Search results for: protein prediction

Authors: Xiao Zhou, Jianlin Cheng

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A single amino acid mutation can have a significant impact on the stability of protein structure. Thus, the prediction of protein stability change induced by single site mutations is critical and useful for studying protein function and structure. Here, we presented a deep learning network with the dropout technique for predicting protein stability changes upon single amino acid substitution. While using only protein sequence as input, the overall prediction accuracy of the method on a standard benchmark is >85%, which is higher than existing sequence-based methods and is comparable to the methods that use not only protein sequence but also tertiary structure, pH value and temperature. The results demonstrate that deep learning is a promising technique for protein stability prediction. The good performance of this sequence-based method makes it a valuable tool for predicting the impact of mutations on most proteins whose experimental structures are not available. Both the downloadable software package and the user-friendly web server (DNpro) that implement the method for predicting protein stability changes induced by amino acid mutations are freely available for the community to use.

Keywords: bioinformatics, deep learning, protein stability prediction, biological data mining

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Authors: Alexandre Barbosa de Almeida, Telma Woerle de Lima Soares

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Protein structure prediction is a challenging task in the bioinformatics field. The biological function of all proteins majorly relies on the shape of their three-dimensional conformational structure, but less than 1% of all known proteins in the world have their structure solved. This work proposes a deep learning model to address this problem, attempting to predict some aspects of the protein conformations. Throughout a process of multiobjective dominance, a recurrent neural network was trained to abstract the particular bias of each individual multiobjective algorithm, generating a heuristic that could be useful to predict some of the relevant aspects of the three-dimensional conformation process formation, known as protein folding.

Keywords: Ab initio heuristic modeling, multiobjective optimization, protein structure prediction, recurrent neural network

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Authors: Jamerson Felipe Pereira Lima, Jeane Cecília Bezerra de Melo

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The difficulty and cost related to obtaining of protein tertiary structure information through experimental methods, such as X-ray crystallography or NMR spectroscopy, helped raising the development of computational methods to do so. An approach used in these last is prediction of tridimensional structure based in the residue chain, however, this has been proved an NP-hard problem, due to the complexity of this process, explained by the Levinthal paradox. An alternative solution is the prediction of intermediary structures, such as the secondary structure of the protein. Artificial Intelligence methods, such as Bayesian statistics, artificial neural networks (ANN), support vector machines (SVM), among others, were used to predict protein secondary structure. Due to its good results, artificial neural networks have been used as a standard method to predict protein secondary structure. Recent published methods that use this technique, in general, achieved a Q3 accuracy between 75% and 83%, whereas the theoretical accuracy limit for protein prediction is 88%. Alternatively, to achieve better results, support vector machines prediction methods have been developed. The statistical evaluation of methods that use different AI techniques, such as ANNs and SVMs, for example, is not a trivial problem, since different training sets, validation techniques, as well as other variables can influence the behavior of a prediction method. In this study, we propose a prediction method based on artificial neural networks, which is then compared with a selected SVM method. The chosen SVM protein secondary structure prediction method is the one proposed by Huang in his work Extracting Physico chemical Features to Predict Protein Secondary Structure (2013). The developed ANN method has the same training and testing process that was used by Huang to validate his method, which comprises the use of the CB513 protein data set and three-fold cross-validation, so that the comparative analysis of the results can be made comparing directly the statistical results of each method.

Keywords: artificial neural networks, protein secondary structure, protein structure prediction, support vector machines

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Authors: Aakansha Gupta, Neha Vadnere, Tapasvi Soni, M. Anbarsi

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Protein secondary structure prediction is one of the most important goals pursued by bioinformatics and theoretical chemistry; it is highly important in medicine and biotechnology. Some aspects of protein functions and genome analysis can be predicted by secondary structure prediction. This is used to help annotate sequences, classify proteins, identify domains, and recognize functional motifs. In this paper, we represent protein secondary structure as a mathematical model. To extract and predict the protein secondary structure from the primary structure, we require a set of parameters. Any constants appearing in the model are specified by these parameters, which also provide a mechanism for efficient and accurate use of data. To estimate these model parameters there are many algorithms out of which the most popular one is the EM algorithm or called the Expectation Maximization Algorithm. These model parameters are estimated with the use of protein datasets like RS126 by using the Bayesian Probabilistic method (data set being categorical). This paper can then be extended into comparing the efficiency of EM algorithm to the other algorithms for estimating the model parameters, which will in turn lead to an efficient component for the Protein Secondary Structure Prediction. Further this paper provides a scope to use these parameters for predicting secondary structure of proteins using machine learning techniques like neural networks and fuzzy logic. The ultimate objective will be to obtain greater accuracy better than the previously achieved.

Keywords: model parameters, expectation maximization algorithm, protein secondary structure prediction, bioinformatics

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Authors: Alireza Dantism

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Presently, describing the function of a protein sequence is one of the most common problems in biology. Usually, this problem can be facilitated by studying the three-dimensional structure of proteins. In the absence of a protein structure, comparative modeling often provides a useful three-dimensional model of the protein that is dependent on at least one known protein structure. Comparative modeling predicts the three-dimensional structure of a given protein sequence (target) mainly based on its alignment with one or more proteins of known structure (templates). Comparative modeling consists of four main steps 1. Similarity between the target sequence and at least one known template structure 2. Alignment of target sequence and template(s) 3. Build a model based on alignment with the selected template(s). 4. Prediction of model errors 5. Optimization of the built model There are many computer programs and web servers that automate the comparative modeling process. One of the most important advantages of these servers is that it makes comparative modeling available to both experts and non-experts, and they can easily do their own modeling without the need for programming knowledge, but some other experts prefer using programming knowledge and do their modeling manually because by doing this they can maximize the accuracy of their modeling. In this study, a web-based tool has been designed to predict the tertiary structure of proteins using PHP and Python programming languages. This tool is called EasyModel. EasyModel can receive, according to the user's inputs, the desired unknown sequence (which we know as the target) in this study, the protein sequence file (template), etc., which also has a percentage of similarity with the primary sequence, and its third structure Predict the unknown sequence and present the results in the form of graphs and constructed protein files.

Keywords: structural bioinformatics, protein tertiary structure prediction, modeling, comparative modeling, modeller

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Authors: K. Tejasri, K. Suvarna Vani, S. Prathyusha, S. Ramya

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Proteins are chained sequences of amino acids which are brought together by the peptide bonds. Many varying formations of the chains are possible due to multiple combinations of amino acids and rotation in numerous positions along the chain. Protein structure prediction is one of the crucial goals worked towards by the members of bioinformatics and theoretical chemistry backgrounds. Among the four different structure levels in proteins, we emphasize mainly the secondary level structure. Generally, the secondary protein basically comprises alpha-helix and beta-sheets. Multi-class classification problem of data with disparity is truly a challenge to overcome and has to be addressed for the beta strands. Imbalanced data distribution constitutes a couple of the classes of data having very limited training samples collated with other classes. The secondary structure data is extracted from the protein primary sequence, and the beta-strands are predicted using suitable machine learning algorithms.

Keywords: proteins, secondary structure elements, beta-sheets, beta-strands, alpha-helices, machine learning algorithms

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Authors: Alexander Hudson, Shaogang Gong

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Structure determination is key to understanding protein function at a molecular level. Whilst significant advances have been made in predicting structure and function from amino acid sequence, researchers must still rely on expensive, time-consuming analytical methods to visualise detailed protein conformation. In this study, we demonstrate that it is possible to make accurate (≥80%) predictions of protein class and architecture from structures determined at low (>3A) resolution, using a deep convolutional neural network trained on high-resolution (≤3A) structures represented as 2D matrices. Thus, we provide proof of concept for high-speed, low-cost protein structure classification at low resolution, and a basis for extension to prediction of function. We investigate the impact of the input representation on classification performance, showing that side-chain information may not be necessary for fine-grained structure predictions. Finally, we confirm that high resolution, low-resolution and NMR-determined structures inhabit a common feature space, and thus provide a theoretical foundation for boosting with single-image super-resolution.

Keywords: transfer learning, protein distance maps, protein structure classification, neural networks

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Authors: Sandhya Devi A

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The protein content of the cranberry squash (protein: 0g) may be increased by extracting protein from the lentils (9 g), which is particularly linked to a lower risk of developing heart disease. Using the technique of alkaline extraction from the lentils flour, protein may be extracted. Alkaline extraction of protein from lentil flour was optimized utilizing response surface approach in order to maximize both protein content and yield. Cranberry squash may be taken if a protein fortification syrup is prepared and processed into the squash.

Keywords: alkaline extraction, cranberry squash, protein fortification, response surface methodology

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Authors: Amita Barik, Ranjit Prasad Bahadur

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We investigate the role of water molecules in 89 protein-RNA complexes taken from the Protein Data Bank. Those with tRNA and single-stranded RNA are less hydrated than with duplex or ribosomal proteins. Protein-RNA interfaces are hydrated less than protein-DNA interfaces, but more than protein-protein interfaces. Majority of the waters at protein-RNA interfaces makes multiple H-bonds; however, a fraction does not make any. Those making Hbonds have preferences for the polar groups of RNA than its partner protein. The spatial distribution of waters makes interfaces with ribosomal proteins and single-stranded RNA relatively ‘dry’ than interfaces with tRNA and duplex RNA. In contrast to protein-DNA interfaces, mainly due to the presence of the 2’OH, the ribose in protein-RNA interfaces is hydrated more than the phosphate or the bases. The minor groove in protein-RNA interfaces is hydrated more than the major groove, while in protein-DNA interfaces it is reverse. The strands make the highest number of water-mediated H-bonds per unit interface area followed by the helices and the non-regular structures. The preserved waters at protein-RNA interfaces make higher number of H-bonds than the other waters. Preserved waters contribute toward the affinity in protein-RNA recognition and should be carefully treated while engineering protein-RNA interfaces.

Keywords: h-bonds, minor-major grooves, preserved water, protein-RNA interfaces

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Authors: Tetsuo Okutsu

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We have developed a crystallization plate with the function of promoting protein crystallization. A gold thin film is deposited on the crystallization plate. A protein solution is dropped thereon, and crystallization is promoted when the protein is irradiated with light of a wavelength that protein does not absorb. Protein is densely adsorbed on the gold thin film surface. The light excites the surface plasmon resonance of the gold thin film, the protein is excited by the generated enhanced electric field induced by surface plasmon resonance, and the amino acid residues are radicalized to produce protein dimers. The dimers function as templates for protein crystals, crystallization is promoted.

Keywords: lysozyme, plasmon, protein, crystallization, RNaseA

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Authors: Agustyas Tjiptaningrum, Intanri Kurniati, Fadilah Fadilah, Linda Erlina, Tiwuk Susantiningsih

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Coronavirus disease-19 (COVID-19) is still one of the health problems. It can be a severe condition that is caused by a cytokine storm. In a cytokine storm, several proinflammatory cytokines are released massively. It destroys epithelial cells, and subsequently, it can cause death. The anti-inflammatory agent can be used to decrease the number of severe Covid-19 conditions. Melaleuca cajuputi is a plant that has antiviral, antibiotic, antioxidant, and anti-inflammatory activities. This study was carried out to analyze the prediction mechanism of the M. cajuputi extract from Lampung, Indonesia, as an anti-inflammatory agent for COVID-19. This study constructed a database of protein host target that was involved in the inflammation process of COVID-19 using data retrieval from GeneCards with the keyword “SARS-CoV2”, “inflammation,” “cytokine storm,” and “acute respiratory distress syndrome.” Subsequent protein-protein interaction was generated by using Cytoscape version 3.9.1. It can predict the significant target protein. Then the analysis of the Gene Ontology (GO) and KEGG pathways was conducted to generate the genes and components that play a role in COVID-19. The result of this study was 30 nodes representing significant proteins, namely NF-κβ, IL-6, IL-6R, IL-2RA, IL-2, IFN2, C3, TRAF6, IFNAR1, and DOX58. From the KEGG pathway, we obtained the result that NF-κβ has a role in the production of proinflammatory cytokines, which play a role in the COVID-19 cytokine storm. It is an important factor for macrophage transcription; therefore, it will induce inflammatory gene expression that encodes proinflammatory cytokines such as IL-6, TNF-α, and IL-1β. In conclusion, the blocking of NF-κβ is the prediction mechanism of the M. cajuputi extract as an anti-inflammation agent for COVID-19.

Keywords: antiinflammation, COVID-19, cytokine storm, NF-κβ, M. cajuputi

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Authors: Kamalpreet Kaur, Renu Dhir

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Climate prediction is an essential component of climate research, which helps evaluate possible effects on economies, communities, and ecosystems. Climate prediction involves short-term weather prediction, seasonal prediction, and long-term climate change prediction. Climate prediction can use the information gathered from satellites, ground-based stations, and ocean buoys, among other sources. The paper's four architectures, such as ResNet50, VGG19, Inception-v3, and Xception, have been combined using an ensemble approach for overall performance and robustness. An ensemble of different models makes a prediction, and the majority vote determines the final prediction. The various architectures such as ResNet50, VGG19, Inception-v3, and Xception efficiently classify the dataset RSI-CB256, which contains satellite images into cloudy and non-cloudy. The generated ensembled S-E model (Sar-ensembled model) provides an accuracy of 99.25%.

Keywords: climate, satellite images, prediction, classification

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Authors: Alexander A. Tokmakov

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Cell-free protein synthesis is widely used to synthesize recombinant proteins. It allows genome-scale expression of various polypeptides under strictly controlled uniform conditions. However, only a minor fraction of all proteins can be successfully expressed in the systems of protein synthesis that are currently used. The factors determining expression success are poorly understood. At present, the vast volume of data is accumulated in cell-free expression databases. It makes possible comprehensive bioinformatics analysis and identification of multiple features associated with successful cell-free expression. Here, we describe an approach aimed at identification of multiple physicochemical and structural properties of amino acid sequences associated with protein solubility and aggregation and highlight major correlations obtained using this approach. The developed method includes: categorical assessment of the protein expression data, calculation and prediction of multiple properties of expressed amino acid sequences, correlation of the individual properties with the expression scores, and evaluation of statistical significance of the observed correlations. Using this approach, we revealed a number of statistically significant correlations between calculated and predicted features of protein sequences and their amenability to cell-free expression. It was found that some of the features, such as protein pI, hydrophobicity, presence of signal sequences, etc., are mostly related to protein solubility, whereas the others, such as protein length, number of disulfide bonds, content of secondary structure, etc., affect mainly the expression propensity. We also demonstrated that amenability of polypeptide sequences to cell-free expression correlates with the presence of multiple sites of post-translational modifications. The correlations revealed in this study provide a plethora of important insights into protein folding and rationalization of protein production. The developed bioinformatics approach can be of practical use for predicting expression success and optimizing cell-free protein synthesis.

Keywords: bioinformatics analysis, cell-free protein synthesis, expression success, optimization, recombinant proteins

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Authors: Yuexin Liu, Gordon B. Mills, Russell R. Broaddus, John N. Weinstein

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The lethality of endometrioid endometrial cancer (EEC) is primarily attributable to the high-stage diseases. However, there are no available biomarkers that predict EEC patient staging at the time of diagnosis. We aim to develop a predictive scheme to help in this regards. Using reverse-phase protein array expression profiles for 210 EEC cases from The Cancer Genome Atlas (TCGA), we constructed a Protein Scoring of EEC Staging (PSES) scheme for surgical stage prediction. We validated and evaluated its diagnostic potential in an independent cohort of 184 EEC cases obtained at MD Anderson Cancer Center (MDACC) using receiver operating characteristic curve analyses. Kaplan-Meier survival analysis was used to examine the association of PSES score with patient outcome, and Ingenuity pathway analysis was used to identify relevant signaling pathways. Two-sided statistical tests were used. PSES robustly distinguished high- from low-stage tumors in the TCGA cohort (area under the ROC curve [AUC]=0.74; 95% confidence interval [CI], 0.68 to 0.82) and in the validation cohort (AUC=0.67; 95% CI, 0.58 to 0.76). Even among grade 1 or 2 tumors, PSES was significantly higher in high- than in low-stage tumors in both the TCGA (P = 0.005) and MDACC (P = 0.006) cohorts. Patients with positive PSES score had significantly shorter progression-free survival than those with negative PSES in the TCGA (hazard ratio [HR], 2.033; 95% CI, 1.031 to 3.809; P = 0.04) and validation (HR, 3.306; 95% CI, 1.836 to 9.436; P = 0.0007) cohorts. The ErbB signaling pathway was most significantly enriched in the PSES proteins and downregulated in high-stage tumors. PSES may provide clinically useful prediction of high-risk tumors and offer new insights into tumor biology in EEC.

Keywords: endometrial carcinoma, protein, protein scoring of EEC staging (PSES), stage

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Authors: Bin Liu

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Protein remote homology detection and fold recognition are two most important tasks in protein sequence analysis, which is critical for protein structure and function studies. In this study, we combined the profile-based features with various string kernels, and constructed several computational predictors for protein remote homology detection and fold recognition. Experimental results on two widely used benchmark datasets showed that these methods outperformed the competing methods, indicating that these predictors are useful computational tools for protein sequence analysis. By analyzing the discriminative features of the training models, some interesting patterns were discovered, reflecting the characteristics of protein superfamilies and folds, which are important for the researchers who are interested in finding the patterns of protein folds.

Keywords: protein remote homology detection, protein fold recognition, profile-based features, Support Vector Machines (SVMs)

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Authors: B. Sir, M. Podhoranyi, S. Kuchar, T. Kocyan

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Rainfall-runoff models play important role in hydrological predictions. However, the model is only one part of the process for creation of flood prediction. The aim of this paper is to show the process of successful prediction for flood event (May 15–May 18 2014). The prediction was performed by rainfall runoff model HEC–HMS, one of the models computed within Floreon+ system. The paper briefly evaluates the results of automatic hydrologic prediction on the river Olše catchment and its gages Český Těšín and Věřňovice.

Keywords: flood, HEC-HMS, prediction, rainfall, runoff

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Authors: Genevieve Pugh, Johnathan Burns, Stefan Howorka

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Single-molecule sensing via protein nanopores has achieved a step-change in portable and label-free DNA sequencing. However, protein pores of both natural or engineered origin are not able to produce the tunable diameters needed for effective protein sensing. Here, we describe a generic strategy to build synthetic DNA nanopores that are wide enough to accommodate folded protein. The pores are composed of interlinked DNA duplexes and carry lipid anchors to achieve the required membrane insertion. Our demonstrator pore has a contiguous cross-sectional channel area of 50 nm2 which is 6-times larger than the largest protein pore. Consequently, transport of folded protein across bilayers is possible. The modular design is amenable for different pore dimensions and can be adapted for protein sensing or to create molecular gates in synthetic biology.

Keywords: biosensing, DNA nanotechnology, DNA origami, nanopore sensing

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Authors: N. H. Adenan, M. S. M. Noorani

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River flow prediction is an essential to ensure proper management of water resources can be optimally distribute water to consumers. This study presents an analysis and prediction by using nonlinear prediction method involving monthly river flow data in Tanjung Tualang from 1976 to 2006. Nonlinear prediction method involves the reconstruction of phase space and local linear approximation approach. The phase space reconstruction involves the reconstruction of one-dimensional (the observed 287 months of data) in a multidimensional phase space to reveal the dynamics of the system. Revenue of phase space reconstruction is used to predict the next 72 months. A comparison of prediction performance based on correlation coefficient (CC) and root mean square error (RMSE) have been employed to compare prediction performance for nonlinear prediction method, ARIMA and SVM. Prediction performance comparisons show the prediction results using nonlinear prediction method is better than ARIMA and SVM. Therefore, the result of this study could be used to developed an efficient water management system to optimize the allocation water resources.

Keywords: river flow, nonlinear prediction method, phase space, local linear approximation

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Authors: Guido Trautmann-Sáez, Mario Pérez-Won, Vilbett Briones, María José Bugueño, Gipsy Tabilo-Munizaga, Luis Gonzáles-Cavieres

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Shrimp by-products are a valuable source of protein. However, traditional protein extraction methods have limitations in terms of their efficiency. Protein extraction from shrimp (Pleuroncodes monodon) industrial by-products assisted with ohmic heating (OH), microwave (MW) and pulsed electric field (PEF). It was performed by chemical method (using NaOH and HCl 2M) assisted with OH, MW and PEF in a continuous flow system (5 ml/s). Protein determination, differential scanning calorimetry (DSC) and Fourier-transform infrared (FTIR). Results indicate a 19.25% (PEF) 3.65% (OH) and 28.19% (MW) improvement in protein extraction efficiency. The most efficient method was selected for the electrospinning process and obtaining fiber.

Keywords: electrospinning process, emerging technology, protein extraction, shrimp by-products

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Authors: Gulcin Yildiz, Hao Feng

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In recent years there is growing interested in using soy protein because of several advantages compared to other protein sources, such as high nutritional value, steady supply, and low cost. Soy protein isolate (SPI) is the most refined soy protein product. It contains 90% protein in a moisture-free form and has some desirable functionalities. Creating a protein-polysaccharide conjugate to be the emulsifying agent rather than the protein alone can markedly enhance its stability. This study was undertaken to examine the effects of ultrasound treatments on the physicochemical properties of SPI-starch conjugates. The soy protein isolate (SPI, Pro-Fam® 955) samples were obtained from the Archer Daniels Midland Company. Protein concentrations were analyzed by the Bardford method using BSA as the standard. The volume-weighted mean diameters D [4,3] of protein–polysaccharide conjugates were measured by dynamic light scattering (DLS). Surface hydrophobicity of the conjugates was measured by using 1-anilino-8-naphthalenesulfonate (ANS) (Sigma-Aldrich, St. Louis, MO, USA). Increasing the pH from 2 to 12 resulted in increased protein solubility. The highest solubility was 69.2% for the sample treated with ultrasonication at pH 12, while the lowest (9.13%) was observed in the Control. For the other pH conditions, the protein solubility values ranged from 40.53 to 49.65%. The ultrasound treatment significantly decreased the particle sizes of the SPI-modified starch conjugates. While the D [4,3] for the Control was 731.6 nm, it was 293.7 nm for the samples treated by sonication at pH 12. The surface hydrophobicity (H0) of SPI-starch at all pH conditions were significantly higher than those in the Control. Ultrasonication was proven to be effective in improving the solubility and emulsifying properties of soy protein isolate-starch conjugates.

Keywords: particle size, solubility, soy protein isolate, ultrasonication

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Authors: Ravid Inbar

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CaMKII (calcium-calmodulin dependent protein kinase II) makes up 2% of the protein in our brain and has a critical role in memory formation and long-term potentiation of neurons. Despite this, research has yet to uncover the role of one of the domains on the activation of this kinase. The following proposes to express the protein without the hub domain in E. coli, leaving only the kinase and regulatory segment of the protein. Next, a series of kinase assays will be conducted to elucidate the role the hub domain plays on CaMKII sensitivity to calcium/calmodulin activation. The hub domain may be important for activation; however, it may also be a variety of domains working together to influence protein activation and not the hub alone. Characterization of a protein is critical to the future understanding of the protein's function, as well as for producing pharmacological targets in cases of patients with diseases.

Keywords: CaMKII, hub domain, kinase assays, kinase + reg seg

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Authors: Muhammet Baldan, Emel Timuçin

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Generally, absorption and bioavailability increase if solubility increases; therefore, it is crucial to predict them in drug discovery applications. Molecular descriptors and Molecular properties are traditionally used for the prediction of water solubility. There are various key descriptors that are used for this purpose, namely Drogan Descriptors, Morgan Descriptors, Maccs keys, etc., and each has different prediction capabilities with differentiating successes between different data sets. Another source for the prediction of solubility is structural features; they are commonly used for the prediction of solubility. However, there are little to no studies that combine three or more properties or descriptors for prediction to produce a more powerful prediction model. Unlike available models, we used a combination of those features in a random forest machine learning model for improved solubility prediction to better predict and, therefore, contribute to drug discovery systems.

Keywords: solubility, molecular descriptors, machine learning, random forest

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Authors: Yichao Liang, Biye Chen, Xiang Li, Steven R. Dimler

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This study investigated the effects of adding calcium and magnesium chloride on heat and storage stability of milk protein concentrate-soy protein isolate (8:2 respectively) mixtures containing 10% w/w total protein subjected to the in-container sterilization (115 °C x 15 min). The particle size does not change when emulsions are heated at pH between 6.7 and 7.3 irrespective of the mixed protein ratio. Increasing concentration of divalent cation salts resulted in an increase in protein particle size, dry sediment formation and sediment height and a decrease in pH, heat stability and hydration in milk protein concentrate-soy protein isolate mixtures solutions on sterilization at 115°C. Fortification of divalent cation salts in milk protein concentrate-soy protein isolate mixture solutions resulted in an accelerated protein sedimentation and two unique sediment regions during accelerated storage stability testing. Moreover, the heat stability decreased upon sterilization at 115°C, with addition of MgCl₂ causing a greater increase in sedimentation velocity and compressibility than CaCl₂. Increasing pH value of protein milk concentrate-soy protein isolate mixtures solutions from 6.7 to 7.2 resulted in an increase in viscosity following the heat treatment. The study demonstrated that the type and concentration of divalent cation salts used strongly impact heat and storage stability of milk protein concentrate-soy protein isolate mixture nutritional beverages.

Keywords: divalent cation salts, heat stability, milk protein concentrate, soy protein isolate, storage stability

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Authors: Mehrnaz Aminifar

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The relation between textural parameters and casein network on release of aromatic compounds was investigated over 90-days of ripening. Low DE maltodextrin and WPI were used to modify the textural properties of low fat brined cheese. Hardness, brittleness and compaction of casein network were affected by addition of maltodextrin and WPI. Textural properties and aroma release from cheese texture were affected by interaction of WPI protein-cheese protein and maltodexterin-cheese protein.

Keywords: aroma release, brined cheese, maltodexterin, WPI

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Authors: C. E. Chinma, S. O. Azeez, J. C. Anuonye, O. B. Ocheme, C. M. Yakubu, S. James, E. U. Ohuoba, I. A. Baba

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There is growing interest in the use of rice bran protein in food formulation due to its hypoallergenic protein, high nutritional value and health promoting potentials. For the first time, the amino acid profile, protein digestibility, antioxidant, and functional properties of protein concentrate from some local varieties of rice bran from Nigeria were studied for possible food applications. Protein concentrates were prepared from rice bran and analysed using standard methods. Results showed that protein content of Kwandala, Yardass, Jeep, and Jamila were 69.24%, 69.97%, 68.73%, and 71.62%, respectively while total essential amino acid were 52.71, 53.03, 51.86, and 55.75g/100g protein, respectively. In vitro protein digestibility of protein concentrate from Kwandala, Yardass, Jeep and Jamila were 90.70%, 91.39%, 90.57% and 91.63% respectively. DPPH radical inhibition of protein from Kwandala, Yardass, Jeep, and Jamila were 48.15%, 48.90%, 47.56%, and 53.29%, respectively while ferric reducing ability power were 0.52, 0.55, 0.47 and 0.67mmol TE per gram, respectively. Protein concentrate from Jamila had higher onset (92.57oC) and denaturation temperature (102.13oC), and enthalpy (0.72J/g) than Jeep (91.46oC, 101.76oC, and 0.68J/g, respectively), Kwandala (90.32oC, 100.54oC and 0.57J/g, respectively), and Yardass (88.94oC, 99.45oC, and 0.51J/g, respectively). In vitro digestibility of protein from Kwandala, Yardas, Jeep, and Jamila were 90.70%, 91.39%, 90.57% and 91.63% respectively. Oil absorption capacity of Kwandala, Yardass, Jeep, and Jamila were 3.61, 3.73, 3.40, and 4.23g oil/g sample respectively, while water absorption capacity were 4.19, 4.32, 3.55 and 4.48g water/g sample, respectively. Protein concentrates had low bulk density (0.37-0.43g/ml). Protein concentrate from Jamila rice bran had the highest foam capacity (37.25%), followed by Yardass (34.20%), Kwandala (30.14%) and Jeep (28.90%). Protein concentrates showed low emulsifying and gelling capacities. In conclusion, protein concentrate prepared from these local rice bran varieties could serve as functional ingredients in food formulations and for enriching low protein foods.

Keywords: rice bran protein, amino acid profile, protein digestibility, antioxidant and functional properties

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Authors: Prajna Metta, Yanti Rusyanti, Nunung Rusminah, Bremmy Laksono

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The decrease of neutrophil chemotaxis function may cause increased susceptibility to aggressive periodontitis (AP). Neutrophil chemotaxis is affected by formyl peptide receptor 1 (FPR1), which when activated will respond to bacterial chemotactic peptide formyl methionyl leusyl phenylalanine (FMLP). FPR1 protein value is decreased in response to a wide number of inflammatory stimuli in AP patients. This study was aimed to assess the alteration of FPR1 protein value in AP patients and if FPR1 protein value could be used as an indicator of neutrophil chemotaxis dysfunction in AP. This is a case control study with 20 AP patients and 20 control subjects. Three milliliters of peripheral blood were drawn and analyzed for FPR1 protein value with ELISA. The data were statistically analyzed with Mann-Whitney test (p>0,05). Results showed that the mean value of FPR1 protein value in AP group is 0,353 pg/mL (0,11 to 1,18 pg/mL) and the mean value of FPR1 protein value in control group is 0,296 pg/mL (0,05 to 0,88 pg/mL). P value 0,787 > 0,05 suggested that there is no significant difference of FPR1 protein value in both groups. The present study suggests that FPR1 protein value has no significance alteration in AP patients and could not be used as an indicator of neutrophil chemotaxis dysfunction.

Keywords: aggressive periodontitis, chemotaxis dysfunction, FPR1 protein value, neutrophil

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Authors: Kefaya Qaddoum

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The aim of this study is to predict breast cancer and to construct a supportive model that will stimulate a more reliable prediction as a factor that is fundamental for public health. In this study, we utilize general regression neural networks (GRNN) to replace the normal predictions with prediction periods to achieve a reasonable percentage of confidence. The mechanism employed here utilises a machine learning system called conformal prediction (CP), in order to assign consistent confidence measures to predictions, which are combined with GRNN. We apply the resulting algorithm to the problem of breast cancer diagnosis. The results show that the prediction constructed by this method is reasonable and could be useful in practice.

Keywords: neural network, conformal prediction, cancer classification, regression

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Authors: Phakamas Rachamontree, Theerawut Phusantisampan, Natthakorn Woravutthikul, Peerapong Pornwongthong, Malinee Sriariyanun

Abstract:

A total of 115 yeast strains isolated from local cassava processing wastes were measured for crude protein content. Among these strains, the strain MSY-2 possessed the highest protein concentration (>3.5 mg protein/mL). By using molecular identification tools, it was identified to be a strain of Pichia kudriavzevii based on similarity of D1/D2 domain of 26S rDNA region. In this study, to optimize the protein production by MSY-2 strain, Response Surface Methodology (RSM) was applied. The tested parameters were the carbon content, nitrogen content, and incubation time. Here, the value of regression coefficient (R2) = 0.7194 could be explained by the model, which is high to support the significance of the model. Under the optimal condition, the protein content was produced up to 3.77 g per L of the culture and MSY-2 strain contain 66.8 g protein per 100 g of cell dry weight. These results revealed the plausibility of applying the novel strain of yeast in single-cell protein production.

Keywords: single cell protein, response surface methodology, yeast, cassava processing waste

Procedia PDF Downloads 365

Authors: Mohammad Hossein Modarressi, Mozhgan Mondeali, Khabat Barkhordari, Ali Etemadi

Abstract:

The COVID-19 pandemic, caused by SARS-CoV-2, has led to significant concerns worldwide due to its catastrophic effects on public health. The SARS-CoV-2 infection is initiated with the binding of the receptor-binding domain (RBD) in its spike protein to the ACE2 receptor in the host cell membrane. Due to the error-prone entity of the viral RNA-dependent polymerase complex, the virus genome, including the coding region for the RBD, acquires new mutations, leading to the appearance of multiple variants. These variants can potentially impact transmission, virulence, antigenicity and evasive immune properties. S477N mutation located in the RBD has been observed in the SARS-CoV-2 omicron (B.1.1. 529) variant. In this study, we investigated the consequences of S477N mutation at the molecular level using computational approaches such as molecular dynamics simulation, protein-protein interaction analysis, immunoinformatics and free energy computation. We showed that displacement of Ser with Asn increases the stability of the spike protein and its affinity to ACE2 and thus increases the transmission potential of the virus. This mutation changes the folding and secondary structure of the spike protein. Also, it reduces antibody neutralization, raising concern about re-infection, vaccine breakthrough and therapeutic values.

Keywords: S477N, COVID-19, molecular dynamic, SARS-COV2 mutations

Procedia PDF Downloads 140

Authors: Seyed Mohammad Hosein Al Omrani Nejad, Ali Rezvani Aghdam

Abstract:

This study was done in order to evaluation different irrigation intervals on amount of protein, and gel production in Aloe vera, a traditional medicinal plant. Plants was plnted in Greenhouse and irrigated according to Accumulative Pan Evaporation(APE). The treatments were included 20, 40, 60, 80, 100, 120, 140, 160, 180, and 200 mm APE which has been showed W1,W2, W3, W4, W5, W6, W7, W8,W9 and W10 respectively.The amount of protein and gel production was measured seperately. Results showed that highest protein and fresh weight of gel obtained plants which irrigated W6 and W7 respectively. According to these results can recomend which if plant irrigatedwhen APE reached 120 and 140 mm by Class A Evaporation Pan method gel production and protein would besuitable in north of khozestan province in limited irrigation conditions.

Keywords: irrigation, protein, gel, aloe vera, Iran

Procedia PDF Downloads 355