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 structure</title> <meta name="description" content="Search results for: protein structure"> <meta name="keywords" content="protein 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 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 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> 9804</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: protein structure</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">9804</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">9803</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">9802</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">9801</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">9800</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">9799</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">9798</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">9797</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">9796</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">9795</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">9794</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">9793</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">9792</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">9791</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">9790</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">9789</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">9788</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">9787</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> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">9786</span> Spectrofluorometric Studies on the Interactions of Bovine Serum Albumin with Dimeric Cationic Surfactants</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Srishti%20Sinha">Srishti Sinha</a>, <a href="https://publications.waset.org/abstracts/search?q=Deepti%20Tikariha"> Deepti Tikariha</a>, <a href="https://publications.waset.org/abstracts/search?q=Kallol%20K.%20Ghosh"> Kallol K. Ghosh</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Over the past few decades protein-surfactant interactions have been a subject of extensive studies as they are of great importance in wide variety of industries, biological, pharmaceutical and cosmetic systems. Protein-surfactant interactions have been explored the effect of surfactants on structure of protein in the form of solubilization and denaturing or renaturing of protein. Globular proteins are frequently used as functional ingredients in healthcare and pharmaceutical products, due to their ability to catalyze biochemical reactions, to be adsorbed on the surface of some substance and to bind other moieties and form molecular aggregates. One of the most widely used globular protein is bovine serum albumin (BSA), since it has a well-known primary structure and been associated with the binding of many different categories of molecules, such as dyes, drugs and toxic chemicals. Protein−surfactant interactions are usually dependent on the surfactant features. Most of the research has been focused on single-chain surfactants. More recently, the binding between proteins and dimeric surfactants has been discussed. In present study interactions of one dimeric surfactant Butanediyl-1,4-bis (dimethylhexadecylammonium bromide) (16-4-16, 2Br-) and the corresponding single-chain surfactant cetyl trimethylammonium bromide (CTAB) with bovine serum albumin (BSA) have been investigated by surface tension and spectrofluoremetric methods. It has been found that the bindings of all gemini surfactant to BSA were cooperatively driven by electrostatic and hydrophobic interactions. The gemini surfactant carrying more charges and hydrophobic tails, showed stronger interactions with BSA than the single-chain surfactant. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=bovine%20serum%20albumin" title="bovine serum albumin">bovine serum albumin</a>, <a href="https://publications.waset.org/abstracts/search?q=gemini%20surfactants" title=" gemini surfactants"> gemini surfactants</a>, <a href="https://publications.waset.org/abstracts/search?q=hydrophobic%20interactions" title=" hydrophobic interactions"> hydrophobic interactions</a>, <a href="https://publications.waset.org/abstracts/search?q=protein%20surfactant%20interaction" title=" protein surfactant interaction"> protein surfactant interaction</a> </p> <a href="https://publications.waset.org/abstracts/35047/spectrofluorometric-studies-on-the-interactions-of-bovine-serum-albumin-with-dimeric-cationic-surfactants" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/35047.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">509</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">9785</span> Bioinformatics Approach to Support Genetic Research in Autism in Mali</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=M.%20Kouyate">M. Kouyate</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20Sangare"> M. Sangare</a>, <a href="https://publications.waset.org/abstracts/search?q=S.%20Samake"> S. Samake</a>, <a href="https://publications.waset.org/abstracts/search?q=S.%20Keita"> S. Keita</a>, <a href="https://publications.waset.org/abstracts/search?q=H.%20G.%20Kim"> H. G. Kim</a>, <a href="https://publications.waset.org/abstracts/search?q=D.%20H.%20Geschwind"> D. H. Geschwind</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Background & Objectives: Human genetic studies can be expensive, even unaffordable, in developing countries, partly due to the sequencing costs. Our aim is to pilot the use of bioinformatics tools to guide scientifically valid, locally relevant, and economically sound autism genetic research in Mali. Methods: The following databases, NCBI, HGMD, and LSDB, were used to identify hot point mutations. Phenotype, transmission pattern, theoretical protein expression in the brain, the impact of the mutation on the 3D structure of the protein) were used to prioritize selected autism genes. We used the protein database, Modeller, and clustal W. Results: We found Mef2c (Gly27Ala/Leu38Gln), Pten (Thr131IIle), Prodh (Leu289Met), Nme1 (Ser120Gly), and Dhcr7 (Pro227Thr/Glu224Lys). These mutations were associated with endonucleases BseRI, NspI, PfrJS2IV, BspGI, BsaBI, and SpoDI, respectively. Gly27Ala/Leu38Gln mutations impacted the 3D structure of the Mef2c protein. Mef2c protein sequences across species showed a high percentage of similarity with a highly conserved MADS domain. Discussion: Mef2c, Pten, Prodh, Nme1, and Dhcr 7 gene mutation frequencies in the Malian population will be very informative. PCR coupled with restriction enzyme digestion can be used to screen the targeted gene mutations. Sanger sequencing will be used for confirmation only. This will cut down considerably the sequencing cost for gene-to-gene mutation screening. The knowledge of the 3D structure and potential impact of the mutations on Mef2c protein informed the protein family and altered function (ex. Leu38Gln). Conclusion & Future Work: Bio-informatics will positively impact autism research in Mali. Our approach can be applied to another neuropsychiatric disorder. <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=endonucleases" title=" endonucleases"> endonucleases</a>, <a href="https://publications.waset.org/abstracts/search?q=autism" title=" autism"> autism</a>, <a href="https://publications.waset.org/abstracts/search?q=Sanger%20sequencing" title=" Sanger sequencing"> Sanger sequencing</a>, <a href="https://publications.waset.org/abstracts/search?q=point%20mutations" title=" point mutations"> point mutations</a> </p> <a href="https://publications.waset.org/abstracts/164430/bioinformatics-approach-to-support-genetic-research-in-autism-in-mali" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/164430.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">83</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">9784</span> Glycation of Serum Albumin: Cause Remarkable Alteration in Protein Structure and Generation of Early Glycation End Products</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ishrat%20Jahan%20Saifi">Ishrat Jahan Saifi</a>, <a href="https://publications.waset.org/abstracts/search?q=Sheelu%20Shafiq%20Siddiqi"> Sheelu Shafiq Siddiqi</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20R.%20Ajmal"> M. R. Ajmal</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Glycation of protein is very important as well as a harmful process, which may lead to develop DM in human body. Human Serum Albumin (HSA) is the most abundant protein in blood and it is highly prone to glycation by the reducing sugars. 2-¬deoxy d-¬Ribose (dRib) is a highly reactive reducing sugar which is produced in cells as a product of the enzyme thymidine phosphorylase. It is generated during the degradation of DNA in human body. It may cause glycation in HSA rapidly and is involved in the development of DM. In present study, we did in¬vitro glycation of HSA with different concentrations of 2-¬deoxy d-¬ribose and found that dRib glycated HSA rapidly within 4h incubation at 37◦C. UV¬ Spectroscopy, Fluorescence spectroscopy, Fourier transform infrared spectroscopy (FTIR) and Circular Dichroism (CD) technique have been done to determine the structural changes in HSA upon glycation. Results of this study suggested that dRib is the potential glycating agent and it causes alteration in protein structure and biophysical properties which may lead to development and progression of Diabetes mellitus. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=2-deoxy%20D-ribose" title="2-deoxy D-ribose">2-deoxy D-ribose</a>, <a href="https://publications.waset.org/abstracts/search?q=human%20serum%20albumin" title=" human serum albumin"> human serum albumin</a>, <a href="https://publications.waset.org/abstracts/search?q=glycation" title=" glycation"> glycation</a>, <a href="https://publications.waset.org/abstracts/search?q=diabetes%20mellitus" title=" diabetes mellitus"> diabetes mellitus</a> </p> <a href="https://publications.waset.org/abstracts/60529/glycation-of-serum-albumin-cause-remarkable-alteration-in-protein-structure-and-generation-of-early-glycation-end-products" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/60529.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">210</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">9783</span> Bioinformatics Identification of Rare Codon Clusters in Proteins Structure of HBV</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Abdorrasoul%20Malekpour">Abdorrasoul Malekpour</a>, <a href="https://publications.waset.org/abstracts/search?q=Mohammad%20Ghorbani%20Mojtaba%20Mortazavi"> Mohammad Ghorbani Mojtaba Mortazavi</a>, <a href="https://publications.waset.org/abstracts/search?q=Mohammadreza%20Fattahi"> Mohammadreza Fattahi</a>, <a href="https://publications.waset.org/abstracts/search?q=Mohammad%20Hassan%20Meshkibaf"> Mohammad Hassan Meshkibaf</a>, <a href="https://publications.waset.org/abstracts/search?q=Ali%20Fakhrzad"> Ali Fakhrzad</a>, <a href="https://publications.waset.org/abstracts/search?q=Saeid%20Salehi"> Saeid Salehi</a>, <a href="https://publications.waset.org/abstracts/search?q=Saeideh%20Zahedi"> Saeideh Zahedi</a>, <a href="https://publications.waset.org/abstracts/search?q=Amir%20Ahmadimoghaddam"> Amir Ahmadimoghaddam</a>, <a href="https://publications.waset.org/abstracts/search?q=Parviz%20Farzadnia%20Dr."> Parviz Farzadnia Dr.</a>, <a href="https://publications.waset.org/abstracts/search?q=Mohammadreza%20Hajyani%20Asl%20Bs"> Mohammadreza Hajyani Asl Bs</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Hepatitis B as an infectious disease has eight main genotypes (A–H). The aim of this study is to Bioinformatically identify Rare Codon Clusters (RCC) in proteins structure of HBV. For detection of protein family accession numbers (Pfam) of HBV proteins; used of uni-prot database and Pfam search tool were used. Obtained Pfam IDs were analyzed in Sherlocc program and RCCs in HBV proteins were detected. In further, the structures of TrEMBL entries proteins studied in PDB database and 3D structures of the HBV proteins and locations of RCCs were visualized and studied using Swiss PDB Viewer software. Pfam search tool have found nine significant hits and 0 insignificant hits in 3 frames. Results of Pfams studied in the Sherlocc program show this program not identified RCCs in the external core antigen (PF08290) and truncated HBeAg protein (PF08290). By contrast the RCCs become identified in Hepatitis core antigen (PF00906) Large envelope protein S (PF00695), X protein (PF00739), DNA polymerase (viral) N-terminal domain (PF00242) and Protein P (Pf00336). In HBV genome, seven RCC identified that found in hepatitis core antigen, large envelope protein S and DNA polymerase proteins and proteins structures of TrEMBL entries sequences that reported in Sherlocc program outputs are not complete. Based on situation of RCC in structure of HBV proteins, it suggested those RCCs are important in HBV life cycle. We hoped that this study provide a new and deep perspective in protein research and drug design for treatment of HBV. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=rare%20codon%20clusters" title="rare codon clusters">rare codon clusters</a>, <a href="https://publications.waset.org/abstracts/search?q=hepatitis%20B%20virus" title=" hepatitis B virus"> hepatitis B virus</a>, <a href="https://publications.waset.org/abstracts/search?q=bioinformatic%20study" title=" bioinformatic study"> bioinformatic study</a>, <a href="https://publications.waset.org/abstracts/search?q=infectious%20disease" title=" infectious disease "> infectious disease </a> </p> <a href="https://publications.waset.org/abstracts/24687/bioinformatics-identification-of-rare-codon-clusters-in-proteins-structure-of-hbv" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/24687.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">488</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">9782</span> Membrane Spanning DNA Origami Nanopores for Protein Translocation</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Genevieve%20Pugh">Genevieve Pugh</a>, <a href="https://publications.waset.org/abstracts/search?q=Johnathan%20Burns"> Johnathan Burns</a>, <a href="https://publications.waset.org/abstracts/search?q=Stefan%20Howorka"> Stefan Howorka</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Single-molecule sensing via protein nanopores has achieved a step-change in portable and label-free DNA sequencing. However, protein pores of both natural or engineered origin are not able to produce the tunable diameters needed for effective protein sensing. Here, we describe a generic strategy to build synthetic DNA nanopores that are wide enough to accommodate folded protein. The pores are composed of interlinked DNA duplexes and carry lipid anchors to achieve the required membrane insertion. Our demonstrator pore has a contiguous cross-sectional channel area of 50 nm2 which is 6-times larger than the largest protein pore. Consequently, transport of folded protein across bilayers is possible. The modular design is amenable for different pore dimensions and can be adapted for protein sensing or to create molecular gates in synthetic biology. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=biosensing" title="biosensing">biosensing</a>, <a href="https://publications.waset.org/abstracts/search?q=DNA%20nanotechnology" title=" DNA nanotechnology"> DNA nanotechnology</a>, <a href="https://publications.waset.org/abstracts/search?q=DNA%20origami" title=" DNA origami"> DNA origami</a>, <a href="https://publications.waset.org/abstracts/search?q=nanopore%20sensing" title=" nanopore sensing"> nanopore sensing</a> </p> <a href="https://publications.waset.org/abstracts/78556/membrane-spanning-dna-origami-nanopores-for-protein-translocation" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/78556.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">324</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">9781</span> The Development of an Automated Computational Workflow to Prioritize Potential Resistance Variants in HIV Integrase Subtype C</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Keaghan%20Brown">Keaghan Brown</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The prioritization of drug resistance mutations impacting protein folding or protein-drug and protein-DNA interactions within macromolecular systems is critical to the success of treatment regimens. With a continual increase in computational tools to assess these impacts, the need for scalability and reproducibility became an essential component of computational analysis and experimental research. Here it introduce a bioinformatics pipeline that combines several structural analysis tools in a simplified workflow, by optimizing the present computational hardware and software to automatically ease the flow of data transformations. Utilizing preestablished software tools, it was possible to develop a pipeline with a set of pre-defined functions that will automate mutation introduction into the HIV-1 Integrase protein structure, calculate the gain and loss of polar interactions and calculate the change in energy of protein fold. Additionally, an automated molecular dynamics analysis was implemented which reduces the constant need for user input and output management. The resulting pipeline, Automated Mutation Introduction and Analysis (AMIA) is an open source set of scripts designed to introduce and analyse the effects of mutations on the static protein structure as well as the results of the multi-conformational states from molecular dynamic simulations. The workflow allows the user to visualize all outputs in a user friendly manner thereby successfully enabling the prioritization of variant systems for experimental validation. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=automated%20workflow" title="automated workflow">automated workflow</a>, <a href="https://publications.waset.org/abstracts/search?q=variant%20prioritization" title=" variant prioritization"> variant prioritization</a>, <a href="https://publications.waset.org/abstracts/search?q=drug%20resistance" title=" drug resistance"> drug resistance</a>, <a href="https://publications.waset.org/abstracts/search?q=HIV%20Integrase" title=" HIV Integrase"> HIV Integrase</a> </p> <a href="https://publications.waset.org/abstracts/178777/the-development-of-an-automated-computational-workflow-to-prioritize-potential-resistance-variants-in-hiv-integrase-subtype-c" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/178777.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">77</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">9780</span> Homology Modelling of Beta Defensin 3 of Bos taurus and Its Docking Studies with Molecules Responsible for Formation of Biofilm</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ravinder%20Singh">Ravinder Singh</a>, <a href="https://publications.waset.org/abstracts/search?q=Ankita%20Gurao"> Ankita Gurao</a>, <a href="https://publications.waset.org/abstracts/search?q=Saroj%20Bandhan"> Saroj Bandhan</a>, <a href="https://publications.waset.org/abstracts/search?q=Sudhir%20Kumar%20Kashyap"> Sudhir Kumar Kashyap </a> </p> <p class="card-text"><strong>Abstract:</strong></p> The Bos taurus Beta defensin 3 is a defensin peptide secreted by neutrophils and epithelial that exhibits anti-microbial activity. It is one of the crucial components forming an innate defense against intra mammary infections in livestock. The beta defensin 3 by virtue of its anti-microbial activity inhibits major mastitis pathogens including Staphylococcus aureus and Pseudomonas aeruginosa etc, which are also responsible for biofilm formation leading to antibiotic resistance phenomenon. Therefore, the defensin may prove as a non-conventional option to treat mastitis. In this study, computational analysis has been performed including sequence comparison among species and homology modeling of Bos taurus beta defensin 3 protein. The assessments of protein structure were done using the protein structure and model assessment tools integrated in Swiss Model server, which employs various local and global quality evaluation parameters. Further, molecular docking was also carried out between the defensin peptide and the components of biofilm to gain insight into various interactions and structural differences crucial for functionality of this protein. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=beta%20defensin%203" title="beta defensin 3">beta defensin 3</a>, <a href="https://publications.waset.org/abstracts/search?q=bos%20taurus" title=" bos taurus"> bos taurus</a>, <a href="https://publications.waset.org/abstracts/search?q=docking" title=" docking"> docking</a>, <a href="https://publications.waset.org/abstracts/search?q=homology%20modeling" title=" homology modeling"> homology modeling</a> </p> <a href="https://publications.waset.org/abstracts/64346/homology-modelling-of-beta-defensin-3-of-bos-taurus-and-its-docking-studies-with-molecules-responsible-for-formation-of-biofilm" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/64346.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">291</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">9779</span> Insights of Interaction Studies between HSP-60, HSP-70 Proteins and HSF-1 in Bubalus bubalis</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ravinder%20Singh">Ravinder Singh</a>, <a href="https://publications.waset.org/abstracts/search?q=C%20Rajesh"> C Rajesh</a>, <a href="https://publications.waset.org/abstracts/search?q=Saroj%20Badhan"> Saroj Badhan</a>, <a href="https://publications.waset.org/abstracts/search?q=Shailendra%20Mishra"> Shailendra Mishra</a>, <a href="https://publications.waset.org/abstracts/search?q=Ranjit%20Singh%20Kataria"> Ranjit Singh Kataria</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Heat shock protein 60 and 70 are crucial chaperones that guide appropriate folding of denatured proteins under heat stress conditions. HSP60 and HSP70 provide assistance in correct folding of a multitude of denatured proteins. The heat shock factors are the family of some transcription factors which controls the regulation of gene expression of proteins involved in folding of damaged or improper folded proteins during stress conditions. Under normal condition heat shock proteins bind with HSF-1 and act as its repressor as well as aids in maintaining the HSF-1’s nonactive and monomeric confirmation. The experimental protein structure for all these proteins in Bubalus bubalis is not known till date. Therefore computational approach was explored to identify three-dimensional structure analysis of all these proteins. In this study, an extensive in silico analysis has been performed including sequence comparison among species to comparative modeling of Bubalus bubalis HSP60, HSP70 and HSF-1 protein. The stereochemical properties of proteins were assessed by utilizing several scrutiny bioinformatics tools to ensure model accuracy. Further docking approach was used to study interactions between Heat shock proteins and HSF-1. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Bubalus%20bubalis" title="Bubalus bubalis">Bubalus bubalis</a>, <a href="https://publications.waset.org/abstracts/search?q=comparative%20modelling" title=" comparative modelling"> comparative modelling</a>, <a href="https://publications.waset.org/abstracts/search?q=docking" title=" docking"> docking</a>, <a href="https://publications.waset.org/abstracts/search?q=heat%20shock%20protein" title=" heat shock protein"> heat shock protein</a> </p> <a href="https://publications.waset.org/abstracts/64431/insights-of-interaction-studies-between-hsp-60-hsp-70-proteins-and-hsf-1-in-bubalus-bubalis" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/64431.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">322</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">9778</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">9777</span> Effect of Electromagnetic Fields on Protein Extraction from Shrimp By-Products for Electrospinning Process</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Guido%20Trautmann-S%C3%A1ez">Guido Trautmann-Sáez</a>, <a href="https://publications.waset.org/abstracts/search?q=Mario%20P%C3%A9rez-Won"> Mario Pérez-Won</a>, <a href="https://publications.waset.org/abstracts/search?q=Vilbett%20Briones"> Vilbett Briones</a>, <a href="https://publications.waset.org/abstracts/search?q=Mar%C3%ADa%20Jos%C3%A9%20Bugue%C3%B1o"> María José Bugueño</a>, <a href="https://publications.waset.org/abstracts/search?q=Gipsy%20Tabilo-Munizaga"> Gipsy Tabilo-Munizaga</a>, <a href="https://publications.waset.org/abstracts/search?q=Luis%20Gonz%C3%A1les-Cavieres"> Luis Gonzáles-Cavieres</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Shrimp by-products are a valuable source of protein. However, traditional protein extraction methods have limitations in terms of their efficiency. Protein extraction from shrimp (Pleuroncodes monodon) industrial by-products assisted with ohmic heating (OH), microwave (MW) and pulsed electric field (PEF). It was performed by chemical method (using NaOH and HCl 2M) assisted with OH, MW and PEF in a continuous flow system (5 ml/s). Protein determination, differential scanning calorimetry (DSC) and Fourier-transform infrared (FTIR). Results indicate a 19.25% (PEF) 3.65% (OH) and 28.19% (MW) improvement in protein extraction efficiency. The most efficient method was selected for the electrospinning process and obtaining fiber. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=electrospinning%20process" title="electrospinning process">electrospinning process</a>, <a href="https://publications.waset.org/abstracts/search?q=emerging%20technology" title=" emerging technology"> emerging technology</a>, <a href="https://publications.waset.org/abstracts/search?q=protein%20extraction" title=" protein extraction"> protein extraction</a>, <a href="https://publications.waset.org/abstracts/search?q=shrimp%20by-products" title=" shrimp by-products"> shrimp by-products</a> </p> <a href="https://publications.waset.org/abstracts/171420/effect-of-electromagnetic-fields-on-protein-extraction-from-shrimp-by-products-for-electrospinning-process" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/171420.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">89</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">9776</span> Physicochemical Properties of Soy Protein Isolate (SPI): Starch Conjugates Treated by Sonication</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Gulcin%20Yildiz">Gulcin Yildiz</a>, <a href="https://publications.waset.org/abstracts/search?q=Hao%20Feng"> Hao Feng</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In recent years there is growing interested in using soy protein because of several advantages compared to other protein sources, such as high nutritional value, steady supply, and low cost. Soy protein isolate (SPI) is the most refined soy protein product. It contains 90% protein in a moisture-free form and has some desirable functionalities. Creating a protein-polysaccharide conjugate to be the emulsifying agent rather than the protein alone can markedly enhance its stability. This study was undertaken to examine the effects of ultrasound treatments on the physicochemical properties of SPI-starch conjugates. The soy protein isolate (SPI, Pro-Fam® 955) samples were obtained from the Archer Daniels Midland Company. Protein concentrations were analyzed by the Bardford method using BSA as the standard. The volume-weighted mean diameters D [4,3] of protein–polysaccharide conjugates were measured by dynamic light scattering (DLS). Surface hydrophobicity of the conjugates was measured by using 1-anilino-8-naphthalenesulfonate (ANS) (Sigma-Aldrich, St. Louis, MO, USA). Increasing the pH from 2 to 12 resulted in increased protein solubility. The highest solubility was 69.2% for the sample treated with ultrasonication at pH 12, while the lowest (9.13%) was observed in the Control. For the other pH conditions, the protein solubility values ranged from 40.53 to 49.65%. The ultrasound treatment significantly decreased the particle sizes of the SPI-modified starch conjugates. While the D [4,3] for the Control was 731.6 nm, it was 293.7 nm for the samples treated by sonication at pH 12. The surface hydrophobicity (H0) of SPI-starch at all pH conditions were significantly higher than those in the Control. Ultrasonication was proven to be effective in improving the solubility and emulsifying properties of soy protein isolate-starch conjugates. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=particle%20size" title="particle size">particle size</a>, <a href="https://publications.waset.org/abstracts/search?q=solubility" title=" solubility"> solubility</a>, <a href="https://publications.waset.org/abstracts/search?q=soy%20protein%20isolate" title=" soy protein isolate"> soy protein isolate</a>, <a href="https://publications.waset.org/abstracts/search?q=ultrasonication" title=" ultrasonication"> ultrasonication</a> </p> <a href="https://publications.waset.org/abstracts/64023/physicochemical-properties-of-soy-protein-isolate-spi-starch-conjugates-treated-by-sonication" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/64023.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">422</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">9775</span> Effect of Removing Hub Domain on Human CaMKII Isoforms Sensitivity to Calcium/Calmodulin</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ravid%20Inbar">Ravid Inbar</a> </p> <p class="card-text"><strong>Abstract:</strong></p> CaMKII (calcium-calmodulin dependent protein kinase II) makes up 2% of the protein in our brain and has a critical role in memory formation and long-term potentiation of neurons. Despite this, research has yet to uncover the role of one of the domains on the activation of this kinase. The following proposes to express the protein without the hub domain in E. coli, leaving only the kinase and regulatory segment of the protein. Next, a series of kinase assays will be conducted to elucidate the role the hub domain plays on CaMKII sensitivity to calcium/calmodulin activation. The hub domain may be important for activation; however, it may also be a variety of domains working together to influence protein activation and not the hub alone. Characterization of a protein is critical to the future understanding of the protein's function, as well as for producing pharmacological targets in cases of patients with diseases. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=CaMKII" title="CaMKII">CaMKII</a>, <a href="https://publications.waset.org/abstracts/search?q=hub%20domain" title=" hub domain"> hub domain</a>, <a href="https://publications.waset.org/abstracts/search?q=kinase%20assays" title=" kinase assays"> kinase assays</a>, <a href="https://publications.waset.org/abstracts/search?q=kinase%20%2B%20reg%20seg" title=" kinase + reg seg"> kinase + reg seg</a> </p> <a href="https://publications.waset.org/abstracts/157748/effect-of-removing-hub-domain-on-human-camkii-isoforms-sensitivity-to-calciumcalmodulin" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/157748.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">89</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%20structure&page=2">2</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=protein%20structure&page=3">3</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=protein%20structure&page=4">4</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=protein%20structure&page=5">5</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=protein%20structure&page=6">6</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=protein%20structure&page=7">7</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=protein%20structure&page=8">8</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=protein%20structure&page=9">9</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=protein%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%20structure&page=326">326</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=protein%20structure&page=327">327</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=protein%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>