proteins have been predicted. By comparing the preexisting structures of virus in different phases of life cycle, the changes taking place in these predicted protein-protein interaction interfaces were followed as a function of maturation process of the virus. Besides changing the current notion about the presence of only homodimers in the mature viral coat, the present analysis indicated presence of a proline-rich motif at the protein-protein interaction interface of the coat protein. Investigating the conservation status of these seemingly functionally crucial residues across other members of flaviviridae family enabled dissecting common mechanisms used for infections by these viruses. Conclusions Thus, using computational approach the present analysis has provided better insights into the preexisting low resolution structures of virus assemblies, the findings of which can be made use of in designing effective antivirals against these deadly human pathogens.

  10. Protein energy malnutrition predicts complications in liver cirrhosis.

    Science.gov (United States)

    Huisman, Ellen J; Trip, Evelien J; Siersema, Peter D; van Hoek, Bart; van Erpecum, Karel J

    2011-11-01

    Protein energy malnutrition frequently occurs in liver cirrhosis. Hand-grip strength according to Jamar is most reliable to predict protein energy malnutrition. We aimed to determine whether protein energy malnutrition affects complication risk. In 84 cirrhotics, baseline nutritional state was determined and subsequent complications prospectively assessed. Influence of potentially relevant factors including malnutrition (by Jamar hand-grip strength) on complication rates were evaluated with univariate analysis. Effect of malnutrition was subsequently evaluated by multivariate logistic regression with adjustment for possible confounders. Underlying causes of cirrhosis were viral hepatitis in 31%, alcohol in 26%, and other in 43%. Baseline Child-Pugh (CP) class was A, B, or C in 58, 35, and 7%, respectively. Energy and protein intake decreased significantly with increasing CP class, with shift from proteins to carbohydrates. At baseline, according to Jamar hand-grip strength, malnutrition occurred in 67% (n=56). Malnutrition was associated with older age and higher CP class (CP class A 57%, B 79%, C 100%) but not with underlying disease or comorbidity. Complications occurred in 18 and 48% in well-nourished and malnourished patients, respectively, (P=0.007) during 13 ± 6 months follow-up. In multivariate analysis, malnutrition was an independent predictor of complications, after correcting for comorbidity, age, and CP score (adjusted odds ratio 4.230; 95% confidence interval 1.090-16.422; P=0.037). In univariate analysis, mortality (4 vs. 18%; P=0.1) tended to be worse in malnourished patients, but this trend was lost in multivariate analysis. Malnutrition is an independent predictor of complications in cirrhosis.

  11. Grain protein concentration and harvestable protein under future climate conditions. A study of 108 spring barley accessions

    DEFF Research Database (Denmark)

    Ingvordsen, Cathrine Heinz; Gislum, René; Jørgensen, Johannes Ravn

    2016-01-01

    In the present study a set of 108 spring barley (H. vulgare L.) accessions were cultivated under predicted future levels of temperature and [CO2] as single factors and in combination (IPCC, AR5, RCP8.5). Across all genotypes, elevated [CO2] (700 ppm day/night) slightly decreased protein concentra......In the present study a set of 108 spring barley (H. vulgare L.) accessions were cultivated under predicted future levels of temperature and [CO2] as single factors and in combination (IPCC, AR5, RCP8.5). Across all genotypes, elevated [CO2] (700 ppm day/night) slightly decreased protein...

  12. Comparison of total protein concentration in skeletal muscle as measured by the Bradford and Lowry assays.

    Science.gov (United States)

    Seevaratnam, Rajini; Patel, Barkha P; Hamadeh, Mazen J

    2009-06-01

    The Lowry and Bradford assays are the most commonly used methods of total protein quantification, yet vary in several aspects. To date, no comparisons have been made in skeletal muscle. We compared total protein concentrations of mouse red and white gastrocnemius, reagent stability, protein stability and range of linearity using both assays. The Lowry averaged protein concentrations 15% higher than the Bradford with a moderate correlation (r = 0.36, P = 0.01). However, Bland-Altman analysis revealed considerable bias (15.8 +/- 29.7%). Both Lowry reagents and its protein-reagent interactions were less stable over time than the Bradford. The linear range of concentration was smaller for the Lowry (0.05-0.50 mg/ml) than the Bradford (0-2.0 mg/ml). We conclude that the Bradford and Lowry measures of total protein concentration in skeletal muscle are not interchangeable. The Bradford and Lowry assays have various strengths and weaknesses in terms of substance interference and protein size. However, the Bradford provides greater reagent stability, protein-reagent stability and range of linearity, and requires less time to analyse compared to the Lowry assay.

  13. Effects of Ionic Strength on the Enzymatic Hydrolysis of Diluted and Concentrated Whey Protein Isolate

    NARCIS (Netherlands)

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

    2012-01-01

    To identify the parameters that affect enzymatic hydrolysis at high substrate concentrations, whey protein isolate (1–30% w/v) was hydrolyzed by Alcalase and Neutrase at constant enzyme-to-substrate ratio. No changes were observed in the solubility and the aggregation state of the proteins. With

  14. Protein Concentration in Milk Formula, Growth, and Later Risk of Obesity: A Systematic Review

    NARCIS (Netherlands)

    Patro-Gołąb, Bernadeta; Zalewski, Bartłomiej M.; Kouwenhoven, Stefanie M. P.; Karaś, Jacek; Koletzko, Berthold; van Goudoever, Johannes Bernard; Szajewska, Hania

    2016-01-01

    Background: Protein intake may influence important health outcomes in later life. Objective: The objective of this study was to investigate current evidence on the effects of infant formulas and follow-on formulas with different protein concentrations on infants' and children's growth, body

  15. Hybrid ATDL-gamma distribution model for predicting area source acid gas concentrations

    Energy Technology Data Exchange (ETDEWEB)

    Jakeman, A J; Taylor, J A

    1985-01-01

    An air quality model is developed to predict the distribution of concentrations of acid gas in an urban airshed. The model is hybrid in character, combining reliable features of a deterministic ATDL-based model with statistical distributional approaches. The gamma distribution was identified from a range of distributional models as the best model. The paper shows that the assumptions of a previous hybrid model may be relaxed and presents a methodology for characterizing the uncertainty associated with model predictions. Results are demonstrated for the 98-percentile predictions of 24-h average data over annual periods at six monitoring sites. This percentile relates to the World Health Organization goal for acid gas concentrations.

  16. Concentration of Proteins and Protein Fractions in Blood Plasma of Chickens Hatched from Eggs Irradiated with Low Level Gamma Rays

    International Nuclear Information System (INIS)

    Kraljevic, P.; Vilic, M.; Simpraga, M.; Matisic, D.; Miljanic, S.

    2011-01-01

    In literature there are many results which have shown that low dose radiation can stimulate many physiological processes of living organism. In our earlier paper it was shown that low dose of gamma radiation has a stimulative effect upon metabolic process in chickens hatched from eggs irradiated before incubation. This was proved by increase of body weight gain and body weight, as well as by increase of two enzymes activities in blood plasma (aspartate aminotransferase and alanine aminotransferase) which play an important role in protein metabolism. Therefore, an attempt was made to determine the effect of eggs irradiation by low dose gamma rays upon concentration of total proteins and protein fractions in the blood plasma of chickens hatched from irradiated eggs. The eggs of heavy breed chickens were irradiated with a dose of 0.15 Gy gamma radiation (60Co) before incubation. Along with the chickens which were hatched from irradiated eggs, there was a control group of chickens hatched from nonirradiated eggs. All other conditions were the same for both groups of chickens. Blood samples were taken from the right jugular vein on the 1 s t and 3 r d day, or from the wing vein on days 5 and 7 after hatching. The total proteins concentration in the blood plasma was determined by the biuret method using Boehringer Mannheim GmbH optimized kits. The protein fractions (albumin, α 1 -globulin, α 2 -globulin, β- and γ-globulins) were estimated electrophoretically on Cellogel strips. The total proteins concentration was significantly decreased in blood plasma of chickens hatched from irradiated eggs on days 3 (P t h day (P 2 -globulin was decreased on days 1 (P t h day of life. Obtained results indicate that low dose of gamma radiation has mostly inhibitory effect upon concentration of total proteins and protein fractions in the blood plasma of chickens hatched from irradiated eggs before incubation. (author)

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

    Directory of Open Access Journals (Sweden)

    Huiying Zhao

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

  18. Biodynamic modelling and the prediction of accumulated trace metal concentrations in the polychaete Arenicola marina

    International Nuclear Information System (INIS)

    Casado-Martinez, M. Carmen; Smith, Brian D.; DelValls, T. Angel; Luoma, Samuel N.; Rainbow, Philip S.

    2009-01-01

    The use of biodynamic models to understand metal uptake directly from sediments by deposit-feeding organisms still represents a special challenge. In this study, accumulated concentrations of Cd, Zn and Ag predicted by biodynamic modelling in the lugworm Arenicola marina have been compared to measured concentrations in field populations in several UK estuaries. The biodynamic model predicted accumulated field Cd concentrations remarkably accurately, and predicted bioaccumulated Ag concentrations were in the range of those measured in lugworms collected from the field. For Zn the model showed less but still good comparability, accurately predicting Zn bioaccumulation in A. marina at high sediment concentrations but underestimating accumulated Zn in the worms from sites with low and intermediate levels of Zn sediment contamination. Therefore, it appears that the physiological parameters experimentally derived for A. marina are applicable to the conditions encountered in these environments and that the assumptions made in the model are plausible. - Biodynamic modelling predicts accumulated field concentrations of Ag, Cd and Zn in the deposit-feeding polychaete Arenicola marina.

  19. Systematic Prediction of Scaffold Proteins Reveals New Design Principles in Scaffold-Mediated Signal Transduction

    Science.gov (United States)

    Hu, Jianfei; Neiswinger, Johnathan; Zhang, Jin; Zhu, Heng; Qian, Jiang

    2015-01-01

    Scaffold proteins play a crucial role in facilitating signal transduction in eukaryotes by bringing together multiple signaling components. In this study, we performed a systematic analysis of scaffold proteins in signal transduction by integrating protein-protein interaction and kinase-substrate relationship networks. We predicted 212 scaffold proteins that are involved in 605 distinct signaling pathways. The computational prediction was validated using a protein microarray-based approach. The predicted scaffold proteins showed several interesting characteristics, as we expected from the functionality of scaffold proteins. We found that the scaffold proteins are likely to interact with each other, which is consistent with previous finding that scaffold proteins tend to form homodimers and heterodimers. Interestingly, a single scaffold protein can be involved in multiple signaling pathways by interacting with other scaffold protein partners. Furthermore, we propose two possible regulatory mechanisms by which the activity of scaffold proteins is coordinated with their associated pathways through phosphorylation process. PMID:26393507

  20. Cold-set globular protein gels: Interactions, structure and rheology as a function of protein concentration.

    NARCIS (Netherlands)

    Alting, A.C.; Hamer, R.J.; Kruif, de C.G.

    2003-01-01

    We identified the contribution of covalent and noncovalent interactions to the scaling behavior of the structural and rheological properties in a cold gelling protein system. The system we studied consisted of two types of whey protein aggregates, equal in size but different in the amount of

  1. Tools for predicting the PK/PD of therapeutic proteins.

    Science.gov (United States)

    Diao, Lei; Meibohm, Bernd

    2015-07-01

    Assessments of the pharmacokinetic/pharmacodynamic (PK/PD) characteristics are an integral part in the development of novel therapeutic agents. Compared with traditional small molecule drugs, therapeutic proteins possess many distinct PK/PD features that necessitate the application of modified or separate approaches for assessing their PK/PD relationships. In this review, the authors discuss tools that are utilized to describe and predict the PK/PD features of therapeutic proteins and that are valuable additions in the armamentarium of drug development approaches to facilitate and accelerate their successful preclinical and clinical development. A variety of state-of-the-art PK/PD tools is currently being applied and has been adjusted to support the development of proteins as therapeutics, including allometric scaling approaches, target-mediated disposition models, first-in-man dose calculations, physiologically based PK models and empirical and semi-mechanistic PK/PD modeling. With the advent of the next generation of biologics including bioengineered antibody constructs being developed, these tools will need to be further refined and adapted to ensure their applicability and successful facilitation of the drug development process for these novel scaffolds.

  2. Optimal neural networks for protein-structure prediction

    International Nuclear Information System (INIS)

    Head-Gordon, T.; Stillinger, F.H.

    1993-01-01

    The successful application of neural-network algorithms for prediction of protein structure is stymied by three problem areas: the sparsity of the database of known protein structures, poorly devised network architectures which make the input-output mapping opaque, and a global optimization problem in the multiple-minima space of the network variables. We present a simplified polypeptide model residing in two dimensions with only two amino-acid types, A and B, which allows the determination of the global energy structure for all possible sequences of pentamer, hexamer, and heptamer lengths. This model simplicity allows us to compile a complete structural database and to devise neural networks that reproduce the tertiary structure of all sequences with absolute accuracy and with the smallest number of network variables. These optimal networks reveal that the three problem areas are convoluted, but that thoughtful network designs can actually deconvolute these detrimental traits to provide network algorithms that genuinely impact on the ability of the network to generalize or learn the desired mappings. Furthermore, the two-dimensional polypeptide model shows sufficient chemical complexity so that transfer of neural-network technology to more realistic three-dimensional proteins is evident

  3. A domain-based approach to predict protein-protein interactions

    Directory of Open Access Journals (Sweden)

    Resat Haluk

    2007-06-01

    Full Text Available Abstract Background Knowing which proteins exist in a certain organism or cell type and how these proteins interact with each other are necessary for the understanding of biological processes at the whole cell level. The determination of the protein-protein interaction (PPI networks has been the subject of extensive research. Despite the development of reasonably successful methods, serious technical difficulties still exist. In this paper we present DomainGA, a quantitative computational approach that uses the information about the domain-domain interactions to predict the interactions between proteins. Results DomainGA is a multi-parameter optimization method in which the available PPI information is used to derive a quantitative scoring scheme for the domain-domain pairs. Obtained domain interaction scores are then used to predict whether a pair of proteins interacts. Using the yeast PPI data and a series of tests, we show the robustness and insensitivity of the DomainGA method to the selection of the parameter sets, score ranges, and detection rules. Our DomainGA method achieves very high explanation ratios for the positive and negative PPIs in yeast. Based on our cross-verification tests on human PPIs, comparison of the optimized scores with the structurally observed domain interactions obtained from the iPFAM database, and sensitivity and specificity analysis; we conclude that our DomainGA method shows great promise to be applicable across multiple organisms. Conclusion We envision the DomainGA as a first step of a multiple tier approach to constructing organism specific PPIs. As it is based on fundamental structural information, the DomainGA approach can be used to create potential PPIs and the accuracy of the constructed interaction template can be further improved using complementary methods. Explanation ratios obtained in the reported test case studies clearly show that the false prediction rates of the template networks constructed

  4. Structural changes induced by high-pressure processing in micellar casein and milk protein concentrates.

    Science.gov (United States)

    Cadesky, Lee; Walkling-Ribeiro, Markus; Kriner, Kyle T; Karwe, Mukund V; Moraru, Carmen I

    2017-09-01

    Reconstituted micellar casein concentrates and milk protein concentrates of 2.5 and 10% (wt/vol) protein concentration were subjected to high-pressure processing at pressures from 150 to 450 MPa, for 15 min, at ambient temperature. The structural changes induced in milk proteins by high-pressure processing were investigated using a range of physical, physicochemical, and chemical methods, including dynamic light scattering, rheology, mid-infrared spectroscopy, scanning electron microscopy, proteomics, and soluble mineral analyses. The experimental data clearly indicate pressure-induced changes of casein micelles, as well as denaturation of serum proteins. Calcium-binding α S1 - and α S2 -casein levels increased in the soluble phase after all pressure treatments. Pressurization up to 350 MPa also increased levels of soluble calcium and phosphorus, in all samples and concentrations, whereas treatment at 450 MPa reduced the levels of soluble Ca and P. Experimental data suggest dissociation of calcium phosphate and subsequent casein micelle destabilization as a result of pressure treatment. Treatment of 10% micellar casein concentrate and 10% milk protein concentrate samples at 450 MPa resulted in weak, physical gels, which featured aggregates of uniformly distributed, casein substructures of 15 to 20 nm in diameter. Serum proteins were significantly denatured by pressures above 250 MPa. These results provide information on pressure-induced changes in high-concentration protein systems, and may inform the development on new milk protein-based foods with novel textures and potentially high nutritional quality, of particular interest being the soft gel structures formed at high pressure levels. The Authors. Published by the Federation of Animal Science Societies and Elsevier Inc. on behalf of the American Dairy Science Association®. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/3.0/).

  5. Alkali solution extraction of rice residue protein isolates: Influence of alkali concentration on protein functional, structural properties and lysinoalanine formation.

    Science.gov (United States)

    Hou, Furong; Ding, Wenhui; Qu, Wenjuan; Oladejo, Ayobami Olayemi; Xiong, Feng; Zhang, Weiwei; He, Ronghai; Ma, Haile

    2017-03-01

    This study evaluated the nutrient property and safety of the rice residue protein isolates (RRPI) product (extracted by different alkali concentrations) by exploring the protein functional, structural properties and lysinoalanine (LAL) formation. The results showed that with the rising of alkali concentration from 0.03M to 0.15M, the solubility, emulsifying and foaming properties of RRPI increased at first and then descended. When the alkali concentration was greater than 0.03M, the RRPI surface hydrophobicity decreased and the content of thiol and disulfide bond, Lys and Cys significantly reduced. By the analysis of HPLC, the content of LAL rose up from 276.08 to 15,198.07mg/kg and decreased to 1340.98mg/kg crude protein when the alkali concentration increased from 0.03 to 0.09M and until to 0.15M. These results indicated that RRPI alkaline extraction concentration above 0.03M may cause severe nutrient or safety problems of protein. Copyright © 2016 Elsevier Ltd. All rights reserved.

  6. Prediction of Chl-a concentrations in an eutrophic lake using ANN models with hybrid inputs

    Science.gov (United States)

    Aksoy, A.; Yuzugullu, O.

    2017-12-01

    Chlorophyll-a (Chl-a) concentrations in water bodies exhibit both spatial and temporal variations. As a result, frequent sampling is required with higher number of samples. This motivates the use of remote sensing as a monitoring tool. Yet, prediction performances of models that convert radiance values into Chl-a concentrations can be poor in shallow lakes. In this study, Chl-a concentrations in Lake Eymir, a shallow eutrophic lake in Ankara (Turkey), are determined using artificial neural network (ANN) models that use hybrid inputs composed of water quality and meteorological data as well as remotely sensed radiance values to improve prediction performance. Following a screening based on multi-collinearity and principal component analysis (PCA), dissolved-oxygen concentration (DO), pH, turbidity, and humidity were selected among several parameters as the constituents of the hybrid input dataset. Radiance values were obtained from QuickBird-2 satellite. Conversion of the hybrid input into Chl-a concentrations were studied for two different periods in the lake. ANN models were successful in predicting Chl-a concentrations. Yet, prediction performance declined for low Chl-a concentrations in the lake. In general, models with hybrid inputs were superior over the ones that solely used remotely sensed data.

  7. Milk protein concentration, estimated breeding value for fertility, and reproductive performance in lactating dairy cows.

    Science.gov (United States)

    Morton, John M; Auldist, Martin J; Douglas, Meaghan L; Macmillan, Keith L

    2017-07-01

    Milk protein concentration in dairy cows has been positively associated with a range of measures of reproductive performance, and genetic factors affecting both milk protein concentration and reproductive performance may contribute to the observed phenotypic associations. It was of interest to assess whether these beneficial phenotypic associations are accounted for or interact with the effects of estimated breeding values for fertility. The effects of a multitrait estimated breeding value for fertility [the Australian breeding value for daughter fertility (ABV fertility)] on reproductive performance were also of interest. Interactions of milk protein concentration and ABV fertility with the interval from calving date to the start of the herd's seasonally concentrated breeding period were also assessed. A retrospective single cohort study was conducted using data collected from 74 Australian seasonally and split calving dairy herds. Associations between milk protein concentration, ABV fertility, and reproductive performance in Holstein cows were assessed using random effects logistic regression. Between 52,438 and 61,939 lactations were used for analyses of 4 reproductive performance measures. Milk protein concentration was strongly and positively associated with reproductive performance in dairy cows, and this effect was not accounted for by the effects of ABV fertility. Increases in ABV fertility had important additional beneficial effects on the probability of pregnancy by wk 6 and 21 of the herd's breeding period. For cows calved before the start of the breeding period, the effects of increases in both milk protein concentration and ABV fertility were beneficial regardless of their interval from calving to the start of the breeding period. These findings demonstrate the potential for increasing reproductive performance through identifying the causes of the association between milk protein concentration and reproductive performance and then devising management

  8. Evaluation of serum biochemical marker concentrations and survival time in dogs with protein-losing enteropathy

    NARCIS (Netherlands)

    Equilino, Mirjam; Théodoloz, Vincent; Gorgas, Daniela; Doherr, Marcus G.; Heilmann, Romy M.; Suchodolski, Jan S.; Steiner, JöRg M.; Burgener, Iwan A.

    2015-01-01

    Results—Serum C-reactive protein concentration was high in 13 of 18 dogs with PLE and in 2 of 18 dogs with FRD. Serum concentration of canine pancreatic lipase immunoreactivity was high in 3 dogs with PLE but within the reference interval in all dogs with FRD. Serum a1-proteinase inhibitor

  9. The composition and functional properties of whey protein concentrates produced from buttermilk are comparable with those of whey protein concentrates produced from skimmed milk.

    Science.gov (United States)

    Svanborg, Sigrid; Johansen, Anne-Grethe; Abrahamsen, Roger K; Skeie, Siv B

    2015-09-01

    The demand for whey protein is increasing in the food industry. Traditionally, whey protein concentrates (WPC) and isolates are produced from cheese whey. At present, microfiltration (MF) enables the utilization of whey from skim milk (SM) through milk protein fractionation. This study demonstrates that buttermilk (BM) can be a potential source for the production of a WPC with a comparable composition and functional properties to a WPC obtained by MF of SM. Through the production of WPC powder and a casein- and phospholipid (PL)-rich fraction by the MF of BM, sweet BM may be used in a more optimal and economical way. Sweet cream BM from industrial churning was skimmed before MF with 0.2-µm ceramic membranes at 55 to 58°C. The fractionations of BM and SM were performed under the same conditions using the same process, and the whey protein fractions from BM and SM were concentrated by ultrafiltration and diafiltration. The ultrafiltration and diafiltration was performed at 50°C using pasteurized tap water and a membrane with a 20-kDa cut-off to retain as little lactose as possible in the final WPC powders. The ultrafiltrates were subsequently spray dried, and their functional properties and chemical compositions were compared. The amounts of whey protein and PL in the WPC powder from BM (BMWPC) were comparable to the amounts found in the WPC from SM (SMWPC); however, the composition of the PL classes differed. The BMWPC contained less total protein, casein, and lactose compared with SMWPC, as well as higher contents of fat and citric acid. No difference in protein solubility was observed at pH values of 4.6 and 7.0, and the overrun was the same for BMWPC and SMWPC; however, the BMWPC made less stable foam than SMWPC. Copyright © 2015 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

  10. Predicting protein-protein interactions in Arabidopsis thaliana through integration of orthology, gene ontology and co-expression

    Directory of Open Access Journals (Sweden)

    Vandepoele Klaas

    2009-06-01

    Full Text Available Abstract Background Large-scale identification of the interrelationships between different components of the cell, such as the interactions between proteins, has recently gained great interest. However, unraveling large-scale protein-protein interaction maps is laborious and expensive. Moreover, assessing the reliability of the interactions can be cumbersome. Results In this study, we have developed a computational method that exploits the existing knowledge on protein-protein interactions in diverse species through orthologous relations on the one hand, and functional association data on the other hand to predict and filter protein-protein interactions in Arabidopsis thaliana. A highly reliable set of protein-protein interactions is predicted through this integrative approach making use of existing protein-protein interaction data from yeast, human, C. elegans and D. melanogaster. Localization, biological process, and co-expression data are used as powerful indicators for protein-protein interactions. The functional repertoire of the identified interactome reveals interactions between proteins functioning in well-conserved as well as plant-specific biological processes. We observe that although common mechanisms (e.g. actin polymerization and components (e.g. ARPs, actin-related proteins exist between different lineages, they are active in specific processes such as growth, cancer metastasis and trichome development in yeast, human and Arabidopsis, respectively. Conclusion We conclude that the integration of orthology with functional association data is adequate to predict protein-protein interactions. Through this approach, a high number of novel protein-protein interactions with diverse biological roles is discovered. Overall, we have predicted a reliable set of protein-protein interactions suitable for further computational as well as experimental analyses.

  11. Extraction of Protein-Protein Interaction from Scientific Articles by Predicting Dominant Keywords.

    Science.gov (United States)

    Koyabu, Shun; Phan, Thi Thanh Thuy; Ohkawa, Takenao

    2015-01-01

    For the automatic extraction of protein-protein interaction information from scientific articles, a machine learning approach is useful. The classifier is generated from training data represented using several features to decide whether a protein pair in each sentence has an interaction. Such a specific keyword that is directly related to interaction as "bind" or "interact" plays an important role for training classifiers. We call it a dominant keyword that affects the capability of the classifier. Although it is important to identify the dominant keywords, whether a keyword is dominant depends on the context in which it occurs. Therefore, we propose a method for predicting whether a keyword is dominant for each instance. In this method, a keyword that derives imbalanced classification results is tentatively assumed to be a dominant keyword initially. Then the classifiers are separately trained from the instance with and without the assumed dominant keywords. The validity of the assumed dominant keyword is evaluated based on the classification results of the generated classifiers. The assumption is updated by the evaluation result. Repeating this process increases the prediction accuracy of the dominant keyword. Our experimental results using five corpora show the effectiveness of our proposed method with dominant keyword prediction.

  12. A computational tool to predict the evolutionarily conserved protein-protein interaction hot-spot residues from the structure of the unbound protein.

    Science.gov (United States)

    Agrawal, Neeraj J; Helk, Bernhard; Trout, Bernhardt L

    2014-01-21

    Identifying hot-spot residues - residues that are critical to protein-protein binding - can help to elucidate a protein's function and assist in designing therapeutic molecules to target those residues. We present a novel computational tool, termed spatial-interaction-map (SIM), to predict the hot-spot residues of an evolutionarily conserved protein-protein interaction from the structure of an unbound protein alone. SIM can predict the protein hot-spot residues with an accuracy of 36-57%. Thus, the SIM tool can be used to predict the yet unknown hot-spot residues for many proteins for which the structure of the protein-protein complexes are not available, thereby providing a clue to their functions and an opportunity to design therapeutic molecules to target these proteins. Copyright © 2013 Federation of European Biochemical Societies. Published by Elsevier B.V. All rights reserved.

  13. Comparing human-Salmonella with plant-Salmonella protein-protein interaction predictions

    Directory of Open Access Journals (Sweden)

    Sylvia eSchleker

    2015-01-01

    Full Text Available Salmonellosis is the most frequent food-borne disease world-wide and can be transmitted to humans by a variety of routes, especially via animal and plant products. Salmonella bacteria are believed to use not only animal and human but also plant hosts despite their evolutionary distance. This raises the question if Salmonella employs similar mechanisms in infection of these diverse hosts. Given that most of our understanding comes from its interaction with human hosts, we investigate here to what degree knowledge of Salmonella-human interactions can be transferred to the Salmonella-plant system. Reviewed are recent publications on analysis and prediction of Salmonella-host interactomes. Putative protein-protein interactions (PPIs between Salmonella and its human and Arabidopsis hosts were retrieved utilizing purely interolog-based approaches in which predictions were inferred based on available sequence and domain information of known PPIs, and machine learning approaches that integrate a larger set of useful information from different sources. Transfer learning is an especially suitable machine learning technique to predict plant host targets from the knowledge of human host targets. A comparison of the prediction results with transcriptomic data shows a clear overlap between the host proteins predicted to be targeted by PPIs and their gene ontology enrichment in both host species and regulation of gene expression. In particular, the cellular processes Salmonella interferes with in plants and humans are catabolic processes. The details of how these processes are targeted, however, are quite different between the two organisms, as expected based on their evolutionary and habitat differences. Possible implications of this observation on evolution of host-pathogen communication are discussed.

  14. A Rapid Method for Determining the Concentration of Recombinant Protein Secreted from Pichia pastoris

    International Nuclear Information System (INIS)

    Sun, L W; Zhao, Y; Jiang, R; Song, Y; Feng, H; Feng, K; Niu, L P; Qi, C

    2011-01-01

    Pichia secretive expression system is one of powerful eukaryotic expression systems in genetic engineering, which is especially suitable for industrial utilization. Because of the low concentration of the target protein in initial experiment, the methods and conditions for expression of the target protein should be optimized according to the protein yield repetitively. It is necessary to set up a rapid, simple and convenient analysis method for protein expression levels instead of the generally used method such as ultrafiltration, purification, dialysis, lyophilization and so on. In this paper, acetone precipitation method was chosen to concentrate the recombinant protein firstly after comparing with four different protein precipitation methods systematically, and then the protein was analyzed by SDS-Polyacrylamide Gel Electrophoresis. The recombinant protein was determined with the feature of protein band by the Automated Image Capture and 1-D Analysis Software directly. With this method, the optimized expression conditions of basic fibroblast growth factor secreted from pichia were obtained, which is as the same as using traditional methods. Hence, a convenient tool to determine the optimized conditions for the expression of recombinant proteins in Pichia was established.

  15. The effect of dietary protein on reproduction in the mare. VI. Serum progestagen concentrations during pregnancy

    Directory of Open Access Journals (Sweden)

    F.E. Van Niekerk

    1998-07-01

    Full Text Available Sixty-four Thoroughbred and Anglo-Arab mares aged 6-12 years were used, of which 40 were non-lactating and 24 lactating. Foals from these 24 mares were weaned at the age of 6 months. Non-lactating and lactating mares were divided into 4 dietary groups each. The total daily protein intake and the protein quality (essential amino-acid content differed in the 4 groups of non-lactating and 4 groups of lactating mares. The mares were covered and the effect of the quantity and quality of dietary protein on serum progestagen concentrations during pregnancy was studied. A sharp decline in serum progestagen concentrations was recorded in all dietary groups from Days 18 to 40 of pregnancy, with some individual mares reaching values of less than 4 ng/mℓ. Serum progestagen concentrations recorded in some of the non-lactating mares on the low-quality protein diet increased to higher values (p<0.05 than those of mares in the other 3 dietary groups at 35-140 days of pregnancy. A similar trend was observed for the lactating mares on a low-quality protein diet at 30-84 days of pregnancy. No such trends were observed in any of the other dietary groups. High-quality protein supplementation increased serum progestagen concentrations during the 1st 30 days of pregnancy. Lactation depressed serum progestagen concentrations until after the foals were weaned.

  16. CONCENTRATION AND RECOVERY OF PROTEIN FROM TUNA COOKING JUICE BY FORWARD OSMOSIS

    Directory of Open Access Journals (Sweden)

    KHONGNAKORN W.

    2016-07-01

    Full Text Available Tuna cooking processing plants generate large amount of cooking juice containing a significant content of protein. Recovery and concentrating process of this valuable compound together with a low energy consumption process are of interest regarding full utilization concept and green process approach. Forward osmosis (FO was employed in this work to recover and concentrate tuna cooking juice. FO process could increase the protein concentration up to 9% with an average permeate flux of 2.54 L/m2h. The permeate flux however tended to decrease as protein concentration increased due to the impact of osmotic pressure of the feed and fouling on the membrane surface. Since tuna cooking juice consists of protein and minerals, membrane analyses indicated that fouling was more severe compared to the fouling caused by standard bovine serum albumin pure protein. However, the presence of minerals rendered it a quicker and lower energy process by comparison. These results indicated that FO is a promising technique in the recovery and concentration of tuna cooking juice protein.

  17. Influence of watermelon seed protein concentrates on dough handling, textural and sensory properties of cookies.

    Science.gov (United States)

    Wani, Ali Abas; Sogi, D S; Singh, Preeti; Khatkar, B S

    2015-04-01

    Fruit processing wastes contain numerous by products of potential use in food & allied industry. Watermelon seeds represent a major by-product of the processing waste and contain high amount of nutritional proteins. Protein rich cereal based products are in demand due to their health promoting benefits. With this aim, wheat flour was fortified with watermelon seed protein concentrates (2.5 %, 5 %, 7.5 % and 10 % levels) to prepare cookies with desirable physical, nutritional, and textural and sensory properties. Substitution levels of 5 % and 10 % significantly (p ≤ 0.05) increased the dough stability and mixing tolerance index, however pasting properties and dough extensibility decreased considerably above 5 % substitution levels. Cookie fracture force (kg) increased significantly (p ≤ 0.05) above 5 % fortification levels. Cookie spread factor (W/T) increased from 2.5 % to 7.5 % fortification levels, further increase showed negative impact. Sensory scores of the cookies showed that protein concentrate may be added up to 7.5 % fortification levels. This study revealed that watermelon protein concentrates can be fortified with protein concentrates upto 5-7.5 % levels in cookies to improve their protein quality.

  18. Optimization of mold wheat bread fortified with soy flour, pea flour and whey protein concentrate.

    Science.gov (United States)

    Erben, Melina; Osella, Carlos A

    2017-07-01

    The objective of this work was to study the effect of replacing a selected wheat flour for defatted soy flour, pea flour and whey protein concentrate on both dough rheological characteristics and the performance and nutritional quality of bread. A mixture design was used to analyze the combination of the ingredients. The optimization process suggested that a mixture containing 88.8% of wheat flour, 8.2% of defatted soy flour, 0.0% of pea flour and 3.0% of whey protein concentrate could be a good combination to achieve the best fortified-bread nutritional quality. The fortified bread resulted in high protein concentration, with an increase in dietary fiber content and higher calcium levels compared with those of control (wheat flour 100%). Regarding protein quality, available lysine content was significantly higher, thus contributing with the essential amino acid requirement.

  19. Increase in local protein concentration by field-inversion gel electrophoresis

    Directory of Open Access Journals (Sweden)

    Paulus Aran

    2007-09-01

    Full Text Available Abstract Background Proteins that migrate through cross-linked polyacrylamide gels (PAGs under the influence of a constant electric field experience negative factors, such as diffusion and non-specific trapping in the gel matrix. These negative factors reduce protein concentrations within a defined gel volume with increasing migration distance and, therefore, decrease protein separation efficiency. Enhancement of protein separation efficiency was investigated by implementing pulsed field-inversion gel electrophoresis (FIGE. Results Separation of model protein species and large protein complexes was compared between FIGE and constant field electrophoresis (CFE in different percentages of PAGs. Band intensities of proteins in FIGE with appropriate ratios of forward and backward pulse times were superior to CFE despite longer running times. These results revealed an increase in band intensity per defined gel volume. A biphasic protein relative mobility shift was observed in percentages of PAGs up to 14%. However, the effect of FIGE on protein separation was stochastic at higher PAG percentage. Rat liver lysates subjected to FIGE in the second-dimension separation of two-dimensional polyarcylamide gel electrophoresis (2D PAGE showed a 20% increase in the number of discernible spots compared with CFE. Nine common spots from both FIGE and CFE were selected for peptide sequencing by mass spectrometry (MS, which revealed higher final ion scores of all nine protein spots from FIGE. Native protein complexes ranging from 800 kDa to larger than 2000 kDa became apparent using FIGE compared with CFE. Conclusion The present investigation suggests that FIGE under appropriate conditions improves protein separation efficiency during PAGE as a result of increased local protein concentration. FIGE can be implemented with minimal additional instrumentation in any laboratory setting. Despite the tradeoff of longer running times, FIGE can be a powerful protein

  20. Increase in local protein concentration by field-inversion gel electrophoresis.

    Science.gov (United States)

    Tsai, Henghang; Low, Teck Yew; Freeby, Steve; Paulus, Aran; Ramnarayanan, Kalpana; Cheng, Chung-Pui Paul; Leung, Hon-Chiu Eastwood

    2007-09-26

    Proteins that migrate through cross-linked polyacrylamide gels (PAGs) under the influence of a constant electric field experience negative factors, such as diffusion and non-specific trapping in the gel matrix. These negative factors reduce protein concentrations within a defined gel volume with increasing migration distance and, therefore, decrease protein separation efficiency. Enhancement of protein separation efficiency was investigated by implementing pulsed field-inversion gel electrophoresis (FIGE). Separation of model protein species and large protein complexes was compared between FIGE and constant field electrophoresis (CFE) in different percentages of PAGs. Band intensities of proteins in FIGE with appropriate ratios of forward and backward pulse times were superior to CFE despite longer running times. These results revealed an increase in band intensity per defined gel volume. A biphasic protein relative mobility shift was observed in percentages of PAGs up to 14%. However, the effect of FIGE on protein separation was stochastic at higher PAG percentage. Rat liver lysates subjected to FIGE in the second-dimension separation of two-dimensional polyarcylamide gel electrophoresis (2D PAGE) showed a 20% increase in the number of discernible spots compared with CFE. Nine common spots from both FIGE and CFE were selected for peptide sequencing by mass spectrometry (MS), which revealed higher final ion scores of all nine protein spots from FIGE. Native protein complexes ranging from 800 kDa to larger than 2000 kDa became apparent using FIGE compared with CFE. The present investigation suggests that FIGE under appropriate conditions improves protein separation efficiency during PAGE as a result of increased local protein concentration. FIGE can be implemented with minimal additional instrumentation in any laboratory setting. Despite the tradeoff of longer running times, FIGE can be a powerful protein separation tool.

  1. BIPS: BIANA Interolog Prediction Server. A tool for protein-protein interaction inference.

    Science.gov (United States)

    Garcia-Garcia, Javier; Schleker, Sylvia; Klein-Seetharaman, Judith; Oliva, Baldo

    2012-07-01

    Protein-protein interactions (PPIs) play a crucial role in biology, and high-throughput experiments have greatly increased the coverage of known interactions. Still, identification of complete inter- and intraspecies interactomes is far from being complete. Experimental data can be complemented by the prediction of PPIs within an organism or between two organisms based on the known interactions of the orthologous genes of other organisms (interologs). Here, we present the BIANA (Biologic Interactions and Network Analysis) Interolog Prediction Server (BIPS), which offers a web-based interface to facilitate PPI predictions based on interolog information. BIPS benefits from the capabilities of the framework BIANA to integrate the several PPI-related databases. Additional metadata can be used to improve the reliability of the predicted interactions. Sensitivity and specificity of the server have been calculated using known PPIs from different interactomes using a leave-one-out approach. The specificity is between 72 and 98%, whereas sensitivity varies between 1 and 59%, depending on the sequence identity cut-off used to calculate similarities between sequences. BIPS is freely accessible at http://sbi.imim.es/BIPS.php.

  2. Short Term Prediction of PM10 Concentrations Using Seasonal Time Series Analysis

    Directory of Open Access Journals (Sweden)

    Hamid Hazrul Abdul

    2016-01-01

    Full Text Available Air pollution modelling is one of an important tool that usually used to make short term and long term prediction. Since air pollution gives a big impact especially to human health, prediction of air pollutants concentration is needed to help the local authorities to give an early warning to people who are in risk of acute and chronic health effects from air pollution. Finding the best time series model would allow prediction to be made accurately. This research was carried out to find the best time series model to predict the PM10 concentrations in Nilai, Negeri Sembilan, Malaysia. By considering two seasons which is wet season (north east monsoon and dry season (south west monsoon, seasonal autoregressive integrated moving average model were used to find the most suitable model to predict the PM10 concentrations in Nilai, Negeri Sembilan by using three error measures. Based on AIC statistics, results show that ARIMA (1, 1, 1 × (1, 0, 012 is the most suitable model to predict PM10 concentrations in Nilai, Negeri Sembilan.

  3. Photonic reagents for concentration measurement of flu-orescent proteins with overlapping spectra

    Science.gov (United States)

    Goun, Alexei; Bondar, Denys I.; Er, Ali O.; Quine, Zachary; Rabitz, Herschel A.

    2016-05-01

    By exploiting photonic reagents (i.e., coherent control by shaped laser pulses), we employ Optimal Dynamic Discrimination (ODD) as a novel means for quantitatively characterizing mixtures of fluorescent proteins with a large spectral overlap. To illustrate ODD, we simultaneously measured concentrations of in vitro mixtures of Enhanced Blue Fluorescent Protein (EBFP) and Enhanced Cyan Fluorescent Protein (ECFP). Building on this foundational study, the ultimate goal is to exploit the capabilities of ODD for parallel monitoring of genetic and protein circuits by suppressing the spectral cross-talk among multiple fluorescent reporters.

  4. Determination of possible effects of mineral concentration on protein synthesis by rumen microbes in vitro

    International Nuclear Information System (INIS)

    Nikolic, J.A.; Jovanovic, M.; Andric, R.

    1976-01-01

    The aim of the present investigation was to determine the effect of different concentrations of sulphide, magnesium and zinc on protein synthesis by rumen micro-organisms in vitro. Rumen content was taken from a young bull fed a diet based on maize and dried sugar beet pulp (2/1) supplemented with urea. The rate of incorporation of 35 S from Na 2 35 SO 4 in relation to the mean specific radioactivity of the sulphide pool was used to estimate the overall rate of microbial protein synthesis. It was found that the rate of protein synthesis and the net rate of utilization of ammonia-N were not affected by differences in mean sulphide concentration from 3.6-8.0 mg/litre. The rate of reduction of sulphate appeared not to be affected by the addition of sodium sulphide to the medium. The rate and efficiency of protein synthesis by rumen micro-organisms were not significantly affected by increasing the concentration of total magnesium from 8.4-15.3 mg/100 ml. The values for soluble magnesium varied widely (1.2-7.8 mg/100 ml), and appeared to be partly dependent on the pH of the medium. Zinc concentrations varying from 5.2-12.4 mg/litre did not influence the overall rate of protein synthesis, although the efficiency tended to be higher when the concentration of zinc was greater. Concentrations of soluble zinc were low (0.3-1.15 mg/litre), and not influenced by changes in the concentration of total zinc. It was concluded that increasing the concentrations of the examined elements above the basic values did not lead consistently to an improved production of microbial protein but, on the other hand, had no obvious detrimental effect on microbial metabolic activity within the limits studied. (author)

  5. The Protein Cost of Metabolic Fluxes: Prediction from Enzymatic Rate Laws and Cost Minimization.

    Directory of Open Access Journals (Sweden)

    Elad Noor

    2016-11-01

    Full Text Available Bacterial growth depends crucially on metabolic fluxes, which are limited by the cell's capacity to maintain metabolic enzymes. The necessary enzyme amount per unit flux is a major determinant of metabolic strategies both in evolution and bioengineering. It depends on enzyme parameters (such as kcat and KM constants, but also on metabolite concentrations. Moreover, similar amounts of different enzymes might incur different costs for the cell, depending on enzyme-specific properties such as protein size and half-life. Here, we developed enzyme cost minimization (ECM, a scalable method for computing enzyme amounts that support a given metabolic flux at a minimal protein cost. The complex interplay of enzyme and metabolite concentrations, e.g. through thermodynamic driving forces and enzyme saturation, would make it hard to solve this optimization problem directly. By treating enzyme cost as a function of metabolite levels, we formulated ECM as a numerically tractable, convex optimization problem. Its tiered approach allows for building models at different levels of detail, depending on the amount of available data. Validating our method with measured metabolite and protein levels in E. coli central metabolism, we found typical prediction fold errors of 4.1 and 2.6, respectively, for the two kinds of data. This result from the cost-optimized metabolic state is significantly better than randomly sampled metabolite profiles, supporting the hypothesis that enzyme cost is important for the fitness of E. coli. ECM can be used to predict enzyme levels and protein cost in natural and engineered pathways, and could be a valuable computational tool to assist metabolic engineering projects. Furthermore, it establishes a direct connection between protein cost and thermodynamics, and provides a physically plausible and computationally tractable way to include enzyme kinetics into constraint-based metabolic models, where kinetics have usually been ignored or

  6. MEGADOCK-Web: an integrated database of high-throughput structure-based protein-protein interaction predictions.

    Science.gov (United States)

    Hayashi, Takanori; Matsuzaki, Yuri; Yanagisawa, Keisuke; Ohue, Masahito; Akiyama, Yutaka

    2018-05-08

    Protein-protein interactions (PPIs) play several roles in living cells, and computational PPI prediction is a major focus of many researchers. The three-dimensional (3D) structure and binding surface are important for the design of PPI inhibitors. Therefore, rigid body protein-protein docking calculations for two protein structures are expected to allow elucidation of PPIs different from known complexes in terms of 3D structures because known PPI information is not explicitly required. We have developed rapid PPI prediction software based on protein-protein docking, called MEGADOCK. In order to fully utilize the benefits of computational PPI predictions, it is necessary to construct a comprehensive database to gather prediction results and their predicted 3D complex structures and to make them easily accessible. Although several databases exist that provide predicted PPIs, the previous databases do not contain a sufficient number of entries for the purpose of discovering novel PPIs. In this study, we constructed an integrated database of MEGADOCK PPI predictions, named MEGADOCK-Web. MEGADOCK-Web provides more than 10 times the number of PPI predictions than previous databases and enables users to conduct PPI predictions that cannot be found in conventional PPI prediction databases. In MEGADOCK-Web, there are 7528 protein chains and 28,331,628 predicted PPIs from all possible combinations of those proteins. Each protein structure is annotated with PDB ID, chain ID, UniProt AC, related KEGG pathway IDs, and known PPI pairs. Additionally, MEGADOCK-Web provides four powerful functions: 1) searching precalculated PPI predictions, 2) providing annotations for each predicted protein pair with an experimentally known PPI, 3) visualizing candidates that may interact with the query protein on biochemical pathways, and 4) visualizing predicted complex structures through a 3D molecular viewer. MEGADOCK-Web provides a huge amount of comprehensive PPI predictions based on

  7. High serum uric acid concentration predicts poor survival in patients with breast cancer.

    Science.gov (United States)

    Yue, Cai-Feng; Feng, Pin-Ning; Yao, Zhen-Rong; Yu, Xue-Gao; Lin, Wen-Bin; Qian, Yuan-Min; Guo, Yun-Miao; Li, Lai-Sheng; Liu, Min

    2017-10-01

    Uric acid is a product of purine metabolism. Recently, uric acid has gained much attraction in cancer. In this study, we aim to investigate the clinicopathological and prognostic significance of serum uric acid concentration in breast cancer patients. A total of 443 female patients with histopathologically diagnosed breast cancer were included. After a mean follow-up time of 56months, survival was analysed using the Kaplan-Meier method. To further evaluate the prognostic significance of uric acid concentrations, univariate and multivariate Cox regression analyses were applied. Of the clinicopathological parameters, uric acid concentration was associated with age, body mass index, ER status and PR status. Univariate analysis identified that patients with increased uric acid concentration had a significantly inferior overall survival (HR 2.13, 95% CI 1.15-3.94, p=0.016). In multivariate analysis, we found that high uric acid concentration is an independent prognostic factor predicting death, but insufficient to predict local relapse or distant metastasis. Kaplan-Meier analysis indicated that high uric acid concentration is related to the poor overall survival (p=0.013). High uric acid concentration predicts poor survival in patients with breast cancer, and might serve as a potential marker for appropriate management of breast cancer patients. Copyright © 2017 Elsevier B.V. All rights reserved.

  8. Replacing soybean meal with gelatin extracted from cow skin and corn protein concentrate as a protein source in broiler diets.

    Science.gov (United States)

    Khalaji, S; Manafi, M; Olfati, Z; Hedyati, M; Latifi, M; Veysi, A

    2016-02-01

    Two experiments were conducted to investigate the effects of replacing soybean meal with gelatin extracted from cow skin and corn protein concentrate as a protein source in broiler diets. Experiments were carried out as a completely randomized design where each experiment involved 4 treatments of 6 replicates and 10 chicks in each pen. Soybean meal proteins in a corn-soy control diet were replaced with 15, 30, and 45% of cow skin gelatin (CSG) or corn protein concentrate (CPC), respectively, in experiments 1 and 2. BW and cumulative feed intake were measured at 7, 21, and 42 d of age. Blood characteristics, relative organs weight and length, ileal digesta viscosity, ileal morphology, and cecal coliform and Salmonella population were measured at 42 d of age. Apparent total tract digestibility of protein was determined during 35 to 42 d of age. Replacement of soybean meal with CSG severely inhibited BW gain, decreased feed intake, and increased FCR in broilers during the experimental period (P ≤ 0.01). The inclusion of CPC reduced BW and increased FCR significantly (P ≤ 0.05) at 21 and 42 d of age without any consequence in feed intake. Protein digestibility was reduced and ileal digesta viscosity was increased linearly by increasing the amount of CSG and CPC in the control diet (P ≤ 0.01). Replacement of soybean meal with CSG and CPC did not significantly alter blood cell profile and plasma phosphorus, creatinine, blood urea nitrogen, Aspartate transaminase, and HDL and LDL cholesterol concentration. The inclusion of CSG linearly (P ≤ 0.05) increased plasma uric acid concentration and alkaline phosphatase activity. Triglyceride and cholesterol levels were decreased significantly (P ≤ 0.05) when the amount of CSG replacement was 15%. The results of this experiment showed that using CSG and CPC negatively affects broiler performance and therefore is not a suitable alternative to soybean meal in commercial diets. © 2015 Poultry Science Association Inc.

  9. Protein Loop Structure Prediction Using Conformational Space Annealing.

    Science.gov (United States)

    Heo, Seungryong; Lee, Juyong; Joo, Keehyoung; Shin, Hang-Cheol; Lee, Jooyoung

    2017-05-22

    We have developed a protein loop structure prediction method by combining a new energy function, which we call E PLM (energy for protein loop modeling), with the conformational space annealing (CSA) global optimization algorithm. The energy function includes stereochemistry, dynamic fragment assembly, distance-scaled finite ideal gas reference (DFIRE), and generalized orientation- and distance-dependent terms. For the conformational search of loop structures, we used the CSA algorithm, which has been quite successful in dealing with various hard global optimization problems. We assessed the performance of E PLM with two widely used loop-decoy sets, Jacobson and RAPPER, and compared the results against the DFIRE potential. The accuracy of model selection from a pool of loop decoys as well as de novo loop modeling starting from randomly generated structures was examined separately. For the selection of a nativelike structure from a decoy set, E PLM was more accurate than DFIRE in the case of the Jacobson set and had similar accuracy in the case of the RAPPER set. In terms of sampling more nativelike loop structures, E PLM outperformed E DFIRE for both decoy sets. This new approach equipped with E PLM and CSA can serve as the state-of-the-art de novo loop modeling method.

  10. Brown pigment formation in heated sugar-protein mixed suspensions containing unmodified and peptically modified whey protein concentrates.

    Science.gov (United States)

    Rongsirikul, Narumol; Hongsprabhas, Parichat

    2016-01-01

    Commercial whey protein concentrate (WPC) was modified by heating the acidified protein suspensions (pH 2.0) at 80 °C for 30 min and treating with pepsin at 37 °C for 60 min. Prior to spray-drying, such modification did not change the molecular weights (MWs) of whey proteins determined by sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE). After spray-drying the modified whey protein concentrate with trehalose excipient (MWPC-TH), it was found that the α-lactalbumin (α-La) was the major protein that was further hydrolyzed the most. The reconstituted MWPC-TH contained β-lactoglobulin (β-Lg) as the major protein and small molecular weight (MW) peptides of less than 6.5 kDa. The reconstituted MWPC-TH had higher NH2 group, Trolox equivalent antioxidant capacity (TEAC), lower exposed aromatic ring and thiol (SH) contents than did the commercial WPC. Kinetic studies revealed that the addition of MWPC-TH in fructose-glycine solution was able to reduce brown pigment formation in the mixtures heated at 80 to 95 °C by increasing the activation energy (Ea) of brown pigment formation due to the retardation of fluoresced advanced glycation end product (AGEs) formation. The addition of MWPC to reducing sugar-glycine/commercial WPC was also able to lower brown pigment formation in the sterilized (121 °C, 15 min) mixed suspensions containing 0.1 M reducing sugar and 0.5-1.0 % glycine and/or commercial (P < 0.05). It was demonstrated that the modification investigated in this study selectively hydrolyzed α-La and retained β-Lg for the production of antibrowning whey protein concentrate.

  11. Replacement of fish meal by protein soybean concentrate in practical diets for Pacific white shrimp

    Directory of Open Access Journals (Sweden)

    Mariana Soares

    2015-10-01

    Full Text Available ABSTRACTThe objective of this work was to evaluate the performance of Litopenaeus vannameifed different levels (0, 25, 50, 75, and 100% of soybean protein concentrate (63.07% crude protein, CP to replace fish meal-by product (61.24% CP. The study was conducted in clear water in fifteen 800 L tanks equipped with aeration systems, constant heating (29 ºC, and daily water exchange (30%. Each tank was stocked with 37.5 shrimp/m3 (3.03±0.14 g. Feed was supplied four times a day, at 6% of the initial biomass, adjusted daily. After 42 days, the weight gain of shrimp fed diets with 0 and 25% protein replacement was higher than that observed in shrimp fed 100% replacement, and there were no differences among those fed the other diets. Feed efficiency and survival did not differ among shrimp fed different protein replacements. There was a negative linear trend for growth parameters and feed intake as protein replacement with soybean protein concentrate increased. Fish meal by-product can be replaced by up to 75% of soybean protein concentrate, with no harm to the growth of Pacific white shrimp.

  12. DC biased low-frequency insulating constriction dielectrophoresis for protein biomolecules concentration.

    Science.gov (United States)

    Zhang, Peng; Liu, Yuxin

    2017-09-01

    Sample enrichment or molecules concentration is considered an essential step in sample processing of miniaturized devices aimed at biosensing and bioanalysis. Among all the means involved to achieve this aim, dielectrophoresis (DEP) is increasingly employed in molecules manipulation and concentration because it is non-destructive and high efficiency. This paper presents a methodology to achieve protein concentration utilizing the combination effects of electrokinetics and low frequency insulating dielectrophoresis (iDEP) generated within a microfluidic device, in which a submicron constricted channel was fabricated using DNA molecular combing and replica molding. This fabrication technique avoids using e-beam lithography or other complicated nanochannel fabrication methods, and provides an easy and low cost approach with the flexibility of controlling channel dimensions to create highly constricted channels embedded in a microfluidic device. With theoretical analysis and experiments, we demonstrated that fluorescein isothiocyanate conjugated bovine serum albumin (FITC-BSA) protein molecules can be significantly concentrated to form an arc-shaped band near the constricted channel under the effects of a negative dielectrophoretic force and DC electrokinetic forces within a short period of time. It was also observed that the amplitudes of the applied DC and AC electric fields, the AC frequencies as well as the suspending medium conductivities had strong effects on the concentration responses of the FITC-BSA molecules, including the concentrated area and position, intensities of the focused molecules, and concentration speed. Our method provides a simple and flexible approach for quickly concentrating protein molecules by controlling the applied electric field parameters. The iDEP device reported in this paper can be used as a stand-alone sensor or worked as a pre-concentration module integrated with biosensors for protein biomarker detection. Furthermore, low

  13. Analysis of predicted and measured performance of an integrated compound parabolic concentrator (ICPC)

    Energy Technology Data Exchange (ETDEWEB)

    Winston, R.; O' Gallagher, J.J.; Muschaweck, J.; Mahoney, A.R.; Dudley, V.

    1999-07-01

    A variety of configurations of evacuated Integrated Compound Parabolic Concentrator (ICPC) tubes have been under development for many years. A particularly favorable optical design corresponds to the unit concentration limit for a fin CPC solution which is then coupled to a practical, thin, wedge-shaped absorber. Prototype collector modules using tubes with two different fin orientations (horizontal and vertical) have been fabricated and tested. Comprehensive measurements of the optical characteristics of the reflector and absorber have been used together with a detailed ray trace analysis to predict the optical performance characteristics of these designs. The observed performance agrees well with the predicted performance.

  14. Intercomparison of model predictions of tritium concentrations in soil and foods following acute airborne HTO exposure

    International Nuclear Information System (INIS)

    Barry, P.J.; Watkins, B.M.; Belot, Y.; Davis, P.A.; Edlund, O.; Galeriu, D.; Raskob, W.; Russell, S.; Togawa, O.

    1998-01-01

    This paper describes the results of a model intercomparision exercise for predicting tritium transport through foodchains. Modellers were asked to assume that farmland was exposed for one hour to an average concentration in air of 10 4 MBq tritium m -3 . They were given the initial soil moisture content and 30 days of hourly averaged historical weather and asked to predict HTO and OBT concentrations in foods at selected times up to 30 days later when crops were assumed to be harvested. Two fumigations were postulated, one at 10.00 h (i.e., in day-light), and the other at 24.00 h (i.e., in darkness).Predicted environmental media concentrations after the daytime exposure agreed within an order of magnitude in most cases. Important sources of differences were variations in choices of numerical values for transport parameters. The different depths of soil layers used in the models appeared to make important contributions to differences in predictions for the given scenario. Following the night-time exposure, however, greater differences in predicted concentrations appeared. These arose largely because of different ways key processes were assumed to be affected by darkness. Uptake of HTO by vegetation and the rate it is converted to OBT were prominent amongst these processes. Further research, experimental data and modelling intercomparisons are required to resolve some of these issues. (Copyright (c) 1998 Elsevier Science B.V., Amsterdam. All rights reserved.)

  15. Effect of protein binding on unbound atazanavir and darunavir cerebrospinal fluid concentrations.

    Science.gov (United States)

    Delille, Cecile A; Pruett, Sarah T; Marconi, Vincent C; Lennox, Jeffrey L; Armstrong, Wendy S; Arrendale, Richard F; Sheth, Anandi N; Easley, Kirk A; Acosta, Edward P; Vunnava, Aswani; Ofotokun, Ighovwerha

    2014-09-01

    HIV-1 protease inhibitors (PIs) exhibit different protein binding affinities and achieve variable plasma and tissue concentrations. Degree of plasma protein binding may impact central nervous system penetration. This cross-sectional study assessed cerebrospinal fluid (CSF) unbound PI concentrations, HIV-1 RNA, and neopterin levels in subjects receiving either ritonavir-boosted darunavir (DRV), 95% plasma protein bound, or atazanavir (ATV), 86% bound. Unbound PI trough concentrations were measured using rapid equilibrium dialysis and liquid chromatography/tandem mass spectrometry. Plasma and CSF HIV-1 RNA and neopterin were measured by Ampliprep/COBAS® Taqman® 2.0 assay (Roche) and enzyme-linked immunosorbent assay (ALPCO), respectively. CSF/plasma unbound drug concentration ratio was higher for ATV, 0.09 [95% confidence interval (CI) 0.06-0.12] than DRV, 0.04 (95%CI 0.03-0.06). Unbound CSF concentrations were lower than protein adjusted wild-type inhibitory concentration-50 (IC50 ) in all ATV and 1 DRV-treated subjects (P < 0.001). CSF HIV-1 RNA was detected in 2/15 ATV and 4/15 DRV subjects (P = 0.65). CSF neopterin levels were low and similar between arms. ATV relative to DRV had higher CSF/plasma unbound drug ratio. Low CSF HIV-1 RNA and neopterin suggest that both regimens resulted in CSF virologic suppression and controlled inflammation. © 2014, The American College of Clinical Pharmacology.

  16. Prediction of Protein-Protein Interaction By Metasample-Based Sparse Representation

    Directory of Open Access Journals (Sweden)

    Xiuquan Du

    2015-01-01

    Full Text Available Protein-protein interactions (PPIs play key roles in many cellular processes such as transcription regulation, cell metabolism, and endocrine function. Understanding these interactions takes a great promotion to the pathogenesis and treatment of various diseases. A large amount of data has been generated by experimental techniques; however, most of these data are usually incomplete or noisy, and the current biological experimental techniques are always very time-consuming and expensive. In this paper, we proposed a novel method (metasample-based sparse representation classification, MSRC for PPIs prediction. A group of metasamples are extracted from the original training samples and then use the l1-regularized least square method to express a new testing sample as the linear combination of these metasamples. PPIs prediction is achieved by using a discrimination function defined in the representation coefficients. The MSRC is applied to PPIs dataset; it achieves 84.9% sensitivity, and 94.55% specificity, which is slightly lower than support vector machine (SVM and much higher than naive Bayes (NB, neural networks (NN, and k-nearest neighbor (KNN. The result shows that the MSRC is efficient for PPIs prediction.

  17. Global concentration additivity and prediction of mixture toxicities, taking nitrobenzene derivatives as an example.

    Science.gov (United States)

    Li, Tong; Liu, Shu-Shen; Qu, Rui; Liu, Hai-Ling

    2017-10-01

    The toxicity of a mixture depends not only on the mixture concentration level but also on the mixture ratio. For a multiple-component mixture (MCM) system with a definite chemical composition, the mixture toxicity can be predicted only if the global concentration additivity (GCA) is validated. The so-called GCA means that the toxicity of any mixture in the MCM system is the concentration additive, regardless of what its mixture ratio and concentration level. However, many mixture toxicity reports have usually employed one mixture ratio (such as the EC 50 ratio), the equivalent effect concentration ratio (EECR) design, to specify several mixtures. EECR mixtures cannot simulate the concentration diversity and mixture ratio diversity of mixtures in the real environment, and it is impossible to validate the GCA. Therefore, in this paper, the uniform design ray (UD-Ray) was used to select nine mixture ratios (rays) in the mixture system of five nitrobenzene derivatives (NBDs). The representative UD-Ray mixtures can effectively and rationally describe the diversity in the NBD mixture system. The toxicities of the mixtures to Vibrio qinghaiensis sp.-Q67 were determined by the microplate toxicity analysis (MTA). For each UD-Ray mixture, the concentration addition (CA) model was used to validate whether the mixture toxicity is additive. All of the UD-Ray mixtures of five NBDs are global concentration additive. Afterwards, the CA is employed to predict the toxicities of the external mixtures from three EECR mixture rays with the NOEC, EC 30 , and EC 70 ratios. The predictive toxicities are in good agreement with the experimental toxicities, which testifies to the predictability of the mixture toxicity of the NBDs. Copyright © 2017. Published by Elsevier Inc.

  18. Ground level environmental protein concentrations in various ecuadorian environments: potential uses of aerosolized protein for ecological research

    Science.gov (United States)

    Staton, Sarah J.R.; Woodward, Andrea; Castillo, Josemar A.; Swing, Kelly; Hayes, Mark A.

    2014-01-01

    Large quantities of free protein in the environment and other bioaerosols are ubiquitous throughout terrestrial ground level environments and may be integrative indicators of ecosystem status. Samples of ground level bioaerosols were collected from various ecosystems throughout Ecuador, including pristine humid tropical forest (pristine), highly altered secondary humid tropical forest (highly altered), secondary transitional very humid forest (regrowth transitional), and suburban dry montane deforested (suburban deforested). The results explored the sensitivity of localized aerosol protein concentrations to spatial and temporal variations within ecosystems, and their value for assessing environmental change. Ecosystem specific variations in environmental protein concentrations were observed: pristine 0.32 ± 0.09 μg/m3, highly altered 0.07 ± 0.05 μg/m3, regrowth transitional 0.17 ± 0.06 μg/m3, and suburban deforested 0.09 ± 0.04 μg/m3. Additionally, comparisons of intra-environmental differences in seasonal/daily weather (dry season 0.08 ± 0.03 μg/m3 and wet season 0.10 ± 0.04 μg/m3), environmental fragmentation (buffered 0.19 ± 0.06 μg/m3 and edge 0.15 ± 0.06 μg/m3), and sampling height (ground level 0.32 ± 0.09 μg/m3 and 10 m 0.24 ± 0.04 μg/m3) demonstrated the sensitivity of protein concentrations to environmental conditions. Local protein concentrations in altered environments correlated well with satellite-based spectral indices describing vegetation productivity: normalized difference vegetation index (NDVI) (r2 = 0.801), net primary production (NPP) (r2 = 0.827), leaf area index (LAI) (r2 = 0.410). Moreover, protein concentrations distinguished the pristine site, which was not differentiated in spectral indices, potentially due to spectral saturation typical of highly vegetated environments. Bioaerosol concentrations represent an inexpensive method to increase understanding of environmental changes, especially in densely vegetated

  19. Functional properties and sensory testing of whey protein concentrate sweetened with rebaudioside A

    Directory of Open Access Journals (Sweden)

    Paula Gimenez MILANI

    2016-02-01

    Full Text Available ABSTRACT Objective: To develop a natural dietary product with functional benefits for diabetic patients. Whey protein concentrate was obtained through the separation membrane processes and sweetened with rebaudioside A. This product was submitted to sensory testing in humans and used to evaluate possible functional properties in male Wistar rats models with diabetesMellitus induced by streptozotocin. Methods: Two concentrates were produced. Only the second showed protein content of 74.3 and 17.3% of lactose was used as supplementation in induced diabetic rats. This concentrate was obtained from the concentration by reverse osmosis system (180 k Daltons, followed by nanofiltration in a 500 k Daltons membrane and spray drying at 5.0% solution of the first concentrate developed. The concentrate was sweetened with rebaudioside A (rebaudioside A 26 mg/100 g concentrate. All procedures were performed at the Center for Studies in Natural Products, at the Universidade Estadual de Maringá. Three experimental groups were established (n=6: two groups of diabetic animals, one control group and one supplemented group; and a control group of normal mice (non-diabetic. The supplemented group received concentrates sweetened with rebaudioside A in a dose of 100 mg/kg bw/day by an esophageal tube for 35 days. Fasting, the fed state and body weight were assessed weekly for all groups. At the end of the supplementation period, the following were analyzed: plasma parameters of glucose, total cholesterol, triglycerides and fructosamine; the serum levels of aspartate aminotransferase and alanine aminotransferase, water and food intake. Organs and tissues were removed and weighed to assess mass and anatomical changes. Results: The product presented 74% of proteins and 17% of lactose and showed satisfactory sensory testing by the addition of 26 mg of rebaudioside A/100 g concentrate. Supplementation of the product reduced hyperglycemia, plasma fructosamine levels

  20. Predictive value of C-reactive protein in critically ill patients after abdominal surgery

    Directory of Open Access Journals (Sweden)

    Frédéric Sapin

    Full Text Available OBJECTIVES: The development of sepsis after abdominal surgery is associated with high morbidity and mortality. Due to inflammation, it may be difficult to diagnose infection when it occurs, but measurement of C-reactive protein could facilitate this diagnosis. In the present study, we evaluated the predictive value and time course of C-reactive protein in relation to outcome in patients admitted to the intensive care unit (ICU after abdominal surgery. METHODS: We included patients admitted to the ICU after abdominal surgery over a period of two years. The patients were divided into two groups according to their outcome: favorable (F; left the ICU alive, without modification of the antibiotic regimen and unfavorable (D; death in the ICU, surgical revision with or without modification of the antibiotic regimen or just modification of the regimen. We then compared the highest C-reactive protein level on the first day of admission between the two groups. RESULTS: A total of 308 patients were included: 86 patients had an unfavorable outcome (group D and 222 had a favorable outcome (group F. The groups were similar in terms of leukocytosis, neutrophilia, and platelet count. C-reactive protein was significantly higher at admission in group D and was the best predictor of an unfavorable outcome, with a sensitivity of 74% and a specificity of 72% for a threshold of 41 mg/L. No changes in C-reactive protein, as assessed based on the delta C-reactive protein, especially at days 4 and 5, were associated with a poor prognosis. CONCLUSIONS: A C-reactive protein cut-off of 41 mg/L during the first day of ICU admission after abdominal surgery was a predictor of an adverse outcome. However, no changes in the C-reactive protein concentration, especially by day 4 or 5, could identify patients at risk of death.

  1. Computational Prediction of Human Salivary Proteins from Blood Circulation and Application to Diagnostic Biomarker Identification

    Science.gov (United States)

    Wang, Jiaxin; Liang, Yanchun; Wang, Yan; Cui, Juan; Liu, Ming; Du, Wei; Xu, Ying

    2013-01-01

    Proteins can move from blood circulation into salivary glands through active transportation, passive diffusion or ultrafiltration, some of which are then released into saliva and hence can potentially serve as biomarkers for diseases if accurately identified. We present a novel computational method for predicting salivary proteins that come from circulation. The basis for the prediction is a set of physiochemical and sequence features we found to be discerning between human proteins known to be movable from circulation to saliva and proteins deemed to be not in saliva. A classifier was trained based on these features using a support-vector machine to predict protein secretion into saliva. The classifier achieved 88.56% average recall and 90.76% average precision in 10-fold cross-validation on the training data, indicating that the selected features are informative. Considering the possibility that our negative training data may not be highly reliable (i.e., proteins predicted to be not in saliva), we have also trained a ranking method, aiming to rank the known salivary proteins from circulation as the highest among the proteins in the general background, based on the same features. This prediction capability can be used to predict potential biomarker proteins for specific human diseases when coupled with the information of differentially expressed proteins in diseased versus healthy control tissues and a prediction capability for blood-secretory proteins. Using such integrated information, we predicted 31 candidate biomarker proteins in saliva for breast cancer. PMID:24324552

  2. Perioperative plasma concentrations of stable nitric oxide products are predictive of cognitive dysfunction after laparoscopic cholecystectomy.

    LENUS (Irish Health Repository)

    Iohom, G

    2012-02-03

    In this study our objectives were to determine the incidence of postoperative cognitive dysfunction (POCD) after laparoscopic cholecystectomy under sevoflurane anesthesia in patients aged >40 and <85 yr and to examine the associations between plasma concentrations of i) S-100beta protein and ii) stable nitric oxide (NO) products and POCD in this clinical setting. Neuropsychological tests were performed on 42 ASA physical status I-II patients the day before, and 4 days and 6 wk after surgery. Patient spouses (n = 13) were studied as controls. Cognitive dysfunction was defined as deficit in one or more cognitive domain(s). Serial measurements of serum concentrations of S-100beta protein and plasma concentrations of stable NO products (nitrate\\/nitrite, NOx) were performed perioperatively. Four days after surgery, new cognitive deficit was present in 16 (40%) patients and in 1 (7%) control subject (P = 0.01). Six weeks postoperatively, new cognitive deficit was present in 21 (53%) patients and 3 (23%) control subjects (P = 0.03). Compared with the "no deficit" group, patients who demonstrated a new cognitive deficit 4 days postoperatively had larger plasma NOx at each perioperative time point (P < 0.05 for each time point). Serum S-100beta protein concentrations were similar in the 2 groups. In conclusion, preoperative (and postoperative) plasma concentrations of stable NO products (but not S-100beta) are associated with early POCD. The former represents a potential biochemical predictor of POCD.

  3. Validation of Molecular Dynamics Simulations for Prediction of Three-Dimensional Structures of Small Proteins.

    Science.gov (United States)

    Kato, Koichi; Nakayoshi, Tomoki; Fukuyoshi, Shuichi; Kurimoto, Eiji; Oda, Akifumi

    2017-10-12

    Although various higher-order protein structure prediction methods have been developed, almost all of them were developed based on the three-dimensional (3D) structure information of known proteins. Here we predicted the short protein structures by molecular dynamics (MD) simulations in which only Newton's equations of motion were used and 3D structural information of known proteins was not required. To evaluate the ability of MD simulationto predict protein structures, we calculated seven short test protein (10-46 residues) in the denatured state and compared their predicted and experimental structures. The predicted structure for Trp-cage (20 residues) was close to the experimental structure by 200-ns MD simulation. For proteins shorter or longer than Trp-cage, root-mean square deviation values were larger than those for Trp-cage. However, secondary structures could be reproduced by MD simulations for proteins with 10-34 residues. Simulations by replica exchange MD were performed, but the results were similar to those from normal MD simulations. These results suggest that normal MD simulations can roughly predict short protein structures and 200-ns simulations are frequently sufficient for estimating the secondary structures of protein (approximately 20 residues). Structural prediction method using only fundamental physical laws are useful for investigating non-natural proteins, such as primitive proteins and artificial proteins for peptide-based drug delivery systems.

  4. Determination of adsorbed protein concentration in aluminum hydroxide suspensions by near-infrared transmittance Spectroscopy

    DEFF Research Database (Denmark)

    Lai, Xuxin; Zheng, Yiwu; Jacobsen, Susanne

    2008-01-01

    , using the partial least square regression (PLSR) method to construct a calibration model. The linear concentration range of adsorbed BSA is from 0 to 1.75 mg/mL by using 10 mm path length cuvettes. The influence of the sedimentation in suspension, different buffers, and different aluminum hydroxide......Analysis of aluminum hydroxide based vaccines is difficult after antigen adsorption. Adsorbed protein is often assessed by measuring residual unadsorbed protein for quality control. A new method for the direct determination of adsorbed protein concentration in suspension using near-infrared (NIR......) transmittance spectroscopy is proposed here. A simple adsorption system using albumin from bovine serum (BSA) and aluminum hydroxide as a model system is employed. The results show that the NIR absorbance at 700-1300 nm is correlated to the adsorbed BSA concentration, measured by the ultraviolet (UV) method...

  5. Microvolume protein concentration determination using the NanoDrop 2000c spectrophotometer.

    Science.gov (United States)

    Desjardins, Philippe; Hansen, Joel B; Allen, Michael

    2009-11-04

    Traditional spectrophotometry requires placing samples into cuvettes or capillaries. This is often impractical due to the limited sample volumes often used for protein analysis. The Thermo Scientific NanoDrop 2000c Spectrophotometer solves this issue with an innovative sample retention system that holds microvolume samples between two measurement surfaces using the surface tension properties of liquids, enabling the quantification of samples in volumes as low as 0.5-2 microL. The elimination of cuvettes or capillaries allows real time changes in path length, which reduces the measurement time while greatly increasing the dynamic range of protein concentrations that can be measured. The need for dilutions is also eliminated, and preparations for sample quantification are relatively easy as the measurement surfaces can be simply wiped with laboratory wipe. This video article presents modifications to traditional protein concentration determination methods for quantification of microvolume amounts of protein using A280 absorbance readings or the BCA colorimetric assay.

  6. Effects of plant proteins on postprandial, free plasma amino acid concentrations in rainbow trout (Oncorhynchus mykiss)

    DEFF Research Database (Denmark)

    Larsen, Bodil Katrine; Dalsgaard, Anne Johanne Tang; Pedersen, Per Bovbjerg

    2012-01-01

    proteins from wheat, peas, field beans, sunflower and soybean. Blood samples were obtained from the caudal vein of 7 fish in each dietary treatment group prior to feeding, as well as: 2, 4, 6, 8, 12, 24, 48 and 72 h after feeding (sampling 7 new fish at each time point), and plasma amino acid......Postprandial patterns in plasma free amino acid concentrations were investigated in juvenile rainbow trout (Oncorhynchus mykiss) fed either a fish meal based diet (FM) or a diet (VEG) where 59% of fish meal protein (corresponding to 46% of total dietary protein) was replaced by a matrix of plant...... the two dietary treatment groups correlated largely with the amino acid content of the two diets except for methionine, lysine and arginine, where the differences were more extreme than what would be expected from differences in dietary concentrations. The apparent protein digestibility coefficient...

  7. Prediction and Observation of Electron Instabilities and Phase Space Holes Concentrated in the Lunar Plasma Wake

    Science.gov (United States)

    Hutchinson, Ian H.; Malaspina, David M.

    2018-05-01

    Recent theory and numerical simulation predicts that the wake of the solar wind flow past the Moon should be the site of electrostatic instabilities that give rise to electron holes. These play an important role in the eventual merging of the wake with the background solar wind. Analysis of measurements from the ARTEMIS satellites, orbiting the Moon at distances from 1.2 to 11 RM, detects holes highly concentrated in the wake, in agreement with prediction. The theory also predicts that the hole flux density observed should be hollow, peaking away from the wake axis. Observation statistics qualitatively confirm this hollowness, lending extra supporting evidence for the identification of their generation mechanism.

  8. Prediction of CO concentrations based on a hybrid Partial Least Square and Support Vector Machine model

    Science.gov (United States)

    Yeganeh, B.; Motlagh, M. Shafie Pour; Rashidi, Y.; Kamalan, H.

    2012-08-01

    Due to the health impacts caused by exposures to air pollutants in urban areas, monitoring and forecasting of air quality parameters have become popular as an important topic in atmospheric and environmental research today. The knowledge on the dynamics and complexity of air pollutants behavior has made artificial intelligence models as a useful tool for a more accurate pollutant concentration prediction. This paper focuses on an innovative method of daily air pollution prediction using combination of Support Vector Machine (SVM) as predictor and Partial Least Square (PLS) as a data selection tool based on the measured values of CO concentrations. The CO concentrations of Rey monitoring station in the south of Tehran, from Jan. 2007 to Feb. 2011, have been used to test the effectiveness of this method. The hourly CO concentrations have been predicted using the SVM and the hybrid PLS-SVM models. Similarly, daily CO concentrations have been predicted based on the aforementioned four years measured data. Results demonstrated that both models have good prediction ability; however the hybrid PLS-SVM has better accuracy. In the analysis presented in this paper, statistic estimators including relative mean errors, root mean squared errors and the mean absolute relative error have been employed to compare performances of the models. It has been concluded that the errors decrease after size reduction and coefficients of determination increase from 56 to 81% for SVM model to 65-85% for hybrid PLS-SVM model respectively. Also it was found that the hybrid PLS-SVM model required lower computational time than SVM model as expected, hence supporting the more accurate and faster prediction ability of hybrid PLS-SVM model.

  9. Pharmacological considerations for predicting PK/PD at the site of action for therapeutic proteins.

    Science.gov (United States)

    Wang, Weirong; Zhou, Honghui

    For therapeutic proteins whose sites of action are distal to the systemic circulation, both drug and target concentrations at the tissue sites are not necessarily proportional to those in systemic circulation, highlighting the importance of understanding pharmacokinetic/pharmacodynamic (PK/PD) relationship at the sites of action. This review summarizes the pharmacological considerations for predicting local PK/PD and the importance of measuring PK and PD at site of action. Three case examples are presented to show how mechanistic and physiologically based PK/PD (PBPK/PD) models which incorporated the PK and PD at the tissue site can be used to facilitate understanding the exposure-response relationship for therapeutic proteins. Copyright © 2016. Published by Elsevier Ltd.

  10. Feature Selection and the Class Imbalance Problem in Predicting Protein Function from Sequence

    NARCIS (Netherlands)

    Al-Shahib, A.; Breitling, R.; Gilbert, D.

    2005-01-01

    Abstract: When the standard approach to predict protein function by sequence homology fails, other alternative methods can be used that require only the amino acid sequence for predicting function. One such approach uses machine learning to predict protein function directly from amino acid sequence

  11. A novel calibration approach of MODIS AOD data to predict PM2.5 concentrations

    Directory of Open Access Journals (Sweden)

    P. Koutrakis

    2011-08-01

    Full Text Available Epidemiological studies investigating the human health effects of PM2.5 are susceptible to exposure measurement errors, a form of bias in exposure estimates, since they rely on data from a limited number of PM2.5 monitors within their study area. Satellite data can be used to expand spatial coverage, potentially enhancing our ability to estimate location- or subject-specific exposures to PM2.5, but some have reported poor predictive power. A new methodology was developed to calibrate aerosol optical depth (AOD data obtained from the Moderate Resolution Imaging Spectroradiometer (MODIS. Subsequently, this method was used to predict ground daily PM2.5 concentrations in the New England region. 2003 MODIS AOD data corresponding to the New England region were retrieved, and PM2.5 concentrations measured at 26 US Environmental Protection Agency (EPA PM2.5 monitoring sites were used to calibrate the AOD data. A mixed effects model which allows day-to-day variability in daily PM2.5-AOD relationships was used to predict location-specific PM2.5 levels. PM2.5 concentrations measured at the monitoring sites were compared to those predicted for the corresponding grid cells. Both cross-sectional and longitudinal comparisons between the observed and predicted concentrations suggested that the proposed new calibration approach renders MODIS AOD data a potentially useful predictor of PM2.5 concentrations. Furthermore, the estimated PM2.5 levels within the study domain were examined in relation to air pollution sources. Our approach made it possible to investigate the spatial patterns of PM2.5 concentrations within the study domain.

  12. Hypercalcemia and high parathyroid hormone-related protein concentration associated with malignant melanoma in a dog.

    Science.gov (United States)

    Pressler, Barrak M; Rotstein, David S; Law, Jerry M; Rosol, Thomas J; LeRoy, Bruce; Keene, Bruce W; Jackson, Mark W

    2002-07-15

    A 12-year-old Cocker Spaniel with an oral malignant melanoma was evaluated for progressive lethargy and anorexia. No metastases were identified during antemortem evaluation, but severe hypercalcemia was evident. Antemortem diagnostic testing failed to identify a cause for the hypercalcemia. No neoplasms other than the melanoma were identified on postmortem examination. Serum parathyroid hormone-related protein concentration was markedly high, and the melanoma had moderate to marked immunostaining for this protein. Paraneoplastic syndromes are rare in dogs with malignant melanoma.

  13. Enhanced Temperature During Grain Filling Reduces Protein Concentration of Durum Wheat

    Directory of Open Access Journals (Sweden)

    Franco Miglietta

    2011-02-01

    Full Text Available Durum wheat is cultivated over more than 13 millions of hectares (ha world wide and Italy is the main European producer with 3.5 millions tons per year. The protein concentration of durum wheat is very important, it ensures high nutritional value and is highly appreciated by the pasta production industries. The protein concentration of wheat is determined during the grain filling period when carbon and nitrogen compounds are translocated into the grains. Air temperature affects translocation rates and contributes to final protein concentration of wheat grains. Two common commercial varieties of durum and bread wheat were exposed from anthesis to harvest, to a source of infrared radiation in the field. This allowed to investigate the relative effect of temperature on translocation of carbon and nitrogen compound during grain filling. The heat treatment imposed affected marginally dry mass accumulation of the grains in bread wheat and didn’t affect dry mass in durum wheat. Grain protein was affected by heat treatment in durum but not in bread wheat. Carbon accumulation rate was higher for durum than for bread wheat. The protein concentration was greater in durum than in bread wheat and we can assume that the absolute nitrogen accumulation rates were higher for the former species. Such difference may be either caused by a faster nitrogen uptake rate and translocation or a more efficient relocation of nitrogen accumulated in reserve organs.

  14. Turkish Tombul hazelnut (Corylus avellana L.) protein concentrates: functional and rheological properties.

    Science.gov (United States)

    Tatar, F; Tunç, M T; Kahyaoglu, T

    2015-02-01

    Turkish Tombul hazelnut consumed as natural or processed forms were evaluated to obtain protein concentrate. Defatted hazelnut flour protein (DHFP) and defatted hazelnut cake protein (DHCP) were produced from defatted hazelnut flour (DHF) and defatted hazelnut cake (DHC), respectively. The functional properties (protein solubility, emulsifying properties, foaming capacity, and colour), and dynamic rheological characteristics of protein concentrates were measured. The protein contents of samples varied in the range of 35-48 % (w/w, db) and 91-92 % (w/w, db) for DHF/DHC and DHFP/DHCP samples, respectively. The significant difference for water/fat absorption capacity, emulsion stability between DHF and DHC were determined. On the other hand, the solubility and emulsion activity of DHF and DHC were not significantly different (p > 0.05). Emulsion stability of DHFP (%46) was higher than that of DHCP (%35) but other functional properties were found similar. According to these results, the DHCP could be used as DHFP in food product formulations. The DHFP and DHCP samples showed different apparent viscosity at the same temperature and concentration, the elastic modulus (G' value) of DHPC was also found higher than that of DHFP samples.

  15. Prediction of indoor radon concentration based on residence location and construction

    International Nuclear Information System (INIS)

    Maekelaeinen, I.; Voutilainen, A.; Castren, O.

    1992-01-01

    We have constructed a model for assessing indoor radon concentrations in houses where measurements cannot be performed. It has been used in an epidemiological study and to determine the radon potential of new building sites. The model is based on data from about 10,000 buildings. Integrated radon measurements were made during the cold season in all the houses; their geographic coordinates were also known. The 2-mo measurement results were corrected to annual average concentrations. Construction data were collected from questionnaires completed by residents; geological data were determined from geological maps. Data were classified according to geographical, geological, and construction factors. In order to describe different radon production levels, the country was divided into four zones. We assumed that the factors were multiplicative, and a linear concentration-prediction model was used. The most significant factor in determining radon concentration was the geographical region, followed by soil type, year of construction, and type of foundation. The predicted indoor radon concentrations given by the model varied from 50 to 440 Bq m -3 . The lower figure represents a house with a basement, built in the 1950s on clay soil, in the region with the lowest radon concentration levels. The higher value represents a house with a concrete slab in contact with the ground, built in the 1980s, on gravel, in the region with the highest average radon concentration

  16. Fast computational methods for predicting protein structure from primary amino acid sequence

    Science.gov (United States)

    Agarwal, Pratul Kumar [Knoxville, TN

    2011-07-19

    The present invention provides a method utilizing primary amino acid sequence of a protein, energy minimization, molecular dynamics and protein vibrational modes to predict three-dimensional structure of a protein. The present invention also determines possible intermediates in the protein folding pathway. The present invention has important applications to the design of novel drugs as well as protein engineering. The present invention predicts the three-dimensional structure of a protein independent of size of the protein, overcoming a significant limitation in the prior art.

  17. Dysregulation of autism-associated synaptic proteins by psychoactive pharmaceuticals at environmental concentrations.

    Science.gov (United States)

    Kaushik, Gaurav; Xia, Yu; Pfau, Jean C; Thomas, Michael A

    2017-11-20

    Autism Spectrum Disorders (ASD) are complex neurological disorders for which the prevalence in the U.S. is currently estimated to be 1 in 50 children. A majority of cases of idiopathic autism in children likely result from unknown environmental triggers in genetically susceptible individuals. These triggers may include maternal exposure of a developing embryo to environmentally relevant minute concentrations of psychoactive pharmaceuticals through ineffectively purified drinking water. Previous studies in our lab examined the extent to which gene sets associated with neuronal development were up- and down-regulated (enriched) in the brains of fathead minnows treated with psychoactive pharmaceuticals at environmental concentrations. The aim of this study was to determine whether similar treatments would alter in vitro expression of ASD-associated synaptic proteins on differentiated human neuronal cells. Human SK-N-SH neuroblastoma cells were differentiated for two weeks with 10μM retinoic acid (RA) and treated with environmentally relevant concentrations of fluoxetine, carbamazepine or venlafaxine, and flow cytometry technique was used to analyze expression of ASD-associated synaptic proteins. Data showed that carbamazepine individually, venlafaxine individually and mixture treatment at environmental concentrations significantly altered the expression of key synaptic proteins (NMDAR1, PSD95, SV2A, HTR1B, HTR2C and OXTR). Data indicated that psychoactive pharmaceuticals at extremely low concentrations altered the in vitro expression of key synaptic proteins that may potentially contribute to neurological disorders like ASD by disrupting neuronal development. Copyright © 2017 Elsevier B.V. All rights reserved.

  18. Influence of whey protein concentrate addition on textural properties of corn flour extrudates

    Directory of Open Access Journals (Sweden)

    Mladen Brnčić

    2008-05-01

    Full Text Available Texture is an important propertiy of extruded snack products, and depended on extrusion process conditions, raw material properties and various ingredients properties as well. The main purpose of this research was, using twin-screw extrusion, to manufacture a direct expanded extrudate based on mixtures of corn flour and whey protein concentrate with acceptable textural properties. Mixtures were made of corn flour and three different concentrations of whey protein concentrate (7,5 %, 15 %, 22,5 %. Materials were processed in co-rotating twin-screw extruder APV Baker, MPF 50.15 under input conditions: water intake was 10,08 L/h, 12,18 L/h, 14,28 L/h, screw speed was 300 rpm; expansion temperature was 130 °C; feed rate was 70 kg/h. Textural properties: breaking strength index and expansion ratio were determined. Breaking strength index had largest value for the sample with 22,5 % of whey protein concentrate and water intake of 14,28 L/h. Sample with 7,5 % of whey protein concentrate and 10,08 L/h had largest expansion ratio. Calculated textural properties confirmed validity of samples. This results suggest that enrichment of extrudates with wpc addition up to 22,5 % to improve their nutritional value as well as their textural characteristics can be accomplished. Validation of direct expanded extrudates in dependence of its textural properties have shown validity and justification of this research.

  19. Volumetric interpretation of protein adsorption: interfacial packing of protein adsorbed to hydrophobic surfaces from surface-saturating solution concentrations.

    Science.gov (United States)

    Kao, Ping; Parhi, Purnendu; Krishnan, Anandi; Noh, Hyeran; Haider, Waseem; Tadigadapa, Srinivas; Allara, David L; Vogler, Erwin A

    2011-02-01

    The maximum capacity of a hydrophobic adsorbent is interpreted in terms of square or hexagonal (cubic and face-centered-cubic, FCC) interfacial packing models of adsorbed blood proteins in a way that accommodates experimental measurements by the solution-depletion method and quartz-crystal-microbalance (QCM) for the human proteins serum albumin (HSA, 66 kDa), immunoglobulin G (IgG, 160 kDa), fibrinogen (Fib, 341 kDa), and immunoglobulin M (IgM, 1000 kDa). A simple analysis shows that adsorbent capacity is capped by a fixed mass/volume (e.g. mg/mL) surface-region (interphase) concentration and not molar concentration. Nearly analytical agreement between the packing models and experiment suggests that, at surface saturation, above-mentioned proteins assemble within the interphase in a manner that approximates a well-ordered array. HSA saturates a hydrophobic adsorbent with the equivalent of a single square or hexagonally-packed layer of hydrated molecules whereas the larger proteins occupy two-or-more layers, depending on the specific protein under consideration and analytical method used to measure adsorbate mass (solution depletion or QCM). Square or hexagonal (cubic and FCC) packing models cannot be clearly distinguished by comparison to experimental data. QCM measurement of adsorbent capacity is shown to be significantly different than that measured by solution depletion for similar hydrophobic adsorbents. The underlying reason is traced to the fact that QCM measures contribution of both core protein, water of hydration, and interphase water whereas solution depletion measures only the contribution of core protein. It is further shown that thickness of the interphase directly measured by QCM systematically exceeds that inferred from solution-depletion measurements, presumably because the static model used to interpret solution depletion does not accurately capture the complexities of the viscoelastic interfacial environment probed by QCM. Copyright © 2010

  20. Arsenic concentrations, related environmental factors, and the predicted probability of elevated arsenic in groundwater in Pennsylvania

    Science.gov (United States)

    Gross, Eliza L.; Low, Dennis J.

    2013-01-01

    Analytical results for arsenic in water samples from 5,023 wells obtained during 1969–2007 across Pennsylvania were compiled and related to other associated groundwater-quality and environmental factors and used to predict the probability of elevated arsenic concentrations, defined as greater than or equal to 4.0 micrograms per liter (µg/L), in groundwater. Arsenic concentrations of 4.0 µg/L or greater (elevated concentrations) were detected in 18 percent of samples across Pennsylvania; 8 percent of samples had concentrations that equaled or exceeded the U.S. Environmental Protection Agency’s drinking-water maximum contaminant level of 10.0 µg/L. The highest arsenic concentration was 490.0 µg/L.

  1. HitPredict version 4: comprehensive reliability scoring of physical protein?protein interactions from more than 100 species

    OpenAIRE

    L?pez, Yosvany; Nakai, Kenta; Patil, Ashwini

    2015-01-01

    HitPredict is a consolidated resource of experimentally identified, physical protein?protein interactions with confidence scores to indicate their reliability. The study of genes and their inter-relationships using methods such as network and pathway analysis requires high quality protein?protein interaction information. Extracting reliable interactions from most of the existing databases is challenging because they either contain only a subset of the available interactions, or a mixture of p...

  2. On the analysis of protein-protein interactions via knowledge-based potentials for the prediction of protein-protein docking

    DEFF Research Database (Denmark)

    Feliu, Elisenda; Aloy, Patrick; Oliva, Baldo

    2011-01-01

    Development of effective methods to screen binary interactions obtained by rigid-body protein-protein docking is key for structure prediction of complexes and for elucidating physicochemical principles of protein-protein binding. We have derived empirical knowledge-based potential functions for s...... and with independence of the partner. This information is encoded at the residue level and could be easily incorporated in the initial grid scoring for Fast Fourier Transform rigid-body docking methods.......Development of effective methods to screen binary interactions obtained by rigid-body protein-protein docking is key for structure prediction of complexes and for elucidating physicochemical principles of protein-protein binding. We have derived empirical knowledge-based potential functions...... for selecting rigid-body docking poses. These potentials include the energetic component that provides the residues with a particular secondary structure and surface accessibility. These scoring functions have been tested on a state-of-art benchmark dataset and on a decoy dataset of permanent interactions. Our...

  3. Prediction of Air Pollutants Concentration Based on an Extreme Learning Machine: The Case of Hong Kong

    Directory of Open Access Journals (Sweden)

    Jiangshe Zhang

    2017-01-01

    Full Text Available With the development of the economy and society all over the world, most metropolitan cities are experiencing elevated concentrations of ground-level air pollutants. It is urgent to predict and evaluate the concentration of air pollutants for some local environmental or health agencies. Feed-forward artificial neural networks have been widely used in the prediction of air pollutants concentration. However, there are some drawbacks, such as the low convergence rate and the local minimum. The extreme learning machine for single hidden layer feed-forward neural networks tends to provide good generalization performance at an extremely fast learning speed. The major sources of air pollutants in Hong Kong are mobile, stationary, and from trans-boundary sources. We propose predicting the concentration of air pollutants by the use of trained extreme learning machines based on the data obtained from eight air quality parameters in two monitoring stations, including Sham Shui Po and Tap Mun in Hong Kong for six years. The experimental results show that our proposed algorithm performs better on the Hong Kong data both quantitatively and qualitatively. Particularly, our algorithm shows better predictive ability, with R 2 increased and root mean square error values decreased respectively.

  4. Prediction of hydroxyl concentrations in cement pore water using a numerical cement hydration model

    NARCIS (Netherlands)

    Eijk, van R.J.; Brouwers, H.J.H.

    2000-01-01

    In this paper, a 3D numerical cement hydration model is used for predicting alkali and hydroxyl concentrations in cement pore water. First, this numerical model is calibrated for Dutch cement employing both chemical shrinkage and calorimetric experiments. Secondly, the strength development of some

  5. A study on prediction of uranium concentration in pregnant solution from in-situ leaching

    International Nuclear Information System (INIS)

    Yi Weiping; Zhou Quan; Yu Yunzhen; Wang Shude; Yang Yihan; Lei Qifeng

    2005-01-01

    The modeling course on prediction of uranium concentration in pregnant solution from in-situ leaching of uranium is described, a mathematical model based on grey system theory is put forward, and a set of computer application software is correspondingly developed. (authors)

  6. Aquatic Exposure Predictions of Insecticide Field Concentrations Using a Multimedia Mass-Balance Model.

    Science.gov (United States)

    Knäbel, Anja; Scheringer, Martin; Stehle, Sebastian; Schulz, Ralf

    2016-04-05

    Highly complex process-driven mechanistic fate and transport models and multimedia mass balance models can be used for the exposure prediction of pesticides in different environmental compartments. Generally, both types of models differ in spatial and temporal resolution. Process-driven mechanistic fate models are very complex, and calculations are time-intensive. This type of model is currently used within the European regulatory pesticide registration (FOCUS). Multimedia mass-balance models require fewer input parameters to calculate concentration ranges and the partitioning between different environmental media. In this study, we used the fugacity-based small-region model (SRM) to calculate predicted environmental concentrations (PEC) for 466 cases of insecticide field concentrations measured in European surface waters. We were able to show that the PECs of the multimedia model are more protective in comparison to FOCUS. In addition, our results show that the multimedia model results have a higher predictive power to simulate varying field concentrations at a higher level of field relevance. The adaptation of the model scenario to actual field conditions suggests that the performance of the SRM increases when worst-case conditions are replaced by real field data. Therefore, this study shows that a less complex modeling approach than that used in the regulatory risk assessment exhibits a higher level of protectiveness and predictiveness and that there is a need to develop and evaluate new ecologically relevant scenarios in the context of pesticide exposure modeling.

  7. Intra-Operative Amylase Concentration in Peri-Pancreatic Fluid Predicts Pancreatic Fistula After Distal Pancreatectomy

    NARCIS (Netherlands)

    Nahm, C.B.; Reuver, P.R.; Hugh, T.J.; Pearson, A.; Gill, A.J.; Samra, J.S.; Mittal, A.

    2017-01-01

    Post-operative pancreatic fistula (POPF) is a potentially severe complication following distal pancreatectomy. The aim of this study was to assess the predictive value of intra-operative amylase concentration (IOAC) in peri-pancreatic fluid after distal pancreatectomy for the diagnosis of POPF.

  8. Prediction of Air Pollutants Concentration Based on an Extreme Learning Machine: The Case of Hong Kong.

    Science.gov (United States)

    Zhang, Jiangshe; Ding, Weifu

    2017-01-24

    With the development of the economy and society all over the world, most metropolitan cities are experiencing elevated concentrations of ground-level air pollutants. It is urgent to predict and evaluate the concentration of air pollutants for some local environmental or health agencies. Feed-forward artificial neural networks have been widely used in the prediction of air pollutants concentration. However, there are some drawbacks, such as the low convergence rate and the local minimum. The extreme learning machine for single hidden layer feed-forward neural networks tends to provide good generalization performance at an extremely fast learning speed. The major sources of air pollutants in Hong Kong are mobile, stationary, and from trans-boundary sources. We propose predicting the concentration of air pollutants by the use of trained extreme learning machines based on the data obtained from eight air quality parameters in two monitoring stations, including Sham Shui Po and Tap Mun in Hong Kong for six years. The experimental results show that our proposed algorithm performs better on the Hong Kong data both quantitatively and qualitatively. Particularly, our algorithm shows better predictive ability, with R 2 increased and root mean square error values decreased respectively.

  9. Importance of Foliar Nitrogen Concentration to Predict Forest Productivity in the Mid-Atlantic Region

    Science.gov (United States)

    Yude Pan; John Hom; Jennifer Jenkins; Richard Birdsey

    2004-01-01

    To assess what difference it might make to include spatially defined estimates of foliar nitrogen in the regional application of a forest ecosystem model (PnET-II), we composed model predictions of wood production from extensive ground-based forest inventory analysis data across the Mid-Atlantic region. Spatial variation in foliar N concentration was assigned based on...

  10. Mathematical Model to Predict Skin Concentration after Topical Application of Drugs

    Directory of Open Access Journals (Sweden)

    Hiroaki Todo

    2013-12-01

    Full Text Available Skin permeation experiments have been broadly done since 1970s to 1980s as an evaluation method for transdermal drug delivery systems. In topically applied drug and cosmetic formulations, skin concentration of chemical compounds is more important than their skin permeations, because primary target site of the chemical compounds is skin surface or skin tissues. Furthermore, the direct pharmacological reaction of a metabolically stable drug that binds with specific receptors of known expression levels in an organ can be determined by Hill’s equation. Nevertheless, little investigation was carried out on the test method of skin concentration after topically application of chemical compounds. Recently we investigated an estimating method of skin concentration of the chemical compounds from their skin permeation profiles. In the study, we took care of “3Rs” issues for animal experiments. We have proposed an equation which was capable to estimate animal skin concentration from permeation profile through the artificial membrane (silicone membrane and animal skin. This new approach may allow the skin concentration of a drug to be predicted using Fick’s second law of diffusion. The silicone membrane was found to be useful as an alternative membrane to animal skin for predicting skin concentration of chemical compounds, because an extremely excellent extrapolation to animal skin concentration was attained by calculation using the silicone membrane permeation data. In this chapter, we aimed to establish an accurate and convenient method for predicting the concentration profiles of drugs in the skin based on the skin permeation parameters of topically active drugs derived from steady-state skin permeation experiments.

  11. Long short-term memory neural network for air pollutant concentration predictions: Method development and evaluation

    International Nuclear Information System (INIS)

    Li, Xiang; Peng, Ling; Yao, Xiaojing; Cui, Shaolong; Hu, Yuan; You, Chengzeng; Chi, Tianhe

    2017-01-01

    Air pollutant concentration forecasting is an effective method of protecting public health by providing an early warning against harmful air pollutants. However, existing methods of air pollutant concentration prediction fail to effectively model long-term dependencies, and most neglect spatial correlations. In this paper, a novel long short-term memory neural network extended (LSTME) model that inherently considers spatiotemporal correlations is proposed for air pollutant concentration prediction. Long short-term memory (LSTM) layers were used to automatically extract inherent useful features from historical air pollutant data, and auxiliary data, including meteorological data and time stamp data, were merged into the proposed model to enhance the performance. Hourly PM 2.5 (particulate matter with an aerodynamic diameter less than or equal to 2.5 μm) concentration data collected at 12 air quality monitoring stations in Beijing City from Jan/01/2014 to May/28/2016 were used to validate the effectiveness of the proposed LSTME model. Experiments were performed using the spatiotemporal deep learning (STDL) model, the time delay neural network (TDNN) model, the autoregressive moving average (ARMA) model, the support vector regression (SVR) model, and the traditional LSTM NN model, and a comparison of the results demonstrated that the LSTME model is superior to the other statistics-based models. Additionally, the use of auxiliary data improved model performance. For the one-hour prediction tasks, the proposed model performed well and exhibited a mean absolute percentage error (MAPE) of 11.93%. In addition, we conducted multiscale predictions over different time spans and achieved satisfactory performance, even for 13–24 h prediction tasks (MAPE = 31.47%). - Highlights: • Regional air pollutant concentration shows an obvious spatiotemporal correlation. • Our prediction model presents superior performance. • Climate data and metadata can significantly

  12. Liquid Whey Protein Concentrates Produced by Ultrafiltration as Primary Raw Materials for Thermal Dairy Gels

    Directory of Open Access Journals (Sweden)

    Marta Henriques

    2017-01-01

    Full Text Available The aim of this work is to study the gelation properties of liquid whey protein concentrates (LWPC produced by ultrafiltration (UF as raw material for thermally induced gels intended for food applications. LWPC thermal gelation was performed using different types of LWPC (non-defatted, defatted and diafiltered of different protein mass fractions and pH. Most of the produced gels showed viscoelastic behaviour. Non-defatted LWPC gave stronger heat-induced gels with a more cohesive microstructure, a higher water holding capacity and also higher elastic modulus (G’ and viscous modulus (G’’. Gel properties were not improved in products with lower content of non-protein compounds. As expected, the increase in protein mass fraction positively influences protein interactions. However, the pH is responsible for the equilibrium between attraction and repulsion forces in the gel components that influence gel hardness and water holding capacity.

  13. A new method to measure effective soil solution concentration predicts copper availability to plants.

    Science.gov (United States)

    Zhang, H; Zhao, F J; Sun, B; Davison, W; McGrath, S P

    2001-06-15

    Risk assessments of metal contaminated soils need to address metal bioavailability. To predict the bioavailability of metals to plants, it is necessary to understand both solution and solid phase supply processes in soils. In striving to find surrogate chemical measurements, scientists have focused either on soil solution chemistry, including free ion activities, or operationally defined fractions of metals. Here we introduce the new concept of effective concentration, CE, which includes both the soil solution concentration and an additional term, expressed as a concentration, that represents metal supplied from the solid phase. CE was measured using the technique of diffusive gradients in thin films (DGT) which, like a plant, locally lowers soil solution concentrations, inducing metal supply from the solid phase, as shown by a dynamic model of the DGT-soil system. Measurements of Cu as CE, soil solution concentration, by EDTA extraction and as free Cu2+ activity in soil solution were made on 29 different soils covering a large range of copper concentrations. Theywere compared to Cu concentrations in the plant material of Lepidium heterophyllum grown on the same soils. Plant concentrations were linearly related and highly correlated with CE but were more scattered and nonlinear with respect to free Cu2+ activity, EDTA extraction, or soil solution concentrations. These results demonstrate that the dominant supply processes in these soils are diffusion and labile metal release, which the DGT-soil system mimics. The quantity CE is shown to have promise as a quantitative measure of the bioavailable metal in soils.

  14. Incorporating information on predicted solvent accessibility to the co-evolution-based study of protein interactions.

    Science.gov (United States)

    Ochoa, David; García-Gutiérrez, Ponciano; Juan, David; Valencia, Alfonso; Pazos, Florencio

    2013-01-27

    A widespread family of methods for studying and predicting protein interactions using sequence information is based on co-evolution, quantified as similarity of phylogenetic trees. Part of the co-evolution observed between interacting proteins could be due to co-adaptation caused by inter-protein contacts. In this case, the co-evolution is expected to be more evident when evaluated on the surface of the proteins or the internal layers close to it. In this work we study the effect of incorporating information on predicted solvent accessibility to three methods for predicting protein interactions based on similarity of phylogenetic trees. We evaluate the performance of these methods in predicting different types of protein associations when trees based on positions with different characteristics of predicted accessibility are used as input. We found that predicted accessibility improves the results of two recent versions of the mirrortree methodology in predicting direct binary physical interactions, while it neither improves these methods, nor the original mirrortree method, in predicting other types of interactions. That improvement comes at no cost in terms of applicability since accessibility can be predicted for any sequence. We also found that predictions of protein-protein interactions are improved when multiple sequence alignments with a richer representation of sequences (including paralogs) are incorporated in the accessibility prediction.

  15. Maximum solid concentrations of coal water slurries predicted by neural network models

    Energy Technology Data Exchange (ETDEWEB)

    Cheng, Jun; Li, Yanchang; Zhou, Junhu; Liu, Jianzhong; Cen, Kefa

    2010-12-15

    The nonlinear back-propagation (BP) neural network models were developed to predict the maximum solid concentration of coal water slurry (CWS) which is a substitute for oil fuel, based on physicochemical properties of 37 typical Chinese coals. The Levenberg-Marquardt algorithm was used to train five BP neural network models with different input factors. The data pretreatment method, learning rate and hidden neuron number were optimized by training models. It is found that the Hardgrove grindability index (HGI), moisture and coalification degree of parent coal are 3 indispensable factors for the prediction of CWS maximum solid concentration. Each BP neural network model gives a more accurate prediction result than the traditional polynomial regression equation. The BP neural network model with 3 input factors of HGI, moisture and oxygen/carbon ratio gives the smallest mean absolute error of 0.40%, which is much lower than that of 1.15% given by the traditional polynomial regression equation. (author)

  16. Watershed regressions for pesticides (WARP) for predicting atrazine concentration in Corn Belt streams

    Science.gov (United States)

    Stone, Wesley W.; Gilliom, Robert J.

    2011-01-01

    Watershed Regressions for Pesticides (WARP) models, previously developed for atrazine at the national scale, can be improved for application to the U.S. Corn Belt region by developing region-specific models that include important watershed characteristics that are influential in predicting atrazine concentration statistics within the Corn Belt. WARP models for the Corn Belt (WARP-CB) were developed for predicting annual maximum moving-average (14-, 21-, 30-, 60-, and 90-day durations) and annual 95th-percentile atrazine concentrations in streams of the Corn Belt region. All streams used in development of WARP-CB models drain watersheds with atrazine use intensity greater than 17 kilograms per square kilometer (kg/km2). The WARP-CB models accounted for 53 to 62 percent of the variability in the various concentration statistics among the model-development sites.

  17. [Activity of alpha-amylase and concentration of protein in saliva of pregnant women].

    Science.gov (United States)

    Ciejak, Magdalena; Olszewska, Maria; Jakubowska, Katarzyna; Zebiełowicz, Dariusz; Safranow, Krzysztof; Chlubek, Dariusz

    2007-01-01

    One of the hypothetical reasons of the increased incidence of caries in women during the pregnancy may be the increased activity of alpha-amylase, which can be found in their saliva. The enzyme takes part in the process of decomposition of simple sugars, which make basic substrate for caries-causing bacteria. The aim of the paper was the evaluation of the influence of pregnancy and gestational age on the activity of alpha-amylase and the concentration of protein in women's saliva. The examined group consisted of 64 pregnant women at age 17-39, between 21st and 40th week of pregnancy. The control group consisted of 44 healthy women at age 20-35, who were not pregnant. In saliva, which was taken before morning meal, without stimulation, protein concentration was determined by Bradford method and the activity of amylase was determined by kinetic method. The activity of amylase correlated strongly and positively with protein concentration in saliva of both the pregnant (RS = +0.65; p women. There were no significant differences between examined parameters in the examined and the control group. It has been observed in the examined group, that there is the significant negative correlation between protein concentration in saliva and the week of pregnancy (RS = -0.35; p increased caries incidence of pregnant women. However, the observed changes of total protein concentration in saliva during pregnancy, suggest that the exact cognition of proteins in pregnant women's saliva may reveal new mechanisms, which lead to an increase of caries risk.

  18. Serum protein concentrations from clinically healthy horses determined by agarose gel electrophoresis.

    Science.gov (United States)

    Riond, Barbara; Wenger-Riggenbach, Bettina; Hofmann-Lehmann, Regina; Lutz, Hans

    2009-03-01

    Serum protein electrophoresis is a useful screening test in equine laboratory medicine. The method can provide valuable information about changes in the concentrations of albumin and alpha-, beta-, and gamma-globulins and thereby help characterize dysproteinemias in equine patients. Reference values for horses using agarose gel as a support medium have not been reported. The purpose of this study was to establish reference intervals for serum protein concentrations in adult horses using agarose gel electrophoresis and to assess differences between warm-blooded and heavy draught horses. In addition, the precision of electrophoresis for determining fraction percentages and the detection limit were determined. Blood samples were obtained from 126 clinically healthy horses, including 105 Thoroughbreds and 21 heavy draught horses of both sexes and ranging from 2 to 20 years of age. The total protein concentration was determined by an automated biuret method. Serum protein electrophoresis was performed using a semi-automated agarose gel electrophoresis system. Coefficients of variation (CVs) were calculated for within-run and within-assay precision. Data from warm-blooded and draught horses were compared using the Mann-Whitney U test. Within-run and within-assay CVs were draught horses and so combined reference intervals (2.5-97.5%) were calculated for total protein (51.0-72.0 g/L), albumin (29.6-38.5 g/L), alpha(1)-globulin (1.9-3.1 g/L), alpha(2)-globulin (5.3-8.7 g/L), beta(1)-globulin (2.8-7.3g/L), beta(2)-globulin (2.2-6.0 g/L), and gamma-globulin (5.8-12.7 g/L) concentrations, and albumin/globulin ratio (0.93-1.65). Using agarose gel as the supporting matrix for serum protein electrophoresis in horses resulted in excellent resolution and accurate results that facilitated standardization into 6 protein fractions.

  19. ProteinSplit: splitting of multi-domain proteins using prediction of ordered and disordered regions in protein sequences for virtual structural genomics

    International Nuclear Information System (INIS)

    Wyrwicz, Lucjan S; Koczyk, Grzegorz; Rychlewski, Leszek; Plewczynski, Dariusz

    2007-01-01

    The annotation of protein folds within newly sequenced genomes is the main target for semi-automated protein structure prediction (virtual structural genomics). A large number of automated methods have been developed recently with very good results in the case of single-domain proteins. Unfortunately, most of these automated methods often fail to properly predict the distant homology between a given multi-domain protein query and structural templates. Therefore a multi-domain protein should be split into domains in order to overcome this limitation. ProteinSplit is designed to identify protein domain boundaries using a novel algorithm that predicts disordered regions in protein sequences. The software utilizes various sequence characteristics to assess the local propensity of a protein to be disordered or ordered in terms of local structure stability. These disordered parts of a protein are likely to create interdomain spacers. Because of its speed and portability, the method was successfully applied to several genome-wide fold annotation experiments. The user can run an automated analysis of sets of proteins or perform semi-automated multiple user projects (saving the results on the server). Additionally the sequences of predicted domains can be sent to the Bioinfo.PL Protein Structure Prediction Meta-Server for further protein three-dimensional structure and function prediction. The program is freely accessible as a web service at http://lucjan.bioinfo.pl/proteinsplit together with detailed benchmark results on the critical assessment of a fully automated structure prediction (CAFASP) set of sequences. The source code of the local version of protein domain boundary prediction is available upon request from the authors

  20. Protein-Protein Interactions Prediction Using a Novel Local Conjoint Triad Descriptor of Amino Acid Sequences

    Directory of Open Access Journals (Sweden)

    Jun Wang

    2017-11-01

    Full Text Available Protein-protein interactions (PPIs play crucial roles in almost all cellular processes. Although a large amount of PPIs have been verified by high-throughput techniques in the past decades, currently known PPIs pairs are still far from complete. Furthermore, the wet-lab experiments based techniques for detecting PPIs are time-consuming and expensive. Hence, it is urgent and essential to develop automatic computational methods to efficiently and accurately predict PPIs. In this paper, a sequence-based approach called DNN-LCTD is developed by combining deep neural networks (DNNs and a novel local conjoint triad description (LCTD feature representation. LCTD incorporates the advantage of local description and conjoint triad, thus, it is capable to account for the interactions between residues in both continuous and discontinuous regions of amino acid sequences. DNNs can not only learn suitable features from the data by themselves, but also learn and discover hierarchical representations of data. When performing on the PPIs data of Saccharomyces cerevisiae, DNN-LCTD achieves superior performance with accuracy as 93.12%, precision as 93.75%, sensitivity as 93.83%, area under the receiver operating characteristic curve (AUC as 97.92%, and it only needs 718 s. These results indicate DNN-LCTD is very promising for predicting PPIs. DNN-LCTD can be a useful supplementary tool for future proteomics study.

  1. Prediction of Coal Face Gas Concentration by Multi-Scale Selective Ensemble Hybrid Modeling

    Directory of Open Access Journals (Sweden)

    WU Xiang

    2014-06-01

    Full Text Available A selective ensemble hybrid modeling prediction method based on wavelet transformation is proposed to improve the fitting and generalization capability of the existing prediction models of the coal face gas concentration, which has a strong stochastic volatility. Mallat algorithm was employed for the multi-scale decomposition and single-scale reconstruction of the gas concentration time series. Then, it predicted every subsequence by sparsely weighted multi unstable ELM(extreme learning machine predictor within method SERELM(sparse ensemble regressors of ELM. At last, it superimposed the predicted values of these models to obtain the predicted values of the original sequence. The proposed method takes advantage of characteristics of multi scale analysis of wavelet transformation, accuracy and fast characteristics of ELM prediction and the generalization ability of L1 regularized selective ensemble learning method. The results show that the forecast accuracy has large increase by using the proposed method. The average relative error is 0.65%, the maximum relative error is 4.16% and the probability of relative error less than 1% reaches 0.785.

  2. Direct Analysis of Proteins from Solutions with High Salt Concentration Using Laser Electrospray Mass Spectrometry

    Science.gov (United States)

    Karki, Santosh; Shi, Fengjian; Archer, Jieutonne J.; Sistani, Habiballah; Levis, Robert J.

    2018-05-01

    The detection of lysozyme, or a mixture of lysozyme, cytochrome c, and myoglobin, from solutions with varying salt concentrations (0.1 to 250 mM NaCl) is compared using laser electrospray mass spectrometry (LEMS) and electrospray ionization-mass spectrometry (ESI-MS). Protonated protein peaks were observed up to a concentration of 250 mM NaCl in the case of LEMS. In the case of ESI-MS, a protein solution with salt concentration > 0.5 mM resulted in predominantly salt-adducted features, with suppression of the protonated protein ions. The constituents in the mixture of proteins were assignable up to 250 mM NaCl for LEMS and were not assignable above a NaCl concentration of 0.5 mM for ESI. The average sodium adducts () bound to the 7+ charge state of lysozyme for LEMS measurements from salt concentrations of 2.5, 25, 50, and 100 mM NaCl are 1.71, 5.23, 5.26, and 5.11, respectively. The conventional electrospray measurements for lysozyme solution containing salt concentrations of 0.1, 1, 2, and 5 mM NaCl resulted in of 2.65, 6.44, 7.57, and 8.48, respectively. LEMS displays an approximately two orders of magnitude higher salt tolerance in comparison with conventional ESI-MS. The non-equilibrium partitioning of proteins on the surface of the charged droplets is proposed as the mechanism for the high salt tolerance phenomena observed in the LEMS measurements. [Figure not available: see fulltext.

  3. Protein and oil composition predictions of single soybeans by transmission Raman spectroscopy.

    Science.gov (United States)

    Schulmerich, Matthew V; Walsh, Michael J; Gelber, Matthew K; Kong, Rong; Kole, Matthew R; Harrison, Sandra K; McKinney, John; Thompson, Dennis; Kull, Linda S; Bhargava, Rohit

    2012-08-22

    The soybean industry requires rapid, accurate, and precise technologies for the analyses of seed/grain constituents. While the current gold standard for nondestructive quantification of economically and nutritionally important soybean components is near-infrared spectroscopy (NIRS), emerging technology may provide viable alternatives and lead to next generation instrumentation for grain compositional analysis. In principle, Raman spectroscopy provides the necessary chemical information to generate models for predicting the concentration of soybean constituents. In this communication, we explore the use of transmission Raman spectroscopy (TRS) for nondestructive soybean measurements. We show that TRS uses the light scattering properties of soybeans to effectively homogenize the heterogeneous bulk of a soybean for representative sampling. Working with over 1000 individual intact soybean seeds, we developed a simple partial least-squares model for predicting oil and protein content nondestructively. We find TRS to have a root-mean-standard error of prediction (RMSEP) of 0.89% for oil measurements and 0.92% for protein measurements. In both calibration and validation sets, the predicative capabilities of the model were similar to the error in the reference methods.

  4. Predicting and measurement of pH of seawater reverse osmosis concentrates

    KAUST Repository

    Waly, Tarek

    2011-10-01

    The pH of seawater reverse osmosis plants (SWRO) is the most influential parameter in determining the degree of supersaturation of CaCO3 in the concentrate stream. For this, the results of pH measurements of the concentrate of a seawater reverse osmosis pilot plant were compared with pH calculations based on the CO2-HCO3 --CO3 2- system equilibrium equations. Results were compared with two commercial software programs from membrane suppliers and also the software package Phreeqc. Results suggest that the real concentrate pH is lower than that of the feed and that none of the used programs was able to predict correctly real pH values. In addition, the effect of incorporating the acidity constant calculated for NaCl medium or seawater medium showed a great influence on the concentrate pH determination. The HCO3 - and CO3 2- equilibrium equation using acidity constants developed for seawater medium was the only method able to predict correctly the concentrate pH. The outcome of this study indicated that the saturation level of the concentrate was lower than previously anticipated. This was confirmed by shutting down the acid and the antiscalants dosing without any signs of scaling over a period of 12 months. © 2011 Elsevier B.V.

  5. PANDA: Protein function prediction using domain architecture and affinity propagation.

    Science.gov (United States)

    Wang, Zheng; Zhao, Chenguang; Wang, Yiheng; Sun, Zheng; Wang, Nan

    2018-02-22

    We developed PANDA (Propagation of Affinity and Domain Architecture) to predict protein functions in the format of Gene Ontology (GO) terms. PANDA at first executes profile-profile alignment algorithm to search against PfamA, KOG, COG, and SwissProt databases, and then launches PSI-BLAST against UniProt for homologue search. PANDA integrates a domain architecture inference algorithm based on the Bayesian statistics that calculates the probability of having a GO term. All the candidate GO terms are pooled and filtered based on Z-score. After that, the remaining GO terms are clustered using an affinity propagation algorithm based on the GO directed acyclic graph, followed by a second round of filtering on the clusters of GO terms. We benchmarked the performance of all the baseline predictors PANDA integrates and also for every pooling and filtering step of PANDA. It can be found that PANDA achieves better performances in terms of area under the curve for precision and recall compared to the baseline predictors. PANDA can be accessed from http://dna.cs.miami.edu/PANDA/ .

  6. Convolutional neural network architectures for predicting DNA–protein binding

    Science.gov (United States)

    Zeng, Haoyang; Edwards, Matthew D.; Liu, Ge; Gifford, David K.

    2016-01-01

    Motivation: Convolutional neural networks (CNN) have outperformed conventional methods in modeling the sequence specificity of DNA–protein binding. Yet inappropriate CNN architectures can yield poorer performance than simpler models. Thus an in-depth understanding of how to match CNN architecture to a given task is needed to fully harness the power of CNNs for computational biology applications. Results: We present a systematic exploration of CNN architectures for predicting DNA sequence binding using a large compendium of transcription factor datasets. We identify the best-performing architectures by varying CNN width, depth and pooling designs. We find that adding convolutional kernels to a network is important for motif-based tasks. We show the benefits of CNNs in learning rich higher-order sequence features, such as secondary motifs and local sequence context, by comparing network performance on multiple modeling tasks ranging in difficulty. We also demonstrate how careful construction of sequence benchmark datasets, using approaches that control potentially confounding effects like positional or motif strength bias, is critical in making fair comparisons between competing methods. We explore how to establish the sufficiency of training data for these learning tasks, and we have created a flexible cloud-based framework that permits the rapid exploration of alternative neural network architectures for problems in computational biology. Availability and Implementation: All the models analyzed are available at http://cnn.csail.mit.edu. Contact: gifford@mit.edu Supplementary information: Supplementary data are available at Bioinformatics online. PMID:27307608

  7. Cost Function Network-based Design of Protein-Protein Interactions: predicting changes in binding affinity.

    Science.gov (United States)

    Viricel, Clément; de Givry, Simon; Schiex, Thomas; Barbe, Sophie

    2018-02-20

    Accurate and economic methods to predict change in protein binding free energy upon mutation are imperative to accelerate the design of proteins for a wide range of applications. Free energy is defined by enthalpic and entropic contributions. Following the recent progresses of Artificial Intelligence-based algorithms for guaranteed NP-hard energy optimization and partition function computation, it becomes possible to quickly compute minimum energy conformations and to reliably estimate the entropic contribution of side-chains in the change of free energy of large protein interfaces. Using guaranteed Cost Function Network algorithms, Rosetta energy functions and Dunbrack's rotamer library, we developed and assessed EasyE and JayZ, two methods for binding affinity estimation that ignore or include conformational entropic contributions on a large benchmark of binding affinity experimental measures. If both approaches outperform most established tools, we observe that side-chain conformational entropy brings little or no improvement on most systems but becomes crucial in some rare cases. as open-source Python/C ++ code at sourcesup.renater.fr/projects/easy-jayz. thomas.schiex@inra.fr and sophie.barbe@insa-toulouse.fr. Supplementary data are available at Bioinformatics online.

  8. Preparation and physicochemical properties of protein concentrate and isolate produced from Acacia tortilis (Forssk.) Hayne ssp. raddiana.

    Science.gov (United States)

    Embaby, Hassan E; Swailam, Hesham M; Rayan, Ahmed M

    2018-02-01

    The composition and physicochemical properties of defatted acacia flour (DFAF), acacia protein concentrate (APC) and acacia protein isolate (API) were evaluated. The results indicated that API had lower, ash and fat content, than DFAF and APC. Also, significant difference in protein content was noticed among DFAF, APC and API (37.5, 63.7 and 91.8%, respectively). Acacia protein concentrate and isolates were good sources of essential amino acids except cystine and methionine. The physicochemical and functional properties of acacia protein improved with the processing of acacia into protein concentrate and protein isolate. The results of scanning electron micrographs showed that DFAF had a compact structure; protein concentrate were, flaky, and porous type, and protein isolate had intact flakes morphology.

  9. Fermentation of solutions of glucose-protein concentrate in a cascade-multi-ray unit

    Energy Technology Data Exchange (ETDEWEB)

    Denshchikov, M T; Shashilova, V P

    1964-01-01

    Glucose-protein concentrate is a material obtained by the hydrolysis of corn, containing glucose 75 to 80, maltose, isomaltose, and other non-fermentable sugars 1.5 to 2, H/sub 2/O 15 to 17, mineral matter 1.9 to 1%, and N-containing materials 3.2 to 3.4 g/kg. In earlier fermentation trails with this material, after addition of H/sub 2/O, only 10 to 12% ethanol concentrations were obtained. With period addition of citric acid and replacement of the yeast at regular intervals, using a cascade-multitray unit, 12 to 13% concentrations of ethanol were obtained.

  10. Prediction of protein hydration sites from sequence by modular neural networks

    DEFF Research Database (Denmark)

    Ehrlich, L.; Reczko, M.; Bohr, Henrik

    1998-01-01

    The hydration properties of a protein are important determinants of its structure and function. Here, modular neural networks are employed to predict ordered hydration sites using protein sequence information. First, secondary structure and solvent accessibility are predicted from sequence with two...... separate neural networks. These predictions are used as input together with protein sequences for networks predicting hydration of residues, backbone atoms and sidechains. These networks are teined with protein crystal structures. The prediction of hydration is improved by adding information on secondary...... structure and solvent accessibility and, using actual values of these properties, redidue hydration can be predicted to 77% accuracy with a Metthews coefficient of 0.43. However, predicted property data with an accuracy of 60-70% result in less than half the improvement in predictive performance observed...

  11. Split Nitrogen Application Improves Wheat Baking Quality by Influencing Protein Composition Rather Than Concentration.

    Science.gov (United States)

    Xue, Cheng; Auf'm Erley, Gunda Schulte; Rossmann, Anne; Schuster, Ramona; Koehler, Peter; Mühling, Karl-Hermann

    2016-01-01

    The use of late nitrogen (N) fertilization (N application at late growth stages of wheat, e.g., booting, heading or anthesis) to improve baking quality of wheat has been questioned. Although it increases protein concentration, the beneficial effect on baking quality (bread loaf volume) needs to be clearly understood. Two pot experiments were conducted aiming to evaluate whether late N is effective under controlled conditions and if these effects result from increased N rate or N splitting. Late N fertilizers were applied either as additional N or split from the basal N at late boot stage or heading in the form of nitrate-N or urea. Results showed that late N fertilization improved loaf volume of wheat flour by increasing grain protein concentration and altering its composition. Increasing N rate mainly enhanced grain protein quantitatively. However, N splitting changed grain protein composition by enhancing the percentages of gliadins and glutenins as well as certain high molecular weight glutenin subunits (HMW-GS), which led to an improved baking quality of wheat flour. The late N effects were greater when applied as nitrate-N than urea. The proportions of glutenin and x-type HMW-GS were more important than the overall protein concentration in determining baking quality. N splitting is more effective in improving wheat quality than the increase in the N rate by late N, which offers the potential to cut down N fertilization rates in wheat production systems.

  12. Split nitrogen application improves wheat baking quality by influencing protein composition rather than concentration

    Directory of Open Access Journals (Sweden)

    Cheng eXue

    2016-06-01

    Full Text Available The use of late nitrogen (N fertilization (N application at late growth stages of wheat, e.g. booting, heading or anthesis to improve baking quality of wheat has been questioned. Although it increases protein concentration, the beneficial effect on baking quality (bread loaf volume needs to be clearly understood. Two pot experiments were conducted aiming to evaluate whether late N is effective under controlled conditions and if these effects result from increased N rate or N splitting. Late N fertilizers were applied either as additional N or split from the basal N at late boot stage or heading in the form of nitrate-N or urea. Results showed that late N fertilization improved loaf volume of wheat flour by increasing grain protein concentration and altering its composition. Increasing N rate mainly enhanced grain protein quantitatively. However, N splitting changed grain protein composition by enhancing the percentages of gliadins and glutenins as well as certain high molecular weight glutenin subunits (HMW-GS, which led to an improved baking quality of wheat flour. The late N effects were greater when applied as nitrate-N than urea. The proportions of glutenin and x-type HMW-GS were more important than the overall protein concentration in determining baking quality. N splitting is more effective in improving wheat quality than the increase in the N rate by late N, which offers the potential to cut down N fertilization rates in wheat production systems.

  13. Serum acute phase protein concentrations in female dogs with mammary tumors.

    Science.gov (United States)

    Tecles, Fernando; Caldín, Marco; Zanella, Anna; Membiela, Francisco; Tvarijonaviciute, Asta; Subiela, Silvia Martínez; Cerón, José Joaquín

    2009-03-01

    Acute phase proteins (APPs) are proteins whose concentrations in serum change after any inflammatory stimulus or tissue damage. The aim of the current study was to evaluate 3 positive APPs (C-reactive protein, serum amyloid A, and haptoglobin) and 1 negative APP (albumin) in female dogs with mammary neoplasia. Acute phase proteins were studied in 70 female dogs aged 8-12 years in the following groups: healthy (n = 10); mammary tumors in stages I (n = 19), II (n = 5), III (n = 6), IV (n = 5), and V (n = 7); and with mammary neoplasia plus a concomitant disease (n = 18). In animals with mammary neoplasia, significant increases of positive APPs were only detected in those that had metastasis or a neoplasm with a diameter greater than 5 cm and ulceration. Dogs with mammary neoplasia and a concomitant disease also had high C-reactive protein concentrations. Albumin concentration was decreased in animals with metastasis and with a concomitant disease. The results of the present study indicate that the acute phase response could be stimulated in female dogs with mammary gland tumors because of different factors, such as metastasis, large size of the primary mass, and ulceration or secondary inflammation of the neoplasm.

  14. The elution of certain protein affinity tags with millimolar concentrations of diclofenac.

    Science.gov (United States)

    Baliova, Martina; Juhasova, Anna; Jursky, Frantisek

    2015-12-01

    Diclofenac (2-[(2, 6-dichlorophenyl)amino] benzeneacetic acid) is a sparingly soluble, nonsteroidal anti-inflammatory drug therapeutically acting at low micromolar concentrations. In pH range from 8 to 11, its aqueous solubility can be increased up to 200 times by the presence of counter ions such as sodium. Our protein interaction studies revealed that a millimolar concentration of sodium diclofenac is able to elute glutathione S-transferase (GST), cellulose binding protein (CBD), and maltose binding protein (MBP) but not histidine-tagged or PDZ-tagged proteins from their affinity resins. The elution efficiency of diclofenac is comparable with the eluting agents normally used at similar concentrations. Native gel electrophoresis of sodium diclofenac-treated proteins showed that the interaction is non-covalent and non-denaturing. These results suggest that sodium diclofenac, in addition to its pharmaceutical applications, can also be exploited as a lead for the development of new proteomics reagents. Copyright © 2015 Elsevier B.V. All rights reserved.

  15. Effects of a concentrate of pea antinutritional factors on pea protein digestibility in piglets

    NARCIS (Netherlands)

    Guen, M.P. Le; Huisman, J.; Guéguen, J.; Beelen, G.; Verstegen, M.W.A.

    1995-01-01

    Four experiments were designed to investigate the apparent ileal digestibility of raw pea (Pisum sativum) and two of its components - an isolate of its proteins and a concentrate of its proteinaceous antinutritional factors (ANFs). Three varieties of peas were used: spring varieties Finale and

  16. Quantifying Protein Concentrations Using Smartphone Colorimetry: A New Method for an Established Test

    Science.gov (United States)

    Gee, Clifford T.; Kehoe, Eric; Pomerantz, William C. K.; Penn, R. Lee

    2017-01-01

    Proteins are involved in nearly every biological process, which makes them of interest to a range of scientists. Previous work has shown that hand-held cameras can be used to determine the concentration of colored analytes in solution, and this paper extends the approach to reactions involving a color change in order to quantify protein…

  17. Resting serum concentration of high-sensitivity C-reactive protein ...

    African Journals Online (AJOL)

    Resting serum concentration of high-sensitivity C-reactive protein (hs-CRP) in sportsmen and untrained male adults. F.A. Niyi-Odumosu, O. A. Bello, S.A. Biliaminu, B.V. Owoyele, T.O. Abu, O.L. Dominic ...

  18. Textural behavior of gels formed by rice starch and whey protein isolate: Concentration and crosshead velocities

    Directory of Open Access Journals (Sweden)

    Thiago Novaes Silva

    Full Text Available ABSTRACT Fabricated food gels involving the use of hydrocolloids are gaining polpularity as confectionery/convenience foods. Starch is commonly combined with a hydrocolloid (protein our polyssacharides, particularly in the food industry, since native starches generally do not have ideal properties for the preparation of food products. Therefore the texture studies of starch-protein mixtures could provide a new approach in producing starch-based food products, being thus acritical attribute that needs to be carefully adjusted to the consumer liking. This work investigated the texture and rheological properties of mixed gels of different concentrations of rice starch (15%, 17.5%, and 20% and whey protein isolate (0%, 3%, and 6% with different crosshead velocities (0.05, 5.0, and 10.0 mm/s using a Box-Behnken experimental design. The samples were submitted to uniaxial compression tests with 80% deformation in order to determinate the following rheological parameters: Young’s modulus, fracture stress, fracture deformation, recoverable energy, and apparent biaxial elongational viscosity. Gels with a higher rice starch concentration that were submitted to higher test velocities were more rigid and resistant, while the whey protein isolate concentration had little influence on these properties. The gels showed a higher recoverable energy when the crosshead velocity was higher, and the apparent biaxial elongational viscosity was also influenced by this factor. Therefore, mixed gels exhibit different properties depending on the rice starch concentration and crosshead velocity.

  19. Bioactive Whey Protein Concentrate and Lactose Stimulate Gut Function in Formula-Fed Preterm Pigs

    DEFF Research Database (Denmark)

    Li, Yanqi; Nguyen, Duc Ninh; Ryom, Karina

    2017-01-01

    OBJECTIVE:: Formula feeding is associated with compromised intestinal health in preterm neonates compared with maternal milk, but the mechanisms behind this are unclear. We hypothesized that the use of maltodextrin and whey protein concentrates (WPCs) with reduced bioactivity due to thermal-proce...

  20. Changes in volatile compounds in whey protein concentrate stored at elevated temperature and humidity

    Science.gov (United States)

    Whey protein concentrate (WPC) has been recommended for use in emergency aid programs, but it is often stored overseas without temperature and relative humidity (RH) control, which may cause it to be rejected because of yellowing, off-flavors, or clumping. Therefore, the volatile compounds present ...

  1. Protein Sub-Nuclear Localization Prediction Using SVM and Pfam Domain Information

    Science.gov (United States)

    Kumar, Ravindra; Jain, Sohni; Kumari, Bandana; Kumar, Manish

    2014-01-01

    The nucleus is the largest and the highly organized organelle of eukaryotic cells. Within nucleus exist a number of pseudo-compartments, which are not separated by any membrane, yet each of them contains only a specific set of proteins. Understanding protein sub-nuclear localization can hence be an important step towards understanding biological functions of the nucleus. Here we have described a method, SubNucPred developed by us for predicting the sub-nuclear localization of proteins. This method predicts protein localization for 10 different sub-nuclear locations sequentially by combining presence or absence of unique Pfam domain and amino acid composition based SVM model. The prediction accuracy during leave-one-out cross-validation for centromeric proteins was 85.05%, for chromosomal proteins 76.85%, for nuclear speckle proteins 81.27%, for nucleolar proteins 81.79%, for nuclear envelope proteins 79.37%, for nuclear matrix proteins 77.78%, for nucleoplasm proteins 76.98%, for nuclear pore complex proteins 88.89%, for PML body proteins 75.40% and for telomeric proteins it was 83.33%. Comparison with other reported methods showed that SubNucPred performs better than existing methods. A web-server for predicting protein sub-nuclear localization named SubNucPred has been established at http://14.139.227.92/mkumar/subnucpred/. Standalone version of SubNucPred can also be downloaded from the web-server. PMID:24897370

  2. Association between feline immunodeficiency virus (FIV) plasma viral RNA load, concentration of acute phase proteins and disease severity.

    Science.gov (United States)

    Kann, Rebecca K C; Seddon, Jennifer M; Kyaw-Tanner, Myat T; Henning, Joerg; Meers, Joanne

    2014-08-01

    Veterinarians have few tools to predict the rate of disease progression in FIV-infected cats. In contrast, in HIV infection, plasma viral RNA load and acute phase protein concentrations are commonly used as predictors of disease progression. This study evaluated these predictors in cats naturally infected with FIV. In older cats (>5 years), log10 FIV RNA load was higher in the terminal stages of disease compared to the asymptomatic stage. There was a significant association between log10 FIV RNA load and both log10 serum amyloid A concentration and age in unwell FIV-infected cats. This study suggests that viral RNA load and serum amyloid A warrant further investigation as predictors of disease status and prognosis in FIV-infected cats. Copyright © 2014 Elsevier Ltd. All rights reserved.

  3. Potential utilization of algal protein concentrate as a food ingredient in space habitats

    Science.gov (United States)

    Nakhost, Z.; Karel, M.

    1989-01-01

    Green alga Scenedesmus obliquus was studied as one of the potential sources of macronutrients in a space habitat. Algal protein concentrate (70.5% protein) was incorporated into a variety of food products such as bran muffins, fettuccine (spinach noodle imitation) and chocolate chip cookies. Food products containing 20 to 40% of incorporated algal proteins were considered. In the sensory analysis the greenish color of the bran muffins and cookies was not found to be objectional. The mild spinachy flavor (algae flavor) was less detectable in chocolate chip cookies than in bran muffins. The color and taste of the algae noodles were found to be pleasant and compared well with commercially available spinach noodles. Commercially available spray-dried Spirulina algae was also incorporated so the products can be compared with those containing Scenedesmus obliquus concentrate. Food products containing commercial algae had a dark green color and a "burnt after taste" and were less acceptable to the panelists.

  4. Simultaneous pre-concentration and separation on simple paper-based analytical device for protein analysis.

    Science.gov (United States)

    Niu, Ji-Cheng; Zhou, Ting; Niu, Li-Li; Xie, Zhen-Sheng; Fang, Fang; Yang, Fu-Quan; Wu, Zhi-Yong

    2018-02-01

    In this work, fast isoelectric focusing (IEF) was successfully implemented on an open paper fluidic channel for simultaneous concentration and separation of proteins from complex matrix. With this simple device, IEF can be finished in 10 min with a resolution of 0.03 pH units and concentration factor of 10, as estimated by color model proteins by smartphone-based colorimetric detection. Fast detection of albumin from human serum and glycated hemoglobin (HBA1c) from blood cell was demonstrated. In addition, off-line identification of the model proteins from the IEF fractions with matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF-MS) was also shown. This PAD IEF is potentially useful either for point of care test (POCT) or biomarker analysis as a cost-effective sample pretreatment method.

  5. Comprehensive predictions of target proteins based on protein-chemical interaction using virtual screening and experimental verifications.

    Science.gov (United States)

    Kobayashi, Hiroki; Harada, Hiroko; Nakamura, Masaomi; Futamura, Yushi; Ito, Akihiro; Yoshida, Minoru; Iemura, Shun-Ichiro; Shin-Ya, Kazuo; Doi, Takayuki; Takahashi, Takashi; Natsume, Tohru; Imoto, Masaya; Sakakibara, Yasubumi

    2012-04-05

    Identification of the target proteins of bioactive compounds is critical for elucidating the mode of action; however, target identification has been difficult in general, mostly due to the low sensitivity of detection using affinity chromatography followed by CBB staining and MS/MS analysis. We applied our protocol of predicting target proteins combining in silico screening and experimental verification for incednine, which inhibits the anti-apoptotic function of Bcl-xL by an unknown mechanism. One hundred eighty-two target protein candidates were computationally predicted to bind to incednine by the statistical prediction method, and the predictions were verified by in vitro binding of incednine to seven proteins, whose expression can be confirmed in our cell system.As a result, 40% accuracy of the computational predictions was achieved successfully, and we newly found 3 incednine-binding proteins. This study revealed that our proposed protocol of predicting target protein combining in silico screening and experimental verification is useful, and provides new insight into a strategy for identifying target proteins of small molecules.

  6. Comprehensive predictions of target proteins based on protein-chemical interaction using virtual screening and experimental verifications

    Directory of Open Access Journals (Sweden)

    Kobayashi Hiroki

    2012-04-01

    Full Text Available Abstract Background Identification of the target proteins of bioactive compounds is critical for elucidating the mode of action; however, target identification has been difficult in general, mostly due to the low sensitivity of detection using affinity chromatography followed by CBB staining and MS/MS analysis. Results We applied our protocol of predicting target proteins combining in silico screening and experimental verification for incednine, which inhibits the anti-apoptotic function of Bcl-xL by an unknown mechanism. One hundred eighty-two target protein candidates were computationally predicted to bind to incednine by the statistical prediction method, and the predictions were verified by in vitro binding of incednine to seven proteins, whose expression can be confirmed in our cell system. As a result, 40% accuracy of the computational predictions was achieved successfully, and we newly found 3 incednine-binding proteins. Conclusions This study revealed that our proposed protocol of predicting target protein combining in silico screening and experimental verification is useful, and provides new insight into a strategy for identifying target proteins of small molecules.

  7. Prediction of Dissolved Gas Concentrations in Transformer Oil Based on the KPCA-FFOA-GRNN Model

    Directory of Open Access Journals (Sweden)

    Jun Lin

    2018-01-01

    Full Text Available The purpose of analyzing the dissolved gas in transformer oil is to determine the transformer’s operating status and is an important basis for fault diagnosis. Accurate prediction of the concentration of dissolved gas in oil can provide an important reference for the evaluation of the state of the transformer. A combined predicting model is proposed based on kernel principal component analysis (KPCA and a generalized regression neural network (GRNN using an improved fruit fly optimization algorithm (FFOA to select the smooth factor. Firstly, based on the idea of using the dissolved gas ratio of oil to diagnose the transformer fault, gas concentration ratios are also used as characteristic parameters. Secondly, the main parameters are selected from the feature parameters using the KPCA method, and the GRNN is then used to predict the gas concentration in the transformer oil. In the training process of the network, the FFOA is used to select the smooth factor of the neural network. Through a concrete example, it is shown that the method proposed in this paper has better data fitting ability and more accurate prediction ability compared with the support vector machine (SVM and gray model (GM methods.

  8. Predicting glycogen concentration in the foot muscle of abalone using near infrared reflectance spectroscopy (NIRS).

    Science.gov (United States)

    Fluckiger, Miriam; Brown, Malcolm R; Ward, Louise R; Moltschaniwskyj, Natalie A

    2011-06-15

    Near infrared reflectance spectroscopy (NIRS) was used to predict glycogen concentrations in the foot muscle of cultured abalone. NIR spectra of live, shucked and freeze-dried abalones were modelled against chemically measured glycogen data (range: 0.77-40.9% of dry weight (DW)) using partial least squares (PLS) regression. The calibration models were then used to predict glycogen concentrations of test abalone samples and model robustness was assessed from coefficient of determination of the validation (R2(val)) and standard error of prediction (SEP) values. The model for freeze-dried abalone gave the best prediction (R2(val) 0.97, SEP=1.71), making it suitable for quantifying glycogen. Models for live and shucked abalones had R2(val) of 0.86 and 0.90, and SEP of 3.46 and 3.07 respectively, making them suitable for producing estimations of glycogen concentration. As glycogen is a taste-active component associated with palatability in abalone, this study demonstrated the potential of NIRS as a rapid method to monitor the factors associated with abalone quality. Copyright © 2011 Elsevier Ltd. All rights reserved.

  9. Preservation of grass juice and wet leaf protein concentrate for animal feeds

    Directory of Open Access Journals (Sweden)

    Matti Näsi

    1983-09-01

    Full Text Available Formic acid, mixtures of acids (AIV 1, AIV 2 and formalin-acid mixtures (Viher solution, Viher acid were tested as preservatives of juice and wet leaf protein concentrate (LPC obtained from grass, clover and pea. The main criteria used in judging the success of preservation were changes in the protein fraction, fermentation of sugars, and losses of dry matter and true protein during storage. Fermentation of sugars and moulding could be inhibited in plant juices by adding 0.5 % v/w preservative, but proteolysis continued and true protein was degraded in unheated juices. Ensiling losses of pea juice were considerable, 4.0-15.6 % of DM, in all treatments. For wet leaf protein concentrate precipitated by steaming (85°C, good preservation could be obtained with the additives used in silage making applied at a level of 1 % v/w. In these treatments protein breakdown was minimal, because heating eliminated proteolytic enzymes and partly sterilized the LPC product.

  10. Watershed regressions for pesticides (warp) models for predicting atrazine concentrations in Corn Belt streams

    Science.gov (United States)

    Stone, Wesley W.; Gilliom, Robert J.

    2012-01-01

    Watershed Regressions for Pesticides (WARP) models, previously developed for atrazine at the national scale, are improved for application to the United States (U.S.) Corn Belt region by developing region-specific models that include watershed characteristics that are influential in predicting atrazine concentration statistics within the Corn Belt. WARP models for the Corn Belt (WARP-CB) were developed for annual maximum moving-average (14-, 21-, 30-, 60-, and 90-day durations) and annual 95th-percentile atrazine concentrations in streams of the Corn Belt region. The WARP-CB models accounted for 53 to 62% of the variability in the various concentration statistics among the model-development sites. Model predictions were within a factor of 5 of the observed concentration statistic for over 90% of the model-development sites. The WARP-CB residuals and uncertainty are lower than those of the National WARP model for the same sites. Although atrazine-use intensity is the most important explanatory variable in the National WARP models, it is not a significant variable in the WARP-CB models. The WARP-CB models provide improved predictions for Corn Belt streams draining watersheds with atrazine-use intensities of 17 kg/km2 of watershed area or greater.

  11. Scoring protein relationships in functional interaction networks predicted from sequence data.

    Directory of Open Access Journals (Sweden)

    Gaston K Mazandu

    Full Text Available UNLABELLED: The abundance of diverse biological data from various sources constitutes a rich source of knowledge, which has the power to advance our understanding of organisms. This requires computational methods in order to integrate and exploit these data effectively and elucidate local and genome wide functional connections between protein pairs, thus enabling functional inferences for uncharacterized proteins. These biological data are primarily in the form of sequences, which determine functions, although functional properties of a protein can often be predicted from just the domains it contains. Thus, protein sequences and domains can be used to predict protein pair-wise functional relationships, and thus contribute to the function prediction process of uncharacterized proteins in order to ensure that knowledge is gained from sequencing efforts. In this work, we introduce information-theoretic based approaches to score protein-protein functional interaction pairs predicted from protein sequence similarity and conserved protein signature matches. The proposed schemes are effective for data-driven scoring of connections between protein pairs. We applied these schemes to the Mycobacterium tuberculosis proteome to produce a homology-based functional network of the organism with a high confidence and coverage. We use the network for predicting functions of uncharacterised proteins. AVAILABILITY: Protein pair-wise functional relationship scores for Mycobacterium tuberculosis strain CDC1551 sequence data and python scripts to compute these scores are available at http://web.cbio.uct.ac.za/~gmazandu/scoringschemes.

  12. Improving N-terminal protein annotation of Plasmodium species based on signal peptide prediction of orthologous proteins

    Directory of Open Access Journals (Sweden)

    Neto Armando

    2012-11-01

    Full Text Available Abstract Background Signal peptide is one of the most important motifs involved in protein trafficking and it ultimately influences protein function. Considering the expected functional conservation among orthologs it was hypothesized that divergence in signal peptides within orthologous groups is mainly due to N-terminal protein sequence misannotation. Thus, discrepancies in signal peptide prediction of orthologous proteins were used to identify misannotated proteins in five Plasmodium species. Methods Signal peptide (SignalP and orthology (OrthoMCL were combined in an innovative strategy to identify orthologous groups showing discrepancies in signal peptide prediction among their protein members (Mixed groups. In a comparative analysis, multiple alignments for each of these groups and gene models were visually inspected in search of misannotated proteins and, whenever possible, alternative gene models were proposed. Thresholds for signal peptide prediction parameters were also modified to reduce their impact as a possible source of discrepancy among orthologs. Validation of new gene models was based on RT-PCR (few examples or on experimental evidence already published (ApiLoc. Results The rate of misannotated proteins was significantly higher in Mixed groups than in Positive or Negative groups, corroborating the proposed hypothesis. A total of 478 proteins were reannotated and change of signal peptide prediction from negative to positive was the most common. Reannotations triggered the conversion of almost 50% of all Mixed groups, which were further reduced by optimization of signal peptide prediction parameters. Conclusions The methodological novelty proposed here combining orthology and signal peptide prediction proved to be an effective strategy for the identification of proteins showing wrongly N-terminal annotated sequences, and it might have an important impact in the available data for genome-wide searching of potential vaccine and drug

  13. The specific ion effect on emulsions, foam and gels of a seed protein concentrate

    International Nuclear Information System (INIS)

    Lawal, O.S.

    2008-05-01

    Protein concentrate was prepared from the seeds of jack bean (Canavalia ensiformis) and the influences of selected Hofmeister salts on some functional properties of the protein concentrate were investigated. The results indicate that kosmotropic salts (Na 2 SO 4 , NaCl, NaBr) had improved water absorption capacities over the chaotropic salts (NaI, NaClO 4 , NaSCN) and generally, the reduction in water absorption capacity followed the Hofmeister trend: Na 2 SO 4 > NaCl > NaBr > NaI > NaClO 4 > NaSCN. However, the reverse was observed for the foaming and emulsification properties. The least gelation concentration (LGC) was used as the index of gelation properties and the results showed that LGC were higher in kosmotropic salts than in chaotropic salts. Generally, increases in salt concentration reduced the water absorption capacity, the surfactant properties as well as the gelation property. The findings would provide insight into the understanding of the structure property relations of the protein concentrate. (author)

  14. Long short-term memory neural network for air pollutant concentration predictions: Method development and evaluation.

    Science.gov (United States)

    Li, Xiang; Peng, Ling; Yao, Xiaojing; Cui, Shaolong; Hu, Yuan; You, Chengzeng; Chi, Tianhe

    2017-12-01

    Air pollutant concentration forecasting is an effective method of protecting public health by providing an early warning against harmful air pollutants. However, existing methods of air pollutant concentration prediction fail to effectively model long-term dependencies, and most neglect spatial correlations. In this paper, a novel long short-term memory neural network extended (LSTME) model that inherently considers spatiotemporal correlations is proposed for air pollutant concentration prediction. Long short-term memory (LSTM) layers were used to automatically extract inherent useful features from historical air pollutant data, and auxiliary data, including meteorological data and time stamp data, were merged into the proposed model to enhance the performance. Hourly PM 2.5 (particulate matter with an aerodynamic diameter less than or equal to 2.5 μm) concentration data collected at 12 air quality monitoring stations in Beijing City from Jan/01/2014 to May/28/2016 were used to validate the effectiveness of the proposed LSTME model. Experiments were performed using the spatiotemporal deep learning (STDL) model, the time delay neural network (TDNN) model, the autoregressive moving average (ARMA) model, the support vector regression (SVR) model, and the traditional LSTM NN model, and a comparison of the results demonstrated that the LSTME model is superior to the other statistics-based models. Additionally, the use of auxiliary data improved model performance. For the one-hour prediction tasks, the proposed model performed well and exhibited a mean absolute percentage error (MAPE) of 11.93%. In addition, we conducted multiscale predictions over different time spans and achieved satisfactory performance, even for 13-24 h prediction tasks (MAPE = 31.47%). Copyright © 2017 Elsevier Ltd. All rights reserved.

  15. Ordinary kriging approach to predicting long-term particulate matter concentrations in seven major Korean cities

    Directory of Open Access Journals (Sweden)

    Sun-Young Kim

    2014-09-01

    Full Text Available Objectives Cohort studies of associations between air pollution and health have used exposure prediction approaches to estimate individual-level concentrations. A common prediction method used in Korean cohort studies is ordinary kriging. In this study, performance of ordinary kriging models for long-term particulate matter less than or equal to 10 μm in diameter (PM10 concentrations in seven major Korean cities was investigated with a focus on spatial prediction ability. Methods We obtained hourly PM10 data for 2010 at 226 urban-ambient monitoring sites in South Korea and computed annual average PM10 concentrations at each site. Given the annual averages, we developed ordinary kriging prediction models for each of the seven major cities and for the entire country by using an exponential covariance reference model and a maximum likelihood estimation method. For model evaluation, cross-validation was performed and mean square error and R-squared (R2 statistics were computed. Results Mean annual average PM10 concentrations in the seven major cities ranged between 45.5 and 66.0 μg/m3 (standard deviation=2.40 and 9.51 μg/m3, respectively. Cross-validated R2 values in Seoul and Busan were 0.31 and 0.23, respectively, whereas the other five cities had R2 values of zero. The national model produced a higher crossvalidated R2 (0.36 than those for the city-specific models. Conclusions In general, the ordinary kriging models performed poorly for the seven major cities and the entire country of South Korea, but the model performance was better in the national model. To improve model performance, future studies should examine different prediction approaches that incorporate PM10 source characteristics.

  16. Evaluation of five commercially available assays and measurement of serum total protein concentration via refractometry for the diagnosis of failure of passive transfer of immunity in foals.

    Science.gov (United States)

    Davis, Rachel; Giguère, Steeve

    2005-11-15

    To determine and compare sensitivity, specificity, accuracy, and predictive values of measurement of serum total protein concentration by refractometry as well as 5 commercially available kits for the diagnosis of failure of passive transfer (FPT) of immunity in foals. Prospective study. 65 foals with various medical problems and 35 clinically normal foals. IgG concentration in serum was assessed by use of zinc sulfate turbidity (assay C), glutaraldehyde coagulation (assay D), 2 semiquantitative immunoassays (assays F and G), and a quantitative immunoassay (assay H). Serum total protein concentration was assessed by refractometry. Radial immunodiffusion (assays A and B) was used as the reference method. For detection of IgG or = 6.0 g/dL indicated adequate IgG concentrations. Most assays were adequate as initial screening tests. However, their use as a definitive test would result in unnecessary treatment of foals with adequate IgG concentrations.

  17. Assessment of protein disorder region predictions in CASP10

    KAUST Repository

    Monastyrskyy, Bohdan; Kryshtafovych, Andriy; Moult, John; Tramontano, Anna; Fidelis, Krzysztof

    2013-01-01

    The article presents the assessment of disorder region predictions submitted to CASP10. The evaluation is based on the three measures tested in previous CASPs: (i) balanced accuracy, (ii) the Matthews correlation coefficient for the binary predictions, and (iii) the area under the curve in the receiver operating characteristic (ROC) analysis of predictions using probability annotation. We also performed new analyses such as comparison of the submitted predictions with those obtained with a Naïve disorder prediction method and with predictions from the disorder prediction databases D2P2 and MobiDB. On average, the methods participating in CASP10 demonstrated slightly better performance than those in CASP9.

  18. Assessment of protein disorder region predictions in CASP10

    KAUST Repository

    Monastyrskyy, Bohdan

    2013-11-22

    The article presents the assessment of disorder region predictions submitted to CASP10. The evaluation is based on the three measures tested in previous CASPs: (i) balanced accuracy, (ii) the Matthews correlation coefficient for the binary predictions, and (iii) the area under the curve in the receiver operating characteristic (ROC) analysis of predictions using probability annotation. We also performed new analyses such as comparison of the submitted predictions with those obtained with a Naïve disorder prediction method and with predictions from the disorder prediction databases D2P2 and MobiDB. On average, the methods participating in CASP10 demonstrated slightly better performance than those in CASP9.

  19. A sequence-based dynamic ensemble learning system for protein ligand-binding site prediction

    KAUST Repository

    Chen, Peng

    2015-12-03

    Background: Proteins have the fundamental ability to selectively bind to other molecules and perform specific functions through such interactions, such as protein-ligand binding. Accurate prediction of protein residues that physically bind to ligands is important for drug design and protein docking studies. Most of the successful protein-ligand binding predictions were based on known structures. However, structural information is not largely available in practice due to the huge gap between the number of known protein sequences and that of experimentally solved structures

  20. A sequence-based dynamic ensemble learning system for protein ligand-binding site prediction

    KAUST Repository

    Chen, Peng; Hu, ShanShan; Zhang, Jun; Gao, Xin; Li, Jinyan; Xia, Junfeng; Wang, Bing

    2015-01-01

    Background: Proteins have the fundamental ability to selectively bind to other molecules and perform specific functions through such interactions, such as protein-ligand binding. Accurate prediction of protein residues that physically bind to ligands is important for drug design and protein docking studies. Most of the successful protein-ligand binding predictions were based on known structures. However, structural information is not largely available in practice due to the huge gap between the number of known protein sequences and that of experimentally solved structures

  1. Concentrations of antibiotics predicted to select for resistant bacteria: Proposed limits for environmental regulation.

    Science.gov (United States)

    Bengtsson-Palme, Johan; Larsson, D G Joakim

    2016-01-01

    There are concerns that selection pressure from antibiotics in the environment may accelerate the evolution and dissemination of antibiotic-resistant pathogens. Nevertheless, there is currently no regulatory system that takes such risks into account. In part, this is due to limited knowledge of environmental concentrations that might exert selection for resistant bacteria. To experimentally determine minimal selective concentrations in complex microbial ecosystems for all antibiotics would involve considerable effort. In this work, our aim was to estimate upper boundaries for selective concentrations for all common antibiotics, based on the assumption that selective concentrations a priori need to be lower than those completely inhibiting growth. Data on Minimal Inhibitory Concentrations (MICs) were obtained for 111 antibiotics from the public EUCAST database. The 1% lowest observed MICs were identified, and to compensate for limited species coverage, predicted lowest MICs adjusted for the number of tested species were extrapolated through modeling. Predicted No Effect Concentrations (PNECs) for resistance selection were then assessed using an assessment factor of 10 to account for differences between MICs and minimal selective concentrations. The resulting PNECs ranged from 8 ng/L to 64 μg/L. Furthermore, the link between taxonomic similarity between species and lowest MIC was weak. This work provides estimated upper boundaries for selective concentrations (lowest MICs) and PNECs for resistance selection for all common antibiotics. In most cases, PNECs for selection of resistance were below available PNECs for ecotoxicological effects. The generated PNECs can guide implementation of compound-specific emission limits that take into account risks for resistance promotion. Copyright © 2015 The Authors. Published by Elsevier Ltd.. All rights reserved.

  2. Protein Function Prediction Based on Sequence and Structure Information

    KAUST Repository

    Smaili, Fatima Z.

    2016-01-01

    operate. In this master thesis project, we worked on inferring protein functions based on the primary protein sequence. In the approach we follow, 3D models are first constructed using I-TASSER. Functions are then deduced by structurally matching

  3. A Particle Swarm Optimization-Based Approach with Local Search for Predicting Protein Folding.

    Science.gov (United States)

    Yang, Cheng-Hong; Lin, Yu-Shiun; Chuang, Li-Yeh; Chang, Hsueh-Wei

    2017-10-01

    The hydrophobic-polar (HP) model is commonly used for predicting protein folding structures and hydrophobic interactions. This study developed a particle swarm optimization (PSO)-based algorithm combined with local search algorithms; specifically, the high exploration PSO (HEPSO) algorithm (which can execute global search processes) was combined with three local search algorithms (hill-climbing algorithm, greedy algorithm, and Tabu table), yielding the proposed HE-L-PSO algorithm. By using 20 known protein structures, we evaluated the performance of the HE-L-PSO algorithm in predicting protein folding in the HP model. The proposed HE-L-PSO algorithm exhibited favorable performance in predicting both short and long amino acid sequences with high reproducibility and stability, compared with seven reported algorithms. The HE-L-PSO algorithm yielded optimal solutions for all predicted protein folding structures. All HE-L-PSO-predicted protein folding structures possessed a hydrophobic core that is similar to normal protein folding.

  4. The importance of different frequency bands in predicting subcutaneous glucose concentration in type 1 diabetic patients.

    Science.gov (United States)

    Lu, Yinghui; Gribok, Andrei V; Ward, W Kenneth; Reifman, Jaques

    2010-08-01

    We investigated the relative importance and predictive power of different frequency bands of subcutaneous glucose signals for the short-term (0-50 min) forecasting of glucose concentrations in type 1 diabetic patients with data-driven autoregressive (AR) models. The study data consisted of minute-by-minute glucose signals collected from nine deidentified patients over a five-day period using continuous glucose monitoring devices. AR models were developed using single and pairwise combinations of frequency bands of the glucose signal and compared with a reference model including all bands. The results suggest that: for open-loop applications, there is no need to explicitly represent exogenous inputs, such as meals and insulin intake, in AR models; models based on a single-frequency band, with periods between 60-120 min and 150-500 min, yield good predictive power (error bands produce predictions that are indistinguishable from those of the reference model as long as the 60-120 min period band is included; and AR models can be developed on signals of short length (approximately 300 min), i.e., ignoring long circadian rhythms, without any detriment in prediction accuracy. Together, these findings provide insights into efficient development of more effective and parsimonious data-driven models for short-term prediction of glucose concentrations in diabetic patients.

  5. Prediction of the HBS width and Xe concentration in grain matrix by the INFRA code

    International Nuclear Information System (INIS)

    Yang, Yong Sik; Lee, Chan Bok; Kim, Dae Ho; Kim, Young Min

    2004-01-01

    Formation of a HBS(High Burnup Structure) is an important phenomenon for the high burnup fuel performance and safety. For the prediction of the HBS(so called 'rim microstructure') proposed rim microstructure formation model, which is a function of the fuel temperature, grain size and fission rate, was inserted into the high burnup fuel performance code INFRA. During the past decades, various examinations have been performed to find the HBS formation mechanism and define HBS characteristics. In the HBEP(High Burnup Effects Program), several rods were examined by EPMA analysis to measure HBS width and these results were re-measured by improved technology including XRF and detail microstructure examination. Recently, very high burnup(∼100MWd/kgU) fuel examination results were reported by Manzel et al., and EPMA analysis results have been released. Using the measured EPMA analysis data, HBS formation prediction model of INFRA code are verified. HBS width prediction results are compared with measured ones and Xe concentration profile is compared with measured EPMA data. Calculated HBS width shows good agreement with measured data in a reasonable error range. Though, there are some difference in transition region and central region due to model limitation and fission gas release prediction error respectively, however, predicted Xe concentration in the fully developed HBS region shows a good agreement with the measured data. (Author)

  6. Soil and Water Assessment Tool model predictions of annual maximum pesticide concentrations in high vulnerability watersheds.

    Science.gov (United States)

    Winchell, Michael F; Peranginangin, Natalia; Srinivasan, Raghavan; Chen, Wenlin

    2018-05-01

    Recent national regulatory assessments of potential pesticide exposure of threatened and endangered species in aquatic habitats have led to increased need for watershed-scale predictions of pesticide concentrations in flowing water bodies. This study was conducted to assess the ability of the uncalibrated Soil and Water Assessment Tool (SWAT) to predict annual maximum pesticide concentrations in the flowing water bodies of highly vulnerable small- to medium-sized watersheds. The SWAT was applied to 27 watersheds, largely within the midwest corn belt of the United States, ranging from 20 to 386 km 2 , and evaluated using consistent input data sets and an uncalibrated parameterization approach. The watersheds were selected from the Atrazine Ecological Exposure Monitoring Program and the Heidelberg Tributary Loading Program, both of which contain high temporal resolution atrazine sampling data from watersheds with exceptionally high vulnerability to atrazine exposure. The model performance was assessed based upon predictions of annual maximum atrazine concentrations in 1-d and 60-d durations, predictions critical in pesticide-threatened and endangered species risk assessments when evaluating potential acute and chronic exposure to aquatic organisms. The simulation results showed that for nearly half of the watersheds simulated, the uncalibrated SWAT model was able to predict annual maximum pesticide concentrations within a narrow range of uncertainty resulting from atrazine application timing patterns. An uncalibrated model's predictive performance is essential for the assessment of pesticide exposure in flowing water bodies, the majority of which have insufficient monitoring data for direct calibration, even in data-rich countries. In situations in which SWAT over- or underpredicted the annual maximum concentrations, the magnitude of the over- or underprediction was commonly less than a factor of 2, indicating that the model and uncalibrated parameterization

  7. Lake nutrient stoichiometry is less predictable than nutrient concentrations at regional and sub-continental scales.

    Science.gov (United States)

    Collins, Sarah M; Oliver, Samantha K; Lapierre, Jean-Francois; Stanley, Emily H; Jones, John R; Wagner, Tyler; Soranno, Patricia A

    2017-07-01

    Production in many ecosystems is co-limited by multiple elements. While a known suite of drivers associated with nutrient sources, nutrient transport, and internal processing controls concentrations of phosphorus (P) and nitrogen (N) in lakes, much less is known about whether the drivers of single nutrient concentrations can also explain spatial or temporal variation in lake N:P stoichiometry. Predicting stoichiometry might be more complex than predicting concentrations of individual elements because some drivers have similar relationships with N and P, leading to a weak relationship with their ratio. Further, the dominant controls on elemental concentrations likely vary across regions, resulting in context dependent relationships between drivers, lake nutrients and their ratios. Here, we examine whether known drivers of N and P concentrations can explain variation in N:P stoichiometry, and whether explaining variation in stoichiometry differs across regions. We examined drivers of N:P in ~2,700 lakes at a sub-continental scale and two large regions nested within the sub-continental study area that have contrasting ecological context, including differences in the dominant type of land cover (agriculture vs. forest). At the sub-continental scale, lake nutrient concentrations were correlated with nutrient loading and lake internal processing, but stoichiometry was only weakly correlated to drivers of lake nutrients. At the regional scale, drivers that explained variation in nutrients and stoichiometry differed between regions. In the Midwestern U.S. region, dominated by agricultural land use, lake depth and the percentage of row crop agriculture were strong predictors of stoichiometry because only phosphorus was related to lake depth and only nitrogen was related to the percentage of row crop agriculture. In contrast, all drivers were related to N and P in similar ways in the Northeastern U.S. region, leading to weak relationships between drivers and stoichiometry

  8. The prediction of concentration profiles for a NIMCIX column absorbing uranium from aqueous solution

    International Nuclear Information System (INIS)

    Wright, R.S.

    1979-01-01

    A procedure is proposed for the prediction of concentration profiles for a countercurrent ion-exchange absorption column, use being made of equilibrium and kinetic data derived from small-scale batch tests. A comparison is presented between the predictions and the measured performance of a column (2,5 m in diameter) absorbing uranium from solution. The method is shown to be adequate for design purposes provided that the data used are from tests in which the solution and resin conditions approximate those for which the plant is being designed [af

  9. Do plasma concentrations of apelin predict prognosis in patients with advanced heart failure?

    Science.gov (United States)

    Dalzell, Jonathan R; Jackson, Colette E; Chong, Kwok S; McDonagh, Theresa A; Gardner, Roy S

    2014-01-01

    Apelin is an endogenous vasodilator and inotrope, plasma concentrations of which are reduced in advanced heart failure (HF). We determined the prognostic significance of plasma concentrations of apelin in advanced HF. Plasma concentrations of apelin were measured in 182 patients with advanced HF secondary to left ventricular systolic dysfunction. The predictive value of apelin for the primary end point of all-cause mortality was assessed over a median follow-up period of 544 (IQR: 196-923) days. In total, 30 patients (17%) reached the primary end point. Of those patients with a plasma apelin concentration above the median, 14 (16%) reached the primary end point compared with 16 (17%) of those with plasma apelin levels below the median (p = NS). NT-proBNP was the most powerful prognostic marker in this population (log rank statistic: 10.37; p = 0.001). Plasma apelin concentrations do not predict medium to long-term prognosis in patients with advanced HF secondary to left ventricular systolic dysfunction.

  10. Sequence- and interactome-based prediction of viral protein hotspots targeting host proteins: a case study for HIV Nef.

    Directory of Open Access Journals (Sweden)

    Mahdi Sarmady

    Full Text Available Virus proteins alter protein pathways of the host toward the synthesis of viral particles by breaking and making edges via binding to host proteins. In this study, we developed a computational approach to predict viral sequence hotspots for binding to host proteins based on sequences of viral and host proteins and literature-curated virus-host protein interactome data. We use a motif discovery algorithm repeatedly on collections of sequences of viral proteins and immediate binding partners of their host targets and choose only those motifs that are conserved on viral sequences and highly statistically enriched among binding partners of virus protein targeted host proteins. Our results match experimental data on binding sites of Nef to host proteins such as MAPK1, VAV1, LCK, HCK, HLA-A, CD4, FYN, and GNB2L1 with high statistical significance but is a poor predictor of Nef binding sites on highly flexible, hoop-like regions. Predicted hotspots recapture CD8 cell epitopes of HIV Nef highlighting their importance in modulating virus-host interactions. Host proteins potentially targeted or outcompeted by Nef appear crowding the T cell receptor, natural killer cell mediated cytotoxicity, and neurotrophin signaling pathways. Scanning of HIV Nef motifs on multiple alignments of hepatitis C protein NS5A produces results consistent with literature, indicating the potential value of the hotspot discovery in advancing our understanding of virus-host crosstalk.

  11. Hill-Climbing search and diversification within an evolutionary approach to protein structure prediction.

    Science.gov (United States)

    Chira, Camelia; Horvath, Dragos; Dumitrescu, D

    2011-07-30

    Proteins are complex structures made of amino acids having a fundamental role in the correct functioning of living cells. The structure of a protein is the result of the protein folding process. However, the general principles that govern the folding of natural proteins into a native structure are unknown. The problem of predicting a protein structure with minimum-energy starting from the unfolded amino acid sequence is a highly complex and important task in molecular and computational biology. Protein structure prediction has important applications in fields such as drug design and disease prediction. The protein structure prediction problem is NP-hard even in simplified lattice protein models. An evolutionary model based on hill-climbing genetic operators is proposed for protein structure prediction in the hydrophobic - polar (HP) model. Problem-specific search operators are implemented and applied using a steepest-ascent hill-climbing approach. Furthermore, the proposed model enforces an explicit diversification stage during the evolution in order to avoid local optimum. The main features of the resulting evolutionary algorithm - hill-climbing mechanism and diversification strategy - are evaluated in a set of numerical experiments for the protein structure prediction problem to assess their impact to the efficiency of the search process. Furthermore, the emerging consolidated model is compared to relevant algorithms from the literature for a set of difficult bidimensional instances from lattice protein models. The results obtained by the proposed algorithm are promising and competitive with those of related methods.

  12. Hill-Climbing search and diversification within an evolutionary approach to protein structure prediction

    Directory of Open Access Journals (Sweden)

    Chira Camelia

    2011-07-01

    Full Text Available Abstract Proteins are complex structures made of amino acids having a fundamental role in the correct functioning of living cells. The structure of a protein is the result of the protein folding process. However, the general principles that govern the folding of natural proteins into a native structure are unknown. The problem of predicting a protein structure with minimum-energy starting from the unfolded amino acid sequence is a highly complex and important task in molecular and computational biology. Protein structure prediction has important applications in fields such as drug design and disease prediction. The protein structure prediction problem is NP-hard even in simplified lattice protein models. An evolutionary model based on hill-climbing genetic operators is proposed for protein structure prediction in the hydrophobic - polar (HP model. Problem-specific search operators are implemented and applied using a steepest-ascent hill-climbing approach. Furthermore, the proposed model enforces an explicit diversification stage during the evolution in order to avoid local optimum. The main features of the resulting evolutionary algorithm - hill-climbing mechanism and diversification strategy - are evaluated in a set of numerical experiments for the protein structure prediction problem to assess their impact to the efficiency of the search process. Furthermore, the emerging consolidated model is compared to relevant algorithms from the literature for a set of difficult bidimensional instances from lattice protein models. The results obtained by the proposed algorithm are promising and competitive with those of related methods.

  13. Meal composition and plasma amino acid ratios: Effect of various proteins or carbohydrates, and of various protein concentrations

    Science.gov (United States)

    Yokogoshi, Hidehiko; Wurtman, Richard J.

    1986-01-01

    The effects of meals containing various proteins and carbohydrates, and of those containing various proportions of protein (0 percent to 20 percent of a meal, by weight) or of carbohydrate (0 percent to 75 percent), on plasma levels of certain large neutral amino acids (LNAA) in rats previously fasted for 19 hours were examined. Also the plasma tryptophan ratios (the ratio of the plasma trytophan concentration to the summed concentrations of the other large neutral amino acids) and other plasma amino acid ratios were calculated. (The plasma tryptophan ratio has been shown to determine brain tryptophan levels and, thereby, to affect the synthesis and release of the neurotransmitter serotonin). A meal containing 70 percent to 75 percent of an insulin-secreting carbohydrate (dextrose or dextrin) increased plasma insulin levels and the tryptophan ratio; those containing 0 percent or 25 percent carbohydrate failed to do so. Addition of as little as 5 percent casein to a 70 percent carbohydrate meal fully blocked the increase in the plasma tryptophan ratio without affecting the secretion of insulin - probably by contributing much larger quantities of the other LNAA than of tryptophan to the blood. Dietary proteins differed in their ability to suppress the carbohydrate-induced rise in the plasma tryptophan ratio. Addition of 10 percent casein, peanut meal, or gelatin fully blocked this increase, but lactalbumin failed to do so, and egg white did so only partially. (Consumption of the 10 percent gelatin meal also produced a major reduction in the plasma tyrosine ratio, and may thereby have affected brain tyrosine levels and catecholamine synthesis.) These observations suggest that serotonin-releasing neurons in brains of fasted rats are capable of distinguishing (by their metabolic effects) between meals poor in protein but rich in carbohydrates that elicit insulin secretion, and all other meals. The changes in brain serotonin caused by carbohydrate-rich, protein

  14. HitPredict version 4: comprehensive reliability scoring of physical protein-protein interactions from more than 100 species.

    Science.gov (United States)

    López, Yosvany; Nakai, Kenta; Patil, Ashwini

    2015-01-01

    HitPredict is a consolidated resource of experimentally identified, physical protein-protein interactions with confidence scores to indicate their reliability. The study of genes and their inter-relationships using methods such as network and pathway analysis requires high quality protein-protein interaction information. Extracting reliable interactions from most of the existing databases is challenging because they either contain only a subset of the available interactions, or a mixture of physical, genetic and predicted interactions. Automated integration of interactions is further complicated by varying levels of accuracy of database content and lack of adherence to standard formats. To address these issues, the latest version of HitPredict provides a manually curated dataset of 398 696 physical associations between 70 808 proteins from 105 species. Manual confirmation was used to resolve all issues encountered during data integration. For improved reliability assessment, this version combines a new score derived from the experimental information of the interactions with the original score based on the features of the interacting proteins. The combined interaction score performs better than either of the individual scores in HitPredict as well as the reliability score of another similar database. HitPredict provides a web interface to search proteins and visualize their interactions, and the data can be downloaded for offline analysis. Data usability has been enhanced by mapping protein identifiers across multiple reference databases. Thus, the latest version of HitPredict provides a significantly larger, more reliable and usable dataset of protein-protein interactions from several species for the study of gene groups. Database URL: http://hintdb.hgc.jp/htp. © The Author(s) 2015. Published by Oxford University Press.

  15. MLP based models to predict PM10, O3 concentrations, in Sines industrial area

    Science.gov (United States)

    Durao, R.; Pereira, M. J.

    2012-04-01

    Sines is an important Portuguese industrial area located southwest cost of Portugal with important nearby protected natural areas. The main economical activities are related with this industrial area, the deep-water port, petrochemical and thermo-electric industry. Nevertheless, tourism is also an important economic activity especially in summer time with potential to grow. The aim of this study is to develop prediction models of pollutant concentration categories (e.g. low concentration and high concentration) in order to provide early warnings to the competent authorities who are responsible for the air quality management. The knowledge in advanced of pollutant high concentrations occurrence will allow the implementation of mitigation actions and the release of precautionary alerts to population. The regional air quality monitoring network consists in three monitoring stations where a set of pollutants' concentrations are registered on a continuous basis. From this set stands out the tropospheric ozone (O3) and particulate matter (PM10) due to the high concentrations occurring in the region and their adverse effects on human health. Moreover, the major industrial plants of the region monitor SO2, NO2 and particles emitted flows at the principal chimneys (point sources), also on a continuous basis,. Therefore Artificial neuronal networks (ANN) were the applied methodology to predict next day pollutant concentrations; due to the ANNs structure they have the ability to capture the non-linear relationships between predictor variables. Hence the first step of this study was to apply multivariate exploratory techniques to select the best predictor variables. The classification trees methodology (CART) was revealed to be the most appropriate in this case.. Results shown that pollutants atmospheric concentrations are mainly dependent on industrial emissions and a complex combination of meteorological factors and the time of the year. In the second step, the Multi

  16. Predicting the concentration of residual methanol in industrial formalin using machine learning

    OpenAIRE

    Heidkamp, William

    2016-01-01

    In this thesis, a machine learning approach was used to develop a predictive model for residual methanol concentration in industrial formalin produced at the Akzo Nobel factory in Kristinehamn, Sweden. The MATLABTM computational environment supplemented with the Statistics and Machine LearningTM toolbox from the MathWorks were used to test various machine learning algorithms on the formalin production data from Akzo Nobel. As a result, the Gaussian Process Regression algorithm was found to pr...

  17. Predicting protein folding pathways at the mesoscopic level based on native interactions between secondary structure elements

    Directory of Open Access Journals (Sweden)

    Sze Sing-Hoi

    2008-07-01

    Full Text Available Abstract Background Since experimental determination of protein folding pathways remains difficult, computational techniques are often used to simulate protein folding. Most current techniques to predict protein folding pathways are computationally intensive and are suitable only for small proteins. Results By assuming that the native structure of a protein is known and representing each intermediate conformation as a collection of fully folded structures in which each of them contains a set of interacting secondary structure elements, we show that it is possible to significantly reduce the conformation space while still being able to predict the most energetically favorable folding pathway of large proteins with hundreds of residues at the mesoscopic level, including the pig muscle phosphoglycerate kinase with 416 residues. The model is detailed enough to distinguish between different folding pathways of structurally very similar proteins, including the streptococcal protein G and the peptostreptococcal protein L. The model is also able to recognize the differences between the folding pathways of protein G and its two structurally similar variants NuG1 and NuG2, which are even harder to distinguish. We show that this strategy can produce accurate predictions on many other proteins with experimentally determined intermediate folding states. Conclusion Our technique is efficient enough to predict folding pathways for both large and small proteins at the mesoscopic level. Such a strategy is often the only feasible choice for large proteins. A software program implementing this strategy (SSFold is available at http://faculty.cs.tamu.edu/shsze/ssfold.

  18. Effects of ventilation on hyaluronan and protein concentration in pleural liquid of anesthetized and conscious rabbits.

    Science.gov (United States)

    Wang, P M; Lai-Fook, S J

    1998-01-01

    The hypothesis of this study is that pleural lubrication is enhanced by hyaluronan acting as a boundary lubricant in pleural liquid and by pleural filtration as reflected in changes in protein concentration with ventilation. Anesthetized rabbits were injected intravenously with Evans blue dye and ventilated with 100% O2 at either of two levels of ventilation for 6 h. Postmortem values of hyaluronan, total protein, and Evans blue-dyed albumin (EBA) concentrations in pleural liquid were greater at the higher ventilation, consistent with increases in boundary lubrication, pleural membrane permeability, and pleural filtration. To determine whether these effects were caused by hyperoxia or anesthesia, conscious rabbits were ventilated with either 3% CO2 or room air in a box for 6, 12, or 24 h. Similar to the anesthetized rabbits, pleural liquid hyaluronan concentration after 24 h was higher in the conscious rabbits with the hypercapnic-induced greater ventilation. By contrast, the time course of total protein and EBA in pleural liquid was similar in both groups of conscious rabbits, indicating no effect of ventilation on pleural permeability. The increase in pleural liquid hyaluronan concentration might be the result of mesothelial cell stimulation by a ventilation-induced increase in pleural liquid shear stress.

  19. StaRProtein, A Web Server for Prediction of the Stability of Repeat Proteins

    Science.gov (United States)

    Xu, Yongtao; Zhou, Xu; Huang, Meilan

    2015-01-01

    Repeat proteins have become increasingly important due to their capability to bind to almost any proteins and the potential as alternative therapy to monoclonal antibodies. In the past decade repeat proteins have been designed to mediate specific protein-protein interactions. The tetratricopeptide and ankyrin repeat proteins are two classes of helical repeat proteins that form different binding pockets to accommodate various partners. It is important to understand the factors that define folding and stability of repeat proteins in order to prioritize the most stable designed repeat proteins to further explore their potential binding affinities. Here we developed distance-dependant statistical potentials using two classes of alpha-helical repeat proteins, tetratricopeptide and ankyrin repeat proteins respectively, and evaluated their efficiency in predicting the stability of repeat proteins. We demonstrated that the repeat-specific statistical potentials based on these two classes of repeat proteins showed paramount accuracy compared with non-specific statistical potentials in: 1) discriminate correct vs. incorrect models 2) rank the stability of designed repeat proteins. In particular, the statistical scores correlate closely with the equilibrium unfolding free energies of repeat proteins and therefore would serve as a novel tool in quickly prioritizing the designed repeat proteins with high stability. StaRProtein web server was developed for predicting the stability of repeat proteins. PMID:25807112

  20. Basic model for the prediction of 137Cs concentration in the organisms of detritus food chain

    International Nuclear Information System (INIS)

    Tateda, Yuzuru

    1997-01-01

    In order to predict 137 Cs concentrations in marine organisms for monitoring, a basic model for the prediction of nuclide levels in marine organisms of detritus food chain was studied. The equilibrated values of ( 137 Cs level in organism)/( 137 Cs level in seawater) derived from calculation agreed with the observed data, indicating validity of modeling conditions. The result of simulation by this basic model showed the following conclusions. 1) ''Ecological half-life'' of 137 Cs in organisms of food chain were 35 and 130 days for detritus feeder and benthic teleosts, respectively, indicating that there was no difference of the ecological half lives in organisms between in detritus food chain and in other food chains. 2) The 137 Cs concentration in organisms showed a peak at 18 and 100 days in detritus and detritus feeder, respectively, after the introduction of 137 Cs into environmental seawater. Their concentration ratios to 137 Cs peak level in seawater were within a range of 2.7-3.8, indicating insignificant difference in the response to 137 Cs change in seawater between in the organisms of detritus food chain and of other food chain. 3) The basic model studies makes it available that the prediction of 137 Cs level in organisms of food chain can be simulated in coastal ecosystem. (author)

  1. Amikacin Concentrations Predictive of Ototoxicity in Multidrug-Resistant Tuberculosis Patients.

    Science.gov (United States)

    Modongo, Chawangwa; Pasipanodya, Jotam G; Zetola, Nicola M; Williams, Scott M; Sirugo, Giorgio; Gumbo, Tawanda

    2015-10-01

    Aminoglycosides, such as amikacin, are used to treat multidrug-resistant tuberculosis. However, ototoxicity is a common problem and is monitored using peak and trough amikacin concentrations based on World Health Organization recommendations. Our objective was to identify clinical factors predictive of ototoxicity using an agnostic machine learning method. We used classification and regression tree (CART) analyses to identify clinical factors, including amikacin concentration thresholds that predicted audiometry-confirmed ototoxicity among 28 multidrug-resistant pulmonary tuberculosis patients in Botswana. Amikacin concentrations were measured for all patients. The quantitative relationship between predictive factors and the probability of ototoxicity were then identified using probit analyses. The primary predictors of ototoxicity on CART analyses were cumulative days of therapy, followed by cumulative area under the concentration-time curve (AUC), which improved on the primary predictor by 87%. The area under the receiver operating curve was 0.97 on the test set. Peak and trough were not predictors in any tree. When algorithms were forced to pick peak and trough as primary predictors, the area under the receiver operating curve fell to 0.46. Probit analysis revealed that the probability of ototoxicity increased sharply starting after 6 months of therapy to near maximum at 9 months. A 10% probability of ototoxicity occurred with a threshold cumulative AUC of 87,232 days · mg · h/liter, while that of 20% occurred at 120,000 days · mg · h/liter. Thus, cumulative amikacin AUC and duration of therapy, and not peak and trough concentrations, should be used as the primary decision-making parameters to minimize the likelihood of ototoxicity in multidrug-resistant tuberculosis. Copyright © 2015, Modongo et al.

  2. Update on protein structure prediction: results of the 1995 IRBM workshop

    DEFF Research Database (Denmark)

    Hubbard, Tim; Tramontano, Anna; Hansen, Jan

    1996-01-01

    Computational tools for protein structure prediction are of great interest to molecular, structural and theoretical biologists due to a rapidly increasing number of protein sequences with no known structure. In October 1995, a workshop was held at IRBM to predict as much as possible about a numbe...

  3. Update on protein structure prediction: results of the 1995 IRBM workshop

    DEFF Research Database (Denmark)

    Hubbard, Tim; Tramontano, Anna; Hansen, Jan

    1996-01-01

    Computational tools for protein structure prediction are of great interest to molecular, structural and theoretical biologists due to a rapidly increasing number of protein sequences with no known structure. In October 1995, a workshop was held at IRBM to predict as much as possible about a number...

  4. INTERACTION OF NIZKOMETILIROVANNYJ PECTINS WITH A CONCENTRATE OF PROTEINS OF WHEY

    Directory of Open Access Journals (Sweden)

    H. I. Teshaev

    2012-01-01

    Full Text Available Potentiometric titration method was used to study quality complex formation between low methylated pectin and proteins concentrated from whey. It’s shown that at рН>IEP of the lactoglobulin the interaction occurs between negatively charged chains of LM-pectin and positively charged patches of polypeptide chains. The biopolymers ratio had no significant effect on the initial pH of soluble complex formation (pHc; addition of sodium chloride decreased pHc and pK0 of complexes, which linked to electrostatic nature of complex formation between LM-pectin and whey proteins.

  5. Jatropha curcas Protein Concentrate Stimulates Insulin Signaling, Lipogenesis, Protein Synthesis and the PKCα Pathway in Rat Liver.

    Science.gov (United States)

    León-López, Liliana; Márquez-Mota, Claudia C; Velázquez-Villegas, Laura A; Gálvez-Mariscal, Amanda; Arrieta-Báez, Daniel; Dávila-Ortiz, Gloria; Tovar, Armando R; Torres, Nimbe

    2015-09-01

    Jatropha curcas is an oil seed plant that belongs to the Euphorbiaceae family. Nontoxic genotypes have been reported in Mexico. The purpose of the present work was to evaluate the effect of a Mexican variety of J. curcas protein concentrate (JCP) on weight gain, biochemical parameters, and the expression of genes and proteins involved in insulin signaling, lipogenesis, cholesterol and protein synthesis in rats. The results demonstrated that short-term consumption of JCP increased serum glucose, insulin, triglycerides and cholesterol levels as well as the expression of transcription factors involved in lipogenesis and cholesterol synthesis (SREBP-1 and LXRα). Moreover, there was an increase in insulin signaling mediated by Akt phosphorylation and mTOR. JCP also increased PKCα protein abundance and the activation of downstream signaling pathway targets such as the AP1 and NF-κB transcription factors typically activated by phorbol esters. These results suggested that phorbol esters are present in JCP, and that they could be involved in the activation of PKC which may be responsible for the high insulin secretion and consequently the activation of insulin-dependent pathways. Our data suggest that this Mexican Jatropha variety contains toxic compounds that produce negative metabolic effects which require caution when using in the applications of Jatropha-based products in medicine and nutrition.

  6. Computational Approaches for Prediction of Pathogen-Host Protein-Protein Interactions

    Directory of Open Access Journals (Sweden)

    Esmaeil eNourani

    2015-02-01

    Full Text Available Infectious diseases are still among the major and prevalent health problems, mostly because of the drug resistance of novel variants of pathogens. Molecular interactions between pathogens and their hosts are the key part of the infection mechanisms. Novel antimicrobial therapeutics to fight drug resistance is only possible in case of a thorough understanding of pathogen-host interaction (PHI systems. Existing databases, which contain experimentally verified PHI data, suffer from scarcity of reported interactions due to the technically challenging and time consuming process of experiments. This has motivated many researchers to address the problem by proposing computational approaches for analysis and prediction of PHIs. The computational methods primarily utilize sequence information, protein structure and known interactions. Classic machine learning techniques are used when there are sufficient known interactions to be used as training data. On the opposite case, transfer and multi task learning methods are preferred. Here, we present an overview of these computational approaches for PHI prediction, discussing their weakness and abilities, with future directions.

  7. Stringent homology-based prediction of H. sapiens-M. tuberculosis H37Rv protein-protein interactions.

    Science.gov (United States)

    Zhou, Hufeng; Gao, Shangzhi; Nguyen, Nam Ninh; Fan, Mengyuan; Jin, Jingjing; Liu, Bing; Zhao, Liang; Xiong, Geng; Tan, Min; Li, Shijun; Wong, Limsoon

    2014-04-08

    H. sapiens-M. tuberculosis H37Rv protein-protein interaction (PPI) data are essential for understanding the infection mechanism of the formidable pathogen M. tuberculosis H37Rv. Computational prediction is an important strategy to fill the gap in experimental H. sapiens-M. tuberculosis H37Rv PPI data. Homology-based prediction is frequently used in predicting both intra-species and inter-species PPIs. However, some limitations are not properly resolved in several published works that predict eukaryote-prokaryote inter-species PPIs using intra-species template PPIs. We develop a stringent homology-based prediction approach by taking into account (i) differences between eukaryotic and prokaryotic proteins and (ii) differences between inter-species and intra-species PPI interfaces. We compare our stringent homology-based approach to a conventional homology-based approach for predicting host-pathogen PPIs, based on cellular compartment distribution analysis, disease gene list enrichment analysis, pathway enrichment analysis and functional category enrichment analysis. These analyses support the validity of our prediction result, and clearly show that our approach has better performance in predicting H. sapiens-M. tuberculosis H37Rv PPIs. Using our stringent homology-based approach, we have predicted a set of highly plausible H. sapiens-M. tuberculosis H37Rv PPIs which might be useful for many of related studies. Based on our analysis of the H. sapiens-M. tuberculosis H37Rv PPI network predicted by our stringent homology-based approach, we have discovered several interesting properties which are reported here for the first time. We find that both host proteins and pathogen proteins involved in the host-pathogen PPIs tend to be hubs in their own intra-species PPI network. Also, both host and pathogen proteins involved in host-pathogen PPIs tend to have longer primary sequence, tend to have more domains, tend to be more hydrophilic, etc. And the protein domains from both

  8. Protein Secondary Structure Prediction Using AutoEncoder Network and Bayes Classifier

    Science.gov (United States)

    Wang, Leilei; Cheng, Jinyong

    2018-03-01

    Protein secondary structure prediction is belong to bioinformatics,and it's important in research area. In this paper, we propose a new prediction way of protein using bayes classifier and autoEncoder network. Our experiments show some algorithms including the construction of the model, the classification of parameters and so on. The data set is a typical CB513 data set for protein. In terms of accuracy, the method is the cross validation based on the 3-fold. Then we can get the Q3 accuracy. Paper results illustrate that the autoencoder network improved the prediction accuracy of protein secondary structure.

  9. Calorie Restricted High Protein Diets Downregulate Lipogenesis and Lower Intrahepatic Triglyceride Concentrations in Male Rats

    Directory of Open Access Journals (Sweden)

    Lee M. Margolis

    2016-09-01

    Full Text Available The purpose of this investigation was to assess the influence of calorie restriction (CR alone, higher-protein/lower-carbohydrate intake alone, and combined CR higher-protein/lower-carbohydrate intake on glucose homeostasis, hepatic de novo lipogenesis (DNL, and intrahepatic triglycerides. Twelve-week old male Sprague Dawley rats consumed ad libitum (AL or CR (40% restriction, adequate (10%, or high (32% protein (PRO milk-based diets for 16 weeks. Metabolic profiles were assessed in serum, and intrahepatic triglyceride concentrations and molecular markers of de novo lipogenesis were determined in liver. Independent of calorie intake, 32% PRO tended to result in lower homeostatic model assessment of insulin resistance (HOMA-IR values compared to 10% PRO, while insulin and homeostatic model assessment of β-cell function (HOMA-β values were lower in CR than AL, regardless of protein intake. Intrahepatic triglyceride concentrations were 27.4 ± 4.5 and 11.7 ± 4.5 µmol·g−1 lower (p < 0.05 in CR and 32% PRO compared to AL and 10% PRO, respectively. Gene expression of fatty acid synthase (FASN, stearoyl-CoA destaurase-1 (SCD1 and pyruvate dehydrogenase kinase, isozyme 4 (PDK4 were 45% ± 1%, 23% ± 1%, and 57% ± 1% lower (p < 0.05, respectively, in CR than AL, regardless of protein intake. Total protein of FASN and SCD were 50% ± 1% and 26% ± 1% lower (p < 0.05 in 32% PRO compared to 10% PRO, independent of calorie intake. Results from this investigation provide evidence that the metabolic health benefits associated with CR—specifically reduction in intrahepatic triglyceride content—may be enhanced by consuming a higher-protein/lower-carbohydrate diet.

  10. Predicting redox-sensitive contaminant concentrations in groundwater using random forest classification

    Science.gov (United States)

    Tesoriero, Anthony J.; Gronberg, Jo Ann; Juckem, Paul F.; Miller, Matthew P.; Austin, Brian P.

    2017-08-01

    Machine learning techniques were applied to a large (n > 10,000) compliance monitoring database to predict the occurrence of several redox-active constituents in groundwater across a large watershed. Specifically, random forest classification was used to determine the probabilities of detecting elevated concentrations of nitrate, iron, and arsenic in the Fox, Wolf, Peshtigo, and surrounding watersheds in northeastern Wisconsin. Random forest classification is well suited to describe the nonlinear relationships observed among several explanatory variables and the predicted probabilities of elevated concentrations of nitrate, iron, and arsenic. Maps of the probability of elevated nitrate, iron, and arsenic can be used to assess groundwater vulnerability and the vulnerability of streams to contaminants derived from groundwater. Processes responsible for elevated concentrations are elucidated using partial dependence plots. For example, an increase in the probability of elevated iron and arsenic occurred when well depths coincided with the glacial/bedrock interface, suggesting a bedrock source for these constituents. Furthermore, groundwater in contact with Ordovician bedrock has a higher likelihood of elevated iron concentrations, which supports the hypothesis that groundwater liberates iron from a sulfide-bearing secondary cement horizon of Ordovician age. Application of machine learning techniques to existing compliance monitoring data offers an opportunity to broadly assess aquifer and stream vulnerability at regional and national scales and to better understand geochemical processes responsible for observed conditions.

  11. Outcome prediction value of determination of cord blood ADM concentrations in neonates with perinatal asphyxia events

    International Nuclear Information System (INIS)

    Zhang Shifa; Zhou Mingxiong; Zhang Xinlu

    2006-01-01

    Objective: To investigate the clinical value of determination of cord blood adrenomedullin (ADM) concentration for predicting development of hypoxic ischemic encephalopathy (HIE) in neonates suffered from perinatal asphyxia. Methods: Cord blood plasma ADM concentrations were measured with RIA in 77 full-ferm neonates with perinatal asphyxia and 30 controls. Results: In the 77 neonates with perinatal asphyxia, 32 developed clinical evidence of HIE within 7 days after birth (HIE group) and 45 didn't (non-HIE group). Cord blood plasma ADM concentrations in the HIE group (160.30 ± 41.3pg/ml) were significantly higher than those in the non-HIE group (112.26 ± 22.90 pg/ml) and controls (102.90 ± 19.43pg/ml). The cord blood plasma ADH concentrations in HIE group were also significantly positively correlated with the severity of the disease (r s = 0. 752, P < 0. 01 ). From our data, taking 117.93pg/ml as cut-off value for diagnosis of HIE would result in a sensitivity of 90.63%, specificity of 80%, and accuracy of 84.42%. Conclusion: High level of ADM in cord blood of neonates with perinatal asphyxia (≥117.93pg/ml) would predict development of HIE with a reasonable accuracy. (authors)

  12. Numerical predictions of the separation of heavy components inside the trace gas concentrator

    International Nuclear Information System (INIS)

    Mo, J.D.

    1995-01-01

    The component with a heavier molecular weight can be separated from the one with a lighter molecular weight in a binary mixture by applying an appropriate pressure gradient. A centrifugal force field effectively generates the required pressure gradient and a favorable flow field along the radial direction in a trace gas concentrator for such an application. This paper presents the numerical predictions of the mass separation inside a trace gas concentrator, which enriches Xenon in air. A Navier-Stokes solver in primitive variables using a pressure based algorithm has been applied to solve for the flow fields. Subsequently, the transport equations with a strong centrifugal field are solved for the mass concentration. This study is the continued effort for the proof-of-concept of centrifugal separation of components with a considerable difference in their molecular weight in a binary mixture. The significant effects of rotational speed, flow field, and the geometrical configuration on the mass separation are presented in this paper

  13. Structural and Function Prediction of Musa acuminata subsp. Malaccensis Protein

    Directory of Open Access Journals (Sweden)

    Anum Munir

    2016-03-01

    Full Text Available Hypothetical proteins (HPs are the proteins whose presence has been anticipated, yet in vivo function has not been built up. Illustrating the structural and functional privileged insights of these HPs might likewise prompt a superior comprehension of the protein-protein associations or networks in diverse types of life. Bananas (Musa acuminata spp., including sweet and cooking types, are giant perennial monocotyledonous herbs of the order Zingiberales, a sister grouped to the all-around considered Poales, which incorporate oats. Bananas are crucial for nourishment security in numerous tropical and subtropical nations and the most prominent organic product in industrialized nations. In the present study, the hypothetical protein of M. acuminata (Banana was chosen for analysis and modeling by distinctive bioinformatics apparatuses and databases. As indicated by primary and secondary structure analysis, XP_009393594.1 is a stable hydrophobic protein containing a noteworthy extent of α-helices; Homology modeling was done utilizing SWISS-MODEL server where the templates identity with XP_009393594.1 protein was less which demonstrated novelty of our protein. Ab initio strategy was conducted to produce its 3D structure. A few evaluations of quality assessment and validation parameters determined the generated protein model as stable with genuinely great quality. Functional analysis was completed by ProtFun 2.2, and KEGG (KAAS, recommended that the hypothetical protein is a transcription factor with cytoplasmic domain as zinc finger. The protein was observed to be vital for translation process, involved in metabolism, signaling and cellular processes, genetic information processing and Zinc ion binding. It is suggested that further test approval would help to anticipate the structures and functions of other uncharacterized proteins of different plants and living being.

  14. Parathyroid hormone related protein concentration in human serum and CSF correlates with age.

    Science.gov (United States)

    Kushnir, Mark M; Peterson, Lisa K; Strathmann, Frederick G

    2018-02-01

    Parathyroid Hormone-Related Protein (PTHrP) is involved in intracellular calcium (Ca) regulation, and has been demonstrated to participate in regulation of Ca in brain cells, activation of neurons, and modulation of pain. However, there are conflicting reports regarding the presence of PTHrP in CSF. PTHrP and Ca were quantified in paired CSF and serum samples using mass spectrometry-based methods. Associations between PTHrP and Ca concentrations with age, sex and concentrations of nine CSF diagnostic markers in a set of 140 paired serum and CSF patient samples were evaluated. The observed median PTHrP concentration in CSF was 51 times higher than in serum; the median concentration of Ca in CSF was 1.8 times lower than in serum. We observed positive correlation between concentrations of PTHrP in CSF and serum (p=0.013). Distribution of PTHrP concentrations in serum was associated with age (p=0.0068) and the concentrations were higher in women. In samples with serum calcium concentrations within the reference intervals (n=118), central 95% distribution of concentrations for Ca-CSF, PTHrP-serum and PTHrP-CSF were 5.4 (4.5-6.1) mg/dL, 1.2 (0.5-2.5) pmol/L, 62 (22-125) pmol/L, respectively. Our data demonstrate that PTHrP is a normal constituent of human CSF with median concentrations 51 fold higher than in serum. Elevated serum PTHrP concentrations were positively correlated with age and significantly higher in women. Our data suggest that CSF could be a significant source of circulating PTHrP. Copyright © 2017 The Canadian Society of Clinical Chemists. Published by Elsevier Inc. All rights reserved.

  15. Stringent DDI-based prediction of H. sapiens-M. tuberculosis H37Rv protein-protein interactions.

    Science.gov (United States)

    Zhou, Hufeng; Rezaei, Javad; Hugo, Willy; Gao, Shangzhi; Jin, Jingjing; Fan, Mengyuan; Yong, Chern-Han; Wozniak, Michal; Wong, Limsoon

    2013-01-01

    H. sapiens-M. tuberculosis H37Rv protein-protein interaction (PPI) data are very important information to illuminate the infection mechanism of M. tuberculosis H37Rv. But current H. sapiens-M. tuberculosis H37Rv PPI data are very scarce. This seriously limits the study of the interaction between this important pathogen and its host H. sapiens. Computational prediction of H. sapiens-M. tuberculosis H37Rv PPIs is an important strategy to fill in the gap. Domain-domain interaction (DDI) based prediction is one of the frequently used computational approaches in predicting both intra-species and inter-species PPIs. However, the performance of DDI-based host-pathogen PPI prediction has been rather limited. We develop a stringent DDI-based prediction approach with emphasis on (i) differences between the specific domain sequences on annotated regions of proteins under the same domain ID and (ii) calculation of the interaction strength of predicted PPIs based on the interacting residues in their interaction interfaces. We compare our stringent DDI-based approach to a conventional DDI-based approach for predicting PPIs based on gold standard intra-species PPIs and coherent informative Gene Ontology terms assessment. The assessment results show that our stringent DDI-based approach achieves much better performance in predicting PPIs than the conventional approach. Using our stringent DDI-based approach, we have predicted a small set of reliable H. sapiens-M. tuberculosis H37Rv PPIs which could be very useful for a variety of related studies. We also analyze the H. sapiens-M. tuberculosis H37Rv PPIs predicted by our stringent DDI-based approach using cellular compartment distribution analysis, functional category enrichment analysis and pathway enrichment analysis. The analyses support the validity of our prediction result. Also, based on an analysis of the H. sapiens-M. tuberculosis H37Rv PPI network predicted by our stringent DDI-based approach, we have discovered some

  16. Analysis and Prediction of Energy Production in Concentrating Photovoltaic (CPV Installations

    Directory of Open Access Journals (Sweden)

    Allen Barnett

    2012-03-01

    Full Text Available A method for the prediction of Energy Production (EP in Concentrating Photovoltaic (CPV installations is examined in this study. It presents a new method that predicts EP by using Global Horizontal Irradiation (GHI and the Photovoltaic Geographical Information System (PVGIS database, instead of Direct Normal Irradiation (DNI data, which are rarely recorded at most locations. EP at four Spanish CPV installations is analyzed: two are based on silicon solar cells and the other two on multi-junction III-V solar cells. The real EP is compared with the predicted EP. Two methods for EP prediction are presented. In the first preliminary method, a monthly Performance Ratio (PR is used as an arbitrary constant value (75% and an estimation of the DNI. The DNI estimation is obtained from GHI measurements and the PVGIS database. In the second method, a lineal model is proposed for the first time in this paper to obtain the predicted EP from the estimated DNI. This lineal model is the regression line that correlates the real monthly EP and the estimated DNI in 2009. This new method implies that the monthly PR is variable. Using the new method, the difference between the predicted and the real EP values is less than 2% for the annual EP and is in the range of 5.6%–16.1% for the monthly EP. The method that uses the variable monthly PR allows the prediction of the EP with reasonable accuracy. It is therefore possible to predict the CPV EP for any location, using only widely available GHI data and the PVGIS database.

  17. ComplexContact: a web server for inter-protein contact prediction using deep learning

    KAUST Repository

    Zeng, Hong; Wang, Sheng; Zhou, Tianming; Zhao, Feifeng; Li, Xiufeng; Wu, Qing; Xu, Jinbo

    2018-01-01

    ComplexContact (http://raptorx2.uchicago.edu/ComplexContact/) is a web server for sequence-based interfacial residue-residue contact prediction of a putative protein complex. Interfacial residue-residue contacts are critical for understanding how proteins form complex and interact at residue level. When receiving a pair of protein sequences, ComplexContact first searches for their sequence homologs and builds two paired multiple sequence alignments (MSA), then it applies co-evolution analysis and a CASP-winning deep learning (DL) method to predict interfacial contacts from paired MSAs and visualizes the prediction as an image. The DL method was originally developed for intra-protein contact prediction and performed the best in CASP12. Our large-scale experimental test further shows that ComplexContact greatly outperforms pure co-evolution methods for inter-protein contact prediction, regardless of the species.

  18. ComplexContact: a web server for inter-protein contact prediction using deep learning

    KAUST Repository

    Zeng, Hong

    2018-05-20

    ComplexContact (http://raptorx2.uchicago.edu/ComplexContact/) is a web server for sequence-based interfacial residue-residue contact prediction of a putative protein complex. Interfacial residue-residue contacts are critical for understanding how proteins form complex and interact at residue level. When receiving a pair of protein sequences, ComplexContact first searches for their sequence homologs and builds two paired multiple sequence alignments (MSA), then it applies co-evolution analysis and a CASP-winning deep learning (DL) method to predict interfacial contacts from paired MSAs and visualizes the prediction as an image. The DL method was originally developed for intra-protein contact prediction and performed the best in CASP12. Our large-scale experimental test further shows that ComplexContact greatly outperforms pure co-evolution methods for inter-protein contact prediction, regardless of the species.

  19. ComplexContact: a web server for inter-protein contact prediction using deep learning.

    Science.gov (United States)

    Zeng, Hong; Wang, Sheng; Zhou, Tianming; Zhao, Feifeng; Li, Xiufeng; Wu, Qing; Xu, Jinbo

    2018-05-22

    ComplexContact (http://raptorx2.uchicago.edu/ComplexContact/) is a web server for sequence-based interfacial residue-residue contact prediction of a putative protein complex. Interfacial residue-residue contacts are critical for understanding how proteins form complex and interact at residue level. When receiving a pair of protein sequences, ComplexContact first searches for their sequence homologs and builds two paired multiple sequence alignments (MSA), then it applies co-evolution analysis and a CASP-winning deep learning (DL) method to predict interfacial contacts from paired MSAs and visualizes the prediction as an image. The DL method was originally developed for intra-protein contact prediction and performed the best in CASP12. Our large-scale experimental test further shows that ComplexContact greatly outperforms pure co-evolution methods for inter-protein contact prediction, regardless of the species.

  20. GRIP: A web-based system for constructing Gold Standard datasets for protein-protein interaction prediction

    Directory of Open Access Journals (Sweden)

    Zheng Huiru

    2009-01-01

    Full Text Available Abstract Background Information about protein interaction networks is fundamental to understanding protein function and cellular processes. Interaction patterns among proteins can suggest new drug targets and aid in the design of new therapeutic interventions. Efforts have been made to map interactions on a proteomic-wide scale using both experimental and computational techniques. Reference datasets that contain known interacting proteins (positive cases and non-interacting proteins (negative cases are essential to support computational prediction and validation of protein-protein interactions. Information on known interacting and non interacting proteins are usually stored within databases. Extraction of these data can be both complex and time consuming. Although, the automatic construction of reference datasets for classification is a useful resource for researchers no public resource currently exists to perform this task. Results GRIP (Gold Reference dataset constructor from Information on Protein complexes is a web-based system that provides researchers with the functionality to create reference datasets for protein-protein interaction prediction in Saccharomyces cerevisiae. Both positive and negative cases for a reference dataset can be extracted, organised and downloaded by the user. GRIP also provides an upload facility whereby users can submit proteins to determine protein complex membership. A search facility is provided where a user can search for protein complex information in Saccharomyces cerevisiae. Conclusion GRIP is developed to retrieve information on protein complex, cellular localisation, and physical and genetic interactions in Saccharomyces cerevisiae. Manual construction of reference datasets can be a time consuming process requiring programming knowledge. GRIP simplifies and speeds up this process by allowing users to automatically construct reference datasets. GRIP is free to access at http://rosalind.infj.ulst.ac.uk/GRIP/.

  1. PIPE: a protein-protein interaction prediction engine based on the re-occurring short polypeptide sequences between known interacting protein pairs

    Directory of Open Access Journals (Sweden)

    Greenblatt Jack

    2006-07-01

    Full Text Available Abstract Background Identification of protein interaction networks has received considerable attention in the post-genomic era. The currently available biochemical approaches used to detect protein-protein interactions are all time and labour intensive. Consequently there is a growing need for the development of computational tools that are capable of effectively identifying such interactions. Results Here we explain the development and implementation of a novel Protein-Protein Interaction Prediction Engine termed PIPE. This tool is capable of predicting protein-protein interactions for any target pair of the yeast Saccharomyces cerevisiae proteins from their primary structure and without the need for any additional information or predictions about the proteins. PIPE showed a sensitivity of 61% for detecting any yeast protein interaction with 89% specificity and an overall accuracy of 75%. This rate of success is comparable to those associated with the most commonly used biochemical techniques. Using PIPE, we identified a novel interaction between YGL227W (vid30 and YMR135C (gid8 yeast proteins. This lead us to the identification of a novel yeast complex that here we term vid30 complex (vid30c. The observed interaction was confirmed by tandem affinity purification (TAP tag, verifying the ability of PIPE to predict novel protein-protein interactions. We then used PIPE analysis to investigate the internal architecture of vid30c. It appeared from PIPE analysis that vid30c may consist of a core and a secondary component. Generation of yeast gene deletion strains combined with TAP tagging analysis indicated that the deletion of a member of the core component interfered with the formation of vid30c, however, deletion of a member of the secondary component had little effect (if any on the formation of vid30c. Also, PIPE can be used to analyse yeast proteins for which TAP tagging fails, thereby allowing us to predict protein interactions that are not

  2. Serum protein concentration in low-dose total body irradiation of normal and malnourished rats

    International Nuclear Information System (INIS)

    Viana, W.C.M.; Lambertz, D.; Borges, E.S.; Neto, A.M.O.; Lambertz, K.M.F.T.; Amaral, A.

    2016-01-01

    Among the radiotherapeutics' modalities, total body irradiation (TBI) is used as treatment for certain hematological, oncological and immunological diseases. The aim of this study was to evaluate the long-term effects of low-dose TBI on plasma concentration of total protein and albumin using prematurely and undernourished rats as animal model. For this, four groups with 9 animals each were formed: Normal nourished (N); Malnourished (M); Irradiated Normal nourished (IN); Irradiated Malnourished (IM). At the age of 28 days, rats of the IN and IM groups underwent total body gamma irradiation with a source of cobalt-60. Total protein and Albumin in the blood serum was quantified by colorimetry. This research indicates that procedures involving low-dose total body irradiation in children have repercussions in the reduction in body-mass as well as in the plasma levels of total protein and albumin. Our findings reinforce the periodic monitoring of total serum protein and albumin levels as an important tool in long-term follow-up of pediatric patients in treatments associated to total body irradiation. - Highlights: • Low-dose total body irradiation (TBI) in children have repercussions in their body-mass. • Long-term total protein and albumin levels are affected by TBI. • The monitoring of total protein and albumin levels are useful in the follow-up of TBI pediatric patients.

  3. InterMap3D: predicting and visualizing co-evolving protein residues

    DEFF Research Database (Denmark)

    Oliveira, Rodrigo Gouveia; Roque, francisco jose sousa simôes almeida; Wernersson, Rasmus

    2009-01-01

    InterMap3D predicts co-evolving protein residues and plots them on the 3D protein structure. Starting with a single protein sequence, InterMap3D automatically finds a set of homologous sequences, generates an alignment and fetches the most similar 3D structure from the Protein Data Bank (PDB......). It can also accept a user-generated alignment. Based on the alignment, co-evolving residues are then predicted using three different methods: Row and Column Weighing of Mutual Information, Mutual Information/Entropy and Dependency. Finally, InterMap3D generates high-quality images of the protein...

  4. Binding Ligand Prediction for Proteins Using Partial Matching of Local Surface Patches

    Directory of Open Access Journals (Sweden)

    Lee Sael

    2010-12-01

    Full Text Available Functional elucidation of uncharacterized protein structures is an important task in bioinformatics. We report our new approach for structure-based function prediction which captures local surface features of ligand binding pockets. Function of proteins, specifically, binding ligands of proteins, can be predicted by finding similar local surface regions of known proteins. To enable partial comparison of binding sites in proteins, a weighted bipartite matching algorithm is used to match pairs of surface patches. The surface patches are encoded with the 3D Zernike descriptors. Unlike the existing methods which compare global characteristics of the protein fold or the global pocket shape, the local surface patch method can find functional similarity between non-homologous proteins and binding pockets for flexible ligand molecules. The proposed method improves prediction results over global pocket shape-based method which was previously developed by our group.

  5. Binding ligand prediction for proteins using partial matching of local surface patches.

    Science.gov (United States)

    Sael, Lee; Kihara, Daisuke

    2010-01-01

    Functional elucidation of uncharacterized protein structures is an important task in bioinformatics. We report our new approach for structure-based function prediction which captures local surface features of ligand binding pockets. Function of proteins, specifically, binding ligands of proteins, can be predicted by finding similar local surface regions of known proteins. To enable partial comparison of binding sites in proteins, a weighted bipartite matching algorithm is used to match pairs of surface patches. The surface patches are encoded with the 3D Zernike descriptors. Unlike the existing methods which compare global characteristics of the protein fold or the global pocket shape, the local surface patch method can find functional similarity between non-homologous proteins and binding pockets for flexible ligand molecules. The proposed method improves prediction results over global pocket shape-based method which was previously developed by our group.

  6. [Application of predictive model to estimate concentrations of chemical substances in the work environment].

    Science.gov (United States)

    Kupczewska-Dobecka, Małgorzata; Czerczak, Sławomir; Jakubowski, Marek; Maciaszek, Piotr; Janasik, Beata

    2010-01-01

    Based on the Estimation and Assessment of Substance Exposure (EASE) predictive model implemented into the European Union System for the Evaluation of Substances (EUSES 2.1.), the exposure to three chosen organic solvents: toluene, ethyl acetate and acetone was estimated and compared with the results of measurements in workplaces. Prior to validation, the EASE model was pretested using three exposure scenarios. The scenarios differed in the decision tree of pattern of use. Five substances were chosen for the test: 1,4-dioxane tert-methyl-butyl ether, diethylamine, 1,1,1-trichloroethane and bisphenol A. After testing the EASE model, the next step was the validation by estimating the exposure level and comparing it with the results of measurements in the workplace. We used the results of measurements of toluene, ethyl acetate and acetone concentrations in the work environment of a paint and lacquer factory, a shoe factory and a refinery. Three types of exposure scenarios, adaptable to the description of working conditions were chosen to estimate inhalation exposure. Comparison of calculated exposure to toluene, ethyl acetate and acetone with measurements in workplaces showed that model predictions are comparable with the measurement results. Only for low concentration ranges, the measured concentrations were higher than those predicted. EASE is a clear, consistent system, which can be successfully used as an additional component of inhalation exposure estimation. If the measurement data are available, they should be preferred to values estimated from models. In addition to inhalation exposure estimation, the EASE model makes it possible not only to assess exposure-related risk but also to predict workers' dermal exposure.

  7. Agarose gel electrophoresis of cerebrospinal fluid proteins of dogs after sample concentration using a membrane microconcentrator technique.

    Science.gov (United States)

    Gama, Fernanda Gomes Velasque; Santana, Aureo Evangelista; Filho, Eugênio de Campos; Nogueira, Cláudia Aparecida da Silva

    2007-03-01

    Cerebrospinal fluid (CSF) is produced in the cerebral ventricles through ultrafiltration of plasma and active transport mechanisms. Evaluation of proteins in CSF may provide important information about the production of immunoglobulins within the central nervous system as well as possible disturbances in the blood-brain barrier. The objective of this study was to measure the concentration and fractions of protein in CSF samples using a membrane microconcentrator technique followed by electrophoresis, and to compare the protein fractions obtained with those in serum. CSF samples from 3 healthy dogs and 3 dogs with canine distemper virus infection were concentrated using a membrane microconcentrator having a 0.5 to 30,000 d nominal molecular weight limit (Ultrafree, Millipore, Billerica, MA, USA). Protein concentration was determined before and after concentration. Agarose gel electrophoresis was done on concentrated CSF samples, serum, and serial dilutions of one of the CSF samples. Electrophoretic bands were clearly identified in densitometer tracings in CSF samples with protein concentrations as low as 1.3 g/dL. The higher CSF protein concentration in dogs with distemper was mainly the result of increased albumin concentration. The microconcentrating method used in this study enables characterization of the main protein fractions in CSF by routine electrophoresis and may be useful for interpreting the underlying cause of changes in CSF protein concentrations.

  8. Measurement of the concentration of plasmatic cortisol by competition to the binding protein

    International Nuclear Information System (INIS)

    Okada, H.; Tambascia, M.A.; Wajchenberg, B.L.; Pieroni, R.R.

    1977-01-01

    The concentration of plasmatic cortisol was measured by competition to the binding protein (transcortin), after extracting the samples with dicloromethane. It is a suitable method for clinical routine, 100μl of plasma being used in each analysis. The normal mean +- standard error mean in 8:00 a.m. fasting subjects was 13,62 +- 5,43 μl/100 ml of plasma [pt

  9. Modified procedure for rapid labelling of low concentrations of bioactive proteins with indium-111

    Energy Technology Data Exchange (ETDEWEB)

    Zoghbi, S S; Neumann, R D; Gottschalk, A

    1985-01-01

    The authors describe the conjugation of DTPA to 100-500 g of protein in concentrations of 0.6-1.0 mg mL utilizing the mixed anhydride method. Free DTPA is removed by minicolumn gel filtration and centrifugation with minimal protein dilution. Radiolabelling process can be monitored by instant thin layer chromatography. Any radiochemical impurity detected can be eliminated either by additional minicolumn filtration of further chelation with more conjugated protein. In citrate buffer at pH 6 with minicolumn gel chromatography the authors prepared In-DTPA-D3 (3.0 Ci g) monoclonal antibody and used it to image hepatocarcinoma in guinea pigs. 13 references, 5 figures, 2 tables.

  10. Predicting protein subcellular locations using hierarchical ensemble of Bayesian classifiers based on Markov chains

    Directory of Open Access Journals (Sweden)

    Eils Roland

    2006-06-01

    Full Text Available Abstract Background The subcellular location of a protein is closely related to its function. It would be worthwhile to develop a method to predict the subcellular location for a given protein when only the amino acid sequence of the protein is known. Although many efforts have been made to predict subcellular location from sequence information only, there is the need for further research to improve the accuracy of prediction. Results A novel method called HensBC is introduced to predict protein subcellular location. HensBC is a recursive algorithm which constructs a hierarchical ensemble of classifiers. The classifiers used are Bayesian classifiers based on Markov chain models. We tested our method on six various datasets; among them are Gram-negative bacteria dataset, data for discriminating outer membrane proteins and apoptosis proteins dataset. We observed that our method can predict the subcellular location with high accuracy. Another advantage of the proposed method is that it can improve the accuracy of the prediction of some classes with few sequences in training and is therefore useful for datasets with imbalanced distribution of classes. Conclusion This study introduces an algorithm which uses only the primary sequence of a protein to predict its subcellular location. The proposed recursive scheme represents an interesting methodology for learning and combining classifiers. The method is computationally efficient and competitive with the previously reported approaches in terms of prediction accuracies as empirical results indicate. The code for the software is available upon request.

  11. Effect of whey protein concentrate on texture of fat-free desserts: sensory and instrumental measurements

    Directory of Open Access Journals (Sweden)

    Márcia Cristina Teixeira Ribeiro Vidigal

    2012-06-01

    Full Text Available It is important to understand how changes in the product formulation can modify its characteristics. Thus, the objective of this study was to investigate the effect of whey protein concentrate (WPC on the texture of fat-free dairy desserts. The correlation between instrumental and sensory measurements was also investigated. Four formulations were prepared with different WPC concentrations (0, 1.5, 3.0, and 4.5 wt. (% and were evaluated using the texture profile analysis (TPA and rheology. Thickness was evaluated by nine trained panelists. Formulations containing WPC showed higher firmness, elasticity, chewiness, and gumminess and clearly differed from the control as indicated by principal component analysis (PCA. Flow behavior was characterized as time-dependent and pseudoplastic. Formulation with 4.5% WPC at 10 °C showed the highest thixotropic behavior. Experimental data were fitted to Herschel-Bulkley model. The addition of WPC contributed to the texture of the fat-free dairy dessert. The yield stress, apparent viscosity, and perceived thickness in the dairy desserts increased with WPC concentration. The presence of WPC promotes the formation of a stronger gel structure as a result of protein-protein interactions. The correlation between instrumental parameters and thickness provided practical results for food industries.

  12. PerturbationAnalyzer: a tool for investigating the effects of concentration perturbation on protein interaction networks.

    Science.gov (United States)

    Li, Fei; Li, Peng; Xu, Wenjian; Peng, Yuxing; Bo, Xiaochen; Wang, Shengqi

    2010-01-15

    The propagation of perturbations in protein concentration through a protein interaction network (PIN) can shed light on network dynamics and function. In order to facilitate this type of study, PerturbationAnalyzer, which is an open source plugin for Cytoscape, has been developed. PerturbationAnalyzer can be used in manual mode for simulating user-defined perturbations, as well as in batch mode for evaluating network robustness and identifying significant proteins that cause large propagation effects in the PINs when their concentrations are perturbed. Results from PerturbationAnalyzer can be represented in an intuitive and customizable way and can also be exported for further exploration. PerturbationAnalyzer has great potential in mining the design principles of protein networks, and may be a useful tool for identifying drug targets. PerturbationAnalyzer can be accessed from the Cytoscape web site http://www.cytoscape.org/plugins/index.php or http://biotech.bmi.ac.cn/PerturbationAnalyzer. Supplementary data are available at Bioinformatics online.

  13. Chemical Utilization of Albizia lebbeck Leaves for Developing Protein Concentrates as a Dietary Supplement.

    Science.gov (United States)

    Khan, Lutful Haque; Varshney, V K

    2017-08-17

    In search of nonconventional sources of protein to combat widespread malnutrition, the possibility of developing a protein concentrate as an alternative dietary supplement from abundantly available yet poorly valorized leaves of Albizia lebbeck (siris) was examined. A process for recovery of leaf protein concentrate (LPC) from these leaves was optimized and applied for isolation of LPCs from lower, middle, and upper canopies of the tree. The optimized conditions (leaves to water 1:9, coagulation at pH 4.0 using 1 N citric acid at 90°C for 11 minutes) afforded LPCs containing protein 37.15%, 37.57%, and 37.76% in 5.99%, 5.97%, and 6.07% yield, respectively. The proximate nutritional composition, pigments, minerals, in vitro digestibility, and antinutritional factors of these LPCs were determined. Analysis of variance of these data revealed no significant difference with respect to canopy. Use of Albizia lebbeck leaves for development of LPC as a food/feed supplement was revealed.

  14. Using the Relevance Vector Machine Model Combined with Local Phase Quantization to Predict Protein-Protein Interactions from Protein Sequences

    Directory of Open Access Journals (Sweden)

    Ji-Yong An

    2016-01-01

    Full Text Available We propose a novel computational method known as RVM-LPQ that combines the Relevance Vector Machine (RVM model and Local Phase Quantization (LPQ to predict PPIs from protein sequences. The main improvements are the results of representing protein sequences using the LPQ feature representation on a Position Specific Scoring Matrix (PSSM, reducing the influence of noise using a Principal Component Analysis (PCA, and using a Relevance Vector Machine (RVM based classifier. We perform 5-fold cross-validation experiments on Yeast and Human datasets, and we achieve very high accuracies of 92.65% and 97.62%, respectively, which is significantly better than previous works. To further evaluate the proposed method, we compare it with the state-of-the-art support vector machine (SVM classifier on the Yeast dataset. The experimental results demonstrate that our RVM-LPQ method is obviously better than the SVM-based method. The promising experimental results show the efficiency and simplicity of the proposed method, which can be an automatic decision support tool for future proteomics research.

  15. The effects of GH and hormone replacement therapy on serum concentrations of mannan-binding lectin, surfactant protein D and vitamin D binding protein in Turner syndrome

    DEFF Research Database (Denmark)

    Gravholt, Claus Højbjerg; Leth-Larsen, Rikke; Lauridsen, Anna Lis

    2004-01-01

    function. In the present study we examined whether GH or hormone replacement therapy (HRT) in Turner syndrome (TS) influence the serum concentrations of MBL and two other proteins partaking in the innate immune defence, surfactant protein D (SP-D) and vitamin D binding protein (DBP). DESIGN: Study 1...

  16. Type 2 diabetes mellitus is associated with differential effects on plasma cholesteryl ester transfer protein and phospholipid transfer protein activities and concentrations

    NARCIS (Netherlands)

    Dullaart, RPF; De Vries, R; Scheek, L; Borggreve, SE; Van Gent, T; Dallinga-Thie, GM; Ito, M; Nagano, M; Sluiter, WJ; Hattori, H; Van Tol, A

    Background: Human plasma contains two lipid transfer proteins, cholesteryl ester transfer protein (CETP) and phospholipid transfer protein (PLTP), which are crucial in reverse cholesterol transport. Methods: Plasma CETP and PLTP activity levels and concentrations in 16 type 2 diabetic patients and

  17. Predicting protein-binding RNA nucleotides with consideration of binding partners.

    Science.gov (United States)

    Tuvshinjargal, Narankhuu; Lee, Wook; Park, Byungkyu; Han, Kyungsook

    2015-06-01

    In recent years several computational methods have been developed to predict RNA-binding sites in protein. Most of these methods do not consider interacting partners of a protein, so they predict the same RNA-binding sites for a given protein sequence even if the protein binds to different RNAs. Unlike the problem of predicting RNA-binding sites in protein, the problem of predicting protein-binding sites in RNA has received little attention mainly because it is much more difficult and shows a lower accuracy on average. In our previous study, we developed a method that predicts protein-binding nucleotides from an RNA sequence. In an effort to improve the prediction accuracy and usefulness of the previous method, we developed a new method that uses both RNA and protein sequence data. In this study, we identified effective features of RNA and protein molecules and developed a new support vector machine (SVM) model to predict protein-binding nucleotides from RNA and protein sequence data. The new model that used both protein and RNA sequence data achieved a sensitivity of 86.5%, a specificity of 86.2%, a positive predictive value (PPV) of 72.6%, a negative predictive value (NPV) of 93.8% and Matthews correlation coefficient (MCC) of 0.69 in a 10-fold cross validation; it achieved a sensitivity of 58.8%, a specificity of 87.4%, a PPV of 65.1%, a NPV of 84.2% and MCC of 0.48 in independent testing. For comparative purpose, we built another prediction model that used RNA sequence data alone and ran it on the same dataset. In a 10 fold-cross validation it achieved a sensitivity of 85.7%, a specificity of 80.5%, a PPV of 67.7%, a NPV of 92.2% and MCC of 0.63; in independent testing it achieved a sensitivity of 67.7%, a specificity of 78.8%, a PPV of 57.6%, a NPV of 85.2% and MCC of 0.45. In both cross-validations and independent testing, the new model that used both RNA and protein sequences showed a better performance than the model that used RNA sequence data alone in

  18. Use of Artificial Neural Network Models to Predict Indicator Organism Concentrations in an Urban Watershed

    Science.gov (United States)

    Mas, D. M.; Ahlfeld, D. P.

    2004-05-01

    Forecasting stream water quality is important for numerous aspects of resource protection and management. Fecal coliform and enteroccocus are primary indicator organisms used to assess potential pathogen contamination. Consequently, modeling the occurrence and concentration of fecal coliform and enterococcus is an important tool in watershed management. In addition, analyzing the relationship between model input and predicted indicator organisms is useful for elucidating possible sources of contamination and mechanisms of transport. While many process-based, statistical, and empirical models exist for water quality prediction, artificial neural network (ANN) models are increasingly being used for forecasting of water resources variables because ANNs are often capable of modeling complex systems for which behavioral rules are either unknown or difficult to simulate. The performance of ANNs compared to more established modeling approaches such as multiple linear regression (MLR) remains an importance research question. Data collected the U.S. Geological Survey in the lower Charles River in Massachusetts, USA in 1999-2000 was examined to determine correlation between various water quality constituents and indicator organisms and to explore the relationship between rainfall characteristics and indicator organism concentrations. Using the results of the statistical analysis to guide the selection of explanatory variables, MLR was performed to develop predictive equations for wet weather and dry weather conditions. The results show that the best-performing predictor variables are generally consistent for both indicator organisms considered. In addition, the regression equations show increasing indicator organism concentrations as a function of suspended sediment concentrations and length of time since last precipitation event, suggesting accumulation and wash off as a key mechanism of pathogen transport under wet weather conditions. This research also presents the

  19. Predicting co-complexed protein pairs using genomic and proteomic data integration

    Directory of Open Access Journals (Sweden)

    King Oliver D

    2004-04-01

    Full Text Available Abstract Background Identifying all protein-protein interactions in an organism is a major objective of proteomics. A related goal is to know which protein pairs are present in the same protein complex. High-throughput methods such as yeast two-hybrid (Y2H and affinity purification coupled with mass spectrometry (APMS have been used to detect interacting proteins on a genomic scale. However, both Y2H and APMS methods have substantial false-positive rates. Aside from high-throughput interaction screens, other gene- or protein-pair characteristics may also be informative of physical interaction. Therefore it is desirable to integrate multiple datasets and utilize their different predictive value for more accurate prediction of co-complexed relationship. Results Using a supervised machine learning approach – probabilistic decision tree, we integrated high-throughput protein interaction datasets and other gene- and protein-pair characteristics to predict co-complexed pairs (CCP of proteins. Our predictions proved more sensitive and specific than predictions based on Y2H or APMS methods alone or in combination. Among the top predictions not annotated as CCPs in our reference set (obtained from the MIPS complex catalogue, a significant fraction was found to physically interact according to a separate database (YPD, Yeast Proteome Database, and the remaining predictions may potentially represent unknown CCPs. Conclusions We demonstrated that the probabilistic decision tree approach can be successfully used to predict co-complexed protein (CCP pairs from other characteristics. Our top-scoring CCP predictions provide testable hypotheses for experimental validation.

  20. Optimization of expression JTAT protein with emphasis on transformation efficiency and IPTG concentration

    Directory of Open Access Journals (Sweden)

    Endang Tri Margawati

    2017-12-01

    Full Text Available One of small accessory genes between pol and env is tat gene encoding TAT protein. This research was aimed to optimize the expression of Jembrana TAT (JTAT protein with preparing Escherichia coli (E. coli in advance using adopted methods of M1 (MgCl2 + CaCl2 and M2 (CaCl2 + Glycerol. The best transformation efficiency resulting from a better transformation method was used to subsequent expression of JTAT protein. A synthetic tat gene encoding protein JTAT was previously cloned into pBT-hisC. Concentration of 200; 400; 600 µM IPTG was induced to a small volume culture (200 ml; OD600 = 4, incubated for 3 h. Pellets were harvested by centrifugation (4000 rpm; 4 °C; 15 min. Buffer B (10 mM Immidazole was added into pellets, lysed by freeze-thaw followed by sonication. Supernatant was collected by centrifugation (10,000 rpm; 4 °C; 20 min and purified using Ni-NTA Agarose resin, released by elution buffer (E containing 400 mM Immidazole to collect purified protein twice (E1, E2. The protein was characterized by SDS-PAGE and Western Blot (WB, quantified (at λ595 nm with BSA standard method in prior. The result showed that transformation efficiency was better in M2 (2.53 × 106 than M1 (3.10 × 105. The JTAT protein was expressed at a right size of 11.8 kDa. Concentration of 200 µM IPTG produced a significantly better protein yield (1.500 ± 0.089 mg/ml; P < 0.05 than 600 µM IPTG (0.896 ± 0.052 mg/ml and not different to 400 µM IPTG (1.298 ± 0.080 mg/ml. This research indicated that transformation efficiency needs to be taken account in prior of optimization of the protein expression.

  1. Examination of the uncertainty in air concentration predictions using Hanford field data

    International Nuclear Information System (INIS)

    Miller, C.W.; Fields, D.E.; Cotter, S.J.

    1986-10-01

    The accuracy of an environmental transport model is best determined by comparing model predictions with environmental measurements made under conditions similar to those assumed by the model, a process commonly referred to as model validation. Over the past several years, we have done a variety of validation studies with the popular Gaussian plume atmospheric dispersion model using data from tests conducted on the Hanford reservation. Data for short-term releases of small particles for release heights of 2 m, 56 m, and 111 m have been used. Up to six different sets of atmospheric dispersion parameters and three different atmospheric stability class specification schemes have been examined. Overall, dispersion parameters based on measurements made near Juelich, West Germany, give the best comparisons between observed and predicted air concentrations. The commonly-used vertical temperature gradient method for determining atmospheric stability class consistently gives poor results. The accuracy of air concentration predictions improves when dry deposition processes are included in the model. Further validation studies using various Hanford data sets are planned

  2. Prediction of endoplasmic reticulum resident proteins using fragmented amino acid composition and support vector machine

    Directory of Open Access Journals (Sweden)

    Ravindra Kumar

    2017-09-01

    Full Text Available Background The endoplasmic reticulum plays an important role in many cellular processes, which includes protein synthesis, folding and post-translational processing of newly synthesized proteins. It is also the site for quality control of misfolded proteins and entry point of extracellular proteins to the secretory pathway. Hence at any given point of time, endoplasmic reticulum contains two different cohorts of proteins, (i proteins involved in endoplasmic reticulum-specific function, which reside in the lumen of the endoplasmic reticulum, called as endoplasmic reticulum resident proteins and (ii proteins which are in process of moving to the extracellular space. Thus, endoplasmic reticulum resident proteins must somehow be distinguished from newly synthesized secretory proteins, which pass through the endoplasmic reticulum on their way out of the cell. Approximately only 50% of the proteins used in this study as training data had endoplasmic reticulum retention signal, which shows that these signals are not essentially present in all endoplasmic reticulum resident proteins. This also strongly indicates the role of additional factors in retention of endoplasmic reticulum-specific proteins inside the endoplasmic reticulum. Methods This is a support vector machine based method, where we had used different forms of protein features as inputs for support vector machine to develop the prediction models. During training leave-one-out approach of cross-validation was used. Maximum performance was obtained with a combination of amino acid compositions of different part of proteins. Results In this study, we have reported a novel support vector machine based method for predicting endoplasmic reticulum resident proteins, named as ERPred. During training we achieved a maximum accuracy of 81.42% with leave-one-out approach of cross-validation. When evaluated on independent dataset, ERPred did prediction with sensitivity of 72.31% and specificity of 83

  3. Interfacial composition and stability of emulsions made with mixtures of commercial sodium caseinate and whey protein concentrate.

    Science.gov (United States)

    Ye, Aiqian

    2008-10-15

    The interfacial composition and the stability of oil-in-water emulsion droplets (30% soya oil, pH 7.0) made with mixtures of sodium caseinate and whey protein concentrate (WPC) (1:1 by protein weight) at various total protein concentrations were examined. The average volume-surface diameter (d32) and the total surface protein concentration of emulsion droplets were similar to those of emulsions made with both sodium caseinate alone and WPC alone. Whey proteins were adsorbed in preference to caseins at low protein concentrations (caseins were adsorbed in preference to whey proteins at high protein concentrations. The creaming stability of the emulsions decreased markedly as the total protein concentration of the system was increased above 2% (sodium caseinate >1%). This was attributed to depletion flocculation caused by the sodium caseinate in these emulsions. Whey proteins did not retard this instability in the emulsions made with mixtures of sodium caseinate and WPC. Copyright © 2008 Elsevier Ltd. All rights reserved.

  4. QSARs for Plasma Protein Binding: Source Data and Predictions

    Data.gov (United States)

    U.S. Environmental Protection Agency — The dataset has all of the information used to create and evaluate 3 independent QSAR models for the fraction of a chemical unbound by plasma protein (Fub) for...

  5. A Web server for predicting proteins involved in pluripotent network

    Indian Academy of Sciences (India)

    2016-11-04

    Nov 4, 2016 ... which are important in pluripotency from the existing knowledge about pluripotent ... proteins, we took 117 genes with gene ontology term developmental ... space to find a hyperplane which maximizes the margin between two ...

  6. An empirical model for predicting urban roadside nitrogen dioxide concentrations in the UK

    International Nuclear Information System (INIS)

    Stedman, J.R.; Goodwin, J.W.L.; King, K.; Murrells, T.P.; Bush, T.J.

    2001-01-01

    significantly higher than in background locations. 21% of urban major road links are expected to have roadside NO 2 greater than or equal to 40μgm -3 in 2005 for our illustrative emissions scenario. The continuing downward trend in traffic emissions is likely to further reduce the number of links exceeding this value by 2009, with about 6% of urban major road links predicted to have concentrations higher than 40μgm -3 . The majority of these links are in the London area. The remaining links are generally confined to the most heavily trafficked roads in other big cities. (Author)

  7. STUDIES OF METHOD FOR DETERMINING THE PROTEIN CONCENTRATION OF "MALEIN PPD" BY THE KJELDAHL METHOD

    Directory of Open Access Journals (Sweden)

    Ciuca, V

    2017-06-01

    Full Text Available Glanders is a contagious and fatal disease of horses, donkeys, and mules, caused by infection with the bacterium Burkholderia mallei. The pathogen causes nodules and ulcerations in the upper respiratory tract and lungs. Glanders is transmissible to humans by direct contact with diseased animals or with infected or contaminated material. In the untreated acute disease, the mortality rate can reach 95% within 3 weeks Malein PPD - the diagnostic product contain max 2mg/ml Burkholderia mallei. The amount of protein in the biological product "Malein PPD" is measured as nitrogen from protein molecule, applying the Kjeldahl (method determination of nitrogen by sulphuric acid digestion. The validation study aims to demonstrate the determination of the protein of the Malein PPD, by sulphuric acid digestion, it is an appropriate analytical method, reproducible and meets the quality requirements of diagnostic reagents. The paper establishes the performance characteristics of the method considered and identify the factors that influence these characteristics. The method for determining the concentration of protein, by the Kjeldahl method is considered valid if the results obtained for each validation parameter are within the admissibility criteria.The validation procedure includes details on protocol working to determine the protein of the Malein PPD, validation criteria, experimental results, mathematical calculations.

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

    Science.gov (United States)

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

    2017-11-01

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

  9. High concentrations of protein test substances may have non-toxic effects on Daphnia magna: implications for regulatory study designs and ecological risk assessments for GM crops.

    Science.gov (United States)

    Raybould, Alan;