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.

Prediction of Protein Configurational Entropy (Popcoen).

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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/ .

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.

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.

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...

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.

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

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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.

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

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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.

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 ...

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

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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.

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

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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

RHEOLOGY OF CHICKPEA PROTEIN CONCENTRATE DISPERSIONS

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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.

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

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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.

CMAQ predicted concentration files

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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....

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.

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...

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

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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...

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

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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

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...

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

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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

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

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

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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.

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

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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.

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

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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

Computational prediction of protein hot spot residues.

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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.

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

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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.

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.

Prediction and characterization of protein-protein interaction networks in swine

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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/.

Refining intra-protein contact prediction by graph analysis

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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.

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.

A domain-based approach to predict protein-protein interactions

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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

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

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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.

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...

Deep learning methods for protein torsion angle prediction.

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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.

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

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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.

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

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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.

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, ...

CHEMICAL COMPOSITION AND FUNCTIONAL PROPERTIES OF RICE PROTEIN CONCENTRATES

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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.

Topology of membrane proteins-predictions, limitations and variations.

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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.

Text mining improves prediction of protein functional sites.

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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.

Text Mining Improves Prediction of Protein Functional Sites

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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

Protein subcellular localization prediction using artificial intelligence technology.

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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

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

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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

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

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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

Flow-covariate prediction of stream pesticide concentrations.

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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.

Predicting and validating protein interactions using network structure.

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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.

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

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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.

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

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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.

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 ...

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

Construction of ontology augmented networks for protein complex prediction.

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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.

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

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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.

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.

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.

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

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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.

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

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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.

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

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.

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.

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

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

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.

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

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

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

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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

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...

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

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.

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.

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

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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.

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.

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.

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

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.

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.

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.

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

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

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 ...

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)

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.

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

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.

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...

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 ...

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.

An overview of the prediction of protein DNA-binding sites.

Science.gov (United States)

Si, Jingna; Zhao, Rui; Wu, Rongling

2015-03-06

Interactions between proteins and DNA play an important role in many essential biological processes such as DNA replication, transcription, splicing, and repair. The identification of amino acid residues involved in DNA-binding sites is critical for understanding the mechanism of these biological activities. In the last decade, numerous computational approaches have been developed to predict protein DNA-binding sites based on protein sequence and/or structural information, which play an important role in complementing experimental strategies. At this time, approaches can be divided into three categories: sequence-based DNA-binding site prediction, structure-based DNA-binding site prediction, and homology modeling and threading. In this article, we review existing research on computational methods to predict protein DNA-binding sites, which includes data sets, various residue sequence/structural features, machine learning methods for comparison and selection, evaluation methods, performance comparison of different tools, and future directions in protein DNA-binding site prediction. In particular, we detail the meta-analysis of protein DNA-binding sites. We also propose specific implications that are likely to result in novel prediction methods, increased performance, or practical applications.

Mapping monomeric threading to protein-protein structure prediction.

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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.

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....

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

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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.

Stringent homology-based prediction of H. sapiens-M. tuberculosis H37Rv protein-protein interactions.

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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

PIPE: a protein-protein interaction prediction engine based on the re-occurring short polypeptide sequences between known interacting protein pairs

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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

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.

Bioinformatic Prediction of WSSV-Host Protein-Protein Interaction

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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.

Predicting co-complexed protein pairs using genomic and proteomic data integration

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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.

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

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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.

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 ...

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....

Dynamics and Predictive Potential of Antibodies against Insect-Derived Recombinant Leishmania infantum Proteins during Chemotherapy of Naturally Infected Dogs

Science.gov (United States)

Todolí, Felicitat; Galindo, Inmaculada; Gómez-Sebastián, Silvia; Pérez-Filgueira, Mariano; Escribano, José M.; Alberola, Jordi; Rodríguez-Cortés, Alhelí

2010-01-01

A predictive marker for the success treatment of canine leishmaniasis is required for the application of a more rational therapy protocol, which must improve the probability of cure and reduce Leishmania resistance to drugs. We investigated the dynamics and predictive value of antibodies against insect-derived recombinant L. infantum proteins rKMPII and rTRYP by using an enzyme-linked immunosorbent assay with retrospective serum samples from 36 dogs during treatment of canine leishmaniasis. In the entire group of dogs, concentrations of antibodies against rKMPII and rTRYP significantly decreased earlier than concentrations of antibodies against crude total Leishmania antigen (one versus six months), which suggested that the dynamics of antibodies against recombinant proteins may be useful for assessing clinical improvement after treatment. Interestingly, decreases in antibody concentrations against rKMPII occurred earlier in disease-free dogs than in dogs that remain clinically ill one year after beginning of treatment, which suggested that these antibodies may be useful for predicting disease-free survival one year after the beginning of therapy against canine leishmaniasis. PMID:20439957

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.

An Overview of the Prediction of Protein DNA-Binding Sites

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Jingna Si

2015-03-01

Full Text Available Interactions between proteins and DNA play an important role in many essential biological processes such as DNA replication, transcription, splicing, and repair. The identification of amino acid residues involved in DNA-binding sites is critical for understanding the mechanism of these biological activities. In the last decade, numerous computational approaches have been developed to predict protein DNA-binding sites based on protein sequence and/or structural information, which play an important role in complementing experimental strategies. At this time, approaches can be divided into three categories: sequence-based DNA-binding site prediction, structure-based DNA-binding site prediction, and homology modeling and threading. In this article, we review existing research on computational methods to predict protein DNA-binding sites, which includes data sets, various residue sequence/structural features, machine learning methods for comparison and selection, evaluation methods, performance comparison of different tools, and future directions in protein DNA-binding site prediction. In particular, we detail the meta-analysis of protein DNA-binding sites. We also propose specific implications that are likely to result in novel prediction methods, increased performance, or practical applications.

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

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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.

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.

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

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

2015-06-15

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

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.

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

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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.

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...

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

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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.

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

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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

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.

Improving N-terminal protein annotation of Plasmodium species based on signal peptide prediction of orthologous proteins

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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

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

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Shatkay, Hagit

2015-01-01

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

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.

SitesIdentify: a protein functional site prediction tool

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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/

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

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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.

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

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

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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.

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

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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.

Predictability Analysis of PM10 Concentrations in Budapest

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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.

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....

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

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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.

Preclinical models used for immunogenicity prediction of therapeutic proteins.

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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.

Predicting turns in proteins with a unified model.

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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.

Prediction and Dissection of Protein-RNA Interactions by Molecular Descriptors.

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Liu, Zhi-Ping; Chen, Luonan

2016-01-01

Protein-RNA interactions play crucial roles in numerous biological processes. However, detecting the interactions and binding sites between protein and RNA by traditional experiments is still time consuming and labor costing. Thus, it is of importance to develop bioinformatics methods for predicting protein-RNA interactions and binding sites. Accurate prediction of protein-RNA interactions and recognitions will highly benefit to decipher the interaction mechanisms between protein and RNA, as well as to improve the RNA-related protein engineering and drug design. In this work, we summarize the current bioinformatics strategies of predicting protein-RNA interactions and dissecting protein-RNA interaction mechanisms from local structure binding motifs. In particular, we focus on the feature-based machine learning methods, in which the molecular descriptors of protein and RNA are extracted and integrated as feature vectors of representing the interaction events and recognition residues. In addition, the available methods are classified and compared comprehensively. The molecular descriptors are expected to elucidate the binding mechanisms of protein-RNA interaction and reveal the functional implications from structural complementary perspective.

Scoring protein relationships in functional interaction networks predicted from sequence data.

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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.

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

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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.

DeepLoc: prediction of protein subcellular localization using deep learning

DEFF Research Database (Denmark)

Almagro Armenteros, Jose Juan; Sønderby, Casper Kaae; Sønderby, Søren Kaae

2017-01-01

The prediction of eukaryotic protein subcellular localization is a well-studied topic in bioinformatics due to its relevance in proteomics research. Many machine learning methods have been successfully applied in this task, but in most of them, predictions rely on annotation of homologues from...... knowledge databases. For novel proteins where no annotated homologues exist, and for predicting the effects of sequence variants, it is desirable to have methods for predicting protein properties from sequence information only. Here, we present a prediction algorithm using deep neural networks to predict...... current state-of-the-art algorithms, including those relying on homology information. The method is available as a web server at http://www.cbs.dtu.dk/services/DeepLoc . Example code is available at https://github.com/JJAlmagro/subcellular_localization . The dataset is available at http...

Application of XGBoost algorithm in hourly PM2.5 concentration prediction

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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.

Stringent DDI-based prediction of H. sapiens-M. tuberculosis H37Rv protein-protein interactions.

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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

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.

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.

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.

Semi-supervised drug-protein interaction prediction from heterogeneous biological spaces.

Science.gov (United States)

Xia, Zheng; Wu, Ling-Yun; Zhou, Xiaobo; Wong, Stephen T C

2010-09-13

Predicting drug-protein interactions from heterogeneous biological data sources is a key step for in silico drug discovery. The difficulty of this prediction task lies in the rarity of known drug-protein interactions and myriad unknown interactions to be predicted. To meet this challenge, a manifold regularization semi-supervised learning method is presented to tackle this issue by using labeled and unlabeled information which often generates better results than using the labeled data alone. Furthermore, our semi-supervised learning method integrates known drug-protein interaction network information as well as chemical structure and genomic sequence data. Using the proposed method, we predicted certain drug-protein interactions on the enzyme, ion channel, GPCRs, and nuclear receptor data sets. Some of them are confirmed by the latest publicly available drug targets databases such as KEGG. We report encouraging results of using our method for drug-protein interaction network reconstruction which may shed light on the molecular interaction inference and new uses of marketed drugs.

Structural features that predict real-value fluctuations of globular proteins.

Science.gov (United States)

Jamroz, Michal; Kolinski, Andrzej; Kihara, Daisuke

2012-05-01

It is crucial to consider dynamics for understanding the biological function of proteins. We used a large number of molecular dynamics (MD) trajectories of nonhomologous proteins as references and examined static structural features of proteins that are most relevant to fluctuations. We examined correlation of individual structural features with fluctuations and further investigated effective combinations of features for predicting the real value of residue fluctuations using the support vector regression (SVR). It was found that some structural features have higher correlation than crystallographic B-factors with fluctuations observed in MD trajectories. Moreover, SVR that uses combinations of static structural features showed accurate prediction of fluctuations with an average Pearson's correlation coefficient of 0.669 and a root mean square error of 1.04 Å. This correlation coefficient is higher than the one observed in predictions by the Gaussian network model (GNM). An advantage of the developed method over the GNMs is that the former predicts the real value of fluctuation. The results help improve our understanding of relationships between protein structure and fluctuation. Furthermore, the developed method provides a convienient practial way to predict fluctuations of proteins using easily computed static structural features of proteins. Copyright © 2012 Wiley Periodicals, Inc.

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

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....

Whey protein concentration by ultrafiltration and study of functional properties

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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..

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)

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

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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.

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

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

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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

Protein-Based Urine Test Predicts Kidney Transplant Outcomes

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... 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 ...

Prediction of Protein Thermostability by an Efficient Neural Network Approach

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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

Protein complex prediction via dense subgraphs and false positive analysis.

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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

Behavior of whey protein concentrates under extreme storage conditions

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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...

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

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)

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

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

2011-01-01

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

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

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

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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

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

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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...

Constraint Logic Programming approach to protein structure prediction

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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.

Constraint Logic Programming approach to protein structure prediction.

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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.

Which clustering algorithm is better for predicting protein complexes?

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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

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...

Protein Secondary Structure Prediction Using Deep Convolutional Neural Fields.

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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.

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

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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.

Prediction of beta-turns in proteins using the first-order Markov models.

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Lin, Thy-Hou; Wang, Ging-Ming; Wang, Yen-Tseng

2002-01-01

We present a method based on the first-order Markov models for predicting simple beta-turns and loops containing multiple turns in proteins. Sequences of 338 proteins in a database are divided using the published turn criteria into the following three regions, namely, the turn, the boundary, and the nonturn ones. A transition probability matrix is constructed for either the turn or the nonturn region using the weighted transition probabilities computed for dipeptides identified from each region. There are two such matrices constructed for the boundary region since the transition probabilities for dipeptides immediately preceding or following a turn are different. The window used for scanning a protein sequence from amino (N-) to carboxyl (C-) terminal is a hexapeptide since the transition probability computed for a turn tetrapeptide is capped at both the N- and C- termini with a boundary transition probability indexed respectively from the two boundary transition matrices. A sum of the averaged product of the transition probabilities of all the hexapeptides involving each residue is computed. This is then weighted with a probability computed from assuming that all the hexapeptides are from the nonturn region to give the final prediction quantity. Both simple beta-turns and loops containing multiple turns in a protein are then identified by the rising of the prediction quantity computed. The performance of the prediction scheme or the percentage (%) of correct prediction is evaluated through computation of Matthews correlation coefficients for each protein predicted. It is found that the prediction method is capable of giving prediction results with better correlation between the percent of correct prediction and the Matthews correlation coefficients for a group of test proteins as compared with those predicted using some secondary structural prediction methods. The prediction accuracy for about 40% of proteins in the database or 50% of proteins in the test set is

Nanoparticles-cell association predicted by protein corona fingerprints

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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

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

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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

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

PCI-SS: MISO dynamic nonlinear protein secondary structure prediction

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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

Protein thermostability prediction within homologous families using temperature-dependent statistical potentials.

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Fabrizio Pucci

Full Text Available The ability to rationally modify targeted physical and biological features of a protein of interest holds promise in numerous academic and industrial applications and paves the way towards de novo protein design. In particular, bioprocesses that utilize the remarkable properties of enzymes would often benefit from mutants that remain active at temperatures that are either higher or lower than the physiological temperature, while maintaining the biological activity. Many in silico methods have been developed in recent years for predicting the thermodynamic stability of mutant proteins, but very few have focused on thermostability. To bridge this gap, we developed an algorithm for predicting the best descriptor of thermostability, namely the melting temperature Tm, from the protein's sequence and structure. Our method is applicable when the Tm of proteins homologous to the target protein are known. It is based on the design of several temperature-dependent statistical potentials, derived from datasets consisting of either mesostable or thermostable proteins. Linear combinations of these potentials have been shown to yield an estimation of the protein folding free energies at low and high temperatures, and the difference of these energies, a prediction of the melting temperature. This particular construction, that distinguishes between the interactions that contribute more than others to the stability at high temperatures and those that are more stabilizing at low T, gives better performances compared to the standard approach based on T-independent potentials which predict the thermal resistance from the thermodynamic stability. Our method has been tested on 45 proteins of known Tm that belong to 11 homologous families. The standard deviation between experimental and predicted Tm's is equal to 13.6°C in cross validation, and decreases to 8.3°C if the 6 worst predicted proteins are excluded. Possible extensions of our approach are discussed.

Improving prediction of heterodimeric protein complexes using combination with pairwise kernel.

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Ruan, Peiying; Hayashida, Morihiro; Akutsu, Tatsuya; Vert, Jean-Philippe

2018-02-19

Since many proteins become functional only after they interact with their partner proteins and form protein complexes, it is essential to identify the sets of proteins that form complexes. Therefore, several computational methods have been proposed to predict complexes from the topology and structure of experimental protein-protein interaction (PPI) network. These methods work well to predict complexes involving at least three proteins, but generally fail at identifying complexes involving only two different proteins, called heterodimeric complexes or heterodimers. There is however an urgent need for efficient methods to predict heterodimers, since the majority of known protein complexes are precisely heterodimers. In this paper, we use three promising kernel functions, Min kernel and two pairwise kernels, which are Metric Learning Pairwise Kernel (MLPK) and Tensor Product Pairwise Kernel (TPPK). We also consider the normalization forms of Min kernel. Then, we combine Min kernel or its normalization form and one of the pairwise kernels by plugging. We applied kernels based on PPI, domain, phylogenetic profile, and subcellular localization properties to predicting heterodimers. Then, we evaluate our method by employing C-Support Vector Classification (C-SVC), carrying out 10-fold cross-validation, and calculating the average F-measures. The results suggest that the combination of normalized-Min-kernel and MLPK leads to the best F-measure and improved the performance of our previous work, which had been the best existing method so far. We propose new methods to predict heterodimers, using a machine learning-based approach. We train a support vector machine (SVM) to discriminate interacting vs non-interacting protein pairs, based on informations extracted from PPI, domain, phylogenetic profiles and subcellular localization. We evaluate in detail new kernel functions to encode these data, and report prediction performance that outperforms the state-of-the-art.

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.)

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.

Exploiting protein flexibility to predict the location of allosteric sites

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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

Paracetamol (acetaminophen) protein adduct concentrations during therapeutic dosing.

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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.

Predicting nucleic acid binding interfaces from structural models of proteins.

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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.

Protein Secondary Structure Prediction Using AutoEncoder Network and Bayes Classifier

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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.

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

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.

RSARF: Prediction of residue solvent accessibility from protein sequence using random forest method

KAUST Repository

Ganesan, Pugalenthi; Kandaswamy, Krishna Kumar Umar; Chou -, Kuochen; Vivekanandan, Saravanan; Kolatkar, Prasanna R.

2012-01-01

Prediction of protein structure from its amino acid sequence is still a challenging problem. The complete physicochemical understanding of protein folding is essential for the accurate structure prediction. Knowledge of residue solvent accessibility gives useful insights into protein structure prediction and function prediction. In this work, we propose a random forest method, RSARF, to predict residue accessible surface area from protein sequence information. The training and testing was performed using 120 proteins containing 22006 residues. For each residue, buried and exposed state was computed using five thresholds (0%, 5%, 10%, 25%, and 50%). The prediction accuracy for 0%, 5%, 10%, 25%, and 50% thresholds are 72.9%, 78.25%, 78.12%, 77.57% and 72.07% respectively. Further, comparison of RSARF with other methods using a benchmark dataset containing 20 proteins shows that our approach is useful for prediction of residue solvent accessibility from protein sequence without using structural information. The RSARF program, datasets and supplementary data are available at http://caps.ncbs.res.in/download/pugal/RSARF/. - See more at: http://www.eurekaselect.com/89216/article#sthash.pwVGFUjq.dpuf

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

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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.

BetaTPred: prediction of beta-TURNS in a protein using statistical algorithms.

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Kaur, Harpreet; Raghava, G P S

2002-03-01

beta-turns play an important role from a structural and functional point of view. beta-turns are the most common type of non-repetitive structures in proteins and comprise on average, 25% of the residues. In the past numerous methods have been developed to predict beta-turns in a protein. Most of these prediction methods are based on statistical approaches. In order to utilize the full potential of these methods, there is a need to develop a web server. This paper describes a web server called BetaTPred, developed for predicting beta-TURNS in a protein from its amino acid sequence. BetaTPred allows the user to predict turns in a protein using existing statistical algorithms. It also allows to predict different types of beta-TURNS e.g. type I, I', II, II', VI, VIII and non-specific. This server assists the users in predicting the consensus beta-TURNS in a protein. The server is accessible from http://imtech.res.in/raghava/betatpred/

Critical Features of Fragment Libraries for Protein Structure Prediction.

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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.

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

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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.

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

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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

Short Term Prediction of PM10 Concentrations Using Seasonal Time Series Analysis

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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.

Integration of relational and hierarchical network information for protein function prediction

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Jiang Xiaoyu

2008-08-01

Full Text Available Abstract Background In the current climate of high-throughput computational biology, the inference of a protein's function from related measurements, such as protein-protein interaction relations, has become a canonical task. Most existing technologies pursue this task as a classification problem, on a term-by-term basis, for each term in a database, such as the Gene Ontology (GO database, a popular rigorous vocabulary for biological functions. However, ontology structures are essentially hierarchies, with certain top to bottom annotation rules which protein function predictions should in principle follow. Currently, the most common approach to imposing these hierarchical constraints on network-based classifiers is through the use of transitive closure to predictions. Results We propose a probabilistic framework to integrate information in relational data, in the form of a protein-protein interaction network, and a hierarchically structured database of terms, in the form of the GO database, for the purpose of protein function prediction. At the heart of our framework is a factorization of local neighborhood information in the protein-protein interaction network across successive ancestral terms in the GO hierarchy. We introduce a classifier within this framework, with computationally efficient implementation, that produces GO-term predictions that naturally obey a hierarchical 'true-path' consistency from root to leaves, without the need for further post-processing. Conclusion A cross-validation study, using data from the yeast Saccharomyces cerevisiae, shows our method offers substantial improvements over both standard 'guilt-by-association' (i.e., Nearest-Neighbor and more refined Markov random field methods, whether in their original form or when post-processed to artificially impose 'true-path' consistency. Further analysis of the results indicates that these improvements are associated with increased predictive capabilities (i.e., increased

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

Prediction of RNA-Binding Proteins by Voting Systems

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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.

Fast dynamics perturbation analysis for prediction of protein functional sites

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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.

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.

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

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Lee Hyunju

2009-12-01

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

A probabilistic fragment-based protein structure prediction algorithm.

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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].

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

Science.gov (United States)

Lee, Hui Sun; Im, Wonpil

2017-01-01

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

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

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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

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

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

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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

Predicting Protein Function via Semantic Integration of Multiple Networks.

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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.

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

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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.

The elution of certain protein affinity tags with millimolar concentrations of diclofenac.

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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.

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

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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.

Feature-Based and String-Based Models for Predicting RNA-Protein Interaction

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Donald Adjeroh

2018-03-01

Full Text Available In this work, we study two approaches for the problem of RNA-Protein Interaction (RPI. In the first approach, we use a feature-based technique by combining extracted features from both sequences and secondary structures. The feature-based approach enhanced the prediction accuracy as it included much more available information about the RNA-protein pairs. In the second approach, we apply search algorithms and data structures to extract effective string patterns for prediction of RPI, using both sequence information (protein and RNA sequences, and structure information (protein and RNA secondary structures. This led to different string-based models for predicting interacting RNA-protein pairs. We show results that demonstrate the effectiveness of the proposed approaches, including comparative results against leading state-of-the-art methods.

Prediction of toxic metals concentration using artificial intelligence techniques

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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.

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.

Evaluation of multiple protein docking structures using correctly predicted pairwise subunits

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Esquivel-Rodríguez Juan

2012-03-01

Full Text Available Abstract Background Many functionally important proteins in a cell form complexes with multiple chains. Therefore, computational prediction of multiple protein complexes is an important task in bioinformatics. In the development of multiple protein docking methods, it is important to establish a metric for evaluating prediction results in a reasonable and practical fashion. However, since there are only few works done in developing methods for multiple protein docking, there is no study that investigates how accurate structural models of multiple protein complexes should be to allow scientists to gain biological insights. Methods We generated a series of predicted models (decoys of various accuracies by our multiple protein docking pipeline, Multi-LZerD, for three multi-chain complexes with 3, 4, and 6 chains. We analyzed the decoys in terms of the number of correctly predicted pair conformations in the decoys. Results and conclusion We found that pairs of chains with the correct mutual orientation exist even in the decoys with a large overall root mean square deviation (RMSD to the native. Therefore, in addition to a global structure similarity measure, such as the global RMSD, the quality of models for multiple chain complexes can be better evaluated by using the local measurement, the number of chain pairs with correct mutual orientation. We termed the fraction of correctly predicted pairs (RMSD at the interface of less than 4.0Å as fpair and propose to use it for evaluation of the accuracy of multiple protein docking.

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

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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.

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...

Protein (multi-)location prediction: using location inter-dependencies in a probabilistic framework

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2014-01-01

Motivation Knowing the location of a protein within the cell is important for understanding its function, role in biological processes, and potential use as a drug target. Much progress has been made in developing computational methods that predict single locations for proteins. Most such methods are based on the over-simplifying assumption that proteins localize to a single location. However, it has been shown that proteins localize to multiple locations. While a few recent systems attempt to predict multiple locations of proteins, their performance leaves much room for improvement. Moreover, they typically treat locations as independent and do not attempt to utilize possible inter-dependencies among locations. Our hypothesis is that directly incorporating inter-dependencies among locations into both the classifier-learning and the prediction process can improve location prediction performance. Results We present a new method and a preliminary system we have developed that directly incorporates inter-dependencies among locations into the location-prediction process of multiply-localized proteins. Our method is based on a collection of Bayesian network classifiers, where each classifier is used to predict a single location. Learning the structure of each Bayesian network classifier takes into account inter-dependencies among locations, and the prediction process uses estimates involving multiple locations. We evaluate our system on a dataset of single- and multi-localized proteins (the most comprehensive protein multi-localization dataset currently available, derived from the DBMLoc dataset). Our results, obtained by incorporating inter-dependencies, are significantly higher than those obtained by classifiers that do not use inter-dependencies. The performance of our system on multi-localized proteins is comparable to a top performing system (YLoc+), without being restricted only to location-combinations present in the training set. PMID:24646119

Protein (multi-)location prediction: using location inter-dependencies in a probabilistic framework.

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Simha, Ramanuja; Shatkay, Hagit

2014-03-19

Knowing the location of a protein within the cell is important for understanding its function, role in biological processes, and potential use as a drug target. Much progress has been made in developing computational methods that predict single locations for proteins. Most such methods are based on the over-simplifying assumption that proteins localize to a single location. However, it has been shown that proteins localize to multiple locations. While a few recent systems attempt to predict multiple locations of proteins, their performance leaves much room for improvement. Moreover, they typically treat locations as independent and do not attempt to utilize possible inter-dependencies among locations. Our hypothesis is that directly incorporating inter-dependencies among locations into both the classifier-learning and the prediction process can improve location prediction performance. We present a new method and a preliminary system we have developed that directly incorporates inter-dependencies among locations into the location-prediction process of multiply-localized proteins. Our method is based on a collection of Bayesian network classifiers, where each classifier is used to predict a single location. Learning the structure of each Bayesian network classifier takes into account inter-dependencies among locations, and the prediction process uses estimates involving multiple locations. We evaluate our system on a dataset of single- and multi-localized proteins (the most comprehensive protein multi-localization dataset currently available, derived from the DBMLoc dataset). Our results, obtained by incorporating inter-dependencies, are significantly higher than those obtained by classifiers that do not use inter-dependencies. The performance of our system on multi-localized proteins is comparable to a top performing system (YLoc+), without being restricted only to location-combinations present in the training set.

Enhanced Temperature During Grain Filling Reduces Protein Concentration of Durum Wheat

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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.

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

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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.

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

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.

Hill-Climbing search and diversification within an evolutionary approach to protein structure prediction

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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.

An update of the DEF database of protein fold class predictions

DEFF Research Database (Denmark)

Reczko, Martin; Karras, Dimitris; Bohr, Henrik

1997-01-01

An update is given on the Database of Expected Fold classes (DEF) that contains a collection of fold-class predictions made from protein sequences and a mail server that provides new predictions for new sequences. To any given sequence one of 49 fold-classes is chosen to classify the structure re...... related to the sequence with high accuracy. The updated predictions system is developed using data from the new version of the 3D-ALI database of aligned protein structures and thus is giving more reliable and more detailed predictions than the previous DEF system.......An update is given on the Database of Expected Fold classes (DEF) that contains a collection of fold-class predictions made from protein sequences and a mail server that provides new predictions for new sequences. To any given sequence one of 49 fold-classes is chosen to classify the structure...

Minimum curvilinearity to enhance topological prediction of protein interactions by network embedding

KAUST Repository

Cannistraci, Carlo

2013-06-21

Motivation: Most functions within the cell emerge thanks to protein-protein interactions (PPIs), yet experimental determination of PPIs is both expensive and time-consuming. PPI networks present significant levels of noise and incompleteness. Predicting interactions using only PPI-network topology (topological prediction) is difficult but essential when prior biological knowledge is absent or unreliable.Methods: Network embedding emphasizes the relations between network proteins embedded in a low-dimensional space, in which protein pairs that are closer to each other represent good candidate interactions. To achieve network denoising, which boosts prediction performance, we first applied minimum curvilinear embedding (MCE), and then adopted shortest path (SP) in the reduced space to assign likelihood scores to candidate interactions. Furthermore, we introduce (i) a new valid variation of MCE, named non-centred MCE (ncMCE); (ii) two automatic strategies for selecting the appropriate embedding dimension; and (iii) two new randomized procedures for evaluating predictions.Results: We compared our method against several unsupervised and supervisedly tuned embedding approaches and node neighbourhood techniques. Despite its computational simplicity, ncMCE-SP was the overall leader, outperforming the current methods in topological link prediction.Conclusion: Minimum curvilinearity is a valuable non-linear framework that we successfully applied to the embedding of protein networks for the unsupervised prediction of novel PPIs. The rationale for our approach is that biological and evolutionary information is imprinted in the non-linear patterns hidden behind the protein network topology, and can be exploited for predicting new protein links. The predicted PPIs represent good candidates for testing in high-throughput experiments or for exploitation in systems biology tools such as those used for network-based inference and prediction of disease-related functional modules. The

Increase in local protein concentration by field-inversion gel electrophoresis

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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

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.

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

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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.

Predicting the subcellular localization of viral proteins within a mammalian host cell

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Thomas DY

2006-04-01

Full Text Available Abstract Background The bioinformatic prediction of protein subcellular localization has been extensively studied for prokaryotic and eukaryotic organisms. However, this is not the case for viruses whose proteins are often involved in extensive interactions at various subcellular localizations with host proteins. Results Here, we investigate the extent of utilization of human cellular localization mechanisms by viral proteins and we demonstrate that appropriate eukaryotic subcellular localization predictors can be used to predict viral protein localization within the host cell. Conclusion Such predictions provide a method to rapidly annotate viral proteomes with subcellular localization information. They are likely to have widespread applications both in the study of the functions of viral proteins in the host cell and in the design of antiviral drugs.

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.

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.

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.

Building a better fragment library for de novo protein structure prediction.

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Saulo H P de Oliveira

Full Text Available Fragment-based approaches are the current standard for de novo protein structure prediction. These approaches rely on accurate and reliable fragment libraries to generate good structural models. In this work, we describe a novel method for structure fragment library generation and its application in fragment-based de novo protein structure prediction. The importance of correct testing procedures in assessing the quality of fragment libraries is demonstrated. In particular, the exclusion of homologs to the target from the libraries to correctly simulate a de novo protein structure prediction scenario, something which surprisingly is not always done. We demonstrate that fragments presenting different predominant predicted secondary structures should be treated differently during the fragment library generation step and that exhaustive and random search strategies should both be used. This information was used to develop a novel method, Flib. On a validation set of 41 structurally diverse proteins, Flib libraries presents both a higher precision and coverage than two of the state-of-the-art methods, NNMake and HHFrag. Flib also achieves better precision and coverage on the set of 275 protein domains used in the two previous experiments of the the Critical Assessment of Structure Prediction (CASP9 and CASP10. We compared Flib libraries against NNMake libraries in a structure prediction context. Of the 13 cases in which a correct answer was generated, Flib models were more accurate than NNMake models for 10. "Flib is available for download at: http://www.stats.ox.ac.uk/research/proteins/resources".

Building a Better Fragment Library for De Novo Protein Structure Prediction

Science.gov (United States)

de Oliveira, Saulo H. P.; Shi, Jiye; Deane, Charlotte M.

2015-01-01

Fragment-based approaches are the current standard for de novo protein structure prediction. These approaches rely on accurate and reliable fragment libraries to generate good structural models. In this work, we describe a novel method for structure fragment library generation and its application in fragment-based de novo protein structure prediction. The importance of correct testing procedures in assessing the quality of fragment libraries is demonstrated. In particular, the exclusion of homologs to the target from the libraries to correctly simulate a de novo protein structure prediction scenario, something which surprisingly is not always done. We demonstrate that fragments presenting different predominant predicted secondary structures should be treated differently during the fragment library generation step and that exhaustive and random search strategies should both be used. This information was used to develop a novel method, Flib. On a validation set of 41 structurally diverse proteins, Flib libraries presents both a higher precision and coverage than two of the state-of-the-art methods, NNMake and HHFrag. Flib also achieves better precision and coverage on the set of 275 protein domains used in the two previous experiments of the the Critical Assessment of Structure Prediction (CASP9 and CASP10). We compared Flib libraries against NNMake libraries in a structure prediction context. Of the 13 cases in which a correct answer was generated, Flib models were more accurate than NNMake models for 10. “Flib is available for download at: http://www.stats.ox.ac.uk/research/proteins/resources”. PMID:25901595

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...

Binding Ligand Prediction for Proteins Using Partial Matching of Local Surface Patches

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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.

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.

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.

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

2016-06-15

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, threading and alignment-free methods, protein homology detection remains a challenging open problem. Recently, network methods that try to find transitive paths in the protein structure space demonstrate the importance of incorporating network information of the structure space. Yet, current methods merge the sequence space and the structure space into a single space, and thus introduce inconsistency in combining different sources of information. Method: We present a novel network-based protein homology detection method, CMsearch, based on cross-modal learning. Instead of exploring a single network built from the mixture of sequence and structure space information, CMsearch builds two separate networks to represent the sequence space and the structure space. It then learns sequence–structure correlation by simultaneously taking sequence information, structure information, sequence space information and structure space information into consideration. Results: We tested CMsearch on two challenging tasks, protein homology detection and protein structure prediction, by querying all 8332 PDB40 proteins. Our results demonstrate that CMsearch is insensitive to the similarity metrics used to define the sequence and the structure spaces. By using HMM–HMM alignment as the sequence similarity metric, CMsearch clearly outperforms state-of-the-art homology detection methods and the CASP-winning template-based protein structure prediction methods.

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.

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.

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

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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.

Prediction of Protein–Protein Interactions by Evidence Combining Methods

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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.

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...

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...

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...

Knowledge base and neural network approach for protein secondary structure prediction.

Science.gov (United States)

Patel, Maulika S; Mazumdar, Himanshu S

2014-11-21

Protein structure prediction is of great relevance given the abundant genomic and proteomic data generated by the genome sequencing projects. Protein secondary structure prediction is addressed as a sub task in determining the protein tertiary structure and function. In this paper, a novel algorithm, KB-PROSSP-NN, which is a combination of knowledge base and modeling of the exceptions in the knowledge base using neural networks for protein secondary structure prediction (PSSP), is proposed. The knowledge base is derived from a proteomic sequence-structure database and consists of the statistics of association between the 5-residue words and corresponding secondary structure. The predicted results obtained using knowledge base are refined with a Backpropogation neural network algorithm. Neural net models the exceptions of the knowledge base. The Q3 accuracy of 90% and 82% is achieved on the RS126 and CB396 test sets respectively which suggest improvement over existing state of art methods. Copyright © 2014 Elsevier Ltd. All rights reserved.

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.

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.

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.

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.

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.

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.

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

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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

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...

MU-LOC: A Machine-Learning Method for Predicting Mitochondrially Localized Proteins in Plants

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Ning Zhang

2018-05-01

Full Text Available Targeting and translocation of proteins to the appropriate subcellular compartments are crucial for cell organization and function. Newly synthesized proteins are transported to mitochondria with the assistance of complex targeting sequences containing either an N-terminal pre-sequence or a multitude of internal signals. Compared with experimental approaches, computational predictions provide an efficient way to infer subcellular localization of a protein. However, it is still challenging to predict plant mitochondrially localized proteins accurately due to various limitations. Consequently, the performance of current tools can be improved with new data and new machine-learning methods. We present MU-LOC, a novel computational approach for large-scale prediction of plant mitochondrial proteins. We collected a comprehensive dataset of plant subcellular localization, extracted features including amino acid composition, protein position weight matrix, and gene co-expression information, and trained predictors using deep neural network and support vector machine. Benchmarked on two independent datasets, MU-LOC achieved substantial improvements over six state-of-the-art tools for plant mitochondrial targeting prediction. In addition, MU-LOC has the advantage of predicting plant mitochondrial proteins either possessing or lacking N-terminal pre-sequences. We applied MU-LOC to predict candidate mitochondrial proteins for the whole proteome of Arabidopsis and potato. MU-LOC is publicly available at http://mu-loc.org.

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/).

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

Improving the accuracy of protein secondary structure prediction using structural alignment

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Gallin Warren J

2006-06-01

Full Text Available Abstract Background The accuracy of protein secondary structure prediction has steadily improved over the past 30 years. Now many secondary structure prediction methods routinely achieve an accuracy (Q3 of about 75%. We believe this accuracy could be further improved by including structure (as opposed to sequence database comparisons as part of the prediction process. Indeed, given the large size of the Protein Data Bank (>35,000 sequences, the probability of a newly identified sequence having a structural homologue is actually quite high. Results We have developed a method that performs structure-based sequence alignments as part of the secondary structure prediction process. By mapping the structure of a known homologue (sequence ID >25% onto the query protein's sequence, it is possible to predict at least a portion of that query protein's secondary structure. By integrating this structural alignment approach with conventional (sequence-based secondary structure methods and then combining it with a "jury-of-experts" system to generate a consensus result, it is possible to attain very high prediction accuracy. Using a sequence-unique test set of 1644 proteins from EVA, this new method achieves an average Q3 score of 81.3%. Extensive testing indicates this is approximately 4–5% better than any other method currently available. Assessments using non sequence-unique test sets (typical of those used in proteome annotation or structural genomics indicate that this new method can achieve a Q3 score approaching 88%. Conclusion By using both sequence and structure databases and by exploiting the latest techniques in machine learning it is possible to routinely predict protein secondary structure with an accuracy well above 80%. A program and web server, called PROTEUS, that performs these secondary structure predictions is accessible at http://wishart.biology.ualberta.ca/proteus. For high throughput or batch sequence analyses, the PROTEUS programs

Incorporating functional inter-relationships into protein function prediction algorithms

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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

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

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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.

Prediction of essential proteins based on subcellular localization and gene expression correlation.

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Fan, Yetian; Tang, Xiwei; Hu, Xiaohua; Wu, Wei; Ping, Qing

2017-12-01

Essential proteins are indispensable to the survival and development process of living organisms. To understand the functional mechanisms of essential proteins, which can be applied to the analysis of disease and design of drugs, it is important to identify essential proteins from a set of proteins first. As traditional experimental methods designed to test out essential proteins are usually expensive and laborious, computational methods, which utilize biological and topological features of proteins, have attracted more attention in recent years. Protein-protein interaction networks, together with other biological data, have been explored to improve the performance of essential protein prediction. The proposed method SCP is evaluated on Saccharomyces cerevisiae datasets and compared with five other methods. The results show that our method SCP outperforms the other five methods in terms of accuracy of essential protein prediction. In this paper, we propose a novel algorithm named SCP, which combines the ranking by a modified PageRank algorithm based on subcellular compartments information, with the ranking by Pearson correlation coefficient (PCC) calculated from gene expression data. Experiments show that subcellular localization information is promising in boosting essential protein prediction.

Parallel protein secondary structure prediction based on neural networks.

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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.

Using support vector machine to predict beta- and gamma-turns in proteins.

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Hu, Xiuzhen; Li, Qianzhong

2008-09-01

By using the composite vector with increment of diversity, position conservation scoring function, and predictive secondary structures to express the information of sequence, a support vector machine (SVM) algorithm for predicting beta- and gamma-turns in the proteins is proposed. The 426 and 320 nonhomologous protein chains described by Guruprasad and Rajkumar (Guruprasad and Rajkumar J. Biosci 2000, 25,143) are used for training and testing the predictive model of the beta- and gamma-turns, respectively. The overall prediction accuracy and the Matthews correlation coefficient in 7-fold cross-validation are 79.8% and 0.47, respectively, for the beta-turns. The overall prediction accuracy in 5-fold cross-validation is 61.0% for the gamma-turns. These results are significantly higher than the other algorithms in the prediction of beta- and gamma-turns using the same datasets. In addition, the 547 and 823 nonhomologous protein chains described by Fuchs and Alix (Fuchs and Alix Proteins: Struct Funct Bioinform 2005, 59, 828) are used for training and testing the predictive model of the beta- and gamma-turns, and better results are obtained. This algorithm may be helpful to improve the performance of protein turns' prediction. To ensure the ability of the SVM method to correctly classify beta-turn and non-beta-turn (gamma-turn and non-gamma-turn), the receiver operating characteristic threshold independent measure curves are provided. (c) 2008 Wiley Periodicals, Inc.

Plasma proteins predict conversion to dementia from prodromal disease.

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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.

Enhancing the prediction of protein pairings between interacting families using orthology information

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Pazos Florencio

2008-01-01

Full Text Available Abstract Background It has repeatedly been shown that interacting protein families tend to have similar phylogenetic trees. These similarities can be used to predicting the mapping between two families of interacting proteins (i.e. which proteins from one family interact with which members of the other. The correct mapping will be that which maximizes the similarity between the trees. The two families may eventually comprise orthologs and paralogs, if members of the two families are present in more than one organism. This fact can be exploited to restrict the possible mappings, simply by impeding links between proteins of different organisms. We present here an algorithm to predict the mapping between families of interacting proteins which is able to incorporate information regarding orthologues, or any other assignment of proteins to "classes" that may restrict possible mappings. Results For the first time in methods for predicting mappings, we have tested this new approach on a large number of interacting protein domains in order to statistically assess its performance. The method accurately predicts around 80% in the most favourable cases. We also analysed in detail the results of the method for a well defined case of interacting families, the sensor and kinase components of the Ntr-type two-component system, for which up to 98% of the pairings predicted by the method were correct. Conclusion Based on the well established relationship between tree similarity and interactions we developed a method for predicting the mapping between two interacting families using genomic information alone. The program is available through a web interface.

Buffer-free therapeutic antibody preparations provide a viable alternative to conventionally buffered solutions: from protein buffer capacity prediction to bioprocess applications.

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Bahrenburg, Sven; Karow, Anne R; Garidel, Patrick

2015-04-01

Protein therapeutics, including monoclonal antibodies (mAbs), have significant buffering capacity, particularly at concentrations>50 mg/mL. This report addresses pH-related issues critical to adoption of self-buffered monoclonal antibody formulations. We evaluated solution conditions with protein concentrations ranging from 50 to 250 mg/mL. Samples were both buffer-free and conventionally buffered with citrate. Samples were non-isotonic or adjusted for isotonicity with NaCl or trehalose. Studies included accelerated temperature stability tests, shaking stability studies, and pH changes in infusion media as protein concentrate is added. We present averaged buffering slopes of capacity that can be applied to any mAb and present a general method for calculating buffering capacity of buffer-free, highly concentrated antibody liquid formulations. In temperature stability tests, neither buffer-free nor conventionally buffered solution conditions showed significant pH changes. Conventionally buffered solutions showed significantly higher opalescence than buffer-free ones. In general, buffer-free solution conditions showed less aggregation than conventionally buffered solutions. Shaking stability tests showed no differences between buffer-free and conventionally buffered solutions. "In-use" preparation experiments showed that pH in infusion bag medium can rapidly approximate that of self-buffered protein concentrate as concentrate is added. In summary, the buffer capacity of proteins can be predicted and buffer-free therapeutic antibody preparations provide a viable alternative to conventionally buffered solutions. Copyright © 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

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

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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

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

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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.

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

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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

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

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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

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

HMMBinder: DNA-Binding Protein Prediction Using HMM Profile Based Features.

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Zaman, Rianon; Chowdhury, Shahana Yasmin; Rashid, Mahmood A; Sharma, Alok; Dehzangi, Abdollah; Shatabda, Swakkhar

2017-01-01

DNA-binding proteins often play important role in various processes within the cell. Over the last decade, a wide range of classification algorithms and feature extraction techniques have been used to solve this problem. In this paper, we propose a novel DNA-binding protein prediction method called HMMBinder. HMMBinder uses monogram and bigram features extracted from the HMM profiles of the protein sequences. To the best of our knowledge, this is the first application of HMM profile based features for the DNA-binding protein prediction problem. We applied Support Vector Machines (SVM) as a classification technique in HMMBinder. Our method was tested on standard benchmark datasets. We experimentally show that our method outperforms the state-of-the-art methods found in the literature.

HMMBinder: DNA-Binding Protein Prediction Using HMM Profile Based Features

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Rianon Zaman

2017-01-01

Full Text Available DNA-binding proteins often play important role in various processes within the cell. Over the last decade, a wide range of classification algorithms and feature extraction techniques have been used to solve this problem. In this paper, we propose a novel DNA-binding protein prediction method called HMMBinder. HMMBinder uses monogram and bigram features extracted from the HMM profiles of the protein sequences. To the best of our knowledge, this is the first application of HMM profile based features for the DNA-binding protein prediction problem. We applied Support Vector Machines (SVM as a classification technique in HMMBinder. Our method was tested on standard benchmark datasets. We experimentally show that our method outperforms the state-of-the-art methods found in the literature.

Automatic generation of bioinformatics tools for predicting protein-ligand binding sites.

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Komiyama, Yusuke; Banno, Masaki; Ueki, Kokoro; Saad, Gul; Shimizu, Kentaro

2016-03-15

Predictive tools that model protein-ligand binding on demand are needed to promote ligand research in an innovative drug-design environment. However, it takes considerable time and effort to develop predictive tools that can be applied to individual ligands. An automated production pipeline that can rapidly and efficiently develop user-friendly protein-ligand binding predictive tools would be useful. We developed a system for automatically generating protein-ligand binding predictions. Implementation of this system in a pipeline of Semantic Web technique-based web tools will allow users to specify a ligand and receive the tool within 0.5-1 day. We demonstrated high prediction accuracy for three machine learning algorithms and eight ligands. The source code and web application are freely available for download at http://utprot.net They are implemented in Python and supported on Linux. shimizu@bi.a.u-tokyo.ac.jp Supplementary data are available at Bioinformatics online. © The Author 2015. Published by Oxford University Press.

Distance matrix-based approach to protein structure prediction.

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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

Prediction of Hydrophobic Cores of Proteins Using Wavelet Analysis.

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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.

Effects of correcting in situ ruminal microbial colonization of feed particles on the relationship between ruminally undegraded and intestinally digested crude protein in concentrate feeds.

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González, Javier; Mouhbi, Rabiaa; Guevara-González, Jesús Alberto; Arroyo, José María

2018-02-01

In situ estimates of ruminally undegraded protein (RUP) and intestinally digested protein (IDP) of ten concentrates, uncorrected or corrected for the ruminal microbial colonization, were used to examine the effects of this correction on the relationship between IDP and RUP values. Both variables were established for three rumen and duodenum cannulated wethers using 15 N labeling-techniques and considering measured rates of ruminal particle comminution (k c ) and outflow (k p ). A covariance analysis showed that the close relationship found between both variables (IDP = -0.0132 ± 0.00679 + 0.776 ± 0.0002 RUP; n = 60; P content in concentrates and industrial by-products can be predicted from RUP values, thus avoiding the laborious and complex procedure of determining intestinal digestibility; however, a larger sample of feeds is necessary to achieve more accurate predictions. The lack of influence of the correction for microbial contamination on the prediction observed in the present study increases the data available for this prediction. However, only the use of corrected values may provide an accurate evaluation. © 2017 Society of Chemical Industry. © 2017 Society of Chemical Industry.

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

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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.

Predicting protein structures with a multiplayer online game.

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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.

An ensemble method for predicting subnuclear localizations from primary protein structures.

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Guo Sheng Han

Full Text Available BACKGROUND: Predicting protein subnuclear localization is a challenging problem. Some previous works based on non-sequence information including Gene Ontology annotations and kernel fusion have respective limitations. The aim of this work is twofold: one is to propose a novel individual feature extraction method; another is to develop an ensemble method to improve prediction performance using comprehensive information represented in the form of high dimensional feature vector obtained by 11 feature extraction methods. METHODOLOGY/PRINCIPAL FINDINGS: A novel two-stage multiclass support vector machine is proposed to predict protein subnuclear localizations. It only considers those feature extraction methods based on amino acid classifications and physicochemical properties. In order to speed up our system, an automatic search method for the kernel parameter is used. The prediction performance of our method is evaluated on four datasets: Lei dataset, multi-localization dataset, SNL9 dataset and a new independent dataset. The overall accuracy of prediction for 6 localizations on Lei dataset is 75.2% and that for 9 localizations on SNL9 dataset is 72.1% in the leave-one-out cross validation, 71.7% for the multi-localization dataset and 69.8% for the new independent dataset, respectively. Comparisons with those existing methods show that our method performs better for both single-localization and multi-localization proteins and achieves more balanced sensitivities and specificities on large-size and small-size subcellular localizations. The overall accuracy improvements are 4.0% and 4.7% for single-localization proteins and 6.5% for multi-localization proteins. The reliability and stability of our classification model are further confirmed by permutation analysis. CONCLUSIONS: It can be concluded that our method is effective and valuable for predicting protein subnuclear localizations. A web server has been designed to implement the proposed method

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

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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.

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...

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 ...

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/.

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.

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

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/.

[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.

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.

Electrostatics, structure prediction, and the energy landscapes for protein folding and binding.

Science.gov (United States)

Tsai, Min-Yeh; Zheng, Weihua; Balamurugan, D; Schafer, Nicholas P; Kim, Bobby L; Cheung, Margaret S; Wolynes, Peter G

2016-01-01

While being long in range and therefore weakly specific, electrostatic interactions are able to modulate the stability and folding landscapes of some proteins. The relevance of electrostatic forces for steering the docking of proteins to each other is widely acknowledged, however, the role of electrostatics in establishing specifically funneled landscapes and their relevance for protein structure prediction are still not clear. By introducing Debye-Hückel potentials that mimic long-range electrostatic forces into the Associative memory, Water mediated, Structure, and Energy Model (AWSEM), a transferable protein model capable of predicting tertiary structures, we assess the effects of electrostatics on the landscapes of thirteen monomeric proteins and four dimers. For the monomers, we find that adding electrostatic interactions does not improve structure prediction. Simulations of ribosomal protein S6 show, however, that folding stability depends monotonically on electrostatic strength. The trend in predicted melting temperatures of the S6 variants agrees with experimental observations. Electrostatic effects can play a range of roles in binding. The binding of the protein complex KIX-pKID is largely assisted by electrostatic interactions, which provide direct charge-charge stabilization of the native state and contribute to the funneling of the binding landscape. In contrast, for several other proteins, including the DNA-binding protein FIS, electrostatics causes frustration in the DNA-binding region, which favors its binding with DNA but not with its protein partner. This study highlights the importance of long-range electrostatics in functional responses to problems where proteins interact with their charged partners, such as DNA, RNA, as well as membranes. © 2015 The Protein Society.

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.

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

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

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...

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

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.

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.

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.

Supervised maximum-likelihood weighting of composite protein networks for complex prediction

Directory of Open Access Journals (Sweden)

Yong Chern Han

2012-12-01

Full Text Available Abstract Background Protein complexes participate in many important cellular functions, so finding the set of existent complexes is essential for understanding the organization and regulation of processes in the cell. With the availability of large amounts of high-throughput protein-protein interaction (PPI data, many algorithms have been proposed to discover protein complexes from PPI networks. However, such approaches are hindered by the high rate of noise in high-throughput PPI data, including spurious and missing interactions. Furthermore, many transient interactions are detected between proteins that are not from the same complex, while not all proteins from the same complex may actually interact. As a result, predicted complexes often do not match true complexes well, and many true complexes go undetected. Results We address these challenges by integrating PPI data with other heterogeneous data sources to construct a composite protein network, and using a supervised maximum-likelihood approach to weight each edge based on its posterior probability of belonging to a complex. We then use six different clustering algorithms, and an aggregative clustering strategy, to discover complexes in the weighted network. We test our method on Saccharomyces cerevisiae and Homo sapiens, and show that complex discovery is improved: compared to previously proposed supervised and unsupervised weighting approaches, our method recalls more known complexes, achieves higher precision at all recall levels, and generates novel complexes of greater functional similarity. Furthermore, our maximum-likelihood approach allows learned parameters to be used to visualize and evaluate the evidence of novel predictions, aiding human judgment of their credibility. Conclusions Our approach integrates multiple data sources with supervised learning to create a weighted composite protein network, and uses six clustering algorithms with an aggregative clustering strategy to

Multi-level machine learning prediction of protein–protein interactions in Saccharomyces cerevisiae

Directory of Open Access Journals (Sweden)

Julian Zubek

2015-07-01

Full Text Available Accurate identification of protein–protein interactions (PPI is the key step in understanding proteins’ biological functions, which are typically context-dependent. Many existing PPI predictors rely on aggregated features from protein sequences, however only a few methods exploit local information about specific residue contacts. In this work we present a two-stage machine learning approach for prediction of protein–protein interactions. We start with the carefully filtered data on protein complexes available for Saccharomyces cerevisiae in the Protein Data Bank (PDB database. First, we build linear descriptions of interacting and non-interacting sequence segment pairs based on their inter-residue distances. Secondly, we train machine learning classifiers to predict binary segment interactions for any two short sequence fragments. The final prediction of the protein–protein interaction is done using the 2D matrix representation of all-against-all possible interacting sequence segments of both analysed proteins. The level-I predictor achieves 0.88 AUC for micro-scale, i.e., residue-level prediction. The level-II predictor improves the results further by a more complex learning paradigm. We perform 30-fold macro-scale, i.e., protein-level cross-validation experiment. The level-II predictor using PSIPRED-predicted secondary structure reaches 0.70 precision, 0.68 recall, and 0.70 AUC, whereas other popular methods provide results below 0.6 threshold (recall, precision, AUC. Our results demonstrate that multi-scale sequence features aggregation procedure is able to improve the machine learning results by more than 10% as compared to other sequence representations. Prepared datasets and source code for our experimental pipeline are freely available for download from: http://zubekj.github.io/mlppi/ (open source Python implementation, OS independent.

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.

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.

NetTurnP – Neural Network Prediction of Beta-turns by Use of Evolutionary Information and Predicted Protein Sequence Features

DEFF Research Database (Denmark)

Petersen, Bent; Lundegaard, Claus; Petersen, Thomas Nordahl

2010-01-01

is the highest reported performance on a two-class prediction of β-turn and not-β-turn. Furthermore NetTurnP shows improved performance on some of the specific β-turn types. In the present work, neural network methods have been trained to predict β-turn or not and individual β-turn types from the primary amino......β-turns are the most common type of non-repetitive structures, and constitute on average 25% of the amino acids in proteins. The formation of β-turns plays an important role in protein folding, protein stability and molecular recognition processes. In this work we present the neural network method...... NetTurnP, for prediction of two-class β-turns and prediction of the individual β-turn types, by use of evolutionary information and predicted protein sequence features. It has been evaluated against a commonly used dataset BT426, and achieves a Matthews correlation coefficient of 0.50, which...

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

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.

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.

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....

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

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

Energy Technology Data Exchange (ETDEWEB)

Karp, Jerome M.; Erylimaz, Ertan; Cowburn, David, E-mail: cowburn@cowburnlab.org, E-mail: David.cowburn@einstein.yu.edu [Albert Einstein College of Medicine of Yeshiva University, Department of Biochemistry (United States)

2015-01-15

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

Sequence-based prediction of protein-binding sites in DNA: comparative study of two SVM models.

Science.gov (United States)

Park, Byungkyu; Im, Jinyong; Tuvshinjargal, Narankhuu; Lee, Wook; Han, Kyungsook

2014-11-01

As many structures of protein-DNA complexes have been known in the past years, several computational methods have been developed to predict DNA-binding sites in proteins. However, its inverse problem (i.e., predicting protein-binding sites in DNA) has received much less attention. One of the reasons is that the differences between the interaction propensities of nucleotides are much smaller than those between amino acids. Another reason is that DNA exhibits less diverse sequence patterns than protein. Therefore, predicting protein-binding DNA nucleotides is much harder than predicting DNA-binding amino acids. We computed the interaction propensity (IP) of nucleotide triplets with amino acids using an extensive dataset of protein-DNA complexes, and developed two support vector machine (SVM) models that predict protein-binding nucleotides from sequence data alone. One SVM model predicts protein-binding nucleotides using DNA sequence data alone, and the other SVM model predicts protein-binding nucleotides using both DNA and protein sequences. In a 10-fold cross-validation with 1519 DNA sequences, the SVM model that uses DNA sequence data only predicted protein-binding nucleotides with an accuracy of 67.0%, an F-measure of 67.1%, and a Matthews correlation coefficient (MCC) of 0.340. With an independent dataset of 181 DNAs that were not used in training, it achieved an accuracy of 66.2%, an F-measure 66.3% and a MCC of 0.324. Another SVM model that uses both DNA and protein sequences achieved an accuracy of 69.6%, an F-measure of 69.6%, and a MCC of 0.383 in a 10-fold cross-validation with 1519 DNA sequences and 859 protein sequences. With an independent dataset of 181 DNAs and 143 proteins, it showed an accuracy of 67.3%, an F-measure of 66.5% and a MCC of 0.329. Both in cross-validation and independent testing, the second SVM model that used both DNA and protein sequence data showed better performance than the first model that used DNA sequence data. To the best of

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.

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

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.

Amikacin Concentrations Predictive of Ototoxicity in Multidrug-Resistant Tuberculosis Patients.

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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.

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

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Fang, Chao; Shang, Yi; Xu, Dong

2018-05-01

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

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

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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.

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...

The Phyre2 web portal for protein modeling, prediction and analysis.

Science.gov (United States)

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

2015-06-01

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

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...

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...

System and methods for predicting transmembrane domains in membrane proteins and mining the genome for recognizing G-protein coupled receptors

Science.gov (United States)

Trabanino, Rene J; Vaidehi, Nagarajan; Hall, Spencer E; Goddard, William A; Floriano, Wely

2013-02-05

The invention provides computer-implemented methods and apparatus implementing a hierarchical protocol using multiscale molecular dynamics and molecular modeling methods to predict the presence of transmembrane regions in proteins, such as G-Protein Coupled Receptors (GPCR), and protein structural models generated according to the protocol. The protocol features a coarse grain sampling method, such as hydrophobicity analysis, to provide a fast and accurate procedure for predicting transmembrane regions. Methods and apparatus of the invention are useful to screen protein or polynucleotide databases for encoded proteins with transmembrane regions, such as GPCRs.

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

Science.gov (United States)

Wang, Xiao; Li, Guo-Zheng

2013-03-12

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

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.

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

Science.gov (United States)

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

2018-03-01

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

MU-LOC: A Machine-Learning Method for Predicting Mitochondrially Localized Proteins in Plants

DEFF Research Database (Denmark)

Zhang, Ning; Rao, R Shyama Prasad; Salvato, Fernanda

2018-01-01

-sequence or a multitude of internal signals. Compared with experimental approaches, computational predictions provide an efficient way to infer subcellular localization of a protein. However, it is still challenging to predict plant mitochondrially localized proteins accurately due to various limitations. Consequently......, the performance of current tools can be improved with new data and new machine-learning methods. We present MU-LOC, a novel computational approach for large-scale prediction of plant mitochondrial proteins. We collected a comprehensive dataset of plant subcellular localization, extracted features including amino...

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.

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.

Genome-scale prediction of proteins with long intrinsically disordered regions.

Science.gov (United States)

Peng, Zhenling; Mizianty, Marcin J; Kurgan, Lukasz

2014-01-01

Proteins with long disordered regions (LDRs), defined as having 30 or more consecutive disordered residues, are abundant in eukaryotes, and these regions are recognized as a distinct class of biologically functional domains. LDRs facilitate various cellular functions and are important for target selection in structural genomics. Motivated by the lack of methods that directly predict proteins with LDRs, we designed Super-fast predictor of proteins with Long Intrinsically DisordERed regions (SLIDER). SLIDER utilizes logistic regression that takes an empirically chosen set of numerical features, which consider selected physicochemical properties of amino acids, sequence complexity, and amino acid composition, as its inputs. Empirical tests show that SLIDER offers competitive predictive performance combined with low computational cost. It outperforms, by at least a modest margin, a comprehensive set of modern disorder predictors (that can indirectly predict LDRs) and is 16 times faster compared to the best currently available disorder predictor. Utilizing our time-efficient predictor, we characterized abundance and functional roles of proteins with LDRs over 110 eukaryotic proteomes. Similar to related studies, we found that eukaryotes have many (on average 30.3%) proteins with LDRs with majority of proteomes having between 25 and 40%, where higher abundance is characteristic to proteomes that have larger proteins. Our first-of-its-kind large-scale functional analysis shows that these proteins are enriched in a number of cellular functions and processes including certain binding events, regulation of catalytic activities, cellular component organization, biogenesis, biological regulation, and some metabolic and developmental processes. A webserver that implements SLIDER is available at http://biomine.ece.ualberta.ca/SLIDER/. Copyright © 2013 Wiley Periodicals, Inc.

Prediction and analysis of near-road concentrations using a reduced-form emission/dispersion model

Directory of Open Access Journals (Sweden)

Kononowech Robert

2010-06-01

Full Text Available Abstract Background Near-road exposures of traffic-related air pollutants have been receiving increased attention due to evidence linking emissions from high-traffic roadways to adverse health outcomes. To date, most epidemiological and risk analyses have utilized simple but crude exposure indicators, most typically proximity measures, such as the distance between freeways and residences, to represent air quality impacts from traffic. This paper derives and analyzes a simplified microscale simulation model designed to predict short- (hourly to long-term (annual average pollutant concentrations near roads. Sensitivity analyses and case studies are used to highlight issues in predicting near-road exposures. Methods Process-based simulation models using a computationally efficient reduced-form response surface structure and a minimum number of inputs integrate the major determinants of air pollution exposures: traffic volume and vehicle emissions, meteorology, and receptor location. We identify the most influential variables and then derive a set of multiplicative submodels that match predictions from "parent" models MOBILE6.2 and CALINE4. The assembled model is applied to two case studies in the Detroit, Michigan area. The first predicts carbon monoxide (CO concentrations at a monitoring site near a freeway. The second predicts CO and PM2.5 concentrations in a dense receptor grid over a 1 km2 area around the intersection of two major roads. We analyze the spatial and temporal patterns of pollutant concentration predictions. Results Predicted CO concentrations showed reasonable agreement with annual average and 24-hour measurements, e.g., 59% of the 24-hr predictions were within a factor of two of observations in the warmer months when CO emissions are more consistent. The highest concentrations of both CO and PM2.5 were predicted to occur near intersections and downwind of major roads during periods of unfavorable meteorology (e.g., low wind

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

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