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 prediction</title> <meta name="description" content="Search results for: protein structure prediction"> <meta name="keywords" content="protein structure prediction"> <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 prediction" 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 prediction"> <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> 11768</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: protein structure prediction</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">11768</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">11767</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">11766</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">480</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">11765</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">467</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">11764</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">11763</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">11762</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">11761</span> Improved 3D Structure Prediction of Beta-Barrel Membrane Proteins by Using Evolutionary Coupling Constraints, Reduced State Space and an Empirical Potential Function</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Wei%20Tian">Wei Tian</a>, <a href="https://publications.waset.org/abstracts/search?q=Jie%20Liang"> Jie Liang</a>, <a href="https://publications.waset.org/abstracts/search?q=Hammad%20Naveed"> Hammad Naveed</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Beta-barrel membrane proteins are found in the outer membrane of gram-negative bacteria, mitochondria, and chloroplasts. They carry out diverse biological functions, including pore formation, membrane anchoring, enzyme activity, and bacterial virulence. In addition, beta-barrel membrane proteins increasingly serve as scaffolds for bacterial surface display and nanopore-based DNA sequencing. Due to difficulties in experimental structure determination, they are sparsely represented in the protein structure databank and computational methods can help to understand their biophysical principles. We have developed a novel computational method to predict the 3D structure of beta-barrel membrane proteins using evolutionary coupling (EC) constraints and a reduced state space. Combined with an empirical potential function, we can successfully predict strand register at > 80% accuracy for a set of 49 non-homologous proteins with known structures. This is a significant improvement from previous results using EC alone (44%) and using empirical potential function alone (73%). Our method is general and can be applied to genome-wide structural prediction. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=beta-barrel%20membrane%20proteins" title="beta-barrel membrane proteins">beta-barrel membrane proteins</a>, <a href="https://publications.waset.org/abstracts/search?q=structure%20prediction" title=" structure prediction"> structure prediction</a>, <a href="https://publications.waset.org/abstracts/search?q=evolutionary%20constraints" title=" evolutionary constraints"> evolutionary constraints</a>, <a href="https://publications.waset.org/abstracts/search?q=reduced%20state%20space" title=" reduced state space"> reduced state space</a> </p> <a href="https://publications.waset.org/abstracts/40565/improved-3d-structure-prediction-of-beta-barrel-membrane-proteins-by-using-evolutionary-coupling-constraints-reduced-state-space-and-an-empirical-potential-function" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/40565.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">618</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">11760</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">11759</span> Development of the Structure of the Knowledgebase for Countermeasures in the Knowledge Acquisition Process for Trouble Prediction in Healthcare Processes</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Shogo%20Kato">Shogo Kato</a>, <a href="https://publications.waset.org/abstracts/search?q=Daisuke%20Okamoto"> Daisuke Okamoto</a>, <a href="https://publications.waset.org/abstracts/search?q=Satoko%20Tsuru"> Satoko Tsuru</a>, <a href="https://publications.waset.org/abstracts/search?q=Yoshinori%20Iizuka"> Yoshinori Iizuka</a>, <a href="https://publications.waset.org/abstracts/search?q=Ryoko%20Shimono"> Ryoko Shimono</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Healthcare safety has been perceived important. It is essential to prevent troubles in healthcare processes for healthcare safety. Trouble prevention is based on trouble prediction using accumulated knowledge on processes, troubles, and countermeasures. However, information on troubles has not been accumulated in hospitals in the appropriate structure, and it has not been utilized effectively to prevent troubles. In the previous study, though a detailed knowledge acquisition process for trouble prediction was proposed, the knowledgebase for countermeasures was not involved. In this paper, we aim to propose the structure of the knowledgebase for countermeasures in the knowledge acquisition process for trouble prediction in healthcare process. We first design the structure of countermeasures and propose the knowledge representation form on countermeasures. Then, we evaluate the validity of the proposal, by applying it into an actual hospital. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=trouble%20prevention" title="trouble prevention">trouble prevention</a>, <a href="https://publications.waset.org/abstracts/search?q=knowledge%20structure" title=" knowledge structure"> knowledge structure</a>, <a href="https://publications.waset.org/abstracts/search?q=structured%20knowledge" title=" structured knowledge"> structured knowledge</a>, <a href="https://publications.waset.org/abstracts/search?q=reusable%20knowledge" title=" reusable knowledge"> reusable knowledge</a> </p> <a href="https://publications.waset.org/abstracts/37281/development-of-the-structure-of-the-knowledgebase-for-countermeasures-in-the-knowledge-acquisition-process-for-trouble-prediction-in-healthcare-processes" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/37281.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">367</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">11758</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">11757</span> BiFormerDTA: Structural Embedding of Protein in Drug Target Affinity Prediction Using BiFormer</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Leila%20Baghaarabani">Leila Baghaarabani</a>, <a href="https://publications.waset.org/abstracts/search?q=Parvin%20Razzaghi"> Parvin Razzaghi</a>, <a href="https://publications.waset.org/abstracts/search?q=Mennatolla%20Magdy%20Mostafa"> Mennatolla Magdy Mostafa</a>, <a href="https://publications.waset.org/abstracts/search?q=Ahmad%20Albaqsami"> Ahmad Albaqsami</a>, <a href="https://publications.waset.org/abstracts/search?q=Al%20Warith%20Al%20Rushaidi"> Al Warith Al Rushaidi</a>, <a href="https://publications.waset.org/abstracts/search?q=Masoud%20Al%20Rawahi"> Masoud Al Rawahi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Predicting the interaction between drugs and their molecular targets is pivotal for advancing drug development processes. Due to the time and cost limitations, computational approaches have emerged as an effective approach to drug-target interaction (DTI) prediction. Most of the introduced computational based approaches utilize the drug molecule and protein sequence as input. This study does not only utilize these inputs, it also introduces a protein representation developed using a masked protein language model. In this representation, for every individual amino acid residue within the protein sequence, there exists a corresponding probability distribution that indicates the likelihood of each amino acid being present at that particular position. Then, the similarity between each pair of amino acids is computed to create a similarity matrix. To encode the knowledge of the similarity matrix, Bi-Level Routing Attention (BiFormer) is utilized, which combines aspects of transformer-based models with protein sequence analysis and represents a significant advancement in the field of drug-protein interaction prediction. BiFormer has the ability to pinpoint the most effective regions of the protein sequence that are responsible for facilitating interactions between the protein and drugs, thereby enhancing the understanding of these critical interactions. Thus, it appears promising in its ability to capture the local structural relationship of the proteins by enhancing the understanding of how it contributes to drugprotein interactions, thereby facilitating more accurate predictions. To evaluate the proposed method, it was tested on two widely recognized datasets: Davis and KIBA. A comprehensive series of experiments was conducted to illustrate its effectiveness in comparison to cutting edge techniques. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=BiFormer" title="BiFormer">BiFormer</a>, <a href="https://publications.waset.org/abstracts/search?q=transformer" title=" transformer"> transformer</a>, <a href="https://publications.waset.org/abstracts/search?q=protein%20language%20processing" title=" protein language processing"> protein language processing</a>, <a href="https://publications.waset.org/abstracts/search?q=self-attention%20mechanism" title=" self-attention mechanism"> self-attention mechanism</a>, <a href="https://publications.waset.org/abstracts/search?q=binding%20affinity" title=" binding affinity"> binding affinity</a>, <a href="https://publications.waset.org/abstracts/search?q=drug%20target%20interaction" title=" drug target interaction"> drug target interaction</a>, <a href="https://publications.waset.org/abstracts/search?q=similarity%20matrix" title=" similarity matrix"> similarity matrix</a>, <a href="https://publications.waset.org/abstracts/search?q=protein%20masked%20representation" title=" protein masked representation"> protein masked representation</a>, <a href="https://publications.waset.org/abstracts/search?q=protein%20language%20model" title=" protein language model"> protein language model</a> </p> <a href="https://publications.waset.org/abstracts/194594/biformerdta-structural-embedding-of-protein-in-drug-target-affinity-prediction-using-biformer" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/194594.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">7</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">11756</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">11755</span> Bioinformatics Approach to Identify Physicochemical and Structural Properties Associated with Successful Cell-free Protein Synthesis</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Alexander%20A.%20Tokmakov">Alexander A. Tokmakov</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Cell-free protein synthesis is widely used to synthesize recombinant proteins. It allows genome-scale expression of various polypeptides under strictly controlled uniform conditions. However, only a minor fraction of all proteins can be successfully expressed in the systems of protein synthesis that are currently used. The factors determining expression success are poorly understood. At present, the vast volume of data is accumulated in cell-free expression databases. It makes possible comprehensive bioinformatics analysis and identification of multiple features associated with successful cell-free expression. Here, we describe an approach aimed at identification of multiple physicochemical and structural properties of amino acid sequences associated with protein solubility and aggregation and highlight major correlations obtained using this approach. The developed method includes: categorical assessment of the protein expression data, calculation and prediction of multiple properties of expressed amino acid sequences, correlation of the individual properties with the expression scores, and evaluation of statistical significance of the observed correlations. Using this approach, we revealed a number of statistically significant correlations between calculated and predicted features of protein sequences and their amenability to cell-free expression. It was found that some of the features, such as protein pI, hydrophobicity, presence of signal sequences, etc., are mostly related to protein solubility, whereas the others, such as protein length, number of disulfide bonds, content of secondary structure, etc., affect mainly the expression propensity. We also demonstrated that amenability of polypeptide sequences to cell-free expression correlates with the presence of multiple sites of post-translational modifications. The correlations revealed in this study provide a plethora of important insights into protein folding and rationalization of protein production. The developed bioinformatics approach can be of practical use for predicting expression success and optimizing cell-free protein synthesis. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=bioinformatics%20analysis" title="bioinformatics analysis">bioinformatics analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=cell-free%20protein%20synthesis" title=" cell-free protein synthesis"> cell-free protein synthesis</a>, <a href="https://publications.waset.org/abstracts/search?q=expression%20success" title=" expression success"> expression success</a>, <a href="https://publications.waset.org/abstracts/search?q=optimization" title=" optimization"> optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=recombinant%20proteins" title=" recombinant proteins"> recombinant proteins</a> </p> <a href="https://publications.waset.org/abstracts/22904/bioinformatics-approach-to-identify-physicochemical-and-structural-properties-associated-with-successful-cell-free-protein-synthesis" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/22904.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">419</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">11754</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">11753</span> Classifying and Predicting Efficiencies Using Interval DEA Grid Setting</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Yiannis%20G.%20Smirlis">Yiannis G. Smirlis </a> </p> <p class="card-text"><strong>Abstract:</strong></p> The classification and the prediction of efficiencies in Data Envelopment Analysis (DEA) is an important issue, especially in large scale problems or when new units frequently enter the under-assessment set. In this paper, we contribute to the subject by proposing a grid structure based on interval segmentations of the range of values for the inputs and outputs. Such intervals combined, define hyper-rectangles that partition the space of the problem. This structure, exploited by Interval DEA models and a dominance relation, acts as a DEA pre-processor, enabling the classification and prediction of efficiency scores, without applying any DEA models. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=data%20envelopment%20analysis" title="data envelopment analysis">data envelopment analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=interval%20DEA" title=" interval DEA"> interval DEA</a>, <a href="https://publications.waset.org/abstracts/search?q=efficiency%20classification" title=" efficiency classification"> efficiency classification</a>, <a href="https://publications.waset.org/abstracts/search?q=efficiency%20prediction" title=" efficiency prediction"> efficiency prediction</a> </p> <a href="https://publications.waset.org/abstracts/86988/classifying-and-predicting-efficiencies-using-interval-dea-grid-setting" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/86988.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">164</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">11752</span> Stress Recovery and Durability Prediction of a Vehicular Structure with Random Road Dynamic Simulation</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Jia-Shiun%20Chen">Jia-Shiun Chen</a>, <a href="https://publications.waset.org/abstracts/search?q=Quoc-Viet%20Huynh"> Quoc-Viet Huynh</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This work develops a flexible-body dynamic model of an all-terrain vehicle (ATV), capable of recovering dynamic stresses while the ATV travels on random bumpy roads. The fatigue life of components is forecasted as well. While considering the interaction between dynamic forces and structure deformation, the proposed model achieves a highly accurate structure stress prediction and fatigue life prediction. During the simulation, stress time history of the ATV structure is retrieved for life prediction. Finally, the hot sports of the ATV frame are located, and the frame life for combined road conditions is forecasted, i.e. 25833.6 hr. If the usage of vehicle is eight hours daily, the total vehicle frame life is 8.847 years. Moreover, the reaction force and deformation due to the dynamic motion can be described more accurately by using flexible body dynamics than by using rigid-body dynamics. Based on recommendations made in the product design stage before mass production, the proposed model can significantly lower development and testing costs. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=flexible-body%20dynamics" title="flexible-body dynamics">flexible-body dynamics</a>, <a href="https://publications.waset.org/abstracts/search?q=veicle" title=" veicle"> veicle</a>, <a href="https://publications.waset.org/abstracts/search?q=dynamics" title=" dynamics"> dynamics</a>, <a href="https://publications.waset.org/abstracts/search?q=fatigue" title=" fatigue"> fatigue</a>, <a href="https://publications.waset.org/abstracts/search?q=durability" title=" durability"> durability</a> </p> <a href="https://publications.waset.org/abstracts/26684/stress-recovery-and-durability-prediction-of-a-vehicular-structure-with-random-road-dynamic-simulation" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/26684.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">394</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">11751</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">11750</span> Computational Prediction of the Effect of S477N Mutation on the RBD Binding Affinity and Structural Characteristic, A Molecular Dynamics Study</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mohammad%20Hossein%20Modarressi">Mohammad Hossein Modarressi</a>, <a href="https://publications.waset.org/abstracts/search?q=Mozhgan%20Mondeali"> Mozhgan Mondeali</a>, <a href="https://publications.waset.org/abstracts/search?q=Khabat%20Barkhordari"> Khabat Barkhordari</a>, <a href="https://publications.waset.org/abstracts/search?q=Ali%20Etemadi"> Ali Etemadi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The COVID-19 pandemic, caused by SARS-CoV-2, has led to significant concerns worldwide due to its catastrophic effects on public health. The SARS-CoV-2 infection is initiated with the binding of the receptor-binding domain (RBD) in its spike protein to the ACE2 receptor in the host cell membrane. Due to the error-prone entity of the viral RNA-dependent polymerase complex, the virus genome, including the coding region for the RBD, acquires new mutations, leading to the appearance of multiple variants. These variants can potentially impact transmission, virulence, antigenicity and evasive immune properties. S477N mutation located in the RBD has been observed in the SARS-CoV-2 omicron (B.1.1. 529) variant. In this study, we investigated the consequences of S477N mutation at the molecular level using computational approaches such as molecular dynamics simulation, protein-protein interaction analysis, immunoinformatics and free energy computation. We showed that displacement of Ser with Asn increases the stability of the spike protein and its affinity to ACE2 and thus increases the transmission potential of the virus. This mutation changes the folding and secondary structure of the spike protein. Also, it reduces antibody neutralization, raising concern about re-infection, vaccine breakthrough and therapeutic values. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=S477N" title="S477N">S477N</a>, <a href="https://publications.waset.org/abstracts/search?q=COVID-19" title=" COVID-19"> COVID-19</a>, <a href="https://publications.waset.org/abstracts/search?q=molecular%20dynamic" title=" molecular dynamic"> molecular dynamic</a>, <a href="https://publications.waset.org/abstracts/search?q=SARS-COV2%20mutations" title=" SARS-COV2 mutations"> SARS-COV2 mutations</a> </p> <a href="https://publications.waset.org/abstracts/145533/computational-prediction-of-the-effect-of-s477n-mutation-on-the-rbd-binding-affinity-and-structural-characteristic-a-molecular-dynamics-study" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/145533.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">175</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">11749</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">11748</span> On the Homology Modeling, Structural Function Relationship and Binding Site Prediction of Human Alsin Protein</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Y.%20Ruchi">Y. Ruchi</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20Prerna"> A. Prerna</a>, <a href="https://publications.waset.org/abstracts/search?q=S.%20Deepshikha"> S. Deepshikha </a> </p> <p class="card-text"><strong>Abstract:</strong></p> Amyotrophic lateral sclerosis (ALS), also known as “Lou Gehrig’s disease”. It is a neurodegenerative disease associated with degeneration of motor neurons in the cerebral cortex, brain stem, and spinal cord characterized by distal muscle weakness, atrophy, normal sensation, pyramidal signs and progressive muscular paralysis reflecting. ALS2 is a juvenile autosomal recessive disorder, slowly progressive, that maps to chromosome 2q33 and is associated with mutations in the alsin gene, a putative GTPase regulator. In this paper we have done homology modeling of alsin2 protein using multiple templates (3KCI_A, 4LIM_A, 402W_A, 4D9S_A, and 4DNV_A) designed using the Prime program in Schrödinger software. Further modeled structure is used to identify effective binding sites on the basis of structural and physical properties using sitemap program in Schrödinger software, structural and function analysis is done by using Prosite and ExPASy server that gives insight into conserved domains and motifs that can be used for protein classification. This paper summarizes the structural, functional and binding site property of alsin2 protein. These binding sites can be potential drug target sites and can be used for docking studies. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=ALS" title="ALS">ALS</a>, <a href="https://publications.waset.org/abstracts/search?q=binding%20site" title=" binding site"> binding site</a>, <a href="https://publications.waset.org/abstracts/search?q=homology%20modeling" title=" homology modeling"> homology modeling</a>, <a href="https://publications.waset.org/abstracts/search?q=neuronal%20degeneration" title=" neuronal degeneration"> neuronal degeneration</a> </p> <a href="https://publications.waset.org/abstracts/20360/on-the-homology-modeling-structural-function-relationship-and-binding-site-prediction-of-human-alsin-protein" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/20360.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">389</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">11747</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">11746</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">11745</span> In Silico Analysis of Deleterious nsSNPs (Missense) of Dihydrolipoamide Branched-Chain Transacylase E2 Gene Associated with Maple Syrup Urine Disease Type II</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Zainab%20S.%20Ahmed">Zainab S. Ahmed</a>, <a href="https://publications.waset.org/abstracts/search?q=Mohammed%20S.%20Ali"> Mohammed S. Ali</a>, <a href="https://publications.waset.org/abstracts/search?q=Nadia%20A.%20Elshiekh"> Nadia A. Elshiekh</a>, <a href="https://publications.waset.org/abstracts/search?q=Sami%20Adam%20Ibrahim"> Sami Adam Ibrahim</a>, <a href="https://publications.waset.org/abstracts/search?q=Ghada%20M.%20El-Tayeb"> Ghada M. El-Tayeb</a>, <a href="https://publications.waset.org/abstracts/search?q=Ahmed%20H.%20Elsadig"> Ahmed H. Elsadig</a>, <a href="https://publications.waset.org/abstracts/search?q=Rihab%20A.%20Omer"> Rihab A. Omer</a>, <a href="https://publications.waset.org/abstracts/search?q=Sofia%20B.%20Mohamed"> Sofia B. Mohamed </a> </p> <p class="card-text"><strong>Abstract:</strong></p> Maple syrup urine (MSUD) is an autosomal recessive disease that causes a deficiency in the enzyme branched-chain alpha-keto acid (BCKA) dehydrogenase. The development of disease has been associated with SNPs in the DBT gene. Despite that, the computational analysis of SNPs in coding and noncoding and their functional impacts on protein level still remains unknown. Hence, in this study, we carried out a comprehensive in silico analysis of missense that was predicted to have a harmful influence on DBT structure and function. In this study, eight different in silico prediction algorithms; SIFT, PROVEAN, MutPred, SNP&GO, PhD-SNP, PANTHER, I-Mutant 2.0 and MUpo were used for screening nsSNPs in DBT including. Additionally, to understand the effect of mutations in the strength of the interactions that bind protein together the ELASPIC servers were used. Finally, the 3D structure of DBT was formed using Mutation3D and Chimera servers respectively. Our result showed that a total of 15 nsSNPs confirmed by 4 software (R301C, R376H, W84R, S268F, W84C, F276C, H452R, R178H, I355T, V191G, M444T, T174A, I200T, R113H, and R178C) were found damaging and can lead to a shift in DBT gene structure. Moreover, we found 7 nsSNPs located on the 2-oxoacid_dh catalytic domain, 5 nsSNPs on the E_3 binding domain and 3 nsSNPs on the Biotin Domain. So these nsSNPs may alter the putative structure of DBT’s domain. Furthermore, we detected all these nsSNPs are on the core residues of the protein and have the ability to change the stability of the protein. Additionally, we found W84R, S268F, and M444T have high significance, and they affected Leucine, Isoleucine, and Valine, which reduces or disrupt the function of BCKD complex, E2-subunit which the DBT gene encodes. In conclusion, based on our extensive in-silico analysis, we report 15 nsSNPs that have possible association with protein deteriorating and disease-causing abilities. These candidate SNPs can aid in future studies on Maple Syrup Urine Disease type II base in the genetic level. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=DBT%20gene" title="DBT gene">DBT gene</a>, <a href="https://publications.waset.org/abstracts/search?q=ELASPIC" title=" ELASPIC"> ELASPIC</a>, <a href="https://publications.waset.org/abstracts/search?q=in%20silico%20analysis" title=" in silico analysis"> in silico analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=UCSF%20chimer" title=" UCSF chimer"> UCSF chimer</a> </p> <a href="https://publications.waset.org/abstracts/83720/in-silico-analysis-of-deleterious-nssnps-missense-of-dihydrolipoamide-branched-chain-transacylase-e2-gene-associated-with-maple-syrup-urine-disease-type-ii" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/83720.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">201</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">11744</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">11743</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">11742</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">11741</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">508</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">11740</span> The Prediction Mechanism of M. cajuputi Extract from Lampung-Indonesia, as an Anti-Inflammatory Agent for COVID-19 by NFκβ Pathway</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Agustyas%20Tjiptaningrum">Agustyas Tjiptaningrum</a>, <a href="https://publications.waset.org/abstracts/search?q=Intanri%20Kurniati"> Intanri Kurniati</a>, <a href="https://publications.waset.org/abstracts/search?q=Fadilah%20Fadilah"> Fadilah Fadilah</a>, <a href="https://publications.waset.org/abstracts/search?q=Linda%20Erlina"> Linda Erlina</a>, <a href="https://publications.waset.org/abstracts/search?q=Tiwuk%20Susantiningsih"> Tiwuk Susantiningsih</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Coronavirus disease-19 (COVID-19) is still one of the health problems. It can be a severe condition that is caused by a cytokine storm. In a cytokine storm, several proinflammatory cytokines are released massively. It destroys epithelial cells, and subsequently, it can cause death. The anti-inflammatory agent can be used to decrease the number of severe Covid-19 conditions. Melaleuca cajuputi is a plant that has antiviral, antibiotic, antioxidant, and anti-inflammatory activities. This study was carried out to analyze the prediction mechanism of the M. cajuputi extract from Lampung, Indonesia, as an anti-inflammatory agent for COVID-19. This study constructed a database of protein host target that was involved in the inflammation process of COVID-19 using data retrieval from GeneCards with the keyword “SARS-CoV2”, “inflammation,” “cytokine storm,” and “acute respiratory distress syndrome.” Subsequent protein-protein interaction was generated by using Cytoscape version 3.9.1. It can predict the significant target protein. Then the analysis of the Gene Ontology (GO) and KEGG pathways was conducted to generate the genes and components that play a role in COVID-19. The result of this study was 30 nodes representing significant proteins, namely NF-κβ, IL-6, IL-6R, IL-2RA, IL-2, IFN2, C3, TRAF6, IFNAR1, and DOX58. From the KEGG pathway, we obtained the result that NF-κβ has a role in the production of proinflammatory cytokines, which play a role in the COVID-19 cytokine storm. It is an important factor for macrophage transcription; therefore, it will induce inflammatory gene expression that encodes proinflammatory cytokines such as IL-6, TNF-α, and IL-1β. In conclusion, the blocking of NF-κβ is the prediction mechanism of the M. cajuputi extract as an anti-inflammation agent for COVID-19. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=antiinflammation" title="antiinflammation">antiinflammation</a>, <a href="https://publications.waset.org/abstracts/search?q=COVID-19" title=" COVID-19"> COVID-19</a>, <a href="https://publications.waset.org/abstracts/search?q=cytokine%20storm" title=" cytokine storm"> cytokine storm</a>, <a href="https://publications.waset.org/abstracts/search?q=NF-%CE%BA%CE%B2" title=" NF-κβ"> NF-κβ</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20cajuputi" title=" M. cajuputi"> M. cajuputi</a> </p> <a href="https://publications.waset.org/abstracts/165831/the-prediction-mechanism-of-m-cajuputi-extract-from-lampung-indonesia-as-an-anti-inflammatory-agent-for-covid-19-by-nfkv-pathway" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/165831.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">87</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">11739</span> SEMCPRA-Sar-Esembled Model for Climate Prediction in Remote Area</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Kamalpreet%20Kaur">Kamalpreet Kaur</a>, <a href="https://publications.waset.org/abstracts/search?q=Renu%20Dhir"> Renu Dhir</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Climate prediction is an essential component of climate research, which helps evaluate possible effects on economies, communities, and ecosystems. Climate prediction involves short-term weather prediction, seasonal prediction, and long-term climate change prediction. Climate prediction can use the information gathered from satellites, ground-based stations, and ocean buoys, among other sources. The paper's four architectures, such as ResNet50, VGG19, Inception-v3, and Xception, have been combined using an ensemble approach for overall performance and robustness. An ensemble of different models makes a prediction, and the majority vote determines the final prediction. The various architectures such as ResNet50, VGG19, Inception-v3, and Xception efficiently classify the dataset RSI-CB256, which contains satellite images into cloudy and non-cloudy. The generated ensembled S-E model (Sar-ensembled model) provides an accuracy of 99.25%. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=climate" title="climate">climate</a>, <a href="https://publications.waset.org/abstracts/search?q=satellite%20images" title=" satellite images"> satellite images</a>, <a href="https://publications.waset.org/abstracts/search?q=prediction" title=" prediction"> prediction</a>, <a href="https://publications.waset.org/abstracts/search?q=classification" title=" classification"> classification</a> </p> <a href="https://publications.waset.org/abstracts/178864/semcpra-sar-esembled-model-for-climate-prediction-in-remote-area" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/178864.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">73</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%20prediction&page=2">2</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=protein%20structure%20prediction&page=3">3</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=protein%20structure%20prediction&page=4">4</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=protein%20structure%20prediction&page=5">5</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=protein%20structure%20prediction&page=6">6</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=protein%20structure%20prediction&page=7">7</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=protein%20structure%20prediction&page=8">8</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=protein%20structure%20prediction&page=9">9</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=protein%20structure%20prediction&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%20prediction&page=392">392</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=protein%20structure%20prediction&page=393">393</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=protein%20structure%20prediction&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>