CINXE.COM

<!DOCTYPE html> <html lang="en" dir="ltr"> <head>  <script async src="https://www.googletagmanager.com/gtag/js?id=G-P63WKM1TM1"></script> <script> window.dataLayer = window.dataLayer || []; function gtag(){dataLayer.push(arguments);} gtag('js', new Date()); gtag('config', 'G-P63WKM1TM1'); </script>  <script type="text/javascript" > (function(m,e,t,r,i,k,a){m[i]=m[i]||function(){(m[i].a=m[i].a||[]).push(arguments)}; m[i].l=1*new Date(); for (var j = 0; j < document.scripts.length; j++) {if (document.scripts[j].src === r) { return; }} k=e.createElement(t),a=e.getElementsByTagName(t)[0],k.async=1,k.src=r,a.parentNode.insertBefore(k,a)}) (window, document, "script", "https://mc.yandex.ru/metrika/tag.js", "ym"); ym(55165297, "init", { clickmap:false, trackLinks:true, accurateTrackBounce:true, webvisor:false }); </script> <noscript><div><img src="https://mc.yandex.ru/watch/55165297" style="position:absolute; left:-9999px;" alt="" /></div></noscript>    <title>Search results for: protein secondary structure</title> <meta name="description" content="Search results for: protein secondary structure"> <meta name="keywords" content="protein secondary structure"> <meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1, maximum-scale=1, user-scalable=no"> <meta charset="utf-8"> <link href="https://cdn.waset.org/favicon.ico" type="image/x-icon" rel="shortcut icon"> <link href="https://cdn.waset.org/static/plugins/bootstrap-4.2.1/css/bootstrap.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/plugins/fontawesome/css/all.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/css/site.css?v=150220211555" rel="stylesheet"> </head> <body> <header> <div class="container"> <nav class="navbar navbar-expand-lg navbar-light"> <a class="navbar-brand" href="https://waset.org"> <img src="https://cdn.waset.org/static/images/wasetc.png" alt="Open Science Research Excellence" title="Open Science Research Excellence" /> </a> <button class="d-block d-lg-none navbar-toggler ml-auto" type="button" data-toggle="collapse" data-target="#navbarMenu" aria-controls="navbarMenu" aria-expanded="false" aria-label="Toggle navigation"> <span class="navbar-toggler-icon"></span> </button> <div class="w-100"> <div class="d-none d-lg-flex flex-row-reverse"> <form method="get" action="https://waset.org/search" class="form-inline my-2 my-lg-0"> <input class="form-control mr-sm-2" type="search" placeholder="Search Conferences" value="protein secondary structure" name="q" aria-label="Search"> <button class="btn btn-light my-2 my-sm-0" type="submit"><i class="fas fa-search"></i></button> </form> </div> <div class="collapse navbar-collapse mt-1" id="navbarMenu"> <ul class="navbar-nav ml-auto align-items-center" id="mainNavMenu"> <li class="nav-item"> <a class="nav-link" href="https://waset.org/conferences" title="Conferences in 2024/2025/2026">Conferences</a> </li> <li class="nav-item"> <a class="nav-link" href="https://waset.org/disciplines" title="Disciplines">Disciplines</a> </li> <li class="nav-item"> <a class="nav-link" href="https://waset.org/committees" rel="nofollow">Committees</a> </li> <li class="nav-item dropdown"> <a class="nav-link dropdown-toggle" href="#" id="navbarDropdownPublications" role="button" data-toggle="dropdown" aria-haspopup="true" aria-expanded="false"> Publications </a> <div class="dropdown-menu" aria-labelledby="navbarDropdownPublications"> <a class="dropdown-item" href="https://publications.waset.org/abstracts">Abstracts</a> <a class="dropdown-item" href="https://publications.waset.org">Periodicals</a> <a class="dropdown-item" href="https://publications.waset.org/archive">Archive</a> </div> </li> <li class="nav-item"> <a class="nav-link" href="https://waset.org/page/support" title="Support">Support</a> </li> </ul> </div> </div> </nav> </div> </header> <main> <div class="container mt-4"> <div class="row"> <div class="col-md-9 mx-auto"> <form method="get" action="https://publications.waset.org/abstracts/search"> <div id="custom-search-input"> <div class="input-group"> <i class="fas fa-search"></i> <input type="text" class="search-query" name="q" placeholder="Author, Title, Abstract, Keywords" value="protein secondary structure"> <input type="submit" class="btn_search" value="Search"> </div> </div> </form> </div> </div> <div class="row mt-3"> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Commenced</strong> in January 2007</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Frequency:</strong> Monthly</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Edition:</strong> International</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Paper Count:</strong> 12656</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: protein secondary structure</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">12656</span> Estimation of Transition and Emission Probabilities</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Aakansha%20Gupta">Aakansha Gupta</a>, <a href="https://publications.waset.org/abstracts/search?q=Neha%20Vadnere"> Neha Vadnere</a>, <a href="https://publications.waset.org/abstracts/search?q=Tapasvi%20Soni"> Tapasvi Soni</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20Anbarsi"> M. Anbarsi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> 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. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=model%20parameters" title="model parameters">model parameters</a>, <a href="https://publications.waset.org/abstracts/search?q=expectation%20maximization%20algorithm" title=" expectation maximization algorithm"> expectation maximization algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=protein%20secondary%20structure%20prediction" title=" protein secondary structure prediction"> protein secondary structure prediction</a>, <a href="https://publications.waset.org/abstracts/search?q=bioinformatics" title=" bioinformatics"> bioinformatics</a> </p> <a href="https://publications.waset.org/abstracts/24070/estimation-of-transition-and-emission-probabilities" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/24070.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">481</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">12655</span> Prediction of All-Beta Protein Secondary Structure Using Garnier-Osguthorpe-Robson Method</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=K.%20Tejasri">K. Tejasri</a>, <a href="https://publications.waset.org/abstracts/search?q=K.%20Suvarna%20Vani"> K. Suvarna Vani</a>, <a href="https://publications.waset.org/abstracts/search?q=S.%20Prathyusha"> S. Prathyusha</a>, <a href="https://publications.waset.org/abstracts/search?q=S.%20Ramya"> S. Ramya</a> </p> <p class="card-text"><strong>Abstract:</strong></p> 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. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=proteins" title="proteins">proteins</a>, <a href="https://publications.waset.org/abstracts/search?q=secondary%20structure%20elements" title=" secondary structure elements"> secondary structure elements</a>, <a href="https://publications.waset.org/abstracts/search?q=beta-sheets" title=" beta-sheets"> beta-sheets</a>, <a href="https://publications.waset.org/abstracts/search?q=beta-strands" title=" beta-strands"> beta-strands</a>, <a href="https://publications.waset.org/abstracts/search?q=alpha-helices" title=" alpha-helices"> alpha-helices</a>, <a href="https://publications.waset.org/abstracts/search?q=machine%20learning%20algorithms" title=" machine learning algorithms"> machine learning algorithms</a> </p> <a href="https://publications.waset.org/abstracts/158938/prediction-of-all-beta-protein-secondary-structure-using-garnier-osguthorpe-robson-method" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/158938.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">94</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">12654</span> Comparison of Different Artificial Intelligence-Based Protein Secondary Structure Prediction Methods</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Jamerson%20Felipe%20Pereira%20Lima">Jamerson Felipe Pereira Lima</a>, <a href="https://publications.waset.org/abstracts/search?q=Jeane%20Cec%C3%ADlia%20Bezerra%20de%20Melo"> Jeane Cecília Bezerra de Melo</a> </p> <p class="card-text"><strong>Abstract:</strong></p> 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. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=artificial%20neural%20networks" title="artificial neural networks">artificial neural networks</a>, <a href="https://publications.waset.org/abstracts/search?q=protein%20secondary%20structure" title=" protein secondary structure"> protein secondary structure</a>, <a href="https://publications.waset.org/abstracts/search?q=protein%20structure%20prediction" title=" protein structure prediction"> protein structure prediction</a>, <a href="https://publications.waset.org/abstracts/search?q=support%20vector%20machines" title=" support vector machines"> support vector machines</a> </p> <a href="https://publications.waset.org/abstracts/22850/comparison-of-different-artificial-intelligence-based-protein-secondary-structure-prediction-methods" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/22850.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">621</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">12653</span> Effect of Low Temperature on Structure and RNA Binding of E.coli CspA: A Molecular Dynamics Based Study</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Amit%20Chaudhary">Amit Chaudhary</a>, <a href="https://publications.waset.org/abstracts/search?q=B.%20S.%20Yadav"> B. S. Yadav</a>, <a href="https://publications.waset.org/abstracts/search?q=P.%20K.%20Maurya"> P. K. Maurya</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20M."> A. M.</a>, <a href="https://publications.waset.org/abstracts/search?q=S.%20Srivastava"> S. Srivastava</a>, <a href="https://publications.waset.org/abstracts/search?q=S.%20Singh"> S. Singh</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20Mani"> A. Mani</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Cold shock protein A (CspA) is major cold inducible protein present in Escherichia coli. The protein is involved in stabilizing secondary structure of RNA by working as chaperone during cold temperature. Two RNA binding motifs play key role in the stabilizing activity. This study aimed to investigate implications of low temperature on structure and RNA binding activity of E. coli CspA. Molecular dynamics simulations were performed to compare the stability of the protein at 37°C and 10 °C. The protein was mutated at RNA binding motifs and docked with RNA to assess the stability of both complexes. Results suggest that CspA as well as CspA-RNA complex is more stable at low temperature. It was also confirmed that RNP1 and RNP2 play key role in RNA binding. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=CspA" title="CspA">CspA</a>, <a href="https://publications.waset.org/abstracts/search?q=homology%20modelling" title=" homology modelling"> homology modelling</a>, <a href="https://publications.waset.org/abstracts/search?q=mutation" title=" mutation"> mutation</a>, <a href="https://publications.waset.org/abstracts/search?q=molecular%20dynamics%20simulation" title=" molecular dynamics simulation"> molecular dynamics simulation</a> </p> <a href="https://publications.waset.org/abstracts/78173/effect-of-low-temperature-on-structure-and-rna-binding-of-ecoli-cspa-a-molecular-dynamics-based-study" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/78173.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">374</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">12652</span> CompPSA: A Component-Based Pairwise RNA Secondary Structure Alignment Algorithm</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ghada%20Badr">Ghada Badr</a>, <a href="https://publications.waset.org/abstracts/search?q=Arwa%20Alturki"> Arwa Alturki</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The biological function of an RNA molecule depends on its structure. The objective of the alignment is finding the homology between two or more RNA secondary structures. Knowing the common functionalities between two RNA structures allows a better understanding and a discovery of other relationships between them. Besides, identifying non-coding RNAs -that is not translated into a protein- is a popular application in which RNA structural alignment is the first step A few methods for RNA structure-to-structure alignment have been developed. Most of these methods are partial structure-to-structure, sequence-to-structure, or structure-to-sequence alignment. Less attention is given in the literature to the use of efficient RNA structure representation and the structure-to-structure alignment methods are lacking. In this paper, we introduce an O(N2) Component-based Pairwise RNA Structure Alignment (CompPSA) algorithm, where structures are given as a component-based representation and where N is the maximum number of components in the two structures. The proposed algorithm compares the two RNA secondary structures based on their weighted component features rather than on their base-pair details. Extensive experiments are conducted illustrating the efficiency of the CompPSA algorithm when compared to other approaches and on different real and simulated datasets. The CompPSA algorithm shows an accurate similarity measure between components. The algorithm gives the flexibility for the user to align the two RNA structures based on their weighted features (position, full length, and/or stem length). Moreover, the algorithm proves scalability and efficiency in time and memory performance. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=alignment" title="alignment">alignment</a>, <a href="https://publications.waset.org/abstracts/search?q=RNA%20secondary%20structure" title=" RNA secondary structure"> RNA secondary structure</a>, <a href="https://publications.waset.org/abstracts/search?q=pairwise" title=" pairwise"> pairwise</a>, <a href="https://publications.waset.org/abstracts/search?q=component-based" title=" component-based"> component-based</a>, <a href="https://publications.waset.org/abstracts/search?q=data%20mining" title=" data mining"> data mining</a> </p> <a href="https://publications.waset.org/abstracts/70264/comppsa-a-component-based-pairwise-rna-secondary-structure-alignment-algorithm" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/70264.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">458</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">12651</span> A Basic Modeling Approach for the 3D Protein Structure of Insulin</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Daniel%20Zarzo%20Montes">Daniel Zarzo Montes</a>, <a href="https://publications.waset.org/abstracts/search?q=Manuel%20Zarzo%20Castell%C3%B3"> Manuel Zarzo Castelló</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Proteins play a fundamental role in biology, but their structure is complex, and it is a challenge for teachers to conceptually explain the differences between their primary, secondary, tertiary, and quaternary structures. On the other hand, there are currently many computer programs to visualize the 3D structure of proteins, but they require advanced training and knowledge. Moreover, it becomes difficult to visualize the sequence of amino acids in these models, and how the protein conformation is reached. Given this drawback, a simple and instructive procedure is proposed in order to teach the protein structure to undergraduate and graduate students. For this purpose, insulin has been chosen because it is a protein that consists of 51 amino acids, a relatively small number. The methodology has consisted of the use of plastic atom models, which are frequently used in organic chemistry and biochemistry to explain the chirality of biomolecules. For didactic purposes, when the aim is to teach the biochemical foundations of proteins, a manipulative system seems convenient, starting from the chemical structure of amino acids. It has the advantage that the bonds between amino acids can be conveniently rotated, following the pattern marked by the 3D models. First, the 51 amino acids were modeled, and then they were linked according to the sequence of this protein. Next, the three disulfide bonds that characterize the stability of insulin have been established, and then the alpha-helix structure has been formed. In order to reach the tertiary 3D conformation of this protein, different interactive models available on the Internet have been visualized. In conclusion, the proposed methodology seems very suitable for biology and biochemistry students because they can learn the fundamentals of protein modeling by means of a manipulative procedure as a basis for understanding the functionality of proteins. This methodology would be conveniently useful for a biology or biochemistry laboratory practice, either at the pre-graduate or university level. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=protein%20structure" title="protein structure">protein structure</a>, <a href="https://publications.waset.org/abstracts/search?q=3D%20model" title=" 3D model"> 3D model</a>, <a href="https://publications.waset.org/abstracts/search?q=insulin" title=" insulin"> insulin</a>, <a href="https://publications.waset.org/abstracts/search?q=biomolecule" title=" biomolecule"> biomolecule</a> </p> <a href="https://publications.waset.org/abstracts/182829/a-basic-modeling-approach-for-the-3d-protein-structure-of-insulin" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/182829.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">55</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">12650</span> Protein Tertiary Structure Prediction by a Multiobjective Optimization and Neural Network Approach</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Alexandre%20Barbosa%20de%20Almeida">Alexandre Barbosa de Almeida</a>, <a href="https://publications.waset.org/abstracts/search?q=Telma%20Woerle%20de%20Lima%20Soares"> Telma Woerle de Lima Soares</a> </p> <p class="card-text"><strong>Abstract:</strong></p> 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. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ab%20initio%20heuristic%20modeling" title="Ab initio heuristic modeling">Ab initio heuristic modeling</a>, <a href="https://publications.waset.org/abstracts/search?q=multiobjective%20optimization" title=" multiobjective optimization"> multiobjective optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=protein%20structure%20prediction" title=" protein structure prediction"> protein structure prediction</a>, <a href="https://publications.waset.org/abstracts/search?q=recurrent%20neural%20network" title=" recurrent neural network"> recurrent neural network</a> </p> <a href="https://publications.waset.org/abstracts/141565/protein-tertiary-structure-prediction-by-a-multiobjective-optimization-and-neural-network-approach" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/141565.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">205</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">12649</span> Transfer Learning for Protein Structure Classification at Low Resolution</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Alexander%20Hudson">Alexander Hudson</a>, <a href="https://publications.waset.org/abstracts/search?q=Shaogang%20Gong"> Shaogang Gong</a> </p> <p class="card-text"><strong>Abstract:</strong></p> 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. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=transfer%20learning" title="transfer learning">transfer learning</a>, <a href="https://publications.waset.org/abstracts/search?q=protein%20distance%20maps" title=" protein distance maps"> protein distance maps</a>, <a href="https://publications.waset.org/abstracts/search?q=protein%20structure%20classification" title=" protein structure classification"> protein structure classification</a>, <a href="https://publications.waset.org/abstracts/search?q=neural%20networks" title=" neural networks"> neural networks</a> </p> <a href="https://publications.waset.org/abstracts/129704/transfer-learning-for-protein-structure-classification-at-low-resolution" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/129704.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">136</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">12648</span> Easymodel: Web-based Bioinformatics Software for Protein Modeling Based on Modeller</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Alireza%20Dantism">Alireza Dantism</a> </p> <p class="card-text"><strong>Abstract:</strong></p> 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. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=structural%20bioinformatics" title="structural bioinformatics">structural bioinformatics</a>, <a href="https://publications.waset.org/abstracts/search?q=protein%20tertiary%20structure%20prediction" title=" protein tertiary structure prediction"> protein tertiary structure prediction</a>, <a href="https://publications.waset.org/abstracts/search?q=modeling" title=" modeling"> modeling</a>, <a href="https://publications.waset.org/abstracts/search?q=comparative%20modeling" title=" comparative modeling"> comparative modeling</a>, <a href="https://publications.waset.org/abstracts/search?q=modeller" title=" modeller"> modeller</a> </p> <a href="https://publications.waset.org/abstracts/156892/easymodel-web-based-bioinformatics-software-for-protein-modeling-based-on-modeller" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/156892.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">97</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">12647</span> Effect of Extrusion Processing Parameters on Protein in Banana Flour Extrudates: Characterisation Using Fourier-Transform Infrared Spectroscopy</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Surabhi%20Pandey">Surabhi Pandey</a>, <a href="https://publications.waset.org/abstracts/search?q=Pavuluri%20Srinivasa%20Rao"> Pavuluri Srinivasa Rao</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Extrusion processing is a high-temperature short time (HTST) treatment which can improve protein quality and digestibility together with retaining active nutrients. In-vitro protein digestibility of plant protein-based foods is generally enhanced by extrusion. The current study aimed to investigate the effect of extrusion cooking on in-vitro protein digestibility (IVPD) and conformational modification of protein in green banana flour extrudates. Green banana flour was extruded through a co-rotating twin-screw extruder varying the moisture content, barrel temperature, screw speed in the range of 10-20 %, 60-80 °C, 200-300 rpm, respectively, at constant feed rate. Response surface methodology was used to optimise the result for IVPD. Fourier-transform infrared spectroscopy (FTIR) analysis provided a convenient and powerful means to monitor interactions and changes in functional and conformational properties of extrudates. Results showed that protein digestibility was highest in extrudate produced at 80°C, 250 rpm and 15% feed moisture. FTIR analysis was done for the optimised sample having highest IVPD. FTIR analysis showed that there were no changes in primary structure of protein while the secondary protein structure changed. In order to explain this behaviour, infrared spectroscopy analysis was carried out, mainly in the amide I and II regions. Moreover, curve fitting analysis showed the conformational changes produced in the flour due to protein denaturation. The quantitative analysis of the changes in the amide I and II regions provided information about the modifications produced in banana flour extrudates. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=extrusion" title="extrusion">extrusion</a>, <a href="https://publications.waset.org/abstracts/search?q=FTIR" title=" FTIR"> FTIR</a>, <a href="https://publications.waset.org/abstracts/search?q=protein%20conformation" title=" protein conformation"> protein conformation</a>, <a href="https://publications.waset.org/abstracts/search?q=raw%20banana%20flour" title=" raw banana flour"> raw banana flour</a>, <a href="https://publications.waset.org/abstracts/search?q=SDS-PAGE%20method" title=" SDS-PAGE method"> SDS-PAGE method</a> </p> <a href="https://publications.waset.org/abstracts/80370/effect-of-extrusion-processing-parameters-on-protein-in-banana-flour-extrudates-characterisation-using-fourier-transform-infrared-spectroscopy" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/80370.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">162</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">12646</span> DNpro: A Deep Learning Network Approach to Predicting Protein Stability Changes Induced by Single-Site Mutations</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Xiao%20Zhou">Xiao Zhou</a>, <a href="https://publications.waset.org/abstracts/search?q=Jianlin%20Cheng"> Jianlin Cheng</a> </p> <p class="card-text"><strong>Abstract:</strong></p> 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. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=bioinformatics" title="bioinformatics">bioinformatics</a>, <a href="https://publications.waset.org/abstracts/search?q=deep%20learning" title=" deep learning"> deep learning</a>, <a href="https://publications.waset.org/abstracts/search?q=protein%20stability%20prediction" title=" protein stability prediction"> protein stability prediction</a>, <a href="https://publications.waset.org/abstracts/search?q=biological%20data%20mining" title=" biological data mining"> biological data mining</a> </p> <a href="https://publications.waset.org/abstracts/48058/dnpro-a-deep-learning-network-approach-to-predicting-protein-stability-changes-induced-by-single-site-mutations" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/48058.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">468</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">12645</span> Biophysical and Structural Characterization of Transcription Factor Rv0047c of Mycobacterium Tuberculosis H37Rv</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Md.%20Samsuddin%20Ansari">Md. Samsuddin Ansari</a>, <a href="https://publications.waset.org/abstracts/search?q=Ashish%20Arora"> Ashish Arora</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Every year 10 million people fall ill with one of the oldest diseases known as tuberculosis, caused by Mycobacterium tuberculosis. The success of M. tuberculosis as a pathogen is because of its ability to persist in host tissues. Multidrug resistance (MDR) mycobacteria cases increase every day, which is associated with efflux pumps controlled at the level of transcription. The transcription regulators of MDR transporters in bacteria belong to one of the following four regulatory protein families: AraC, MarR, MerR, and TetR. Phenolic acid decarboxylase repressor (PadR), like a family of transcription regulators, is closely related to the MarR family. Phenolic acid decarboxylase repressor (PadR) was first identified as a transcription factor involved in the regulation of phenolic acid stress response in various microorganisms (including Mycobacterium tuberculosis H37Rv). Recently research has shown that the PadR family transcription factors are global, multifunction transcription regulators. Rv0047c is a PadR subfamily-1 protein. We are exploring the biophysical and structural characterization of Rv0047c. The Rv0047 gene was amplified by PCR using the primers containing EcoRI and HindIII restriction enzyme sites cloned in pET-NH6 vector and overexpressed in DH5α and BL21 (λDE3) cells of E. coli following purification with Ni2+-NTA column and size exclusion chromatography. We did DSC to know the thermal stability; the Tm (transition temperature) of protein is 55.29ºC, and ΔH (enthalpy change) of 6.92 kcal/mol. Circular dichroism to know the secondary structure and conformation and fluorescence spectroscopy for tertiary structure study of protein. To understand the effect of pH on the structure, function, and stability of Rv0047c we employed spectroscopy techniques such as circular dichroism, fluorescence, and absorbance measurements in a wide range of pH (from pH-2.0 to pH-12). At low and high pH, it shows drastic changes in the secondary and tertiary structure of the protein. EMSA studies showed the specific binding of Rv0047c with its own 30-bp promoter region. To determine the effect of complex formation on the secondary structure of Rv0047c, we examined the CD spectra of the complex of Rv0047c with promoter DNA of rv0047. The functional role of Rv0047c was characterized by over-expressing the Rv0047c gene under the control of hsp60 promoter in Mycobacterium tuberculosis H37Rv. We have predicted the three-dimensional structure of Rv0047c using the Swiss Model and Modeller, with validity checked by the Ramachandra plot. We did molecular docking of Rv0047c with dnaA, through PatchDock following refinement through FireDock. Through this, it is possible to easily identify the binding hot-stop of the receptor molecule with that of the ligand, the nature of the interface itself, and the conformational change undergone by the protein pattern. We are using X-crystallography to unravel the structure of Rv0047c. Overall the studies show that Rv0047c may have transcription regulation along with providing an insight into the activity of Rv0047c in the pH range of subcellular environment and helps to understand the protein-protein interaction, a novel target to kill dormant bacteria and potential strategy for tuberculosis control. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=mycobacterium%20tuberculosis" title="mycobacterium tuberculosis">mycobacterium tuberculosis</a>, <a href="https://publications.waset.org/abstracts/search?q=phenolic%20acid%20decarboxylase%20repressor" title=" phenolic acid decarboxylase repressor"> phenolic acid decarboxylase repressor</a>, <a href="https://publications.waset.org/abstracts/search?q=Rv0047c" title=" Rv0047c"> Rv0047c</a>, <a href="https://publications.waset.org/abstracts/search?q=Circular%20dichroism" title=" Circular dichroism"> Circular dichroism</a>, <a href="https://publications.waset.org/abstracts/search?q=fluorescence%20spectroscopy" title=" fluorescence spectroscopy"> fluorescence spectroscopy</a>, <a href="https://publications.waset.org/abstracts/search?q=docking" title=" docking"> docking</a>, <a href="https://publications.waset.org/abstracts/search?q=protein-protein%20interaction" title=" protein-protein interaction"> protein-protein interaction</a> </p> <a href="https://publications.waset.org/abstracts/161001/biophysical-and-structural-characterization-of-transcription-factor-rv0047c-of-mycobacterium-tuberculosis-h37rv" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/161001.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">121</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">12644</span> Characterization of the GntR Family Transcriptional Regulator Rv0792c: A Potential Drug Target for Mycobacterium tuberculosis</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Thanusha%20D.%20Abeywickrama">Thanusha D. Abeywickrama</a>, <a href="https://publications.waset.org/abstracts/search?q=Inoka%20C.%20Perera"> Inoka C. Perera</a>, <a href="https://publications.waset.org/abstracts/search?q=Genji%20Kurisu"> Genji Kurisu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Tuberculosis, considered being as the ninth leading cause of death worldwide, cause from a single infectious agent M. tuberculosis and the drug resistance nature of this bacterium is a continuing threat to the world. Therefore TB preventing treatment is expanding, where this study designed to analyze the regulatory mechanism of GntR transcriptional regulator gene Rv0792c, which lie between several genes codes for some hypothetical proteins, a monooxygenase and an oxidoreductase. The gene encoding Rv0792c was cloned into pET28a and expressed protein was purified to near homogeneity by Nickel affinity chromatography. It was previously reported that the protein binds within the intergenic region (BS region) between Rv0792c gene and monooxygenase (Rv0793). This resulted in binding of three protein molecules with the BS region suggesting tight control of monooxygenase as well as its own gene. Since monooxygenase plays a key role in metabolism, this gene may have a global regulatory role. The natural ligand for this regulator is still under investigation. In relation to the Rv0792 protein structure, a Circular Dichroism (CD) spectrum was carried out to determine its secondary structure elements. Percentage-wise, 17.4% Helix, 21.8% Antiparallel, 5.1% Parallel, 12.3% turn and 43.5% other were revealed from CD spectrum data under room temperature. Differential Scanning Calorimetry (DSC) was conducted to assess the thermal stability of Rv0792, which the melting temperature of protein is 57.2 ± 0.6 °C. The graph of heat capacity (Cp) versus temperature for the best fit was obtained for non-two-state model, which concludes the folding of Rv0792 protein occurs through stable intermediates. Peak area (∆HCal ) and Peak shape (∆HVant ) was calculated from the graph and ∆HCal / ∆HVant was close to 0.5, suggesting dimeric nature of the protein. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=CD%20spectrum" title="CD spectrum">CD spectrum</a>, <a href="https://publications.waset.org/abstracts/search?q=DSC%20analysis" title=" DSC analysis"> DSC analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=GntR%20transcriptional%20regulator" title=" GntR transcriptional regulator"> GntR transcriptional regulator</a>, <a href="https://publications.waset.org/abstracts/search?q=protein%20structure" title=" protein structure"> protein structure</a> </p> <a href="https://publications.waset.org/abstracts/88842/characterization-of-the-gntr-family-transcriptional-regulator-rv0792c-a-potential-drug-target-for-mycobacterium-tuberculosis" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/88842.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">222</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">12643</span> Towards the Inhibition Mechanism of Lysozyme Fibrillation by Hydrogen Sulfide</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Indra%20Gonzalez%20Ojeda">Indra Gonzalez Ojeda</a>, <a href="https://publications.waset.org/abstracts/search?q=Tatiana%20Quinones"> Tatiana Quinones</a>, <a href="https://publications.waset.org/abstracts/search?q=Manuel%20Rosario"> Manuel Rosario</a>, <a href="https://publications.waset.org/abstracts/search?q=Igor%20Lednev"> Igor Lednev</a>, <a href="https://publications.waset.org/abstracts/search?q=Juan%20Lopez%20Garriga"> Juan Lopez Garriga</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Amyloid fibrils are stable aggregates of misfolded protein associated with many neurodegenerative disorders. It has been shown that hydrogen sulfide (H2S), inhibits the fibrillation of lysozyme through the formation of trisulfide (S-S-S) bonds. However, the overall mechanism remains elusive. Here, the concentration dependence of H2S effect was investigated using Atomic force microscopy (AFM), non-resonance Raman spectroscopy, Deep-UV Raman spectroscopy and circular dichroism (CD). It was found that small spherical aggregates with trisulfide bonds and a unique secondary structure were formed instead of amyloid fibrils when adding concentrations of 25 mM and 50 mM of H2S. This could indicate that H2S might serve as a protecting agent for the protein. However, further characterization of these aggregates and their trisulfide bonds is needed to fully unravel the function H2S has on protein fibrillation. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=amyloid%20fibrils" title="amyloid fibrils">amyloid fibrils</a>, <a href="https://publications.waset.org/abstracts/search?q=hydrogen%20sulfide" title=" hydrogen sulfide"> hydrogen sulfide</a>, <a href="https://publications.waset.org/abstracts/search?q=protein%20folding" title=" protein folding"> protein folding</a>, <a href="https://publications.waset.org/abstracts/search?q=raman%20spectroscopy" title=" raman spectroscopy"> raman spectroscopy</a> </p> <a href="https://publications.waset.org/abstracts/86031/towards-the-inhibition-mechanism-of-lysozyme-fibrillation-by-hydrogen-sulfide" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/86031.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">216</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">12642</span> Role of Estrogen Receptor-alpha in Mammary Carcinoma by Single Nucleotide Polymorphisms and Molecular Docking: An In-silico Analysis</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Asif%20Bilal">Asif Bilal</a>, <a href="https://publications.waset.org/abstracts/search?q=Fouzia%20Tanvir"> Fouzia Tanvir</a>, <a href="https://publications.waset.org/abstracts/search?q=Sibtain%20Ahmad"> Sibtain Ahmad</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Estrogen receptor alpha, also known as estrogen receptor-1, is highly involved in risk of mammary carcinoma. The objectives of this study were to identify non-synonymous SNPs of estrogen receptor and their association with breast cancer and to identify the chemotherapeutic responses of phytochemicals against it via in-silico study design. For this purpose, different online tools. to identify pathogenic SNPs the tools were SIFT, Polyphen, Polyphen-2, fuNTRp, SNAP2, for finding disease associated SNPs the tools SNP&GO, PhD-SNP, PredictSNP, MAPP, SNAP, MetaSNP, PANTHER, and to check protein stability Mu-Pro, I-Mutant, and CONSURF were used. Post-translational modifications (PTMs) were detected by Musitedeep, Protein secondary structure by SOPMA, protein to protein interaction by STRING, molecular docking by PyRx. Seven SNPs having rsIDs (rs760766066, rs779180038, rs956399300, rs773683317, rs397509428, rs755020320, and rs1131692059) showing mutations on I229T, R243C, Y246H, P336R, Q375H, R394S, and R394H, respectively found to be completely deleterious. The PTMs found were 96 times Glycosylation; 30 times Ubiquitination, a single time Acetylation; and no Hydroxylation and Phosphorylation were found. The protein secondary structure consisted of Alpha helix (Hh) is (28%), Extended strand (Ee) is (21%), Beta turn (Tt) is 7.89% and Random coil (Cc) is (44.11%). Protein-protein interaction analysis revealed that it has strong interaction with Myeloperoxidase, Xanthine dehydrogenase, carboxylesterase 1, Glutathione S-transferase Mu 1, and with estrogen receptors. For molecular docking we used Asiaticoside, Ilekudinuside, Robustoflavone, Irinoticane, Withanolides, and 9-amin0-5 as ligands that extract from phytochemicals and docked with this protein. We found that there was great interaction (from -8.6 to -9.7) of these ligands of phytochemicals at ESR1 wild and two mutants (I229T and R394S). It is concluded that these SNPs found in ESR1 are involved in breast cancer and given phytochemicals are highly helpful against breast cancer as chemotherapeutic agents. Further in vitro and in vivo analysis should be performed to conduct these interactions. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=breast%20cancer" title="breast cancer">breast cancer</a>, <a href="https://publications.waset.org/abstracts/search?q=ESR1" title=" ESR1"> ESR1</a>, <a href="https://publications.waset.org/abstracts/search?q=phytochemicals" title=" phytochemicals"> phytochemicals</a>, <a href="https://publications.waset.org/abstracts/search?q=molecular%20docking" title=" molecular docking"> molecular docking</a> </p> <a href="https://publications.waset.org/abstracts/175362/role-of-estrogen-receptor-alpha-in-mammary-carcinoma-by-single-nucleotide-polymorphisms-and-molecular-docking-an-in-silico-analysis" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/175362.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">69</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">12641</span> Protein Remote Homology Detection and Fold Recognition by Combining Profiles with Kernel Methods</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Bin%20Liu">Bin Liu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> 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. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=protein%20remote%20homology%20detection" title="protein remote homology detection">protein remote homology detection</a>, <a href="https://publications.waset.org/abstracts/search?q=protein%20fold%20recognition" title=" protein fold recognition"> protein fold recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=profile-based%20features" title=" profile-based features"> profile-based features</a>, <a href="https://publications.waset.org/abstracts/search?q=Support%20Vector%20Machines%20%28SVMs%29" title=" Support Vector Machines (SVMs)"> Support Vector Machines (SVMs)</a> </p> <a href="https://publications.waset.org/abstracts/104054/protein-remote-homology-detection-and-fold-recognition-by-combining-profiles-with-kernel-methods" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/104054.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">161</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">12640</span> Primer Design for the Detection of Secondary Metabolite Biosynthetic Pathways in Metagenomic Data</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Jeisson%20Alejandro%20Triana">Jeisson Alejandro Triana</a>, <a href="https://publications.waset.org/abstracts/search?q=Maria%20Fernanda%20Quiceno%20Vallejo"> Maria Fernanda Quiceno Vallejo</a>, <a href="https://publications.waset.org/abstracts/search?q=Patricia%20del%20Portillo"> Patricia del Portillo</a>, <a href="https://publications.waset.org/abstracts/search?q=Juan%20Manuel%20Anzola"> Juan Manuel Anzola</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Most of the known antimicrobials so far discovered are secondary metabolites. The potential for new natural products of this category increases as new microbial genomes and metagenomes are being sequenced. Despite the advances, there is no systematic way to interrogate metagenomic clones for their potential to contain clusters of genes related to these pathways. Here we analyzed 52 biosynthetic pathways from the AntiSMASH database at the protein domain level in order to identify domains of high specificity and sensitivity with respect to specific biosynthetic pathways. These domains turned out to have various degrees of divergence at the DNA level. We propose PCR assays targetting such domains in-silico and corroborated one by Sanger sequencing. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=bioinformatic" title="bioinformatic">bioinformatic</a>, <a href="https://publications.waset.org/abstracts/search?q=anti%20smash" title=" anti smash"> anti smash</a>, <a href="https://publications.waset.org/abstracts/search?q=antibiotics" title=" antibiotics"> antibiotics</a>, <a href="https://publications.waset.org/abstracts/search?q=secondary%20metabolites" title=" secondary metabolites"> secondary metabolites</a>, <a href="https://publications.waset.org/abstracts/search?q=natural%20products" title=" natural products"> natural products</a>, <a href="https://publications.waset.org/abstracts/search?q=protein%20domains" title=" protein domains"> protein domains</a> </p> <a href="https://publications.waset.org/abstracts/144241/primer-design-for-the-detection-of-secondary-metabolite-biosynthetic-pathways-in-metagenomic-data" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/144241.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">179</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">12639</span> Potential Use of Cnidoscolus Chayamansa Leaf from Mexico as High-Quality Protein Source</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Diana%20Karina%20Baigts%20Allende">Diana Karina Baigts Allende</a>, <a href="https://publications.waset.org/abstracts/search?q=Mariana%20%20Gonzalez%20Diaz"> Mariana Gonzalez Diaz</a>, <a href="https://publications.waset.org/abstracts/search?q=Luis%20Antonio%20Chel%20Guerrero"> Luis Antonio Chel Guerrero</a>, <a href="https://publications.waset.org/abstracts/search?q=Mukthar%20Sandoval%20Peraza"> Mukthar Sandoval Peraza</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Poverty and food insecurity are still incident problems in the developing countries, where population´s diet is based on cereals which are lack in protein content. Nevertheless, during last years the use of native plants has been studied as an alternative source of protein in order to improve the nutritional intake. Chaya crop also called Spinach tree, is a prehispanic plant native from Central America and South of Mexico (Mayan culture), which has been especially valued due to its high nutritional content particularly protein and some medicinal properties. The aim of this work was to study the effect of protein isolation processing from Chaya leaf harvest in Yucatan, Mexico on its structure quality in order: i) to valorize the Chaya crop and ii) to produce low-cost and high-quality protein. Chaya leaf was extruded, clarified and recovered using: a) acid precipitation by decreasing the pH value until reach the isoelectric point (3.5) and b) thermal coagulation, by heating the protein solution at 80 °C during 30 min. Solubilized protein was re-dissolved in water and spray dried. The presence of Fraction I protein, known as RuBisCO (Rubilose-1,5-biphosfate carboxylase/oxygenase) was confirmed by gel electrophoresis (SDS-PAGE) where molecular weight bands of 55 KDa and 12 KDa were observed. The infrared spectrum showed changes in protein structure due to the isolation method. The use of high temperatures (thermal coagulation) highly decreased protein solubility in comparison to isoelectric precipitated protein, the nutritional properties according to amino acid profile was also disturbed, showing minor amounts of overall essential amino acids from 435.9 to 367.8 mg/g. Chaya protein isolate obtained by acid precipitation showed higher protein quality according to essential amino acid score compared to FAO recommendations, which could represent an important sustainable source of protein for human consumption. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=chaya%20leaf" title="chaya leaf">chaya leaf</a>, <a href="https://publications.waset.org/abstracts/search?q=nutritional%20properties" title=" nutritional properties"> nutritional properties</a>, <a href="https://publications.waset.org/abstracts/search?q=protein%20isolate" title=" protein isolate"> protein isolate</a>, <a href="https://publications.waset.org/abstracts/search?q=protein%20structure" title=" protein structure"> protein structure</a> </p> <a href="https://publications.waset.org/abstracts/56439/potential-use-of-cnidoscolus-chayamansa-leaf-from-mexico-as-high-quality-protein-source" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/56439.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">341</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">12638</span> The Evaluation of Substitution of Acacia villosa in Ruminants Ration</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Hadriana%20Bansi">Hadriana Bansi</a>, <a href="https://publications.waset.org/abstracts/search?q=Elizabeth%20Wina"> Elizabeth Wina</a>, <a href="https://publications.waset.org/abstracts/search?q=Toto%20Toharmat"> Toto Toharmat</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Acacia villosa is thornless shrub legume which contents high crude protein. However, the utilization of A. villosa as ruminant feed is limited by its secondary compounds. The aim of this article is to find out the maximum of substitution A. villosa in sheep ration. The nutritional evaluation consisted of in vitro two stages, in vivo, and in vitro gas production trials. The secondary compounds of A. villosa also were analyzed. Evaluating digestibility of increasing level of substitution A. villosa replacing Pennisetum purpureum was using in vitro two stages. The substitution of 30% A. villosa was compared to 100% P. purpureum by in vitro gas production technique and in vivo digestibility. The results of two stages in vitro showed that total phenol, condensed tannin, and non-protein amino acid (NPAA) were high. Substitution 15% A. villosa reached the highest digestibility for both dry matter (DM) and crude protein (CP) which were 67% and 86% respectively, but it was shown that DM and CP digestibility of substitution 30% of A. villosa was still high which were 61.82% and 75-67% respectively. The pattern of gas production showed that first 8 hours total gas production substitution of 30% A. villosa was higher than 100% P. purpureum and declined after 10 hours incubation. In vivo trials showed that substitution of 30% A. villosa significantly increased CP intake, CP digestibility, and nitrogen retention. It can be concluded that substitution A. villosa until 30% still gave the good impact even though it has high secondary compounds. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Acacia%20villosa" title="Acacia villosa">Acacia villosa</a>, <a href="https://publications.waset.org/abstracts/search?q=digestibility" title=" digestibility"> digestibility</a>, <a href="https://publications.waset.org/abstracts/search?q=gas%20production" title=" gas production"> gas production</a>, <a href="https://publications.waset.org/abstracts/search?q=secondary%20compounds" title=" secondary compounds"> secondary compounds</a> </p> <a href="https://publications.waset.org/abstracts/96358/the-evaluation-of-substitution-of-acacia-villosa-in-ruminants-ration" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/96358.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">163</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">12637</span> Bioinformatics Approach to Identify Physicochemical and Structural Properties Associated with Successful Cell-free Protein Synthesis</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Alexander%20A.%20Tokmakov">Alexander A. Tokmakov</a> </p> <p class="card-text"><strong>Abstract:</strong></p> 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. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=bioinformatics%20analysis" title="bioinformatics analysis">bioinformatics analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=cell-free%20protein%20synthesis" title=" cell-free protein synthesis"> cell-free protein synthesis</a>, <a href="https://publications.waset.org/abstracts/search?q=expression%20success" title=" expression success"> expression success</a>, <a href="https://publications.waset.org/abstracts/search?q=optimization" title=" optimization"> optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=recombinant%20proteins" title=" recombinant proteins"> recombinant proteins</a> </p> <a href="https://publications.waset.org/abstracts/22904/bioinformatics-approach-to-identify-physicochemical-and-structural-properties-associated-with-successful-cell-free-protein-synthesis" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/22904.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">419</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">12636</span> A Novel Protein Elicitor Extracted From Lecanicillium lecanii Induced Resistance Against Whitefly, Bemisia tabaci in Cotton</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Yusuf%20Ali%20Abdulle">Yusuf Ali Abdulle</a>, <a href="https://publications.waset.org/abstracts/search?q=Azhar%20Uddin%20Keerio"> Azhar Uddin Keerio</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Background: Protein elicitors play a key role in signaling or displaying plant defense mechanisms and emerging as vital tools for bio-control of insects. This study was aimed at the characterization of the novel protein elicitor isolated from entomopathogenic fungi Lecanicillium lecanii (V3) strain and its activity against Whitefly, Bemisia tabaci in cotton. The sequence of purified elicitor protein showed 100% similarity with hypothetical protein LEL_00878 [Cordyceps confragosa RCEF 1005], GenBank no (OAA81333.1). This novel protein elicitor has 253 amino acid residues and 762bp with a molecular mass of 29 kDa. The protein recombinant was expressed in Escherichia coli using pET‐28a (+) plasmid. Effects of purified novel protein elicitor on Bemisia tabaci were determined at three concentrations of protein (i.e., 58.32, 41.22, 35.41 μg mL⁻¹) on cotton plants and were exposed to newly molted adult B.tabaci. Bioassay results showed a significant effect of the exogenous application of novel protein elicitor on B. tabaci in cotton. In addition, the gene expression analysis found a significant up-regulation of the major genes associated with salicylic acid (SA) and jasmonic acid (JA) linked plant defense pathways in elicitor protein-treated plants. Our results suggested the potential application of a novel protein elicitor derived from Lecanicillium lecanii as a future bio-intensive controlling approach against the whitefly, Bemisia tabaci. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=resistance" title="resistance">resistance</a>, <a href="https://publications.waset.org/abstracts/search?q=Lecanicillium%20lecanii" title=" Lecanicillium lecanii"> Lecanicillium lecanii</a>, <a href="https://publications.waset.org/abstracts/search?q=secondary%20metabolites" title=" secondary metabolites"> secondary metabolites</a>, <a href="https://publications.waset.org/abstracts/search?q=whitefly" title=" whitefly"> whitefly</a> </p> <a href="https://publications.waset.org/abstracts/151545/a-novel-protein-elicitor-extracted-from-lecanicillium-lecanii-induced-resistance-against-whitefly-bemisia-tabaci-in-cotton" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/151545.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">184</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">12635</span> Lentil Protein Fortification in Cranberry Squash</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sandhya%20Devi%20A">Sandhya Devi A</a> </p> <p class="card-text"><strong>Abstract:</strong></p> 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. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=alkaline%20extraction" title="alkaline extraction">alkaline extraction</a>, <a href="https://publications.waset.org/abstracts/search?q=cranberry%20squash" title=" cranberry squash"> cranberry squash</a>, <a href="https://publications.waset.org/abstracts/search?q=protein%20fortification" title=" protein fortification"> protein fortification</a>, <a href="https://publications.waset.org/abstracts/search?q=response%20surface%20methodology" title=" response surface methodology"> response surface methodology</a> </p> <a href="https://publications.waset.org/abstracts/153178/lentil-protein-fortification-in-cranberry-squash" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/153178.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">111</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">12634</span> Insight into Structure and Functions of of Acyl CoA Binding Protein of Leishmania major</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Rohit%20Singh%20Dangi">Rohit Singh Dangi</a>, <a href="https://publications.waset.org/abstracts/search?q=Ravi%20Kant%20Pal"> Ravi Kant Pal</a>, <a href="https://publications.waset.org/abstracts/search?q=Monica%20Sundd"> Monica Sundd</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Acyl-CoA binding protein (ACBP) is a housekeeping protein which functions as an intracellular carrier of acyl-CoA esters. Given the fact that the amastigote stage (blood stage) of Leishmania depends largely on fatty acids as the energy source, of which a large part is derived from its host, these proteins might have an important role in its survival. In Leishmania major, genome sequencing suggests the presence of six ACBPs, whose function remains largely unknown. For functional and structural characterization, one of the ACBP genes was cloned, and the protein was expressed and purified heterologously. Acyl-CoA ester binding and stoichiometry were analyzed by isothermal titration calorimetry and Dynamic light scattering. Our results shed light on high affinity of ACBP towards longer acyl-CoA esters, such as myristoyl-CoA to arachidonoyl-CoA with single binding site. To understand the binding mechanism & dynamics, Nuclear magnetic resonance assignments of this protein are being done. The protein's crystal structure was determined at 1.5Å resolution and revealed a classical topology for ACBP, containing four alpha-helical bundles. In the binding pocket, the loop between the first and the second helix (16 – 26AA) is four residues longer from other extensively studied ACBPs (PfACBP) and it curls upwards towards the pantothenate moiety of CoA to provide a large tunnel space for long acyl chain insertion. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=acyl-coa%20binding%20protein%20%28ACBP%29" title="acyl-coa binding protein (ACBP)">acyl-coa binding protein (ACBP)</a>, <a href="https://publications.waset.org/abstracts/search?q=acyl-coa%20esters" title=" acyl-coa esters"> acyl-coa esters</a>, <a href="https://publications.waset.org/abstracts/search?q=crystal%20structure" title=" crystal structure"> crystal structure</a>, <a href="https://publications.waset.org/abstracts/search?q=isothermal%20titration" title=" isothermal titration"> isothermal titration</a>, <a href="https://publications.waset.org/abstracts/search?q=calorimetry" title=" calorimetry"> calorimetry</a>, <a href="https://publications.waset.org/abstracts/search?q=Leishmania" title=" Leishmania"> Leishmania</a> </p> <a href="https://publications.waset.org/abstracts/37502/insight-into-structure-and-functions-of-of-acyl-coa-binding-protein-of-leishmania-major" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/37502.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">448</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">12633</span> Computational Prediction of the Effect of S477N Mutation on the RBD Binding Affinity and Structural Characteristic, A Molecular Dynamics Study</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mohammad%20Hossein%20Modarressi">Mohammad Hossein Modarressi</a>, <a href="https://publications.waset.org/abstracts/search?q=Mozhgan%20Mondeali"> Mozhgan Mondeali</a>, <a href="https://publications.waset.org/abstracts/search?q=Khabat%20Barkhordari"> Khabat Barkhordari</a>, <a href="https://publications.waset.org/abstracts/search?q=Ali%20Etemadi"> Ali Etemadi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> 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. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=S477N" title="S477N">S477N</a>, <a href="https://publications.waset.org/abstracts/search?q=COVID-19" title=" COVID-19"> COVID-19</a>, <a href="https://publications.waset.org/abstracts/search?q=molecular%20dynamic" title=" molecular dynamic"> molecular dynamic</a>, <a href="https://publications.waset.org/abstracts/search?q=SARS-COV2%20mutations" title=" SARS-COV2 mutations"> SARS-COV2 mutations</a> </p> <a href="https://publications.waset.org/abstracts/145533/computational-prediction-of-the-effect-of-s477n-mutation-on-the-rbd-binding-affinity-and-structural-characteristic-a-molecular-dynamics-study" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/145533.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">177</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">12632</span> Hydration of Protein-RNA Recognition Sites</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Amita%20Barik">Amita Barik</a>, <a href="https://publications.waset.org/abstracts/search?q=Ranjit%20Prasad%20Bahadur"> Ranjit Prasad Bahadur</a> </p> <p class="card-text"><strong>Abstract:</strong></p> 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. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=h-bonds" title="h-bonds">h-bonds</a>, <a href="https://publications.waset.org/abstracts/search?q=minor-major%20grooves" title=" minor-major grooves"> minor-major grooves</a>, <a href="https://publications.waset.org/abstracts/search?q=preserved%20water" title=" preserved water"> preserved water</a>, <a href="https://publications.waset.org/abstracts/search?q=protein-RNA%20interfaces" title=" protein-RNA interfaces"> protein-RNA interfaces</a> </p> <a href="https://publications.waset.org/abstracts/42932/hydration-of-protein-rna-recognition-sites" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/42932.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">302</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">12631</span> A Comparative Study of Secondary Education Curriculum of Iran with Some Developed Countries in the World</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Seyyed%20Abdollah%20Hojjati">Seyyed Abdollah Hojjati</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Review in the areas of secondary education; it is a kind of comparative requires very careful scrutiny in educational structure of different countries,In upcoming review of the basic structure of our educational system in Islamic republic of Iran with somedeveloped countries in the world, Analyzing of strengthsand weaknesses in main areas, A simple review of the above methods do not consider this particular community, Modifythe desired result can be expressed in the secondary school curriculum and academic guidance of under graduate students in a skill-driven and creativity growth, It not just improves the health and dynamism of this period and increases the secondary teachers' authority and the relationship between teacher and student in this course will be meaningful and attractive, But with reduced of false prosperity and guaranteed institutes and quizzes, areas will be provided for students to enjoy the feeling ofthe psychological comfort and to have the highest growth of creativity . <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=comparative" title="comparative">comparative</a>, <a href="https://publications.waset.org/abstracts/search?q=curriculum%20of%20secondary%20education" title=" curriculum of secondary education"> curriculum of secondary education</a>, <a href="https://publications.waset.org/abstracts/search?q=curriculum" title=" curriculum"> curriculum</a>, <a href="https://publications.waset.org/abstracts/search?q=Iran" title=" Iran"> Iran</a>, <a href="https://publications.waset.org/abstracts/search?q=developed%20countries" title=" developed countries"> developed countries</a> </p> <a href="https://publications.waset.org/abstracts/14988/a-comparative-study-of-secondary-education-curriculum-of-iran-with-some-developed-countries-in-the-world" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/14988.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">493</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">12630</span> Protein Crystallization Induced by Surface Plasmon Resonance</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Tetsuo%20Okutsu">Tetsuo Okutsu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> 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. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=lysozyme" title="lysozyme">lysozyme</a>, <a href="https://publications.waset.org/abstracts/search?q=plasmon" title=" plasmon"> plasmon</a>, <a href="https://publications.waset.org/abstracts/search?q=protein" title=" protein"> protein</a>, <a href="https://publications.waset.org/abstracts/search?q=crystallization" title=" crystallization"> crystallization</a>, <a href="https://publications.waset.org/abstracts/search?q=RNaseA" title=" RNaseA"> RNaseA</a> </p> <a href="https://publications.waset.org/abstracts/85433/protein-crystallization-induced-by-surface-plasmon-resonance" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/85433.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">218</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">12629</span> Physicochemical and Functional Characteristics of Hemp Protein Isolate</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=El-Sohaimy%20Sobhy%20A.">El-Sohaimy Sobhy A.</a>, <a href="https://publications.waset.org/abstracts/search?q=Androsova%20Natalia"> Androsova Natalia</a>, <a href="https://publications.waset.org/abstracts/search?q=Toshev%20Abuvali%20Djabarovec"> Toshev Abuvali Djabarovec</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The conditions of the isolation of proteins from the hemp seeds were optimized in the current work. Moreover, the physicochemical and functional properties of hemp protein isolate were evaluated for its potential application in food manufacturing. The elastin protein is the most predominant protein in the protein profile with a molecular weight of 58.1 KDa, besides albumin, with a molecular weight of 31.5 KDa. The FTIR spectrum detected the absorption peaks of the amide I in 1750 and 1600 cm⁻¹, which pointed to C=O stretching while N-H was stretching at 1650-1580 cm⁻¹. The peak at 3250 was related to N-H stretching of primary aliphatic amine (3400-3300 cm⁻¹), and the N-H stretching for secondary (II) amine appeared at 3350-3310 cm⁻¹. Hemp protein isolate (HPI) was showed high content of arginine (15.52 g/100 g), phenylalanine+tyrosine (9.63 g/100 g), methionine + cysteine (5.49 g/100 g), leucine + isoleucine (5.21 g/100 g) and valine (4.53 g/100 g). It contains a moderate level of threonine (3.29 g/100 g) and lysine (2.50 g/100 g), with the limiting amino acid being a tryptophan (0.22 g/100 g HPI). HPI showed high water-holding capacity (4.5 ± 2.95 ml/g protein) and oil holding capacity (2.33 ± 1.88 ml/g) values. The foaming capacity of HPI was increased with increasing the pH values to reach the maximum value at pH 11 (67.23±3.20 %). The highest emulsion ability index of HPI was noted at pH 9 (91.3±2.57 m2/g) with low stability (19.15±2.03). <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Cannabis%20sativa%20ssp." title="Cannabis sativa ssp.">Cannabis sativa ssp.</a>, <a href="https://publications.waset.org/abstracts/search?q=protein%20isolate" title=" protein isolate"> protein isolate</a>, <a href="https://publications.waset.org/abstracts/search?q=isolation%20conditions" title=" isolation conditions"> isolation conditions</a>, <a href="https://publications.waset.org/abstracts/search?q=amino%20acid%20composition" title=" amino acid composition"> amino acid composition</a>, <a href="https://publications.waset.org/abstracts/search?q=chemical%20properties" title=" chemical properties"> chemical properties</a>, <a href="https://publications.waset.org/abstracts/search?q=functional%20properties" title=" functional properties"> functional properties</a> </p> <a href="https://publications.waset.org/abstracts/150400/physicochemical-and-functional-characteristics-of-hemp-protein-isolate" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/150400.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">180</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">12628</span> Analysis of Replication Protein A (RPA): The Role of Homolog Interaction and Recombination during Meiosis</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Jeong%20Hwan%20Joo">Jeong Hwan Joo</a>, <a href="https://publications.waset.org/abstracts/search?q=Keun%20Pil%20Kim"> Keun Pil Kim</a> </p> <p class="card-text"><strong>Abstract:</strong></p> During meiosis, meiotic recombination is initiated by Spo11-mediated DSB formation and exonuclease-mediated DSB resection occurs to expose single stranded DNA formation. RPA is further required to inhibit secondary structure formation of ssDNA that can be formed Watson-Crick pairing. Rad51-Dmc1, RecA homologs in eukaryote and their accessory factors involve in searching homolog templates to mediate strand exchange. In this study, we investigate the recombinational roles of replication protein A (RPA), which is heterotrimeric protein that is composed of RPA1, RPA2, and RPA3. Here, we investigated meiotic recombination using DNA physical analysis at the HIS4LEU2 hot spot. In rfa1-119 (K45E, N316S) cells, crossover (CO) and non-crossover (NCO) products reduced than WT. rfa1-119 delayed in single end invasion-to-double holiday junction (SEI-to-dHJ) transition and exhibits a defect in second-end capture that is also modulated by Rad52. In the further experiment, we observed that in rfa1-119 mutant, RPA could not be released in timely manner. Furthermore, rfa1-119 exhibits failure in the second end capture, implying reduction of COs and NCOs. In this talk, we will discuss more detail how RPA involves in chromatin axis association via formation of axis-bridge and why RPA is required for Rad52-mediated second-end capture progression. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=homolog%20interaction" title="homolog interaction">homolog interaction</a>, <a href="https://publications.waset.org/abstracts/search?q=meiotic%20recombination" title=" meiotic recombination"> meiotic recombination</a>, <a href="https://publications.waset.org/abstracts/search?q=replication%20protein%20A" title=" replication protein A"> replication protein A</a>, <a href="https://publications.waset.org/abstracts/search?q=RPA1" title=" RPA1"> RPA1</a> </p> <a href="https://publications.waset.org/abstracts/80585/analysis-of-replication-protein-a-rpa-the-role-of-homolog-interaction-and-recombination-during-meiosis" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/80585.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">201</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">12627</span> The Effect of Sorafenibe on Soat1 Protein by Using Molecular Docking Method</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mahdiyeh%20Gholaminezhad">Mahdiyeh Gholaminezhad</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Context: The study focuses on the potential impact of Sorafenib on SOAT1 protein in liver cancer treatment, addressing the need for more effective therapeutic options. Research aim: To explore the effects of Sorafenib on the activity of SOAT1 protein in liver cancer cells. Methodology: Molecular docking was employed to analyze the interaction between Sorafenib and SOAT1 protein. Findings: The study revealed a significant effect of Sorafenib on the stability and activity of SOAT1 protein, suggesting its potential as a treatment for liver cancer. Theoretical importance: This research highlights the molecular mechanism underlying Sorafenib's anti-cancer properties, contributing to the understanding of its therapeutic effects. Data collection: Data on the molecular structure of Sorafenib and SOAT1 protein were obtained from computational simulations and databases. Analysis procedures: Molecular docking simulations were performed to predict the binding interactions between Sorafenib and SOAT1 protein. Question addressed: How does Sorafenib influence the activity of SOAT1 protein and what are the implications for liver cancer treatment? Conclusion: The study demonstrates the potential of Sorafenib as a targeted therapy for liver cancer by affecting the activity of SOAT1 protein. Reviewers' Comments: The study provides valuable insights into the molecular basis of Sorafenib's action on SOAT1 protein, suggesting its therapeutic potential. To enhance the methodology, the authors could consider validating the docking results with experimental data for further validation. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=liver%20cancer" title="liver cancer">liver cancer</a>, <a href="https://publications.waset.org/abstracts/search?q=sorafenib" title=" sorafenib"> sorafenib</a>, <a href="https://publications.waset.org/abstracts/search?q=SOAT1" title=" SOAT1"> SOAT1</a>, <a href="https://publications.waset.org/abstracts/search?q=molecular%20docking" title=" molecular docking"> molecular docking</a> </p> <a href="https://publications.waset.org/abstracts/189263/the-effect-of-sorafenibe-on-soat1-protein-by-using-molecular-docking-method" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/189263.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">26</span> </span> </div> </div> <ul class="pagination"> <li class="page-item disabled"><span class="page-link">&lsaquo;</span></li> <li class="page-item active"><span class="page-link">1</span></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=protein%20secondary%20structure&page=2">2</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=protein%20secondary%20structure&page=3">3</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=protein%20secondary%20structure&page=4">4</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=protein%20secondary%20structure&page=5">5</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=protein%20secondary%20structure&page=6">6</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=protein%20secondary%20structure&page=7">7</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=protein%20secondary%20structure&page=8">8</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=protein%20secondary%20structure&page=9">9</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=protein%20secondary%20structure&page=10">10</a></li> <li class="page-item disabled"><span class="page-link">...</span></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=protein%20secondary%20structure&page=421">421</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=protein%20secondary%20structure&page=422">422</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=protein%20secondary%20structure&page=2" rel="next">&rsaquo;</a></li> </ul> </div> </main> <footer> <div id="infolinks" class="pt-3 pb-2"> <div class="container"> <div style="background-color:#f5f5f5;" class="p-3"> <div class="row"> <div class="col-md-2"> <ul class="list-unstyled"> About <li><a href="https://waset.org/page/support">About Us</a></li> <li><a href="https://waset.org/page/support#legal-information">Legal</a></li> <li><a target="_blank" rel="nofollow" href="https://publications.waset.org/static/files/WASET-16th-foundational-anniversary.pdf">WASET celebrates its 16th foundational anniversary</a></li> </ul> </div> <div class="col-md-2"> <ul class="list-unstyled"> Account <li><a href="https://waset.org/profile">My Account</a></li> </ul> </div> <div class="col-md-2"> <ul class="list-unstyled"> Explore <li><a href="https://waset.org/disciplines">Disciplines</a></li> <li><a href="https://waset.org/conferences">Conferences</a></li> <li><a href="https://waset.org/conference-programs">Conference Program</a></li> <li><a href="https://waset.org/committees">Committees</a></li> <li><a href="https://publications.waset.org">Publications</a></li> </ul> </div> <div class="col-md-2"> <ul class="list-unstyled"> Research <li><a href="https://publications.waset.org/abstracts">Abstracts</a></li> <li><a href="https://publications.waset.org">Periodicals</a></li> <li><a href="https://publications.waset.org/archive">Archive</a></li> </ul> </div> <div class="col-md-2"> <ul class="list-unstyled"> Open Science <li><a target="_blank" rel="nofollow" href="https://publications.waset.org/static/files/Open-Science-Philosophy.pdf">Open Science Philosophy</a></li> <li><a target="_blank" rel="nofollow" href="https://publications.waset.org/static/files/Open-Science-Award.pdf">Open Science Award</a></li> <li><a target="_blank" rel="nofollow" href="https://publications.waset.org/static/files/Open-Society-Open-Science-and-Open-Innovation.pdf">Open Innovation</a></li> <li><a target="_blank" rel="nofollow" href="https://publications.waset.org/static/files/Postdoctoral-Fellowship-Award.pdf">Postdoctoral Fellowship Award</a></li> <li><a target="_blank" rel="nofollow" href="https://publications.waset.org/static/files/Scholarly-Research-Review.pdf">Scholarly Research Review</a></li> </ul> </div> <div class="col-md-2"> <ul class="list-unstyled"> Support <li><a href="https://waset.org/page/support">Support</a></li> <li><a href="https://waset.org/profile/messages/create">Contact Us</a></li> <li><a href="https://waset.org/profile/messages/create">Report Abuse</a></li> </ul> </div> </div> </div> </div> </div> <div class="container text-center"> <hr style="margin-top:0;margin-bottom:.3rem;"> <a href="https://creativecommons.org/licenses/by/4.0/" target="_blank" class="text-muted small">Creative Commons Attribution 4.0 International License</a> <div id="copy" class="mt-2">© 2024 World Academy of Science, Engineering and Technology</div> </div> </footer> <a href="javascript:" id="return-to-top"><i class="fas fa-arrow-up"></i></a> <div class="modal" id="modal-template"> <div class="modal-dialog"> <div class="modal-content"> <div class="row m-0 mt-1"> <div class="col-md-12"> <button type="button" class="close" data-dismiss="modal" aria-label="Close"><span aria-hidden="true">×</span></button> </div> </div> <div class="modal-body"></div> </div> </div> </div> <script src="https://cdn.waset.org/static/plugins/jquery-3.3.1.min.js"></script> <script src="https://cdn.waset.org/static/plugins/bootstrap-4.2.1/js/bootstrap.bundle.min.js"></script> <script src="https://cdn.waset.org/static/js/site.js?v=150220211556"></script> <script> jQuery(document).ready(function() { /*jQuery.get("https://publications.waset.org/xhr/user-menu", function (response) { jQuery('#mainNavMenu').append(response); });*/ jQuery.get({ url: "https://publications.waset.org/xhr/user-menu", cache: false }).then(function(response){ jQuery('#mainNavMenu').append(response); }); }); </script> </body> </html>