Difference between revisions of "Team:IISER Berhampur/Model"

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  <div class="navi">&emsp;<a href="#block1">Mutational Analysis</a> <a href="#block2">2</a> <a href="#block3">3</a> <a href="#block4">4</a> <a href="#block5">5</a>&emsp;<div class="progress" id="progress"></div></div>
  
  
 
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<h1> Overview</h1>
 
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      <p>Our journey throughout our project was not that easy. We got a lot of questions and doubts at every step of our project. Many of them were easy to answer through experience or logic, but some needed to be worked on. Hence, we took help of modelling to answer a few of them. We took an approach of ‘Imagine each step -> Ask Questions: Why? How? -> Modelling to answer the questions -> Integrate the answers into the project -> Proceed ahead.</p><br>
 
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      <p>The flow chart above gives an overview of all such questions and how we tried to answer them. More details on each modelling part is described in the following sections.The whole modelling aspect is divided into 6 sub-sections which are as follows-</p><br>
 
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    <ul>
 
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        <li>Epidemiological Studies- To understand the problem of growing dengue cases and why it is an important topic to move ahead with. Further details are below.(Read more)</li>
 
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        <li>Mutational Analysis- To understand and justify why targeting Non-structural proteins can be a good target for targeting the inhibition of the protein-protein interactions.</li>       
 
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        <li>Target PPI studies- A brief literature review and studies were done to select a set of protein-protein interactions. This part mainly aims towards the validation of the reporter system. </li>
 
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        <li>Peptide Inhibitor Designing-  After a particular PPI was selected for the validation of the system, the peptide inhibitors were designed and appropriate chemical modifications were done. The designed peptide inhibitors were then to be ranked by our reporter system to check the efficiency of the system.</li>       
 
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        <li>Molecular dynamics analysis-  To facilitate the designing of the reporter system. </li>       
 
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        <li>Mathematical Formalisms- To mathematically depict the pipeline for using our reporter system to quantify the efficiency of peptide inhibitors. </li>
 
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  <h1 style="color:#0e5850;top:160px;">1. Content</h1><br>
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  <h1 style="color:#0e5850;top:160px;">2. Mutational Analysis</h1><br>
 
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     <p>Before going into this section, let’s first have a look at the various types of DENV Proteins.</p><br>
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    <br><img src="https://static.igem.org/mediawiki/2020/4/41/T--IISER_Berhampur--epi1.png"><br>
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    <div class="leftSide">
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     <p>The RNA genome of DENV encodes 3 structural proteins (C, prM and E) and 7 non-structural proteins (NS1, NS2A, NS2B, NS3, NS4A, NS4B, NS5). The structural proteins are the molecules that the virus particle comes equipped with, such as the proteins comprising their capsids or envelope. The non-structural proteins are encoded by the viral genome and expressed in the infected host cells. These play important roles in several processes such as replication of the virus.</p><br>
 +
    <p>Now let’s come back to the topic of Mutational analysis.</p><br>
 +
    <p>To answer the question ‘Which proteins to select as the target of the peptide inhibitors: Non-Structural or Structural proteins?’, we studied the mutational landscape of the DENV genome, to infer which proteins are relatively stable in terms of their mutational entropy so that the inhibitor is not quickly rendered ineffective by mutations in the genome.</p><br>
 +
    <p>To study the genome data of dengue virus, we used the data available in the Nextstrain (<a href="https://doi.org/10.1155/2016/6803098" style="color:darkblue;"><b>https://nextstrain.org/dengue</b></a>) open-source project website. This website provides the data for the genetic diversity of the DENV genome over several years. It compares the number of mutation events and Shannon Entropy values(which gives a measure of how frequently a gene mutates) of each codon that codes for an amino acid  in the structural and non-structural proteins of the Dengue virus. It takes into account results from numerous studies from all over the world and provides a timeline for the evolution of the DENV genome and geographical distribution of the different genotypes.</p><br>
 +
    <p>To achieve our aim to understand the difference between the rate of mutations in NS Proteins and that in structural proteins, we took into account the number of mutation events that have occurred in each codon of all the genes of DENV. Out of these, we took those positions where the number of mutational events had been 10 or greater than 10. Some number of mutations is a common occurrence in most genes due to background mutational events. But if this number is significantly high in a gene, it shows that there is some kind of selection pressure. So this bar of 10 mutations was set as our threshold to isolate those positions where mutations were greater than normally occurring  mutations, which may suggest a greater tendency to mutate. These positions would be more prone to undergo changes, thus rendering our inhibitor ineffective if targeted against these sites. </p><br>
 +
 
 +
    <p>After this, the selected codon positions in each of the genes were plotted against ‘The Number of mutations’ and compared.</p><br>
 +
    <p>The results obtained were as follow:</p><br>
 +
    <ul>
 +
          <li>For each of the four serotypes, the C and E structural proteins, along with the NS1 protein contained at least one codon that had undergone 10 or more than 10 mutations, implying that these proteins have evolved the most over the course of time. All the other NS Proteins did not have mutations greater than 10, in some serotype or the other.</li>
 +
          <li>Comparatively less number of positions in the structural proteins where mutational events are high, can be accounted for by the fact that Structural proteins are shorter in length and have less number of amino acids, as it is. Moreover, this fact further proves that rate of mutations is high in structural proteins because despite shorter length, C and E proteins have comparably high number of mutations in all 4 serotypes.</li>
 +
      </ul>
 +
    <p>Hence, it can be concluded that Non-Structural Proteins would be a better target for our Peptide Inhibitor since it was seen that Structural proteins are more prone to mutations. If chosen as a target for our peptide inhibitor, a more mutationally stable protein, would ensure that the peptide inhibitor is viable for a longer time.</p><br>
 +
    <ul>
 +
          <li>Among the Non-structural proteins, it was seen that our target protein NS5 (which was selected later on) did have a significantly high number of mutations, which probably could mean that NS5 too faces high selection pressure. But this could again be partially due to the long length of NS5. Moreover, on taking a closer look, it was found that that the region where STAT-2 interacts with NS5, there is a high number of mutations (In DENV1 and especially DENV2, in which this interaction has been shown). This shows that our chosen interaction is really an important interaction in the DENV pathogenesis since selection pressure is high in that region. Due to this, we stayed with our choice of targeting NS5 and STAT2 interaction (more on this in the ‘Protein-Protein Interaction Studies’ section) in spite of NS5 having a greater number of mutations.</li>
 +
      </ul>
 
   </div>
 
   </div>
 +
    <h1>Graphs</h1><br>
 +
    <h2>1) DENV-1</h2>
 +
    <br><img src="https://static.igem.org/mediawiki/2020/4/41/T--IISER_Berhampur--epi1.png"><br>
 +
    <h2>2)  DENV-2</h2>
 +
    <br><img src="https://static.igem.org/mediawiki/2020/4/41/T--IISER_Berhampur--epi1.png"><br>
 +
    <h2>3) DENV-3</h2>
 +
    <br><img src="https://static.igem.org/mediawiki/2020/4/41/T--IISER_Berhampur--epi1.png"><br>
 +
    <h2>4)- DENV-4</h2>
 +
    <br><img src="https://static.igem.org/mediawiki/2020/4/41/T--IISER_Berhampur--epi1.png"><br>
 +
 +
 +
    <br>
 +
 
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Revision as of 20:27, 22 October 2020

IISER-BPR IGEM
 Mutational Analysis 2 3 4 5 






2. Mutational Analysis


Before going into this section, let’s first have a look at the various types of DENV Proteins.




The RNA genome of DENV encodes 3 structural proteins (C, prM and E) and 7 non-structural proteins (NS1, NS2A, NS2B, NS3, NS4A, NS4B, NS5). The structural proteins are the molecules that the virus particle comes equipped with, such as the proteins comprising their capsids or envelope. The non-structural proteins are encoded by the viral genome and expressed in the infected host cells. These play important roles in several processes such as replication of the virus.


Now let’s come back to the topic of Mutational analysis.


To answer the question ‘Which proteins to select as the target of the peptide inhibitors: Non-Structural or Structural proteins?’, we studied the mutational landscape of the DENV genome, to infer which proteins are relatively stable in terms of their mutational entropy so that the inhibitor is not quickly rendered ineffective by mutations in the genome.


To study the genome data of dengue virus, we used the data available in the Nextstrain (https://nextstrain.org/dengue) open-source project website. This website provides the data for the genetic diversity of the DENV genome over several years. It compares the number of mutation events and Shannon Entropy values(which gives a measure of how frequently a gene mutates) of each codon that codes for an amino acid in the structural and non-structural proteins of the Dengue virus. It takes into account results from numerous studies from all over the world and provides a timeline for the evolution of the DENV genome and geographical distribution of the different genotypes.


To achieve our aim to understand the difference between the rate of mutations in NS Proteins and that in structural proteins, we took into account the number of mutation events that have occurred in each codon of all the genes of DENV. Out of these, we took those positions where the number of mutational events had been 10 or greater than 10. Some number of mutations is a common occurrence in most genes due to background mutational events. But if this number is significantly high in a gene, it shows that there is some kind of selection pressure. So this bar of 10 mutations was set as our threshold to isolate those positions where mutations were greater than normally occurring mutations, which may suggest a greater tendency to mutate. These positions would be more prone to undergo changes, thus rendering our inhibitor ineffective if targeted against these sites.


After this, the selected codon positions in each of the genes were plotted against ‘The Number of mutations’ and compared.


The results obtained were as follow:


  • For each of the four serotypes, the C and E structural proteins, along with the NS1 protein contained at least one codon that had undergone 10 or more than 10 mutations, implying that these proteins have evolved the most over the course of time. All the other NS Proteins did not have mutations greater than 10, in some serotype or the other.
  • Comparatively less number of positions in the structural proteins where mutational events are high, can be accounted for by the fact that Structural proteins are shorter in length and have less number of amino acids, as it is. Moreover, this fact further proves that rate of mutations is high in structural proteins because despite shorter length, C and E proteins have comparably high number of mutations in all 4 serotypes.

Hence, it can be concluded that Non-Structural Proteins would be a better target for our Peptide Inhibitor since it was seen that Structural proteins are more prone to mutations. If chosen as a target for our peptide inhibitor, a more mutationally stable protein, would ensure that the peptide inhibitor is viable for a longer time.


  • Among the Non-structural proteins, it was seen that our target protein NS5 (which was selected later on) did have a significantly high number of mutations, which probably could mean that NS5 too faces high selection pressure. But this could again be partially due to the long length of NS5. Moreover, on taking a closer look, it was found that that the region where STAT-2 interacts with NS5, there is a high number of mutations (In DENV1 and especially DENV2, in which this interaction has been shown). This shows that our chosen interaction is really an important interaction in the DENV pathogenesis since selection pressure is high in that region. Due to this, we stayed with our choice of targeting NS5 and STAT2 interaction (more on this in the ‘Protein-Protein Interaction Studies’ section) in spite of NS5 having a greater number of mutations.

Graphs


1) DENV-1



2) DENV-2



3) DENV-3



4)- DENV-4










2


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1. This is different content


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1. This is ALSO different content


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blah blahblah blahblah blahblah blahblah blahblah blahblah blahblah blahblah blahblah blahblah blahblah blahblah blahblah blahblah blahblah blahblah blahblah blahblah blahblah blahblah blahblah blahblah blahblah blahblah blahblah blahblah blahblah blahblah blahblah blahblah blahblah blahblah blahblah blahblah blahblah blahblah blahblah blahblah blahblah blahblah blahblah blahblah blahblah blahblah blahblah blahblah blahblah blahblah blahblah blahblah blahblah blahblah blahblah blahblah blahblah blahblah blahblah blahblah blahblah blahblah blahblah blahblah blahblah blahblah blahblah blahblah blahblah blahblah blahblah blahblah blahblah blahblah blahblah blahblah blahblah blahblah blahblah blahblah blahblah blahblah blahblah blahblah blahblah blahblah blahblah blahblah blahblah blahblah blahblah blahblah blahblah blahblah blahblah blahblah blahblah blahblah blahblah blahblah blahblah blahblah blahblah blahblah blahblah blahblah blahblah blahblah blahblah blahblah blah









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blah blahblah blahblah blahblah blahblah blahblah blahblah blahblah blahblah blahblah blahblah blahblah blahblah blahblah blahblah blahblah blahblah blahblah blahblah blahblah blahblah blahblah blahblah blahblah blahblah blahblah blahblah blahblah blahblah blahblah blahblah blahblah blahblah blahblah blahblah blahblah blahblah blahblah blahblah blahblah blahblah blahblah blahblah blahblah blahblah blahblah blahblah blahblah blahblah blahblah blahblah blahblah blahblah blahblah blahblah blahblah blahblah blahblah blahblah blahblah blahblah blahblah blahblah blahblah blahblah blahblah blahblah blahblah blahblah blahblah blahblah blahblah blahblah blahblah blahblah blahblah blahblah blahblah blahblah blahblah blahblah blahblah blahblah blahblah blahblah blahblah blahblah blahblah blahblah blahblah blahblah blahblah blahblah blahblah blahblah blahblah blahblah blahblah blahblah blahblah blahblah blahblah blahblah blahblah blahblah blahblah blahblah blahblah blahblah blah



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