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README.html
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<!doctype html>
<html>
<head>
<meta charset='UTF-8'><meta name='viewport' content='width=device-width initial-scale=1'>
<title>README</title><link href='https://fonts.loli.net/css?family=Open+Sans:400italic,700italic,700,400&subset=latin,latin-ext' rel='stylesheet' type='text/css' /><style type='text/css'>html {overflow-x: initial !important;}:root { --bg-color: #ffffff; --text-color: #333333; --select-text-bg-color: #B5D6FC; --select-text-font-color: auto; --monospace: "Lucida Console",Consolas,"Courier",monospace; }
html { font-size: 14px; background-color: var(--bg-color); color: var(--text-color); font-family: "Helvetica Neue", Helvetica, Arial, sans-serif; -webkit-font-smoothing: antialiased; }
body { margin: 0px; padding: 0px; height: auto; bottom: 0px; top: 0px; left: 0px; right: 0px; font-size: 1rem; line-height: 1.42857143; overflow-x: hidden; background-image: inherit; background-size: inherit; background-attachment: inherit; background-origin: inherit; background-clip: inherit; background-color: inherit; tab-size: 4; background-position: inherit inherit; background-repeat: inherit inherit; }
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<div id='write' class = 'is-mac'><h1><a name="dent-disease" class="md-header-anchor"></a><span>Dent Disease</span></h1><p><span>This work aims to understand what is </span><em><span>'wrong'</span></em><span> with the mutated protein, i.e. to understand the proteins' distribution with respect to the molecules in the </span><strong><span>Dent disease</span></strong><span>.</span></p><p><span>We need a way of comparing movements among the different molecular simulations. In order to discover how mutated protein differs from the non-mutated one.</span></p><p><span> As we can see below nothing can be appreciated from the complete system </span><em><span>(membrane, waters, ions and the protein)</span></em><span>. Hence, how can we compare proteins? </span></p><p> </p><p><img src="./images/full.gif" referrerpolicy="no-referrer" alt="full"></p><p> </p><h3><a name="simplified-trajectory-and-structure" class="md-header-anchor"></a><span>Simplified Trajectory and Structure</span></h3><p><span>As it is difficult to see the protein in this system, we simplify trajectory erasing membranes, waters and sodium ions. </span></p><p><img src="./images/sanitosano.gif" referrerpolicy="no-referrer" alt="sanitosano"></p><p><span>After that we only select the most important </span><strong><span>Coordination Chlorines</span></strong><span> as well as </span><strong><span>Proton Pathway</span></strong><span>:</span></p><ul><li><em><span>Upper</span></em><span> chlorine: Gly209, Lys210, Glu211, Gly453, Leu454, Phe455, Ile456, Gly169, Ile170</span></li><li><em><span>Lower</span></em><span> chlorine: Lys210, Glu211, Ser168, His561</span></li><li><em><span>Proton</span></em><span> pathway: Lys174, Glu211, Phe255, Gly260, Phe264, Glu267, Glu268, Ser520, Tyr558</span></li></ul><p><span>Then, we obtained a clearer image of the protein trajectory along the simulation, which is primordial for the study of it.</span></p><p><img src="./images/healthy.gif" referrerpolicy="no-referrer" alt="healthy"></p><h2><a name="kernel-principal-component-analysis" class="md-header-anchor"></a><span>Kernel Principal Component Analysis</span></h2><p><span>The data was structured as follows in a 3-Dimensional Matrix:</span></p><p><img src="./images/image copy 2.png" alt="image copy" style="zoom:33%;" /></p><p> </p><p><span>Once we have structured our data-base, we started computing different correlation matrices frame to frame based on </span><strong><span>Root Mean Square Deviation</span></strong><span> (RMSD) of atomic positions:</span></p><p><span> </span><span class="MathJax_SVG" tabindex="-1" style="font-size: 100%; display: inline-block;"><svg xmlns:xlink="http://www.w3.org/1999/xlink" width="55.258ex" 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of 1 means exactly the same position between frames and similarity of 0 means maximum distance among every frame.</span></p><p><span>From this matrix, we calculated the Principal Component Analysis so that we can see the evolution of the frames by means of the two most important components.</span></p><p> </p><h3><a name="alpha-carbons" class="md-header-anchor"></a><span>Alpha carbons</span></h3><p><span>As previously explained, the first step to follow is to select the desired atoms: </span><strong><span>alpha carbons</span></strong><span>. Which simplifies a lot the representation: </span></p><ul><li><span>No-mutated protein: alpha carbons</span></li></ul><p><img src="./images/mutation_ca.gif" referrerpolicy="no-referrer" alt="mutation_ca"></p><p> </p><ul><li><span>Mutated protein: alpha carbons</span></li></ul><p> </p><p><img src="./images/mutation_ca copy.gif" referrerpolicy="no-referrer" alt="mutation_ca copy"></p><p> </p><p><span>The </span><strong><span>Kernel Matrices</span></strong><span>, or correlation matrices, obtained for the mutated and non-mutated protein based on alpha carbons are:</span></p><p><img src="./images/image copy.png" alt="image copy" style="zoom:33%;" /></p><p><span>From these heat maps, we could conclude that there is a recursive pattern of square shape along the diagonal. Another interesting property is that closer frames appear to be more similar than further frames in no-mutated protein, but slighltly differs in mutated protein.</span></p><p><span>Finally, we used these matrices to compute the </span><strong><span>Principal Component Analysis</span></strong><span>. Then, we projected both matrices to the two most important components in the same space.</span></p><p> </p><p><img src="./images/image.png" referrerpolicy="no-referrer" alt="image"></p><p> </p><p> </p><p> </p><p> </p></div>
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