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{% block page_content %}
<div class="row mt-4">
<h2>Introduction</h2>
<p><b>GRASP</b> - Generating random alanine scanned peptides is a peptide mutator that performs conserved
substitutions
based on inputs from an <b>alanine scan</b>. This was a crucial step in significantly increasing the scope of
possible mutations. Existing softwares in the domain only consider surface-level information, ignoring any
conformational changes that may occur. GRASP creates combinations of <b>conserved substitutions</b> on selected
amino acids that fall within the specified range. Then, five random sequences are chosen from the sample set
created. These are then tested and subjected to further analysis. Thus, our software is a one-of-a-kind approach
to
automating and exponentially simplifying the process of <b>random</b> conserved mutations. This process can be
employed to potentially <b>increase the affinity</b> of the peptide to its interacting ligands. </p>
<h1>GRASP</h1>
<h2 class="reveal-on-scroll">Introduction</h2>
<p class="reveal-on-scroll"><b>GRASP</b> - Generating random alanine scanned peptides is a peptide mutator that
performs conserved substitutions based on inputs from an <b>alanine scan</b>. This was a crucial step in
significantly increasing the scope of possible mutations. Existing softwares in the domain only consider
surface-level information, ignoring any conformational changes that may occur. GRASP creates combinations of
<b>conserved substitutions</b> on selected amino acids that fall within the specified range. Then, five random
sequences are chosen from the sample set created. These are then tested and subjected to further analysis. Thus, our
software is a one-of-a-kind approach to automating and exponentially simplifying the process of <b>random</b>
conserved mutations. This process can be employed to potentially <b>increase the affinity</b> of the peptide to its
interacting ligands. </p>
<h1 class="reveal-on-scroll">GRASP</h1>
<blockquote>
<p><em><strong><span>Peptide sequence mutator through Alanine Scanning</span></strong></em></p>
<p class="reveal-on-scroll"><em><strong><span>Peptide sequence mutator through Alanine Scanning</span></strong></em>
</p>
</blockquote>
<h3>About</h3>
<h3 class="reveal-on-scroll">About</h3>
<blockquote>
<p><em><span>For more information, refer to this page on our wiki: </span><strong> <a
<p class="reveal-on-scroll"><em><span>For more information, refer to this page on our wiki: </span><strong> <a
href="{{ url_for('pages', page='engineering') }}" class='url'>Engineering success</a></strong></em><br>
<em><span>Checkout the software at: </span><strong> <a href="https://gitlab.igem.org/2022/software-tools/mit-mahe"
class='url'>MIT_MAHE Software
tools</a></strong></em>
</p>
class='url'>MIT_MAHE Software tools</a></strong></em> </p>
</blockquote>
<figure class="figure">
<img src="https://static.igem.wiki/teams/4200/wiki/software/cartesianproduct-manimce-v0-16-0-post0.gif"
<figure class="figure reveal-on-scroll"> <img
src="https://static.igem.wiki/teams/4200/wiki/software/cartesianproduct-manimce-v0-16-0-post0.gif"
class="figure-img img-fluid rounded" alt="GRASP GIF animation" style="width: 85%;">
<figcaption class="figure-caption text-center">
Peptide sequence mutations on a sample sequence. Right: The list of all the output sequences.
</figcaption>
<figcaption class="figure-caption text-center"> Peptide sequence mutations on a sample sequence. Right: The list of
all the output sequences. </figcaption>
</figure>
<h3>Description</h3>
<p><span>The software comprises of the following modules:</span></p>
<ol>
<h3 class="reveal-on-scroll">Description</h3>
<p class="reveal-on-scroll"><span>The software comprises of the following modules:</span></p>
<ol class="reveal-on-scroll">
<li>
<p><strong><span>Alanine Scan \( \Delta \Delta G \) values</span></strong>
<span>BUDE Alanine scan is employed to obtain \( \Delta \Delta G \) values of each residue present in the
input.</span>
<p class="reveal-on-scroll"><strong><span>Alanine Scan \( \Delta \Delta G \) values</span></strong> <span>BUDE
Alanine scan is employed to obtain \( \Delta \Delta G \) values of each residue present in the input.</span>
</p>
</li>
<li>
<p><strong><span>Conservative mutations</span></strong></p>
<p><span>Conserved mutation is a process in which mutations are made within the same type of residues.</span>
</p>
<p><em><span>For example:</em><span> We replaced amino acids within the same group, for
<p class="reveal-on-scroll"><strong><span>Conservative mutations</span></strong></p>
<p class="reveal-on-scroll"><span>Conserved mutation is a process in which mutations are made within the same type
of residues.</span> </p>
<p class="reveal-on-scroll"><em><span>For example:</em><span> We replaced amino acids within the same group, for
example, hydrophobic re 3—hydrophobic residues with hydrophobic residues or amino acids of same charge with
each other.</span></p>
</li>
<li>
<p><strong><span>Random sampler</span></strong></p>
<p><span>Random peptides are selected as samples from the huge sequence list received to reduce the user
workload.
Note that this selection is <b>pseudo-random</b>.</span>
</p>
<p class="reveal-on-scroll"><strong><span>Random sampler</span></strong></p>
<p class="reveal-on-scroll"><span>Random peptides are selected as samples from the huge sequence list received to
reduce the user workload. Note that this selection is <b>pseudo-random</b>.</span> </p>
</li>
</ol>
<h3><span>Features</span></h3>
<h3 class="reveal-on-scroll"><span>Features</span></h3>
<figure class="figure">
<img src="https://static.igem.wiki/teams/4200/wiki/software/grasp-help.png" class="figure-img img-fluid rounded"
alt="GRASP Help" width="80%">
<figcaption class="figure-caption text-center">
GRASP Help
</figcaption>
<figure class="figure reveal-on-scroll"> <img src="https://static.igem.wiki/teams/4200/wiki/software/grasp-help.png"
class="figure-img img-fluid rounded" alt="GRASP Help" width="80%">
<figcaption class="figure-caption text-center"> GRASP Help </figcaption>
</figure>
<ol>
<ol class="reveal-on-scroll">
<li>
<p><strong><span>Command Line Interface (CLI)</span></strong>
<span>We can use the software locally on command prompt.</span>
</p>
<p class="reveal-on-scroll"><strong><span>Command Line Interface (CLI)</span></strong> <span>We can use the
software locally on command prompt.</span> </p>
</li>
<li>
<p><strong><span>Integrable modules</span></strong></p>
<p><span>Our software consists of different modules for the mutator and alanine scan to integrate them into
different and more extensive networks by other iGEM teams and parties.</span></p>
<p class="reveal-on-scroll"><strong><span>Integrable modules</span></strong></p>
<p class="reveal-on-scroll"><span>Our software consists of different modules for the mutator and alanine scan to
integrate them into different and more extensive networks by other iGEM teams and parties.</span></p>
</li>
<li>
<p><strong><span>Mutation lock</span></strong>
<span>To prevent the mutation of an already modified residue, that part of the sequence was put under a
mutation
lock</span>
</p>
<p class="reveal-on-scroll"><strong><span>Mutation lock</span></strong> <span>To prevent the mutation of an
already modified residue, that part of the sequence was put under a mutation lock</span> </p>
</li>
<li>
<p><strong><span>Aggrescan</span></strong></p>
<p><span>Inclusion of a <b>BASH</b> script, which takes the output and prepends FASTA headers to each sequence
in
the text file.</span></p>
<p class="reveal-on-scroll"><strong><span>Aggrescan</span></strong></p>
<p class="reveal-on-scroll"><span>Inclusion of a <b>BASH</b> script, which takes the output and prepends FASTA
headers to each sequence in the text file.</span></p>
</li>
</ol>
<h3><span>Background</span></h3>
<p><span>Protein engineering is modifying a protein through substitution, insertion, or deletion to exhibit desired
characteristics and properties.</span></p>
<p><span>During the process of designing our peptide, we came across the problem of having an <b>extensive sample
set</b> of substitutions through trial and error. However, even after this, we could not guarantee an increase
in affinity compared to the original peptide as needed in our case. To overcome this problem, we designed this
software to provide all the most probable mutations that increase the binding affinity.
<h3 class="reveal-on-scroll"><span>Background</span></h3>
<p class="reveal-on-scroll"><span>Protein engineering is modifying a protein through substitution, insertion, or
deletion to exhibit desired characteristics and properties.</span></p>
<p class="reveal-on-scroll"><span>During the process of designing our peptide, we came across the problem of having an
<b>extensive sample set</b> of substitutions through trial and error. However, even after this, we could not
guarantee an increase in affinity compared to the original peptide as needed in our case. To overcome this
problem, we designed this software to provide all the most probable mutations that increase the binding affinity.
</span></p>
<p><span>Furthermore, we explored other point mutation softwares for our problem; however, they only took surface
information into consideration. We decided to take \( \Delta \Delta G \) value of a residue as the parameter for
mutation which
give an increase in the accuracy of the mutation.</span></p>
<h2>Method</h2>
<h3>\( \Delta \Delta G \) threshold</h3>
<p>
We take \( \Delta \Delta G \)value of each residue derived from BUDE alanine scan as a parameter to do mutations.
To
make these
mutations we <b>set a threshold</b> \( \Delta \Delta G \)range for each residue (in our case, 0 to 1). Only if the
\( \Delta \Delta G \) value lies between
this
range will it be mutated.
<p class="reveal-on-scroll"><span>Furthermore, we explored other point mutation softwares for our problem; however,
they only took surface information into consideration. We decided to take \( \Delta \Delta G \) value of a residue
as the parameter for mutation which give an increase in the accuracy of the mutation.</span></p>
<h2 class="reveal-on-scroll">Method</h2>
<h3 class="reveal-on-scroll">\( \Delta \Delta G \) threshold</h3>
<p class="reveal-on-scroll"> We take \( \Delta \Delta G \)value of each residue derived from BUDE alanine scan as a
parameter to do mutations. To make these mutations we <b>set a threshold</b> \( \Delta \Delta G \)range for each
residue (in our case, 0 to 1). Only if the \( \Delta \Delta G \) value lies between this range will it be mutated.
</p>
<h3>Significance of Cartesian product</h3>
<p>
Cartesian Product: The Cartesian Product of sets \(A\) and \(B\) is defined as the set of all ordered pairs \((x,
y)\)
such
that \(x\) belongs to \(A\)and \(y\) belongs to \(B\)
For example, if \(A = {1, 2}\) and \(B = {3, 4, 5}\), then the Cartesian Product of A and B is \({(1, 3), (1, 4),
(1, 5),
(2, 3), (2, 4), (2, 5)}\).
Originally, during a test run, the computational time that the software took for making 3 mutation positions was 2
hours 20 mins.
After incorporating the cartesian product into the mutator, the time reduced to 11 mutation positions in 10
seconds.
<h3 class="reveal-on-scroll">Significance of Cartesian product</h3>
<p class="reveal-on-scroll"> Cartesian Product: The Cartesian Product of sets \(A\) and \(B\) is defined as the set of
all ordered pairs \((x, y)\) such that \(x\) belongs to \(A\)and \(y\) belongs to \(B\) For example, if \(A = {1,
2}\) and \(B = {3, 4, 5}\), then the Cartesian Product of A and B is \({(1, 3), (1, 4), (1, 5), (2, 3), (2, 4), (2,
5)}\). Originally, during a test run, the computational time that the software took for making 3 mutation positions
was 2 hours 20 mins. After incorporating the cartesian product into the mutator, the time reduced to 11 mutation
positions in 10 seconds.
</p>
<h3>Algorithm</h3>
<p>
We first create a Mutater object, which support the following three formats:
<h3 class="reveal-on-scroll">Algorithm</h3>
<p class="reveal-on-scroll"> We first create a Mutater object, which support the following three formats:
</p>
<ol>
<li>
Wild type amino acid, the position of the Amino acid, and the Mutant type it needs to be replaced with.
</li>
<li>
Just the Wild type amino acid and its position, without the Mutant type. This can act as the template to create
new
Mutater objects.
</li>
<li>
Just the position in the sequence.
</li>
<li> Wild type amino acid, the position of the Amino acid, and the Mutant type it needs to be replaced with. </li>
<li> Just the Wild type amino acid and its position, without the Mutant type. This can act as the template to create
new Mutater objects. </li>
<li> Just the position in the sequence. </li>
</ol>
<h3>Alanine scanning</h3>
<p>
The Alanine scanning results from BUDE Alanine scan are parsed through which results in a position array over
which
mutations are possibly required.
<h3 class="reveal-on-scroll">Alanine scanning</h3>
<p class="reveal-on-scroll"> The Alanine scanning results from BUDE Alanine scan are parsed through which results in a
position array over which mutations are possibly required.
</p>
<h3>Group mutations</h3>
<p>
The sequence is taken as a template over which a special array is constructed, with the following steps:
<h3 class="reveal-on-scroll">Group mutations</h3>
<p class="reveal-on-scroll"> The sequence is taken as a template over which a special array is constructed, with the
following steps:
</p>
<ol>
<li>
Iterate over the sequence and get the wild type amino acid at each stage.
</li>
<li>
Retain the wild type if mutation isn't required at this position. This could result when the position doesn't
want
to
be mutated, or when the position is present in the mutation lock array.
</li>
<li>
If a mutation is required in the current position, replace the position with an array of all the possible amino
acids
within the same group. This step is important to achieve Conserved mutations.
</li>
<ol class="reveal-on-scroll">
<li> Iterate over the sequence and get the wild type amino acid at each stage. </li>
<li> Retain the wild type if mutation isn't required at this position. This could result when the position doesn't
want to be mutated, or when the position is present in the mutation lock array. </li>
<li> If a mutation is required in the current position, replace the position with an array of all the possible amino
acids within the same group. This step is important to achieve Conserved mutations. </li>
</ol>
<h3>Cartesian product</h3>
<p>
The special array is then passed to the <code>itertools.product()</code> function, which evaluates the Cartesian
product on the
elements of the special array. This results in an iterable object that contains all the final sequences, which is
then
stored in the text file.
<h3 class="reveal-on-scroll">Cartesian product</h3>
<p class="reveal-on-scroll"> The special array is then passed to the <code>itertools.product()</code> function, which
evaluates the Cartesian product on the elements of the special array. This results in an iterable object that
contains all the final sequences, which is then stored in the text file.
</p>
<h3>Random sample</h3>
<p>
A trivial random sampler is implemented using <code>random.sample()</code> on the sequences array, and then stored
in a text
file.
<h3 class="reveal-on-scroll">Random sample</h3>
<p class="reveal-on-scroll"> A trivial random sampler is implemented using <code>random.sample()</code> on the
sequences array, and then stored in a text file.
</p>
</div>
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