Search results for: protein structure prediction
Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 11411

Search results for: protein structure prediction

11201 Comparative Rumen Degradable and Rumen Undegradable Fractions in Untreated, Formaldehyde and Heat Treated Vegetable Protein Sources of Pakistan

Authors: Illahi Bakhsh Marghazani, Nasrullah, Masood Ul Haq Kakar, Abdul Hameed Baloch, Ahmad Nawaz Khoso, Behram Chacher

Abstract:

Protein sources are the major part of ration fed to dairy buffaloes in Pakistan however, the limited availability and lack of judicious use of protein resources are further aggravating the conditions to enhance milk and meat production. In order to gain maximum production from limited protein source availability, it is necessary to balance feed for rumen degradable and rumen undegradable protein fractions. This study planned to know the rumen degradable and rumen undegradable fractions in all vegetable protein sources with (formaldehyde and heat treatment) and without treatments. Samples of soybean meal, corn gluten meal 60%, maize gluten feed, guar meal, sunflower meal, rapeseed meal, rapeseed cake, canola meal, cottonseed cake, cottonseed meal, coconut cake, coconut meal, palm kernel cake, almond cake and sesame cake were collected from ten different geographical locations of Pakistan. These samples were also subjected to formaldehyde (1% /100g CP of test feed) and heat treatments (1 hr at 15 lb psi/100 g CP of test feed). In situ technique was used to know the ruminal degradability characteristics. Data obtained were fitted to Orskove equation. Results showed that both treatments significantly (P < 0.05) decreased ruminal degradability in all vegetable protein sources than untreated vegetable protein sources, however, of both treatments, heat treatment was more effective than formaldehyde treatment in decreasing ruminal degradability in most of the studied vegetable protein sources.

Keywords: formaldehyde and heat treatments, in situ technique, rumen degradable and rumen undegradable fractions, vegetable protein sources

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11200 Prediction of CO2 Concentration in the Korea Train Express (KTX) Cabins

Authors: Yong-Il Lee, Do-Yeon Hwang, Won-Seog Jeong, Duckshin Park

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Recently, because of the high-speed trains forced ventilation, it is important to control the ventilation. The ventilation is for controlling various contaminants, temperature, and humidity. The high-speed train route is straight to a destination having a high speed. And there are many mountainous areas in Korea. So, tunnel rate is higher then other country. KTX HVAC block off the outdoor air, when entering tunnel. So the high tunnel rate is an effect of ventilation in the KTX cabin. It is important to reduction rate in CO2 concentration prediction. To meet the air quality of the public transport vehicles recommend standards, the KTX cabin of CO2 concentration should be managed. In this study, the concentration change was predicted by CO2 prediction simulation in route to be opened.

Keywords: CO2 prediction, KTX, ventilation, infrastructure and transportation engineering

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11199 Statistical Analysis with Prediction Models of User Satisfaction in Software Project Factors

Authors: Katawut Kaewbanjong

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We analyzed a volume of data and found significant user satisfaction in software project factors. A statistical significance analysis (logistic regression) and collinearity analysis determined the significance factors from a group of 71 pre-defined factors from 191 software projects in ISBSG Release 12. The eight prediction models used for testing the prediction potential of these factors were Neural network, k-NN, Naïve Bayes, Random forest, Decision tree, Gradient boosted tree, linear regression and logistic regression prediction model. Fifteen pre-defined factors were truly significant in predicting user satisfaction, and they provided 82.71% prediction accuracy when used with a neural network prediction model. These factors were client-server, personnel changes, total defects delivered, project inactive time, industry sector, application type, development type, how methodology was acquired, development techniques, decision making process, intended market, size estimate approach, size estimate method, cost recording method, and effort estimate method. These findings may benefit software development managers considerably.

Keywords: prediction model, statistical analysis, software project, user satisfaction factor

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11198 Calibration Model of %Titratable Acidity (Citric Acid) for Intact Tomato by Transmittance SW-NIR Spectroscopy

Authors: K. Petcharaporn, S. Kumchoo

Abstract:

The acidity (citric acid) is one of the chemical contents that can refer to the internal quality and the maturity index of tomato. The titratable acidity (%TA) can be predicted by a non-destructive method prediction by using the transmittance short wavelength (SW-NIR). Spectroscopy in the wavelength range between 665-955 nm. The set of 167 tomato samples divided into groups of 117 tomatoes sample for training set and 50 tomatoes sample for test set were used to establish the calibration model to predict and measure %TA by partial least squares regression (PLSR) technique. The spectra were pretreated with MSC pretreatment and it gave the optimal result for calibration model as (R = 0.92, RMSEC = 0.03%) and this model obtained high accuracy result to use for %TA prediction in test set as (R = 0.81, RMSEP = 0.05%). From the result of prediction in test set shown that the transmittance SW-NIR spectroscopy technique can be used for a non-destructive method for %TA prediction of tomatoes.

Keywords: tomato, quality, prediction, transmittance, titratable acidity, citric acid

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11197 An Improvement of ComiR Algorithm for MicroRNA Target Prediction by Exploiting Coding Region Sequences of mRNAs

Authors: Giorgio Bertolazzi, Panayiotis Benos, Michele Tumminello, Claudia Coronnello

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MicroRNAs are small non-coding RNAs that post-transcriptionally regulate the expression levels of messenger RNAs. MicroRNA regulation activity depends on the recognition of binding sites located on mRNA molecules. ComiR (Combinatorial miRNA targeting) is a user friendly web tool realized to predict the targets of a set of microRNAs, starting from their expression profile. ComiR incorporates miRNA expression in a thermodynamic binding model, and it associates each gene with the probability of being a target of a set of miRNAs. ComiR algorithms were trained with the information regarding binding sites in the 3’UTR region, by using a reliable dataset containing the targets of endogenously expressed microRNA in D. melanogaster S2 cells. This dataset was obtained by comparing the results from two different experimental approaches, i.e., inhibition, and immunoprecipitation of the AGO1 protein; this protein is a component of the microRNA induced silencing complex. In this work, we tested whether including coding region binding sites in the ComiR algorithm improves the performance of the tool in predicting microRNA targets. We focused the analysis on the D. melanogaster species and updated the ComiR underlying database with the currently available releases of mRNA and microRNA sequences. As a result, we find that the ComiR algorithm trained with the information related to the coding regions is more efficient in predicting the microRNA targets, with respect to the algorithm trained with 3’utr information. On the other hand, we show that 3’utr based predictions can be seen as complementary to the coding region based predictions, which suggests that both predictions, from 3'UTR and coding regions, should be considered in a comprehensive analysis. Furthermore, we observed that the lists of targets obtained by analyzing data from one experimental approach only, that is, inhibition or immunoprecipitation of AGO1, are not reliable enough to test the performance of our microRNA target prediction algorithm. Further analysis will be conducted to investigate the effectiveness of the tool with data from other species, provided that validated datasets, as obtained from the comparison of RISC proteins inhibition and immunoprecipitation experiments, will be available for the same samples. Finally, we propose to upgrade the existing ComiR web-tool by including the coding region based trained model, available together with the 3’UTR based one.

Keywords: AGO1, coding region, Drosophila melanogaster, microRNA target prediction

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11196 Ground Surface Temperature History Prediction Using Long-Short Term Memory Neural Network Architecture

Authors: Venkat S. Somayajula

Abstract:

Ground surface temperature history prediction model plays a vital role in determining standards for international nuclear waste management. International standards for borehole based nuclear waste disposal require paleoclimate cycle predictions on scale of a million forward years for the place of waste disposal. This research focuses on developing a paleoclimate cycle prediction model using Bayesian long-short term memory (LSTM) neural architecture operated on accumulated borehole temperature history data. Bayesian models have been previously used for paleoclimate cycle prediction based on Monte-Carlo weight method, but due to limitations pertaining model coupling with certain other prediction networks, Bayesian models in past couldn’t accommodate prediction cycle’s over 1000 years. LSTM has provided frontier to couple developed models with other prediction networks with ease. Paleoclimate cycle developed using this process will be trained on existing borehole data and then will be coupled to surface temperature history prediction networks which give endpoints for backpropagation of LSTM network and optimize the cycle of prediction for larger prediction time scales. Trained LSTM will be tested on past data for validation and then propagated for forward prediction of temperatures at borehole locations. This research will be beneficial for study pertaining to nuclear waste management, anthropological cycle predictions and geophysical features

Keywords: Bayesian long-short term memory neural network, borehole temperature, ground surface temperature history, paleoclimate cycle

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11195 Dissociation of Hydrophobic Interactions in Whey Protein Polymers: Molecular Characterization Using Dilute Solution Viscometry

Authors: Ahmed S. Eissa

Abstract:

Whey represents about 85-95% of the milk volume and about 55% of milk nutrients. Whey proteins are of special importance in formulated foods due to their rich nutritional and functional benefits. Whey proteins form large polymers upon heating to a temperature greater than the denaturation temperature. Hydrophobic interactions play an important role in building whey protein polymers. In this study, dissociation of hydrophobic interactions of whey protein polymers was done by adding Sodium Dodecyl Sulphonate (SDS). At low SDS concentrations, protein polymers were dissociated to smaller chains, as revealed by dilution solution viscometry (DSV). Interestingly, at higher SDS concentrations, polymer molecules got larger in size. Intrinsic viscosity was increased to many folds when raising the SDS concentration from 0.5% to 2%. Complex molecular arrangement leads to the formation of larger macromolecules, due to micelle formation. The study opens a venue for manipulating and enhancing whey protein functional properties by manipulating the hydrophobic interactions.

Keywords: whey proteins, hydrophobic interactions, SDS

Procedia PDF Downloads 215
11194 A Galectin from Rock Bream Oplegnathus fasciatus: Molecular Characterization and Immunological Properties

Authors: W. S. Thulasitha, N. Umasuthan, G. I. Godahewa, Jehee Lee

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In fish, innate immune defense is the first immune response against microbial pathogens which consists of several antimicrobial components. Galectins are one of the carbohydrate binding lectins that have the ability to identify pathogen by recognition of pathogen associated molecular patterns. Galectins play a vital role in the regulation of innate and adaptive immune responses. Rock bream Oplegnathus fasciatus is one of the most important cultured species in Korea and Japan. Considering the losses due to microbial pathogens, present study was carried out to understand the molecular and functional characteristics of a galectin in normal and pathogenic conditions, which could help to establish an understanding about immunological components of rock bream. Complete cDNA of rock bream galectin like protein B (rbGal like B) was identified from the cDNA library, and the in silico analysis was carried out using bioinformatic tools. Genomic structure was derived from the BAC library by sequencing a specific clone and using Spidey. Full length of rbGal like B (contig14775) cDNA containing 517 nucleotides was identified from the cDNA library which comprised of 435 bp in the open reading frame encoding a deduced protein composed of 145 amino acids. The molecular mass of putative protein was predicted as 16.14 kDa with an isoelectric point of 8.55. A characteristic conserved galactose binding domain was located from 12 to 145 amino acids. Genomic structure of rbGal like B consisted of 4 exons and 3 introns. Moreover, pairwise alignment showed that rock bream rbGal like B shares highest similarity (95.9 %) and identity (91 %) with Takifugu rubripes galectin related protein B like and lowest similarity (55.5 %) and identity (32.4 %) with Homo sapiens. Multiple sequence alignment demonstrated that the galectin related protein B was conserved among vertebrates. A phylogenetic analysis revealed that rbGal like B protein clustered together with other fish homologs in fish clade. It showed closer evolutionary link with Takifugu rubripes. Tissue distribution and expression patterns of rbGal like B upon immune challenges were performed using qRT-PCR assays. Among all tested tissues, level of rbGal like B expression was significantly high in gill tissue followed by kidney, intestine, heart and spleen. Upon immune challenges, it showed an up-regulated pattern of expression with Edwardsiella tarda, rock bream irido virus and poly I:C up to 6 h post injection and up to 24 h with LPS. However, In the presence of Streptococcus iniae rbGal like B showed an up and down pattern of expression with the peak at 6 - 12 h. Results from the present study revealed the phylogenetic position and role of rbGal like B in response to microbial infection in rock bream.

Keywords: galectin like protein B, immune response, Oplegnathus fasciatus, molecular characterization

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11193 Removal of Protein from Chromium Tanning Bath by Biological Treatment Using Pseudomonas sp.

Authors: Amel Benhadji, Mourad Taleb Ahmed, Rachida Maachi

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The challenge for the new millennium is to develop an industrial system that has minimal socio-ecological impacts, without compromising quality of life. Leather industry is one of these industries demanding environmentally friendly products. In this study, we investigated the possibility of applying innovative low cost biological treatment using Pseudomonas aeruginosa. This strain tested the efficiency of the batch biological treatment in the recovery of protein and hexavalent chromium from chromium tanning bath. We have compared suspended and fixed bacteria culture. The results showed the removal of the total protein of treatment and a decrease of hexavalent chromium concentration is during the treatment. The better efficiency of the biological treatment is obtained when using fixed culture of P. aeruginosa.

Keywords: tanning wastewater, biological treatment, protein removal, hexavalent chromium

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11192 Recovery of Proteins from EDAM Whey Using Membrane Ultrafiltration

Authors: F. Yelles-Allam, A. A. Nouani

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In Algeria, whey is discarded without any treatment and this causes not only pollution problem, but also a loss in nutritive components of milk. In this paper, characterization of EDAM whey, which is resulted from pasteurised mixture of cow’s milk and skim milk, and recovery of whey protein by ultrafiltration / diafiltration, was studied. The physical-chemical analysis of whey has emphasized on its pollutant and nutritive characteristics. In fact, its DBO5 and DCO are 49.33, and 127.71 gr of O2/l of whey respectively. It contains: fat (1,90±0,1 gr/l), lactose (47.32±1,57 gr/l), proteins (8.04±0,2 gr/l) and ashes (5,20±0,15 gr/l), calcium (0,48±0,04 gr/l), Na (1.104gr/l), K (1.014 gr/l), Mg (0.118 gr/l) and P (0.482 gr/l). Ultrafiltration was carried out in a polyetersulfone membrane with a cut-off of 10K. Its hydraulic intrinsic resistance and permeability are respectively: 2.041.1012 m-1 and 176,32 l/h.m2 at PTM of 1 bar. The retentate obtained at FC6, contains 16,33g/l of proteins and 70,25 g/l of dry matter. The retention rate of protein is 97, 7% and the decrease in DBO5 and DCO are at 18.875 g /l and 42.818 g/l respectively. Diafiltration performed on protein concentrates allowed the complete removal of lactose and minerals. The ultrafiltration of the whey before the disposal is an alternative for Algéria dairy industry.

Keywords: diafiltration, DBO, DCO, protein, ultrafiltration, whey

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11191 Hybrid Fuzzy Weighted K-Nearest Neighbor to Predict Hospital Readmission for Diabetic Patients

Authors: Soha A. Bahanshal, Byung G. Kim

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Identification of patients at high risk for hospital readmission is of crucial importance for quality health care and cost reduction. Predicting hospital readmissions among diabetic patients has been of great interest to many researchers and health decision makers. We build a prediction model to predict hospital readmission for diabetic patients within 30 days of discharge. The core of the prediction model is a modified k Nearest Neighbor called Hybrid Fuzzy Weighted k Nearest Neighbor algorithm. The prediction is performed on a patient dataset which consists of more than 70,000 patients with 50 attributes. We applied data preprocessing using different techniques in order to handle data imbalance and to fuzzify the data to suit the prediction algorithm. The model so far achieved classification accuracy of 80% compared to other models that only use k Nearest Neighbor.

Keywords: machine learning, prediction, classification, hybrid fuzzy weighted k-nearest neighbor, diabetic hospital readmission

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11190 Isolation and Characterization White Spot Syndrome Protein Envelope Protein 19 from Black Tiger Shrimp (Penaeus monodon)

Authors: Andi Aliah Hidayani, Asmi Citra Malina A. R. Tassakka, Andi Parenrengi

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Vanname Shrimp is one of the high yielding varieties that are more resistant to virus attacks. However, now this shrimp more death due to virus attack such as white spot disease caused by white spot syndrome virus (WSSV). Various efforts have done to prevent the disease, like immunostimulatory, probiotics, and vaccine. White spot syndrome virus (WSSV) envelope protein VP19 gene is important because of its involvement in the system infection of shrimp. This study aimed to isolate and characterize an envelope protein VP19 – encoding gene of WSSV using WSSV infected Vanname Shrimp sample from some areas in South Sulawesi (Pangkep, Barru and Pinrang). The genomic of DNA were isolated from shrimp muscle using DTAB-CTAB method. Isolation of gene encoding envelope protein VP19 WSSV ws successfully performed with the results of the length of DNA fragment was 387 bp. The results of homology analysis using BLASTn homology suggested that these isolates genes from Barru, Pangkep and Pinrang have closest relationship with isolates from Mexican.

Keywords: vanname, shrimp, WSSV, viral protein 19

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11189 EverPro as the Missing Piece in the Plant Protein Portfolio to Aid the Transformation to Sustainable Food Systems

Authors: Aylin W Sahin, Alice Jaeger, Laura Nyhan, Gregory Belt, Steffen Münch, Elke K. Arendt

Abstract:

Our current food systems cause an increase in malnutrition resulting in more people being overweight or obese in the Western World. Additionally, our natural resources are under enormous pressure and the greenhouse gas emission increases yearly with a significant contribution to climate change. Hence, transforming our food systems is of highest priority. Plant-based food products have a lower environmental impact compared to their animal-based counterpart, representing a more sustainable protein source. However, most plant-based protein ingredients, such as soy and pea, are lacking indispensable amino acids and extremely limited in their functionality and, thus, in their food application potential. They are known to have a low solubility in water and change their properties during processing. The low solubility displays the biggest challenge in the development of milk alternatives leading to inferior protein content and protein quality in dairy alternatives on the market. Moreover, plant-based protein ingredients often possess an off-flavour, which makes them less attractive to consumers. EverPro, a plant-protein isolate originated from Brewer’s Spent Grain, the most abundant by-product in the brewing industry, represents the missing piece in the plant protein portfolio. With a protein content of >85%, it is of high nutritional value, including all indispensable amino acids which allows closing the protein quality gap of plant proteins. Moreover, it possesses high techno-functional properties. It is fully soluble in water (101.7 ± 2.9%), has a high fat absorption capacity (182.4 ± 1.9%), and a foaming capacity which is superior to soy protein or pea protein. This makes EverPro suitable for a vast range of food applications. Furthermore, it does not cause changes in viscosity during heating and cooling of dispersions, such as beverages. Besides its outstanding nutritional and functional characteristics, the production of EverPro has a much lower environmental impact compared to dairy or other plant protein ingredients. Life cycle assessment analysis showed that EverPro has the lowest impact on global warming compared to soy protein isolate, pea protein isolate, whey protein isolate, and egg white powder. It also contributes significantly less to freshwater eutrophication, marine eutrophication and land use compared the protein sources mentioned above. EverPro is the prime example of sustainable ingredients, and the type of plant protein the food industry was waiting for: nutritious, multi-functional, and environmentally friendly.

Keywords: plant-based protein, upcycled, brewers' spent grain, low environmental impact, highly functional ingredient

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11188 Using High Performance Computing for Online Flood Monitoring and Prediction

Authors: Stepan Kuchar, Martin Golasowski, Radim Vavrik, Michal Podhoranyi, Boris Sir, Jan Martinovic

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The main goal of this article is to describe the online flood monitoring and prediction system Floreon+ primarily developed for the Moravian-Silesian region in the Czech Republic and the basic process it uses for running automatic rainfall-runoff and hydrodynamic simulations along with their calibration and uncertainty modeling. It takes a long time to execute such process sequentially, which is not acceptable in the online scenario, so the use of high-performance computing environment is proposed for all parts of the process to shorten their duration. Finally, a case study on the Ostravice river catchment is presented that shows actual durations and their gain from the parallel implementation.

Keywords: flood prediction process, high performance computing, online flood prediction system, parallelization

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11187 Structure and Mechanics Patterns in the Assembly of Type V Intermediate-Filament Protein-Based Fibers

Authors: Mark Bezner, Shani Deri, Tom Trigano, Kfir Ben-Harush

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Intermediate filament (IF) proteins-based fibers are among the toughest fibers in nature, as was shown by native hagfish slime threads and by synthetic fibers that are based on type V IF-proteins, the nuclear lamins. It is assumed that their mechanical performance stems from two major factors: (1) the transition from elastic -helices to stiff-sheets during tensile load; and (2) the specific organization of the coiled-coil proteins into a hierarchical network of nano-filaments. Here, we investigated the interrelationship between these two factors by using wet-spun fibers based on C. elegans (Ce) lamin. We found that Ce-lamin fibers, whether assembled in aqueous or alcoholic solutions, had the same nonlinear mechanical behavior, with the elastic region ending at ~5%. The pattern of the transition was, however, different: the ratio between -helices and -sheets/random coils was relatively constant until a 20% strain for fibers assembled in an aqueous solution, whereas for fibers assembled in 70% ethanol, the transition ended at a 6% strain. This structural phenomenon in alcoholic solution probably occurred through the transition between compacted and extended conformation of the random coil, and not between -helix and -sheets, as cycle analyses had suggested. The different transition pattern can also be explained by the different higher order organization of Ce-lamins in aqueous or alcoholic solutions, as demonstrated by introducing a point mutation in conserved residue in Ce-lamin gene that alter the structure of the Ce-lamins’ nano-fibrils. In addition, biomimicking the layered structure of silk and hair fibers by coating the Ce-lamin fiber with a hydrophobic layer enhanced fiber toughness and lead to a reversible transition between -helix and the extended conformation. This work suggests that different hierarchical structures, which are formed by specific assembly conditions, lead to diverse secondary structure transitions patterns, which in turn affect the fibers’ mechanical properties.

Keywords: protein-based fibers, intermediate filaments (IF) assembly, toughness, structure-property relationships

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11186 Prediction of PM₂.₅ Concentration in Ulaanbaatar with Deep Learning Models

Authors: Suriya

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Rapid socio-economic development and urbanization have led to an increasingly serious air pollution problem in Ulaanbaatar (UB), the capital of Mongolia. PM₂.₅ pollution has become the most pressing aspect of UB air pollution. Therefore, monitoring and predicting PM₂.₅ concentration in UB is of great significance for the health of the local people and environmental management. As of yet, very few studies have used models to predict PM₂.₅ concentrations in UB. Using data from 0:00 on June 1, 2018, to 23:00 on April 30, 2020, we proposed two deep learning models based on Bayesian-optimized LSTM (Bayes-LSTM) and CNN-LSTM. We utilized hourly observed data, including Himawari8 (H8) aerosol optical depth (AOD), meteorology, and PM₂.₅ concentration, as input for the prediction of PM₂.₅ concentrations. The correlation strengths between meteorology, AOD, and PM₂.₅ were analyzed using the gray correlation analysis method; the comparison of the performance improvement of the model by using the AOD input value was tested, and the performance of these models was evaluated using mean absolute error (MAE) and root mean square error (RMSE). The prediction accuracies of Bayes-LSTM and CNN-LSTM deep learning models were both improved when AOD was included as an input parameter. Improvement of the prediction accuracy of the CNN-LSTM model was particularly enhanced in the non-heating season; in the heating season, the prediction accuracy of the Bayes-LSTM model slightly improved, while the prediction accuracy of the CNN-LSTM model slightly decreased. We propose two novel deep learning models for PM₂.₅ concentration prediction in UB, Bayes-LSTM, and CNN-LSTM deep learning models. Pioneering the use of AOD data from H8 and demonstrating the inclusion of AOD input data improves the performance of our two proposed deep learning models.

Keywords: deep learning, AOD, PM2.5, prediction, Ulaanbaatar

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11185 Folding of β-Structures via the Polarized Structure-Specific Backbone Charge (PSBC) Model

Authors: Yew Mun Yip, Dawei Zhang

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Proteins are the biological machinery that executes specific vital functions in every cell of the human body by folding into their 3D structures. When a protein misfolds from its native structure, the machinery will malfunction and lead to misfolding diseases. Although in vitro experiments are able to conclude that the mutations of the amino acid sequence lead to incorrectly folded protein structures, these experiments are unable to decipher the folding process. Therefore, molecular dynamic (MD) simulations are employed to simulate the folding process so that our improved understanding of the folding process will enable us to contemplate better treatments for misfolding diseases. MD simulations make use of force fields to simulate the folding process of peptides. Secondary structures are formed via the hydrogen bonds formed between the backbone atoms (C, O, N, H). It is important that the hydrogen bond energy computed during the MD simulation is accurate in order to direct the folding process to the native structure. Since the atoms involved in a hydrogen bond possess very dissimilar electronegativities, the more electronegative atom will attract greater electron density from the less electronegative atom towards itself. This is known as the polarization effect. Since the polarization effect changes the electron density of the two atoms in close proximity, the atomic charges of the two atoms should also vary based on the strength of the polarization effect. However, the fixed atomic charge scheme in force fields does not account for the polarization effect. In this study, we introduce the polarized structure-specific backbone charge (PSBC) model. The PSBC model accounts for the polarization effect in MD simulation by updating the atomic charges of the backbone hydrogen bond atoms according to equations derived between the amount of charge transferred to the atom and the length of the hydrogen bond, which are calculated from quantum-mechanical calculations. Compared to other polarizable models, the PSBC model does not require quantum-mechanical calculations of the peptide simulated at every time-step of the simulation and maintains the dynamic update of atomic charges, thereby reducing the computational cost and time while accounting for the polarization effect dynamically at the same time. The PSBC model is applied to two different β-peptides, namely the Beta3s/GS peptide, a de novo designed three-stranded β-sheet whose structure is folded in vitro and studied by NMR, and the trpzip peptides, a double-stranded β-sheet where a correlation is found between the type of amino acids that constitute the β-turn and the β-propensity.

Keywords: hydrogen bond, polarization effect, protein folding, PSBC

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11184 Role of Mismatch Repair Protein Expression in Colorectal Cancer: A Study from North India

Authors: Alka Yadav, Mayank Jain, Rajan Saxena, Niraj Kumari, Narendra Krishnani, Ashok Kumar

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Purpose: To study the mismatch repair (MMR) protein expression and its clinicopathological correlation in colorectal cancer patients in North India. Methods: A prospective study was conducted on histologically proven 52 (38 males and 14 females) patients with adenocarcinoma of colorectum. MMR protein loss was determined by using immunohistochemistry for MLH1, MSH2, PMS2 and MSH6. Results: 52 patients (38 males and 14 females) underwent resection for colorectal cancer with the median age of 52 years (16-81 years). 35% of the patients (n=18) were younger than 50 years of the age. 3 patients had associated history of malignancy in the family. 29 (56%) patients had right colon cancer, 9 (17%) left colon cancer and 14 (27%) rectal cancer. 2 patients each had synchronous and metachronous cancer. Histology revealed well-differentiated tumour in 16, moderately differentiated in 10 and poorly differentiated tumour in 26 patients. MMR protein loss was seen in 15 (29%) patients. Seven (46%) of these patients were less than 50 years of age. Combined loss of MSH2 and MSH6 was seen most commonly and it was found in 6 patients. 12 (80%) patients with MMR protein loss had tumour located proximal to the splenic flexure compared to 3 (20%) located distal to the splenic flexure. There was no difference in MMR protein loss based on patients' age, gender, degree of tumour differentiation, stage of the disease and tumour histological characteristics. Conclusions: This study revealed that there was less than 30% MMR protein loss in colorectal cancer patients. The loss was most commonly seen in right sided colon cancer than left. A larger study is further required to validate these findings.

Keywords: colorectal cancer, mismatch repair protein, immunohitochemistry, clinicopathological correlation

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11183 Recent Trend in Gluten-Free Bakery Products

Authors: Madhuresh Dwivedi, Navneet Singh Deora, H. N. Mishra

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In the context of bakery products, the gluten component of wheat has a crucial role in stabilizing the gas-cell and crumb structures, appearance, mouth feel and maintaining the rheological properties, thus the acceptability of these products. However, because of coeliac disease, some individuals cannot tolerate the protein gliadin present in the gluten fraction of wheat flour. Also termed as gluten-sensitive enteropathy, it is a common chronicle disorder in populations throughout the world with average prevalence of 0.37%. The safest way for celiac sufferers is to stay away from gluten-containing foods such as wheat, rye, barley as well as durum wheat, spelt wheat, and triticale. Thus, in view of the current increasing incidence of gluten intolerant sufferers (due to improved diagnostic procedures), the development of gluten-free cereal-based bakery products suitable for celiac patients represents a challenging and serious task, but also very demanding call for food technologists as well as for the bakers. The use of alternative cereal starches (like rice, soy, maize, potato and so on), gums, hydrocolloids, dietary fibres, alternative protein sources, prebiotics and combinations of them represent the most widespread approach used as replacement to mimic gluten in the manufacture of industrial processable gluten-free bakery products due to their structure-building and water binding properties.

Keywords: gluten-free, coeliac disease, alternative flour, hydrocolloid, crumb structure

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11182 Targetting T6SS of Klebsiella pneumoniae for Assessment of Immune Response in Mice for Therapeutic Lead Development

Authors: Sweta Pandey, Samridhi Dhyani, Susmita Chaudhuri

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Klebsiella pneumoniae bacteria is a global threat to human health due to an increase in multi-drug resistance among strains. The hypervirulent strains of Klebsiella pneumoniae is a major trouble due to their association with life-threatening infections in a healthy population. One of the major virulence factors of hyper virulent strains of Klebsiella pneumoniae is the T6SS (Type six secretary system) which is majorly involved in microbial antagonism and causes interaction with the host eukaryotic cells during infections. T6SS mediates some of the crucial factors for establishing infection by the bacteria, such as cell adherence, invasion, and subsequent in vivo colonisation. The antibacterial activity and the cell invasion property of the T6SS system is a major requirement for the establishment of K. pneumoniae infections within the gut. The T6SS can be an appropriate target for developing therapeutics. The T6SS consists of an inner tube comprising hexamers of Hcp (Haemolysin -regulated protein) protein, and at the top of this tube sits VgrG (Valine glycine repeat protein G); the tip of the machinery consists of PAAR domain containing proteins which act as a delivery system for bacterial effectors. For this study, immune response to recombinant VgrG protein was generated to establish this protein as a potential immunogen for the development of therapeutic leads. The immunogenicity of the selected protein was determined by predicting the B cell epitopes by the BCEP analysis tool. The gene sequence for multiple domains of VgrG protein (phage_base_V, T6SS_Vgr, DUF2345) was selected and cloned in pMAL vector in E. coli. The construct was subcloned and expressed as a fusion protein of 203 residue protein with mannose binding protein tag (MBP) to enhance solubility and purification of this protein. The purified recombinant VgrG fusion protein was used for mice immunisation. The antiserum showed reactivity with the recombinant VgrG in ELISA and western blot. The immunised mice were challenged with K. pneumoniae bacteria and showed bacterial clearance in immunised mice. The recombinant VgrG protein can further be used for studying downstream signalling of VgrG protein in mice during infection and for therapeutic MAb development to eradicate K. pneumoniae infections.

Keywords: immune response, Klebsiella pneumoniae, multi-drug resistance, recombinant protein expression, T6SS, VgrG

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11181 Purification and Pre-Crystallization of Recombinant PhoR Cytoplasmic Domain Protein from Mycobacterium Tuberculosis H37Rv

Authors: Oktira Roka Aji, Maelita R. Moeis, Ihsanawati, Ernawati A. Giri-Rachman

Abstract:

Globally, tuberculosis (TB) remains a leading cause of death. The emergence of multidrug-resistant strains and extensively drug-resistant strains have become a major public concern. One of the potential candidates for drug target is the cytoplasmic domain of PhoR Histidine Kinase, a part of the Two Component System (TCS) PhoR-PhoP in Mycobacterium tuberculosis (Mtb). TCS PhoR-PhoP relay extracellular signal to control the expression of 114 virulent associated genes in Mtb. The 3D structure of PhoR cytoplasmic domain is needed to screen novel drugs using structure based drug discovery. The PhoR cytoplasmic domain from Mtb H37Rv was overexpressed in E. coli BL21(DE3), then purified using IMAC Ni-NTA Agarose his-tag affinity column and DEAE-ion exchange column chromatography. The molecular weight of the purified protein was estimated to be 37 kDa after SDS-PAGE analysis. This sample was used for pre-crystallization screening by applying sitting drop vapor diffusion method using Natrix (HR2-116) 48 solutions crystal screen kit at 25ºC. Needle-like crystals were observed after the seventh day of incubation in test solution No.47 (0.1 M KCl, 0.01 M MgCl2.6H2O, 0.05 M Tris-Cl pH 8.5, 30% v/v PEG 4000). Further testing is required for confirming the crystal.

Keywords: tuberculosis, two component system, histidine kinase, needle-like crystals

Procedia PDF Downloads 408
11180 Probabilistic-Based Design of Bridges under Multiple Hazards: Floods and Earthquakes

Authors: Kuo-Wei Liao, Jessica Gitomarsono

Abstract:

Bridge reliability against natural hazards such as floods or earthquakes is an interdisciplinary problem that involves a wide range of knowledge. Moreover, due to the global climate change, engineers have to design a structure against the multi-hazard threats. Currently, few of the practical design guideline has included such concept. The bridge foundation in Taiwan often does not have a uniform width. However, few of the researches have focused on safety evaluation of a bridge with a complex pier. Investigation of the scouring depth under such situation is very important. Thus, this study first focuses on investigating and improving the scour prediction formula for a bridge with complicated foundation via experiments and artificial intelligence. Secondly, a probabilistic design procedure is proposed using the established prediction formula for practical engineers under the multi-hazard attacks.

Keywords: bridge, reliability, multi-hazards, scour

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11179 Analytical Modeling of Globular Protein-Ferritin in α-Helical Conformation: A White Noise Functional Approach

Authors: Vernie C. Convicto, Henry P. Aringa, Wilson I. Barredo

Abstract:

This study presents a conformational model of the helical structures of globular protein particularly ferritin in the framework of white noise path integral formulation by using Associated Legendre functions, Bessel and convolution of Bessel and trigonometric functions as modulating functions. The model incorporates chirality features of proteins and their helix-turn-helix sequence structural motif.

Keywords: globular protein, modulating function, white noise, winding probability

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11178 Determination of Yield and Some Quality Characteristics of Winter Canola (Brassica napus ssp. oleifera L.) Cultivars

Authors: B. Coşgun, O. Ozturk

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Canola is a specific edible type of rapeseed, developed in the 1970s, which contains about 40 percent oil. This research was carried out to determine the yield and some quality characteristics of some winter canola cultivars during the 2010-2011 vegetation period in Central Anatolia of Turkey. In this research; Oase, Dante, Californium, Excalibur, Elvis, ES Hydromel, Licord, Orkan, Vectra, Nelson, Champlain and NK Petrol winter canola varieties were used as material. The field experiment was set up in a “Randomized Complete Block Design” with three replications on 21 September 2010. In this research; seed yield, oil content, protein content, oil yield and protein yield were examined. As a result of this research; seed yield, oil content, oil yield and protein yield (except protein content) were significant differences between the cultivars. The highest seed yield (6348 kg ha-1) was obtained from the NK Petrol, while the lowest seed yield (3949 kg ha-1) was determined from the Champlain cultivar was obtained. The highest oil content (46.73%) was observed from Oase and the lowest value was obtained from Vectra (41.87%) cultivar. The highest oil yield (2950 kg ha-1) was determined from NK Petrol while the least value (1681 kg ha-1) was determined from Champlain cultivar. The highest protein yield (1539.3 kg ha-1) was obtained from NK Petrol and the lowest protein yield (976.5 kg ha-1) was obtained from Champlain cultivar. The main purpose of the cultivation of oil crops, to increase the yield of oil per unit area. According the result of this research, NK Petrol cultivar which ranks first with regard to both seed yield and oil yield between cultivars as the most suitable winter canola cultivar of local conditions.

Keywords: rapeseed, cultivar, seed yield, crude oil ratio, crude protein ratio, crude oil yield, crude protein yield

Procedia PDF Downloads 243
11177 Life Prediction of Condenser Tubes Applying Fuzzy Logic and Neural Network Algorithms

Authors: A. Majidian

Abstract:

The life prediction of thermal power plant components is necessary to prevent the unexpected outages, optimize maintenance tasks in periodic overhauls and plan inspection tasks with their schedules. One of the main critical components in a power plant is condenser because its failure can affect many other components which are positioned in downstream of condenser. This paper deals with factors affecting life of condenser. Failure rates dependency vs. these factors has been investigated using Artificial Neural Network (ANN) and fuzzy logic algorithms. These algorithms have shown their capabilities as dynamic tools to evaluate life prediction of power plant equipments.

Keywords: life prediction, condenser tube, neural network, fuzzy logic

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11176 Interaction Effects of Dietary Ginger, Zingiber Officinale, on Plasma Protein Fractions in Rainbow Trout, Oncorhynchus Mykiss

Authors: Ali Taheri Mirghaed, Sara Ahani, Ashkan Zargar, Seyyed Morteza Hoseini

Abstract:

Diseases are the major challenges in intensive aquaculture that cause significant annual losses. Antibiotic-therapy is a common way to control bacterial disease in fish, and oxytetracycline (OTC) is the only oral antibiotic in aquaculture approved FDA. OTC has been found to have negative effects on fish, such as oxidative stress and immune-suppression, thus, it is necessary to mitigate such effects. Medicinal herbs have various benefits on fish, including antioxidant, immunostimulant, and anti-microbial effects. Therefore, we hypothesized if dietary ginger meal (GM) interacts with dietary OTC by monitoring plasma protein fractions in rainbow trout. The study was conducted as a 2 × 2 factorial design, including diets containing 0 and 1% GM and 0 and 1.66 % OTC (corresponding to 100 mg/kg fish biomass per day). After ten days treating the fish (60 g individual weight) with these feeds, blood samples were taken from al treatments (n =3). Plasma was separated by centrifugation, and protein fractions were determined by electrophoresis. The results showed that OTC and GM had interaction effects on total protein (P<0.001), albumin (P<0.001), alpha-1 fraction (P=0.010), alpha-2 fraction (P=0.001), beta-2 fraction (P=0.014), and gamma fraction (P<0.001). Beta-1 fraction was significantly (P=0.030) affected by dietary GM. GM decreased plasma total protein, albumin, and beta-2 but increased beta-1 fraction. OTC significantly decreased total protein (P<0.001), albumin (P=0.001), alpha-2 fraction (P<0.001), beta-2 fraction (P=0.004), and gamma fraction (P<0.001) but had no significant effects on alpha-1 and beta-1 fractions. Dietary GM inhibited/suppressed the effects of dietary OTC on the plasma total protein and protein fractions. In conclusion, adding 1% GM to diet can mitigate the negative effects of dietary OTC on plasma proteins. Thus, GM may boost health of rainbow trout during the period of medication with OTC.

Keywords: ginger, plasma protein electrophoresis, dietary additive, rainbow trout

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11175 Tailorability of Poly(Aspartic Acid)/BSA Complex by Self-Assembling in Aqueous Solutions

Authors: Loredana E. Nita, Aurica P. Chiriac, Elena Stoleru, Alina Diaconu, Tudorachi Nita

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Self-assembly processes are an attractive method to form new and complex structures between macromolecular compounds to be used for specific applications. In this context, intramolecular and intermolecular bonds play a key role during self-assembling processes in preparation of carrier systems of bioactive substances. Polyelectrolyte complexes (PECs) are formed through electrostatic interactions, and though they are significantly below of the covalent linkages in their strength, these complexes are sufficiently stable owing to the association processes. The relative ease way of PECs formation makes from them a versatile tool for preparation of various materials, with properties that can be tuned by adjusting several parameters, such as the chemical composition and structure of polyelectrolytes, pH and ionic strength of solutions, temperature and post-treatment procedures. For example, protein-polyelectrolyte complexes (PPCs) are playing an important role in various chemical and biological processes, such as protein separation, enzyme stabilization and polymer drug delivery systems. The present investigation is focused on evaluation of the PPC formation between a synthetic polypeptide (poly(aspartic acid) – PAS) and a natural protein (bovine serum albumin - BSA). The PPC obtained from PAS and BSA in different ratio was investigated by corroboration of various techniques of characterization as: spectroscopy, microscopy, thermo-gravimetric analysis, DLS and zeta potential determination, measurements which were performed in static and/or dynamic conditions. The static contact angle of the sample films was also determined in order to evaluate the changes brought upon surface free energy of the prepared PPCs in interdependence with the complexes composition. The evolution of hydrodynamic diameter and zeta potential of the PPC, recorded in situ, confirm changes of both co-partners conformation, a 1/1 ratio between protein and polyelectrolyte being benefit for the preparation of a stable PPC. Also, the study evidenced the dependence of PPC formation on the temperature of preparation. Thus, at low temperatures the PPC is formed with compact structure, small dimension and hydrodynamic diameter, close to those of BSA. The behavior at thermal treatment of the prepared PPCs is in agreement with the composition of the complexes. From the contact angle determination results the increase of the PPC films cohesion, which is higher than that of BSA films. Also, a higher hydrophobicity corresponds to the new PPC films denoting a good adhesion of the red blood cells onto the surface of PSA/BSA interpenetrated systems. The SEM investigation evidenced as well the specific internal structure of PPC concretized in phases with different size and shape in interdependence with the interpolymer mixture composition.

Keywords: polyelectrolyte – protein complex, bovine serum albumin, poly(aspartic acid), self-assembly

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11174 Use RP-HPLC To Investigate Factors Influencing Sorghum Protein Extraction

Authors: Khaled Khaladi, Rafika Bibi, Hind Mokrane, Boubekeur Nadjemi

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Sorghum (Sorghum bicolor (L.) Moench) is an important cereal crop grown in the semi-arid tropics of Africa and Asia due to its drought tolerance. Sorghum grain has protein content varying from 6 to 18%, with an average of 11%, Sorghum proteins can be broadly classified into prolamin and non-prolamin proteins. Kafirins, the major storage proteins, are classified as prolamins, and as such, they contain high levels of proline and glutamine and are soluble in non-polar solvents such as aqueous alcohols. Kafirins account for 77 to 82% of the protein in the endosperm, whereas non-prolamin proteins (namely, albumins, globulins, and glutelins) make up about 30% of the proteins. To optimize the extraction of sorghum proteins, several variables were examined: detergent type and concentration, reducing agent type and concentration, and buffer pH and concentration. Samples were quantified and characterized by RP-HPLC.

Keywords: sorghum, protein extraction, detergent, food science

Procedia PDF Downloads 287
11173 Quantifying the Protein-Protein Interaction between the Ion-Channel-Forming Colicin A and the Tol Proteins by Potassium Efflux in E. coli Cells

Authors: Fadilah Aleanizy

Abstract:

Colicins are a family of bacterial toxins that kill Escherichia coli and other closely related species. The mode of action of colicins involves binding to an outer membrane receptor and translocation across the cell envelope, leading to cytotoxicity through specific targets. The mechanism of colicin cytotoxicity includes a non-specific endonuclease activity or depolarization of the cytoplasmic membrane by pore-forming activity. For Group A colicins, translocation requires an interaction between the N-terminal domain of the colicin and a series of membrane- bound and periplasmic proteins known as the Tol system (TolB, TolR, TolA, TolQ, and Pal and the active domain must be translocated through the outer membranes. Protein-protein interactions are intrinsic to virtually every cellular process. The transient protein-protein interactions of the colicin include the interaction with much more complicated assemblies during colicin translocation across the cellular membrane to its target. The potassium release assay detects variation in the K+ content of bacterial cells (K+in). This assays is used to measure the effect of pore-forming colicins such as ColA on an indicator organism by measuring the changes of the K+ concentration in the external medium (K+out ) that are caused by cell killing with a K+ selective electrode. One of the goals of this work is to employ a quantifiable in-vivo method to spot which Tol protein are more implicated in the interaction with colicin A as it is translocated to its target.

Keywords: K+ efflux, Colicin A, Tol-proteins, E. coli

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11172 SNR Classification Using Multiple CNNs

Authors: Thinh Ngo, Paul Rad, Brian Kelley

Abstract:

Noise estimation is essential in today wireless systems for power control, adaptive modulation, interference suppression and quality of service. Deep learning (DL) has already been applied in the physical layer for modulation and signal classifications. Unacceptably low accuracy of less than 50% is found to undermine traditional application of DL classification for SNR prediction. In this paper, we use divide-and-conquer algorithm and classifier fusion method to simplify SNR classification and therefore enhances DL learning and prediction. Specifically, multiple CNNs are used for classification rather than a single CNN. Each CNN performs a binary classification of a single SNR with two labels: less than, greater than or equal. Together, multiple CNNs are combined to effectively classify over a range of SNR values from −20 ≤ SNR ≤ 32 dB.We use pre-trained CNNs to predict SNR over a wide range of joint channel parameters including multiple Doppler shifts (0, 60, 120 Hz), power-delay profiles, and signal-modulation types (QPSK,16QAM,64-QAM). The approach achieves individual SNR prediction accuracy of 92%, composite accuracy of 70% and prediction convergence one order of magnitude faster than that of traditional estimation.

Keywords: classification, CNN, deep learning, prediction, SNR

Procedia PDF Downloads 100