Protein Structure Prediction

Na + /H + exchanger isoform one (NHE1) is a mammalian plasma membrane protein that removes intracellular protons, thereby elevating intracellular pH (pH i). NHE1 uses the energy of allowing an extracellular sodium down its gradient into cells to remove one intracellular proton. The ubiquitous protein has several important physiological and pathological influences on mammalian cells as a result of its activity. The three-dimensional structure of human NHE1 (hNHE1) is not known. Here, we modeled NHE1 based on the structure of MjNhaP1 of Methanocaldoccocus jannaschii in combination with biochemical surface accessibility data. hNHE1 contained 12 transmembrane segments including a characteristic Na + /H + antiporter fold of two trans-membrane segments with a helix-extended region-helix conformation crossing each other within the membrane. Amino acids 363-410 mapped principally to the extracellular surface as an extracellular loop (EL5). A large preponderance of amino acids shown to be surface accessible by biochemical experiments mapped near to, or on, the extracellular surface. Docking of Na + /H + exchanger inhibitors to the extracellular surface suggested that inhibitor binding on an extracellular site is made up from several amino acids of different regions of the protein. The results present a novel testable, three-dimensional model illustrating NHE1 structure and accounting for experimental biochemical data. Résumé : L'échangeur Na + /H + NHE1 est une protéine de la membrane plasmique des mammifères qui enlève les protons intracellulaires, élevant ainsi le pH intracellulaire (pH i). NHE1 utilise l'énergie générée par l'entrée spontanée dans les cellules d'un sodium extracellulaire par gradient pour faire sortir un proton intracellulaire. Cette protéine ubiquiste exerce une influence importante sur les plans physiologiques et pathologiques dans les cellules de mammifères conséquemment à son activité. La structure tridimensionnelle de NHE1 humain (hNHE1) n'est pas connue. Les auteurs ont modélisé ici NHE1 à partir de la structure de MjNhaP1 de Methanocaldococcus jannaschii en combinaison avec les données biochimiques d'accessibilité de la surface. Le transporteur hNHE1 comprenait 12 segments transmembranaires dont un repli caractéristique de l'antiport Na + /H + , formé de deux segments transmembranaires dans une conformation hélice-région étendue-hélice qui se croisent à l'intérieur de la membrane. Les acides aminés 363-410 ont été cartographiés principalement à la surface extracellulaire en tant que boucle extracellulaire (EL5). Une forte prépondérance d'acides aminés accessibles à la surface selon des expériences biochimiques était cartographiée à proximité ou sur la surface extracellulaire. L'amarrage d'inhibiteurs de l'échangeur Na + /H + à la surface extra-cellulaire suggérait que la liaison de l'inhibiteur sur un site extracellulaire implique plusieurs acides aminés de différentes régions de la protéine. Les résultats présentent un nouveau modèle tridimensionnel vérifiable qui illustre la structure de NHE1 et explique les données biochimiques expérimentales. [Traduit par la Rédaction]

Stock is a curve with a lot of unknowns. Stock market forecasting is fraught with complications and unpredictability. One of the most challenging and sophisticated methods of doing business is investing in the stock market. Stock forecasting is a difficult and time-consuming activity since the stock market is extremely volatile with stock prices fluctuating due to a variety of variables. Investors nowadays want quick and precise information to make informed decisions, thanks to the rapid growth of technology in stock price prediction. Understanding a company's stock price pattern and estimating its future development and financial growth will be quite advantageous. As the stock is made up of dynamic data, data is the critical source of efficiency. In the current trend of predicting stocks, deep learning is the most popular among the prediction of datasets. To forecast and automate operations, deep learning employs several prediction models and algorithms. The paper briefs about different algorithms and methods used for stock market prediction.

Cellulose, hemicellulose, pectin (carbohydrate), and lignin (noncarbohydrate) polymers are the main substrates of lignocellulose-degrading enzymes. They are present in large amounts in the primary cell wall and dietary fibers of major fruits and vegetables. During processing of fruits and vegetables to the corresponding final food products, lignocellulosic substrates are hydrolyzed by different lignocellulolytic enzymes. Currently, lignocellulolytic enzymes such as cellulases, xylanases, pectinases, and laccases are extensively used during the processing of fruits and vegetables, in applications like texturizing and flavoring of products in the food industries. The present article provides an updated overview of functional applications of lignocellulolytic enzymes in the juice processing, oil extraction, and alcoholic beverage processing industries. Extensive use of lignocellulolytic enzymes in different food processing industries not only accelerates the production rates but also improves product quality. It is also possible to ensure the efficient use of fruits and vegetables globally by employing lignocellulolytic enzymes in the corresponding processing industries to convert them into food commodities, which will not only raise their economic value in the global market but also increase food availability, which will help mitigate nutritional problems worldwide. Practical Application: Lignocellulosic substrates (cellulose, hemicellulose, pectin, and lignin) are abundantly present in fruits and vegetables. Various lignocellulolytic enzymes have been used to hydrolyze these substrates during fruit and vegetable processing. Lignocellulolytic enzymes are widely used in the conversion of fruits and vegetables into various food commodities, from texturizing to flavoring, and their immense potential for such applications makes them very attractive to food manufacturers. Lignocellulolytic enzymes have been extensively used in fruit and vegetable juice processing, vegetable oil extractions, and wine processing.

"Networks are mathematically directed (in practical applications also undirected) graphs and a graph is a one-dimensional abstract complex, i.e., a topological space. Network theory focuses on various topological structures and properties, dynamic properties, and functionality-topology relationship, etc. There are some common mathematical foundations, theories and methodology for network analysis, in which graph theory, statistics, and operational research, etc., are the fundamental sciences of network analysis. Various emerging biological networks, at both micro- and macro- levels, will provide numerous sources for the development of general network theory and methodology and also facilitate the development of theory and methodology of biological networks.

Biological network analysis is a fast moving science. Many core scientific issues, for example, ecological structure, co-evolution, co-extinction and biodiversity conservation in ecology, and cancer development and metabolic regulation in health science, etc., are expected to be addressed by network approaches and network analysis. Network analysis is becoming the core methodology to treat complex biological systems. As the fast development of this area, more and more papers on biological networks are published.

At present explosive numbers of quality papers on biological networks are being published each year. The initiation of the journal, Network Biology, will provide a public and unified platform for the publication of these studies. From this integrated and unique journal, researchers, university teachers and students will thoroughly have an in-depth and complete insight on knowledge, methodology and recent advances of biological networks.

In the view of system dynamics, biological networks are always self-organized systems with emergent, autonomous and adaptive properties. Their dynamics can be represented by agent-based modeling, individual-based modeling and some other methodologies like neural network modeling. Therefore, agent-based modeling, individual-based modeling, self-organization of biological systems, and neural network modeling, etc., fall into the aims and scope of the journal, Network Biology.

The NETWORK BIOLOGY (ISSN 2220-8879) is an open access, peer-reviewed online journal that considers scientific articles in all different areas of network biology. It is the transactions of the International Society of Network Biology.It dedicates to the latest advances in network biology. The goal of this journal is to keep a record of the state-of-the-art research and promote the research work in these fast moving areas. The topics to be covered by Network Biology include, but are not limited to:

•Theories, algorithms and programs of network analysis
•Innovations and applications of biological networks
•Dynamics, optimization and control of biological networks
•Ecological networks, food webs and natural equilibrium
•Co-evolution, co-extinction, biodiversity conservation
•Metabolic networks, protein-protein interaction networks, biochemical reaction networks, gene networks, transcriptional regulatory networks, cell cycle networks, phylogenetic networks, network motifs
•Physiological networks
•Network regulation of metabolic processes, human diseases and ecological systems
•Social networks, epidemiological networks
•System complexity, self-organized systems, emergence of biological systems, agent-based modeling, individual-based modeling, neural network modeling, and other network-based modeling, etc.
We are also interested in short communications that clearly address a specific issue or completely present a new ecological network, food web, or metabolic or gene network, etc.

Authors can submit their works to the email box of this journal, [email protected]. All manuscripts submitted to this journal must be previously unpublished and may not be considered for publication elsewhere at any time during review period of this journal. Authors are asked to read Author Guidelines before submitting manuscripts.

In addition to free submissions from authors around the world, special issues are also accepted. The organizer of a special issue can collect submissions (yielded from a research project, a research group, etc.) on a specific research topic, or submissions of a scientific conference for publication of special issue.

Network Biology 
ISSN 2220-8879
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RSS feed: http://www.iaees.org/publications/journals/nb/rss.xml
E-mail: [email protected]
Editor-in-Chief: WenJun Zhang"

The three areas of food reserves in quinoa seeds are: a large central perisperm, a peripheral embryo and a one to two-cell layered endosperm surrounding the hypocotyl-radicle axis of the embryo. Cytochemical and ultrastructural analysis revealed that starch grains occupy the cells of the perisperm, while lipid bodies, protein bodies with globoid crystals of phytin, and proplastids with deposits of

Bitcoin is currently the leading global provider of cryptocurrency which has recently received a lot of attention from the public. In while facilitating services and product deals. The factors that affect the Bitcoin price and the patterns behind its fluctuations can be predicted by using various mach This prediction may give better insights about the bitcoin price to the people who are investing in the bitcoin. Accurate prediction of the future trend in the closed price of the day for a given Machine Learning models can be used to predict the closed price for a given cryptocurrency Keywords: prediction bitcoin price calibration In today's era, the planet is digitalizing. there's a Cryptocurrency is digital cash that's very fashionable. The cryptocurrency may be a money primarily managed or exchanged on computer systems. Cryptocurrencies use blockchai are highly secure and transparent. So, cryptocurrencies can become a replacement sort of taking advantage of the longer term. There are more than 900 cryptocurrencies currently available to take a position online and this number is consistently growing. of those cryptocurrencies, undoubtedly the foremost popular has been Bitcoin and it had been also the primary cryptocurrency within the market. People can make use of bitcoin rather than cash. A. Literature Survey This section overlooks similar existing solutions and examines the algorithms used and drawbacks. Enhancing Bitcoin Price Fluctuation Using Attentive LSTM and Embedding network [1] study used traditional machine learning including Random Forest, XGBoost, S machine learning methods cannot capture the time dependency of time series. Recently, deep learning methods such as Recurrent Neural Networks (RNN) can handle the issue of time depend out long-term dependencies foremost commonly used variants of RNN can solve the vanishing gradient problem. long-term dependencies due to ARIMA [2] analyses neural network autoregression (NNA Bitcoin price forecast results, considering two different training and test samples. I better than ARIMA, while ARIMA outperforms NNAR in the second training

In order to support and supervise patients, the key detection and estimation of cancer type should establish a compulsion in the cancer research. Many research teams from the biomedical and bioinformatics fields have been advised to learn and evaluate the use of machine learning (ML) methods because of the relevance of classifying cancer patients into high or low risk clusters. To predict breast cancer, the logistic regression method and many classifiers have been proposed to generate profound predictions about breast cancer data in a new environment. This paper discusses the various approaches to data mining using classification to create deep predictions that can be applied to Breast Cancer data. In addition, by testing datasets on different classifiers, this analysis predicts the best model that delivers high efficiency. In this paper, the UCI machine learning repository has 699 instances with 11 attributes collected from the Breast cancer dataset. First, the data set is pre-processed, visualized and fed to different classifiers such as Logistic Regression, Support Vector Classifier, K-Nearest Neighbour, Decision Tree and Random Forest. 10-fold cross validation is implemented and testing is carried out in order to create and validate new models. Effective analysis shows that Logistic Regression generates the deep predictions of all classifiers and obtains the best model delivering strong and precise outcomes, followed by other methods: Support Vector Classifier, K-Nearest Neighbour, Decision Tree and Random Forest. Most models were less reliable compared to the approach of logistic regression.

With this product, my objective was to make cryptocurrency investments simple & profitable. This product operates via an easy to use website allowing customers to buy, sell, and invest in cryptocurrencies, and in stock markets, all in one single place. The vision was to make a product for the global financial system that is easily accessible to everyone. The costumer will first have to make investments in cryptocurrencies, then he/she can invest in share markets as per their needs. This product also has a built-in machine learning based stock prediction model which helps it’s users to decide which stock to invest in & when it is the right time to sell such stocks.

Fire ants are well-known by their aggressive stinging behavior, causing many stinging incidents of medical importance. The limited availability of fire ant venom for scientific and clinical uses has restricted, up to now, the knowledge about the biochemistry, immunology, and pharmacology of these venoms. For this study, S. invicta venom was obtained commercially and used for proteomic characterization. For this purpose, the combination of gel-based and gel-free proteomic strategies was used to assign the proteomic profile of the venom from the fire ant S. invicta. This experimental approach permitted the identification of 46 proteins, which were organized into four different groups according to their potential role in fire ant venom: true venom components, housekeeping proteins, body muscle proteins, and proteins involved in chemical communication. The active venom components that may not present toxic roles were classified into three subgroups according to their potential functions: self-venom protection, colony asepsis, and chemical communication. Meanwhile, the proteins classified as true toxins, based on their functions after being injected into the victims' bodies by the fire ants, were classified in five other subgroups: proteins influencing the homeostasis of the victims, neurotoxins, proteins that promote venom diffusion, proteins that cause tissue damage/inflammation, and allergens.