Difference between revisions of "Team:TU Darmstadt/Model"
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| − | <p> In synthetic biology, theoretical models are often used to gain insights, predict and | + | <p> In synthetic biology, theoretical models are often used to gain insights, to predict and |
| − | improve | + | to improve |
experiments. In our project we are modifying Virus-like particles (VLPs) by attaching | experiments. In our project we are modifying Virus-like particles (VLPs) by attaching | ||
proteins to the | proteins to the | ||
| − | surface of the | + | surface of the <a href="https://2019.igem.org/Team:TU_Darmstadt/Project/P22_VLP" |
| − | + | target="_blank">P22</a> capsid through a linker. The linking is catalyzed using | |
| − | + | the enzyme <a href="https://2019.igem.org/Team:TU_Darmstadt/Project/Sortase" | |
| − | the enzyme Sortase A7M, which is a calcium independent mutant of the wild type | + | target="_blank">Sortase A7M</a>, which is a calcium-independent mutant of the |
| − | + | wild type | |
| + | Sortase A from <i>Staphylococcus aureus</i>. We performed | ||
modeling to predict the unknown structure of the | modeling to predict the unknown structure of the | ||
| − | Sortase A7M | + | Sortase A7M to improve the linking between proteins and therefore to optimize the |
modification | modification | ||
efficiency of our platform. <br> | efficiency of our platform. <br> | ||
| − | Two different modeling approaches were used to determine the structure of Sortase A7M. | + | Two different modeling approaches were used to determine the structure of the |
| + | Sortase A7M. | ||
We compared | We compared | ||
| − | machine learning approaches to traditional | + | machine learning approaches to traditional, comparative Monte-Carlo based modeling |
methods. The | methods. The | ||
| − | results were evaluated using an energy-scoring function and molecular dynamics (MD) | + | results were evaluated using an energy-scoring function and molecular dynamics (MD) |
simulations. The | simulations. The | ||
| − | most promising Sortase A7M structures were used to perform a docking simulation to | + | most promising Sortase A7M structures were used to perform a |
| − | + | docking simulation to | |
| − | + | investigate binding with linkers. | |
</p> | </p> | ||
</div> | </div> | ||
| Line 87: | Line 89: | ||
<p> | <p> | ||
| − | <i>In silico</i> modeling and simulation of proteins requires a 3D structure, | + | <i>In silico</i> modeling and simulation of proteins requires a |
| + | 3D structure, | ||
which can be | which can be | ||
obtained from the <a href="https://www.rcsb.org/" target="_blank">RCSB Protein | obtained from the <a href="https://www.rcsb.org/" target="_blank">RCSB Protein | ||
Data | Data | ||
| − | Bank</a>. However, if no 3D structures are annotated, as | + | Bank</a>. However, if no 3D structures are annotated, as is the case with |
| − | + | Sortase A7M, the structure has to be determined by other means. The | |
| − | + | structure prediction | |
| − | of | + | of Sortase A7M was done using two different approaches. |
</p> | </p> | ||
| Line 103: | Line 106: | ||
datasets. This is commonly | datasets. This is commonly | ||
done by | done by | ||
| − | presenting the algorithm with training data as well as a scoring function to | + | presenting the algorithm with training data, as well as a scoring function to |
measure its | measure its | ||
success at processing the | success at processing the | ||
input data. During training a feedback loop is used to allow the algorithm to | input data. During training a feedback loop is used to allow the algorithm to | ||
automatically | automatically | ||
| − | find a function | + | find a function that fits |
the data. In contrast, classical | the data. In contrast, classical | ||
algorithms are often | algorithms are often | ||
| Line 119: | Line 122: | ||
weights, which are adjusted during its training. Nodes in neural networks are | weights, which are adjusted during its training. Nodes in neural networks are | ||
linked | linked | ||
| − | together: | + | together: one neuron processes |
| − | the | + | the input of other neurons, loosely mimicking the structure of biological |
brains. While one | brains. While one | ||
usually has a fixed | usually has a fixed | ||
| − | amount of input and output neurons limited by the data one wishes to classify, | + | amount of input and output neurons, limited by the data one wishes to classify, |
adding layers of hidden neurons can improve the classification. | adding layers of hidden neurons can improve the classification. | ||
This is often referred to as | This is often referred to as | ||
| Line 130: | Line 133: | ||
</p> | </p> | ||
| − | <p>Using Machine Learning to predict protein structures has many advantages compared | + | <p>Using Machine Learning to predict protein structures has many advantages, |
| + | compared | ||
to | to | ||
| − | conventional methods especially | + | conventional methods, especially |
for iGEM teams who often only have limited access to resources. After training a | for iGEM teams who often only have limited access to resources. After training a | ||
neural | neural | ||
network, which is a | network, which is a | ||
| − | computationally expensive process and often done in centralized data centers, it | + | computationally expensive process and is often done in centralized data centers, |
| + | it | ||
can be used | can be used | ||
to predict the | to predict the | ||
| Line 162: | Line 167: | ||
AlQuarishi demonstrated a complete deep learning approach that is able to make | AlQuarishi demonstrated a complete deep learning approach that is able to make | ||
predictions | predictions | ||
| − | within 1-2 Å of other | + | within 1-2 Å of other |
| − | approaches | + | approaches, |
<sup id="cite_ref-1" class="reference"> | <sup id="cite_ref-1" class="reference"> | ||
<a href="#cite_note-1">[2] </a> | <a href="#cite_note-1">[2] </a> | ||
| − | </sup> | + | </sup> while only using a fraction of the computational power. This enables |
| − | + | accurate | |
structural | structural | ||
prediction with less | prediction with less | ||
| − | powerful as well as less expensive hardware and thus significantly reduces the | + | powerful, as well as less expensive, hardware and thus significantly reduces the |
cost of | cost of | ||
structural modeling.</p> | structural modeling.</p> | ||
| Line 176: | Line 181: | ||
<p> We used AlQuarashi’s approach in combination with his pretrained model, which | <p> We used AlQuarashi’s approach in combination with his pretrained model, which | ||
was trained on | was trained on | ||
| − | the | + | the ProteinNet database |
| − | containing all structures released prior to the start of CASP12 (12th Critical | + | containing all structures released prior to the start of CASP12 (12th |
| + | Critical | ||
Assessment of | Assessment of | ||
Techniques for Protein | Techniques for Protein | ||
<!-- RMSD immer gleich schreiben bindestriche und so --> | <!-- RMSD immer gleich schreiben bindestriche und so --> | ||
| − | Structure Prediction – 2016). The results were tested against the CASP12 | + | Structure Prediction – 2016) our generated candidate structure <i>CASP12</i> is |
| + | named after. The results were tested against the CASP12 | ||
datasets and | datasets and | ||
| − | reached distance root-mean-square deviation (RMSD) values between | + | reached distance root-mean-square deviation (RMSD) values between |
10 and 13 Å. The RMSD is defined as root-mean-square deviation of all atom | 10 and 13 Å. The RMSD is defined as root-mean-square deviation of all atom | ||
positions compared to a template structure. | positions compared to a template structure. | ||
| − | It is defined as: | + | It is defined as: </p> |
| − | + | <div class="row" style="height: 4em;"> | |
| − | + | <img class="img-fluid center" | |
| − | + | src="https://2019.igem.org/wiki/images/3/31/T--TU_Darmstadt--RMSD.png" | |
| − | + | style="max-height: 100%; width: auto; margin: 0 auto;"> | |
| − | + | ||
| − | + | </div> | |
| − | + | <p>where x<sub>i</sub> is a vector of the atomic coordinates of the i-th atom. | |
| − | + | All proteins in the CASP datasets were not published until after the | |
| − | All proteins in the CASP datasets were not published until after the | + | competition, and thus represent an |
| − | and thus represent an | + | |
assessment with only little bias. | assessment with only little bias. | ||
<sup id="cite_ref-4" class="reference"> | <sup id="cite_ref-4" class="reference"> | ||
<a href="#cite_note-4">[4] </a> | <a href="#cite_note-4">[4] </a> | ||
</sup> | </sup> | ||
| − | We used these pretrained datasets to make structural predictions for our | + | We used these pretrained datasets to make structural predictions for our |
| − | A7M. The | + | Sortase A7M. The |
| − | predicted structure was then relaxed in a | + | predicted structure was then relaxed in a |
| − | + | molecular dynamics simulation. | |
| + | |||
</p> | </p> | ||
| Line 216: | Line 222: | ||
<ol> | <ol> | ||
| − | <li>We used the amino acid sequence of the Sortase A7M in the FASTA format to | + | <li>We used the amino acid sequence of the Sortase A7M in the FASTA format |
| + | to | ||
predict the | predict the | ||
tertiary structure of the | tertiary structure of the | ||
| − | amino acid backbone using AlQuarishi’s | + | amino acid backbone using AlQuarishi’s implementation of his |
end-to-end | end-to-end | ||
differentiable learning of | differentiable learning of | ||
| − | protein structure with the pretrained preCASP | + | protein structure with the pretrained preCASP ProteinNet database. |
| − | + | ||
| + | |||
| + | The | ||
| + | output file was a | ||
<i>.tertiary</i> file which | <i>.tertiary</i> file which | ||
contains a sequential 3x3 Matrix with atomic coordinates from each amino | contains a sequential 3x3 Matrix with atomic coordinates from each amino | ||
acid backbone | acid backbone | ||
starting at the | starting at the | ||
| − | N-Terminus.</li> | + | N-Terminus. The raw backbone is depicted in <b>Animation 1</b>.</li> |
| − | <li>As the standard format for protein structure information is the PDB file | + | <li>As the standard format for protein structure information is the PDB (Protein |
| + | Data Bank) file | ||
format, we | format, we | ||
wrote a python script to | wrote a python script to | ||
| − | combine the structural information from the FASTA and .tertiary files into a | + | combine the structural information from the FASTA and <i>.tertiary</i> files |
| + | into a | ||
| + | single | ||
PDB file. | PDB file. | ||
For ease of use we | For ease of use we | ||
| Line 241: | Line 254: | ||
</li> | </li> | ||
| − | <li>Using Rosetta's fixed backbone design program 'fixbb' with the 'hpatch', the | + | <li>Using <i>Rosetta</i>'s fixed backbone design program 'fixbb' with the |
| + | 'hpatch', the | ||
optimal | optimal | ||
position of the side-chains | position of the side-chains | ||
| Line 248: | Line 262: | ||
corresponding | corresponding | ||
side-chains and optimizes | side-chains and optimizes | ||
| − | their conformation. The | + | their conformation. The hpatch database ensures that hydrophilic side-chains |
are to be | are to be | ||
preferred on the surface | preferred on the surface | ||
| − | of the protein as our sortase is present in an aqueous environment.</li> | + | of the protein as our sortase is present in an aqueous environment. The |
| − | + | resulting structure is depicted in <b>Animation 2.</b></li> | |
| − | + | ||
</ol> | </ol> | ||
<p>In order to evaluate the structure obtained, we constructed a Ramachandran plot | <p>In order to evaluate the structure obtained, we constructed a Ramachandran plot | ||
by | by | ||
| − | calculating the dihedral angles of the amino acid backbone as depicted in | + | calculating the dihedral angles of the amino acid backbone, as depicted in |
| − | + | <b>Fig. 1</b>. These can then be compared to the typical dihedral angles | |
| − | secondary structures such as | + | for specific |
| + | secondary structures such as α-Helices and β-Sheets. The typical | ||
| + | angles | ||
from a randomly sampled dataset | from a randomly sampled dataset | ||
| − | are depicted in | + | are depicted in <b>Fig. 2</b>.</p> |
| − | + | ||
<div class="row"> | <div class="row"> | ||
| − | <div class="figurcolumn column" style="width: 50%; float: left; | + | <div class="figurcolumn column" style="width: 50%; float: left; padding: 1em;"> |
| − | < | + | <div class="row"> |
| − | + | <a target="_blank" | |
| − | + | href="https://2019.igem.org/wiki/images/8/8d/T--TU_Darmstadt--dihedral.png"> | |
| − | + | <img class="img-fluid center" | |
| − | + | src="https://2019.igem.org/wiki/images/8/8d/T--TU_Darmstadt--dihedral.png" | |
| − | + | style="width:90%; height: 90%;"></a> | |
| − | + | </div> | |
| − | + | <div class="row"> | |
| − | + | <div class="caption"> | |
| + | <p><b>Figure 1:</b> The dihedral angles of amino acids can be | ||
| + | calculated to create a Ramachandran plot. </p> | ||
| + | </div> | ||
| + | </div> | ||
</div> | </div> | ||
| − | + | <div class="figurcolumn column" style="width: 50%; float: right; padding: 1em;"> | |
| − | + | <div class="row"> | |
| − | <div class="figurcolumn column" style="width: 50%; float: | + | <a target="_blank" |
| − | < | + | href="https://2019.igem.org/wiki/images/7/73/T--TU_Darmstadt--Comp_Ramachandran.PNG"> |
| − | + | <img class="img-fluid center" | |
| − | + | src="https://2019.igem.org/wiki/images/7/73/T--TU_Darmstadt--Comp_Ramachandran.PNG" | |
| − | + | style="width:100%; height: 100% !important;"></a> | |
| − | + | </div> | |
| − | <p><b>Figure | + | <div class="row"> |
| − | + | <div class="caption"> | |
| + | <p><b>Figure 2:</b> The dihedral angles over a range of randomly | ||
| + | sampled | ||
| + | proteins.</p> | ||
| + | </div> | ||
| + | </div> | ||
</div> | </div> | ||
</div> | </div> | ||
| Line 294: | Line 317: | ||
<div class="row"> | <div class="row"> | ||
<div class="figurcolumn column" style="width: 50%; float: left; padding: 1em;"> | <div class="figurcolumn column" style="width: 50%; float: left; padding: 1em;"> | ||
| − | <img class="img-fluid center" | + | <a target="_blank" |
| − | + | href="https://2019.igem.org/wiki/images/5/57/T--TU_Darmstadt--CASP_Nochains.gif"> | |
| − | + | <img class="img-fluid center" | |
| − | <p><b>Animation 1: </b>The raw PDB File converted from the .tertiary file. | + | src="https://2019.igem.org/wiki/images/5/57/T--TU_Darmstadt--CASP_Nochains.gif" |
| − | + | style="width:100%"></a> | |
| + | <div class="caption"> | ||
| + | <p><b>Animation 1: </b>The raw PDB File converted from the | ||
| + | <i>.tertiary</i> | ||
| + | file. | ||
| + | </p> | ||
| + | </div> | ||
</div> | </div> | ||
<div class="figurcolumn column" style="width: 50%; float: right; padding: 1em;"> | <div class="figurcolumn column" style="width: 50%; float: right; padding: 1em;"> | ||
| − | <img class="img-fluid center" | + | <a target="_blank" |
| − | + | href="https://2019.igem.org/wiki/images/c/c1/T--TU_Darmstadt--CASP_Chains.gif"> | |
| − | + | <img class="img-fluid center" | |
| − | <p><b>Animation 2: </b>The PDB-File after Step 3.</p> | + | src="https://2019.igem.org/wiki/images/c/c1/T--TU_Darmstadt--CASP_Chains.gif" |
| + | style="width:100%"></a> | ||
| + | <div class="caption"> | ||
| + | <p><b>Animation 2: </b>The PDB-File after Step 3.</p> | ||
| + | </div> | ||
</div> | </div> | ||
</div> | </div> | ||
| − | <p>For analysis the Strucure was viewed in | + | <p>For analysis the Strucure was viewed in <i>PyMOL</i> |
| − | <!-- | + | <!-- PyMOL REFERENZ -->. As can be seen in <b>Animation 3</b> below, |
| − | no secondary structures could be recognized by | + | no secondary structures could be recognized by PyMOL. Thus, a Ramachandran plot |
was used to | was used to | ||
| − | evaluate the dihedral angles of the backbone. It was found that the angles do | + | evaluate the dihedral angles of the backbone. This is depicted in <b>Fig. 3</b>. |
| − | + | It was found that the angles do not match with | |
the typical angles for α-helices and β-sheets.</p> | the typical angles for α-helices and β-sheets.</p> | ||
<div class="row"> | <div class="row"> | ||
<div class="figurcolumn column" style="width: 50%; float: left; padding: 1em;"> | <div class="figurcolumn column" style="width: 50%; float: left; padding: 1em;"> | ||
| − | <img class="img-fluid center" | + | <a target="_blank" |
| − | + | href="https://2019.igem.org/wiki/images/8/82/T--TU_Darmstadt--CASP_CHAINSCartoon.gif"> | |
| − | + | <img class="img-fluid center" | |
| − | <p><b>Animation 3: </b>The cartoon view in | + | src="https://2019.igem.org/wiki/images/8/82/T--TU_Darmstadt--CASP_CHAINSCartoon.gif" |
| + | style="width:100%"></a> | ||
| + | <div class="caption"> | ||
| + | <p><b>Animation 3: </b>The cartoon view in PyMOL.</p> | ||
| + | </div> | ||
</div> | </div> | ||
<div class="figurcolumn column" | <div class="figurcolumn column" | ||
style="width: 50%; float: right; padding: 1em;"> | style="width: 50%; float: right; padding: 1em;"> | ||
| − | <img class="img-fluid center" | + | <a target="_blank" |
| − | + | href="https://2019.igem.org/wiki/images/1/18/T--TU_Darmstadt--Ramachandran_Plot.png"> | |
| − | + | <img class="img-fluid center" | |
| − | <p> <b>Figure | + | src="https://2019.igem.org/wiki/images/1/18/T--TU_Darmstadt--Ramachandran_Plot.png" |
| + | style="width:100%"></a> | ||
| + | <div class="caption"> | ||
| + | <p> <b>Figure 3: </b>Ramachandran plot of the predicted structure.</p> | ||
| + | </div> | ||
</div> | </div> | ||
</div> | </div> | ||
<p>During training the predictions in AlQuarashi’s Model were optimized for their | <p>During training the predictions in AlQuarashi’s Model were optimized for their | ||
| − | RMSD which is | + | RMSD, which is |
the root-mean-square deviation of the distance between the atoms of the | the root-mean-square deviation of the distance between the atoms of the | ||
prediction and | prediction and | ||
| Line 336: | Line 377: | ||
structure. Thus, even though the predictions are expected to have a similar | structure. Thus, even though the predictions are expected to have a similar | ||
shape to | shape to | ||
| − | the physical structure, they may not be in the energy minimum. | + | the physical structure, they may not be in the energy minimum. |
| − | + | <h2>Rosetta Comparative Modeling</h2> | |
| − | + | <h3 class="ausfahrbarer-boi">Background</h3> | |
| − | + | ||
| − | + | ||
| − | + | ||
| − | + | ||
| − | + | <p>In our second approach we used the <a href="https://www.rosettacommons.org" | |
| − | + | target="_blank"><i>RosettaCommons</a> comparative modeling | |
| − | + | (<a>RosettaCM</a>)</i>, which | |
| − | + | is based on homology modeling | |
| − | + | <sup id="cite_ref-5" class="reference"> | |
| − | + | <a href="#cite_note-5">[5] </a> | |
| − | + | </sup> | |
| − | + | . Homology modeling is a protein | |
| − | + | modeling | |
| − | + | method, which | |
| − | + | requires one or more template structures as a base for the protein to be | |
| − | + | modeled. | |
| − | + | The protein | |
| − | + | sequences are aligned with the sequence of the target protein. Unaligned | |
| − | + | sections are | |
| − | + | modeled using fragment or protein libraries, which leads to creating | |
| − | + | protein structures based | |
| + | on different sequence homologues of the protein of interest. | ||
| + | <i>Ab-initio</i> or <i>de novo</i> modeling on the other hand attempts | ||
| + | to find protein | ||
| + | structures solely based on physicochemical principles applied to the primary | ||
| + | sequence, which | ||
| + | can be compared to the refolding of a denatured protein.</p> | ||
| − | + | <p>RosettaCM combines <i>ab-initio modeling</i> with | |
| − | + | <i>homology modeling</i>. The | |
| − | + | homologous structures, for which a resolved 3D structure with sufficiently | |
| − | + | similar | |
| − | + | sequence exists, are generated using homology modeling. Afterwards the | |
| − | + | unaligned | |
| − | + | sequences are modeled <i>de novo</i>. By combining the two methods, | |
| − | + | RosettaCM | |
| − | + | represents a precise and resource efficient tool for protein structure | |
| − | + | prediction. | |
| − | + | Rosetta applications rely on the Monte-Carlo Optimization, which is a | |
| − | + | probabilistic | |
| − | + | approach to finding a local minimum in the energy landscape of protein | |
| − | <br> where k<sub>B</sub> is the Boltzmann constant, ΔE the difference in | + | conformations. The |
| − | + | underlying equation serving as the foundation of the statistical Monte-Carlo | |
| − | + | <sup id="cite_ref-6" class="reference"> | |
| + | <a href="#cite_note-1">[6] </a> | ||
| + | </sup> | ||
| + | method is the Metropolis acceptance criterion: | ||
| + | </p> | ||
| + | <div class="row" style="height: 4em;"> | ||
| + | <img class="img-fluid center" | ||
| + | src="https://2019.igem.org/wiki/images/3/32/T--TU_Darmstadt--MetAcc.png" | ||
| + | style="max-height: 100%; width: auto; margin: 0 auto;"> | ||
| + | </div> | ||
| + | <p> | ||
| + | <br> where k<sub>B</sub> is the Boltzmann constant, ΔE the difference | ||
| + | in | ||
| + | energy of the two states and T the temperature. The term k<sub>B</sub>T can | ||
| + | also | ||
| + | be written as a single factor β.</p> | ||
| − | + | <p> | |
| − | + | During the statistical protein folding based on the Monte-Carlo method, the | |
| − | + | initial | |
| − | + | structure is changed by small random perturbations of the atom locations. | |
| − | + | Whether the structure is accepted, or | |
| − | + | not, is decided by the Metropolis acceptance criterion. | |
| − | + | If ΔE < 0, the structure is accepted, otherwise the newly proposed | |
| − | + | structure is accepted with probability p as described in the Metropolis | |
| − | + | acceptance criterion. </p> <h3 class="ausfahrbarer-boi">Procedure</h3> | |
| − | + | <p> | |
| − | + | The <i>RosettaCM</i> protocol requires evolutionary related | |
| − | + | structures and | |
| − | + | sequences, | |
| − | + | as well as fragment files of the target structure. | |
| − | + | The fragment files serve as a structure template for the proteins | |
| − | + | and | |
| − | + | consist of peptide fragments of sizes 3 and 9 amino acids. | |
| − | + | We gathered five evolutionary related structures from the PDB with | |
| − | + | the | |
| − | + | accession numbers:</p> | |
| − | + | <ul> | |
| − | + | <li><a href="http://www.rcsb.org/structure/1IJA" | |
| − | + | target="_blank">1ija</a></li> | |
| − | + | <li><a href="http://www.rcsb.org/structure/1ITW" | |
| − | + | target="_blank">1itw</a></li> | |
| − | + | <li><a href="http://www.rcsb.org/structure/1ITP" | |
| − | + | target="_blank">1itp</a></li> | |
| − | + | <li><a href="http://www.rcsb.org/structure/1ITO" | |
| − | + | target="_blank">1ito</a></li> | |
| − | + | <li><a href="http://www.rcsb.org/structure/2MLM" | |
| − | + | target="_blank">2mlm</a></li> | |
| − | + | </ul> | |
| − | + | <br> | |
| − | + | <p> | |
| − | + | The five PDB entries represent different structures of sortases | |
| − | + | from | |
| − | + | <i>Staphylococcus aureus</i>. | |
| − | + | Fragment files can be created with the Robetta <a | |
| − | + | href="http://robetta.bakerlab.org/" | |
| − | <li>sequence and structural alignment of templates</li> | + | target="_blank">online server</a> or with the |
| − | + | <i>Rosetta FragmentPicker</i> | |
| − | + | application. | |
| − | + | </p> | |
| − | + | <p>The <i>RosettaCM</i> procedure is best described in the following | |
| − | + | steps: | |
| + | <sup id="cite_ref-5" class="reference"> | ||
| + | <a href="#cite_note-5">[5] </a> | ||
| + | </sup> | ||
| + | </p> | ||
| + | <ol> | ||
| + | <li>sequence and structural alignment of templates</li> | ||
| + | <li>fragment insertion in unaligned sections</li> | ||
| + | <li>replacement of random segment with segment from a different | ||
| + | template | ||
| + | structure</li> | ||
| + | <li>energy minimization</li> | ||
| + | <li>all-atom optimization</li> | ||
| − | + | </ol> | |
| − | + | <br> | |
| − | + | <p> | |
| − | + | The alignment can be performed with various tools. We used <a | |
| − | + | href="https://mafft.cbrc.jp/alignment/server/" | |
| − | + | target="_blank">MAFFT</a> to | |
| − | + | generate the multiple sequence alignments. | |
| − | + | Prior to using the alignments as an input, they were converted to | |
| − | + | the | |
| − | + | grishin | |
| − | + | alignment format, as <i>RosettaCM</i> requires the alignments to be | |
| − | + | in said | |
| − | + | format. | |
| − | + | The minimization is performed using the Rosetta centroid energy | |
| − | + | function. For | |
| − | + | the centroid function to be applied, the protein is converted to the | |
| − | + | centroid | |
| − | + | representation. A protein in centroid representation consists of the | |
| − | + | backbone | |
| − | + | atoms N, C<sub>α</sub>, O<sub>Carbonyl</sub> and an atom of | |
| − | + | varying size representing the | |
| − | + | side chain. The advantage of using the centroid representation is | |
| − | + | that | |
| − | + | the | |
| − | + | energy landscape can be traversed easier due to the smoother nature | |
| − | + | of the centroid energy landscape. | |
| − | + | Finally, the generated structure undergoes a second minimization in | |
| − | + | an | |
| − | + | all-atom model by | |
| − | + | means of Monte-Carlo optimization. This is similar to the energy | |
| − | + | minimization, but without the amino acids being | |
| − | + | represented as centroids of their functional groups. Structures | |
| − | + | computed | |
| − | + | through | |
| − | + | all-atom optimizations can reach atomic resolutions, | |
| + | <sup id="cite_ref-5" class="reference"> | ||
| + | <a href="#cite_note-5">[5] </a> | ||
| + | </sup> | ||
| + | which is crucial for a model meant to be used to estimate atomic | ||
| + | interactions. | ||
| + | </p> | ||
| − | + | <h3>Results</h3> | |
| − | + | <p> | |
| − | + | The run yielded 15,000 structures, which have been compared using | |
| − | + | the | |
| − | + | Rosetta | |
| − | + | scoring functions (talaris2013). | |
| − | + | <!-- scoring --> | |
| − | + | From the 15,000 structures generated, we inspected the ten best | |
| + | scoring | ||
| + | structures. </p> | ||
| − | + | <p>As can be seen in <b>Fig. 4</b>, the most prominent differences can | |
| − | + | be found in the regions close to the N- and C-terminus. As | |
| − | + | fluctuations in those | |
| − | + | regions are not untypical, we decided to use the best scoring | |
| − | + | structure, candidate <i>S_14771</i> (<b>Animation 4</b>), as | |
| − | + | the input | |
| + | for | ||
| + | the | ||
| + | simulations to follow. | ||
| + | </p> | ||
| − | + | <div class="row"> | |
| − | + | <div class="figurcolumn column" | |
| − | + | style="width: 50%; float: left; padding: 1em;"> | |
| − | + | <a target="_blank" | |
| − | + | href="https://2019.igem.org/wiki/images/4/40/T--TU_Darmstadt--top10_corporate.png"> | |
| − | style="width:100%"> | + | <img class="img-fluid center" |
| + | src="https://2019.igem.org/wiki/images/4/40/T--TU_Darmstadt--top10_corporate.png" | ||
| + | style="width:100%"></a> | ||
| + | <div class="caption"> | ||
| + | <p><b>Figure 4</b>: The structural alignment of the ten best | ||
| + | scoring | ||
| + | sortase structures | ||
| + | displaying minor differences with the exception of the | ||
| + | C- | ||
| + | and | ||
| + | N-terminal | ||
| + | regions. N- and C-terminal regions tend to show strong | ||
| + | fluctuations, thus it is | ||
| + | unsurprising to find the terminal regions to be | ||
| + | unaligned. | ||
| + | </p> | ||
| + | </div> | ||
| + | </div> | ||
| + | <div class="figurcolumn column" | ||
| + | style="width: 50%; float: right; padding: 1em;"> | ||
| + | <a target="_blank" | ||
| + | href="https://2019.igem.org/wiki/images/b/b3/T--TU_Darmstadt--s14771.gif"> | ||
| + | <img class="img-fluid center" | ||
| + | src="https://2019.igem.org/wiki/images/b/b3/T--TU_Darmstadt--s14771.gif" | ||
| + | style="width:100%"></a> | ||
| + | <div class="caption"> | ||
| + | <p><b>Animation 4</b>: Sortase A7M candidate | ||
| + | <i>S_14771</i> | ||
| + | created | ||
| + | through | ||
| + | <i>RosettaCM</i>.</p> | ||
| + | </div> | ||
| + | </div> | ||
</div> | </div> | ||
| − | |||
| − | |||
| − | |||
| − | |||
| − | |||
| − | < | + | <p> In order to evaluate the secondary structure of the Sortase A7M |
| − | + | candidate <i>S_14771</i> a Ramachandran plot was created and | |
| − | + | compared to | |
| − | + | the five sortases used as input for the comparative modeling. | |
| − | + | Comparisons were also drawn with the sortase predicted by Deep | |
| − | < | + | Learning, |
| − | + | as well as a database of randomly sampled proteins. | |
| − | + | Ramachandran plots of dihedral angles (<b>Fig. 5</b>) can be a | |
| − | + | first indicator | |
| − | + | whether the structures computed are valid.</p> | |
| − | + | ||
| − | + | ||
| − | + | ||
| − | + | ||
| − | + | ||
| − | + | ||
| − | + | ||
| − | + | ||
| − | + | ||
| − | + | <div class="row"> | |
| − | + | <div class="figurcolumn column" | |
| − | + | style="width: 50%; float: left; padding: 1em;"> | |
| − | + | <a target="_blank" | |
| − | + | href="https://2019.igem.org/wiki/images/2/28/T--TU_Darmstadt--ramachandran_s14711.png"> | |
| − | + | <img class="img-fluid center" | |
| − | + | src="https://2019.igem.org/wiki/images/2/28/T--TU_Darmstadt--ramachandran_s14711.png" | |
| − | + | style="width:100%"></a> | |
| − | + | </div> | |
| − | + | <div class="figurcolumn column" | |
| − | + | style="width: 50%; float: right; padding: 1em;"> | |
| − | + | <a target="_blank" | |
| − | + | href="https://2019.igem.org/wiki/images/1/18/T--TU_Darmstadt--Ramachandran_Plot.png"> | |
| − | + | <img class="img-fluid center" | |
| + | src="https://2019.igem.org/wiki/images/1/18/T--TU_Darmstadt--Ramachandran_Plot.png" | ||
| + | style="width:100%"></a> | ||
| + | </div> | ||
</div> | </div> | ||
| − | <div class="figurcolumn column" | + | <div class="row"> |
| − | + | <div class="figurcolumn column" | |
| − | + | style="width: 50%; float: left; padding: 1em;"> | |
| − | + | <a target="_blank" | |
| − | + | href="https://2019.igem.org/wiki/images/e/ee/T--TU_Darmstadt--ramachandran_five_sortases.png"> | |
| + | <img class="img-fluid center" | ||
| + | src="https://2019.igem.org/wiki/images/e/ee/T--TU_Darmstadt--ramachandran_five_sortases.png" | ||
| + | style="width:100%"></a> | ||
| + | </div> | ||
| + | <div class="figurcolumn column" | ||
| + | style="width: 50%; float: right; padding: 1em;"> | ||
| + | <a target="_blank" | ||
| + | href="https://2019.igem.org/wiki/images/7/73/T--TU_Darmstadt--Comp_Ramachandran.PNG"> | ||
| + | <img class="img-fluid center" | ||
| + | src="https://2019.igem.org/wiki/images/7/73/T--TU_Darmstadt--Comp_Ramachandran.PNG" | ||
| + | style="height:82.5%; padding-top: 1.8em; padding-bottom: 2.5em;"></a> | ||
| + | </div> | ||
</div> | </div> | ||
| − | + | <div class="row"> | |
| − | + | <div class="caption"> | |
| − | + | <p><b>Figure 5</b>: The Ramachandran plot of randomly sampled | |
| − | + | proteins | |
| − | + | <sup id="cite_ref-1" class="reference"> | |
| − | + | <a href="#cite_note-1">[1] </a> | |
| − | + | </sup> | |
| + | and the input structures of the <i>comparative | ||
| + | modeling</i> | ||
| + | show | ||
| + | similar secondary structures. Secondary structure analysis | ||
| + | of | ||
| + | both | ||
| + | sortase candidates reveals absence | ||
| + | of secondary structures for candidate <i>CASP12</i>. This is | ||
| + | not | ||
| + | the | ||
| + | case | ||
| + | with candidate <i>S_14771</i> as the Ramachandran plot shows | ||
| + | all | ||
| + | relevant | ||
| + | structures.</p> | ||
| + | </div> | ||
</div> | </div> | ||
| − | < | + | <p>The Ramachandran plot (<b>Fig. 5</b>) showing α-helices |
| − | + | and | |
| − | + | β-sheets is a | |
| − | + | strong indicator of a successful structure determination, as those | |
| − | + | secondary | |
| − | + | structures are crucial for the functionality of sortases.</p> | |
| − | + | ||
| − | + | <h2>Conclusion</h2> | |
| − | + | <p> | |
| − | href=" | + | We used machine learning methods, as well as Monte-carlo simulations |
| − | + | to | |
| − | + | determine the structure of the mutated transpeptidase | |
| − | + | Sortase A7M. | |
| − | + | The machine | |
| − | + | learning approach using AlQuarishi's Deep Neural Network yielded a | |
| − | + | structure which seemed to | |
| − | + | not have any secondary structures. To exclude the possibility of an | |
| − | + | error in the | |
| − | + | PyMOL visualization software by Schroedinger, | |
| − | + | <sup id="cite_ref-7" class="reference"> | |
| + | <a href="#cite_note-7">[7] </a> | ||
| + | </sup>a Ramachandran plot | ||
| + | (<b>Fig. 3</b>) | ||
| + | was created. The plot shows that no typical secondary structures are | ||
| + | present, which is a strong indicator of a failed approach to | ||
| + | determine a | ||
| + | structure. | ||
| + | The approach, using <i>Rosetta Comparative Modeling</i>, | ||
| + | yielded | ||
| + | 15,000 | ||
| + | structures scored with the talaris2013 scoring function. The ten | ||
| + | best structures | ||
| + | were aligned and exhibited almost identical secondary structures | ||
| + | (<b>Fig. 4</b>). | ||
| + | The greatest structural differences are present in the N- and | ||
| + | C-terminal | ||
| + | regions. Since terminal regions tend to fluctuate more strongly than | ||
| + | non-terminal segments of the protein, we deemed those fluctuations | ||
| + | non-relevant | ||
| + | for the proteins functionality. | ||
| + | <br> | ||
| + | Being the best scoring candidate, structure <i>S_14771</i> was | ||
| + | analyzed | ||
| + | structurally | ||
| + | using a Ramachandran plot (<b>Fig. 5</b>). The plot shows all | ||
| + | the | ||
| + | relevant and | ||
| + | typical structures sortases exhibit and serves as an indicator for | ||
| + | a | ||
| + | successful structure prediction. | ||
| + | <br> | ||
| + | In the steps to follow, a molecular dynamics (MD) | ||
| + | simulation will be performed on both structures. Even though | ||
| + | structure <i>CASP12</i> | ||
| + | does not seem to be a valid structure, refolding processes during a | ||
| + | MD | ||
| + | simulation might lead to a relaxation of the protein and allow for a | ||
| + | promising | ||
| + | prediction of the Sortase A7M structure. | ||
| + | </p> | ||
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</div> | </div> | ||
</div> | </div> | ||
| Line 675: | Line 738: | ||
</div> | </div> | ||
</div> | </div> | ||
| + | |||
<div class="tab my-3"> | <div class="tab my-3"> | ||
| Line 693: | Line 757: | ||
<div class="col-xs-12 col-sm-12 col-md-10"> | <div class="col-xs-12 col-sm-12 col-md-10"> | ||
<div class="flex-center"> | <div class="flex-center"> | ||
| − | <h2> | + | <h2>Background</h2> |
<p>The structure predictions made so far were based on statistical methods with | <p>The structure predictions made so far were based on statistical methods with | ||
| Line 699: | Line 763: | ||
constraints. The Deep | constraints. The Deep | ||
Learning algorithm uses a neural network trained to find a function associating | Learning algorithm uses a neural network trained to find a function associating | ||
| − | the | + | the amino acid sequence and |
| − | + | ||
the final 3D positions of the atoms within the protein. On the other hand, | the final 3D positions of the atoms within the protein. On the other hand, | ||
predictions | predictions | ||
were made with Rosetta | were made with Rosetta | ||
| − | using the Monte Carlo Method. Here random movement of individual atoms occurs, | + | using the Monte-Carlo Method. Here random movement of individual atoms occurs, |
and the | and the | ||
energy is estimated after | energy is estimated after | ||
| − | each step.</p> | + | each step. |
| + | </p> | ||
| − | |||
| − | |||
| − | |||
| − | |||
| − | |||
| − | |||
| − | |||
| − | |||
| − | |||
| − | |||
<p> | <p> | ||
| Line 732: | Line 786: | ||
discrete molecules, creating a solvated protein. This step is crucial to | discrete molecules, creating a solvated protein. This step is crucial to | ||
validate the structures, as the interaction with water is one of the primary | validate the structures, as the interaction with water is one of the primary | ||
| − | + | mechanism for protein folding. | |
| − | Since neither candidate CASP12 nor S_14771 have been modeled with explicit water | + | Since neither candidate <i>CASP12</i> nor <i>S_14771</i> have been modeled with |
| + | explicit water | ||
an according MD simulation is imperative, to | an according MD simulation is imperative, to | ||
verify the correctness of the candidates conformation. | verify the correctness of the candidates conformation. | ||
| Line 739: | Line 794: | ||
the protein has to be placed in a simulation box | the protein has to be placed in a simulation box | ||
and said box is filled with water molecules. This is called solvation and is | and said box is filled with water molecules. This is called solvation and is | ||
| − | visualized for candidate S_14771 in | + | visualized for candidate <i>S_14771</i> in <b>Fig. 6</b>. |
</p> | </p> | ||
| − | + | <div class="row"> | |
| + | <div class="figurcolumn column" | ||
| + | style="width: 80%; float: right; padding: 1em; margin: 0 auto;"> | ||
| + | <a target="_blank" | ||
| + | href="https://2019.igem.org/wiki/images/0/08/T--TU_Darmstadt--MoleculeInWater.png"> | ||
| + | <img class="img-fluid center" | ||
| + | src="https://2019.igem.org/wiki/images/0/08/T--TU_Darmstadt--MoleculeInWater.png" | ||
| + | style="width:100%"></a> | ||
| + | <div class="caption"> | ||
| + | <p><b>Figure 6: </b>Sortase A7M in a force field surrounded by discrete | ||
| + | water molecules and ions.</p> | ||
| + | </div> | ||
| + | </div> | ||
| + | </div> | ||
<p> | <p> | ||
| − | We used GROMACS (GROningen MAchine for Chemical Simulations) | + | We used <i>GROMACS</i> (GROningen MAchine for Chemical Simulations) |
| − | <!-- cite --> as the tool for our molecular dynamic simulations. GROMACS solves | + | <!-- cite --> as the tool for our molecular dynamic simulations. <i>GROMACS</i> |
| + | solves | ||
Newtons | Newtons | ||
equations of motion for | equations of motion for | ||
individual atoms | individual atoms | ||
| − | <sup id="cite_ref- | + | <sup id="cite_ref-8" class="reference"> |
| − | <a href="#cite_note- | + | <a href="#cite_note-8">[8] </a> |
</sup> | </sup> | ||
. While this classical simulation is much more accurate than predictions made by | . While this classical simulation is much more accurate than predictions made by | ||
| Line 758: | Line 827: | ||
the system | the system | ||
size is quite small. | size is quite small. | ||
| − | <sup id="cite_ref- | + | <sup id="cite_ref-8" class="reference"> |
| − | <a href="#cite_note- | + | <a href="#cite_note-8">[8] </a> |
</sup> | </sup> | ||
Additionally, atoms are assumed to be classical particles, which is not the | Additionally, atoms are assumed to be classical particles, which is not the | ||
| Line 773: | Line 842: | ||
To perform the molecular dynamics simulations we mostly followed the <a | To perform the molecular dynamics simulations we mostly followed the <a | ||
href="http://www.mdtutorials.com/gmx/lysozyme/01_pdb2gmx.html" | href="http://www.mdtutorials.com/gmx/lysozyme/01_pdb2gmx.html" | ||
| − | target="_blank">GROMACS | + | target="_blank"><i>GROMACS</i> |
| + | Lysozyme tutorial</a> as it serves our purpose | ||
perfectly. We created our simulation box to be of dodecahedral shape and a 0.7 | perfectly. We created our simulation box to be of dodecahedral shape and a 0.7 | ||
nm distance of the solute to the box borders. We used periodic boundry | nm distance of the solute to the box borders. We used periodic boundry | ||
conditions and a Na<sup>+</sup> Cl<sup>-</sup> concentration of 0.012 mol/L. The | conditions and a Na<sup>+</sup> Cl<sup>-</sup> concentration of 0.012 mol/L. The | ||
main difference of our approach was that we used the CHARMM36 | main difference of our approach was that we used the CHARMM36 | ||
| − | < | + | <sup id="cite_ref-9" class="reference"> |
| + | <a href="#cite_note-9">[9] </a> | ||
| + | </sup> | ||
| + | force field instead of the OPLS-AA/L force field and have adjusted | ||
our molecular dynamics parameters <a | our molecular dynamics parameters <a | ||
| − | href="http://www. | + | href="http://www.GROMACS.org/Documentation/Terminology/Force_Fields/CHARMM" |
target="_blank">accordingly</a>. | target="_blank">accordingly</a>. | ||
The simulation was performed on a NVIDIA GTX 760 graphics card allowing us to | The simulation was performed on a NVIDIA GTX 760 graphics card allowing us to | ||
| Line 789: | Line 862: | ||
To analyse the MD simulation we used the Python programming language and the <a | To analyse the MD simulation we used the Python programming language and the <a | ||
href="https://www.biotite-python.org/" target="_blank">Biotite package</a> | href="https://www.biotite-python.org/" target="_blank">Biotite package</a> | ||
| − | < | + | <sup id="cite_ref-10" class="reference"> |
| − | < | + | <a href="#cite_note-10">[10] </a> |
| + | </sup> | ||
| + | as well as <i>GROMACS</i> analysis tools as | ||
| + | <a href="http://manual.gromacs.org/documentation/2018/onlinehelp/gmx-covar.html" | ||
| + | target="_blank">covar</a> and | ||
| + | <a href="http://manual.gromacs.org/documentation/2018/onlinehelp/gmx-anaeig.html" | ||
| + | target="_blank">anaeig</a>. | ||
The first analyses are a root-mean-square deviation (RMSD), a root-mean-square | The first analyses are a root-mean-square deviation (RMSD), a root-mean-square | ||
fluctuation (RMSF) and a gyration radius analysis. | fluctuation (RMSF) and a gyration radius analysis. | ||
RMSD calculations have been described in the structure prediction section. To | RMSD calculations have been described in the structure prediction section. To | ||
compute the RMSF the movement distance of each | compute the RMSF the movement distance of each | ||
| − | residue is computed as a root-mean-square over time as: | + | residue is computed as a root-mean-square over time as: </p> |
| − | + | <div class="row" style="height: 4em;"> | |
| − | + | <img class="img-fluid center" | |
| − | gyration is | + | src="https://2019.igem.org/wiki/images/d/d3/T--TU_Darmstadt--RMSF.png" |
| − | + | style="max-height: 100%; width: auto; margin: 0 auto;"> | |
| + | </div> | ||
| + | <p>where v(t)<sub>i</sub> is the position of atom i at time t. The radius of | ||
| + | gyration is a quantity used to describe the expansion or compression of | ||
| + | a particle. | ||
The final analysis performed on the MD simulation is called Principle Component | The final analysis performed on the MD simulation is called Principle Component | ||
Analysis (PCA). | Analysis (PCA). | ||
By applying PCA to a protein it is possible to gain insights into the relevant | By applying PCA to a protein it is possible to gain insights into the relevant | ||
| − | vibrational motions and thereby the physical mechanism of the protein | + | vibrational motions and thereby the physical mechanism of the protein. |
| − | < | + | <sup id="cite_ref-11" class="reference"> |
| + | <a href="#cite_note-11">[11] </a> | ||
| + | </sup> | ||
</p> | </p> | ||
| Line 810: | Line 895: | ||
<h3>First indicators</h3> | <h3>First indicators</h3> | ||
<p> | <p> | ||
| − | The first possible indicators of a stable protein structure are converging RMSD, | + | The first possible indicators of a stable protein structure are converging |
| − | small RMSF values | + | root-mean-square deviation (RMSD), |
| + | small root-mean-square | ||
| + | fluctuation (RMSF) values | ||
as well as converging radii of gyration. Using the Python software package and | as well as converging radii of gyration. Using the Python software package and | ||
the module Biotite we calculated | the module Biotite we calculated | ||
| − | these quantities and plotted the results for both candidate S_14771 and | + | these quantities and plotted the results for both candidate <i>S_14771</i> and |
| − | candidate CASP12. | + | candidate <i>CASP12</i>. |
</p> | </p> | ||
<div class="row"> | <div class="row"> | ||
<div class="figurcolumn column" style="width: 50%; float: left; padding: 1em;"> | <div class="figurcolumn column" style="width: 50%; float: left; padding: 1em;"> | ||
| − | <img class="img-fluid center" | + | <a target="_blank" |
| − | + | href="https://2019.igem.org/wiki/images/4/4f/T--TU_Darmstadt--rmsd_s14771.png"><img | |
| − | + | class="img-fluid center" | |
| − | < | + | src="https://2019.igem.org/wiki/images/4/4f/T--TU_Darmstadt--rmsd_s14771.png" |
| − | <b>Figure 7: </b> The RMSD is one of three main indicators of a stable | + | style="width:100%"></a> |
| − | + | <div class="caption"> | |
| − | + | <p> | |
| − | + | <b>Figure 7: </b> The RMSD is one of three main indicators of a | |
| − | + | stable | |
| − | + | protein structure of the MD simulation of | |
| − | + | <i>S_14771</i> over the period of 200,000 ps. As time progressed the | |
| + | RMSD | ||
| + | increased with a smaller slope. | ||
| + | The value stabilizes at a time of 110,000 ps and fluctuated around | ||
| + | the | ||
| + | value of 6 Å. | ||
| + | </p> | ||
| + | </div> | ||
</div> | </div> | ||
<div class="figurcolumn column" style="width: 50%; float: right; padding: 1em;"> | <div class="figurcolumn column" style="width: 50%; float: right; padding: 1em;"> | ||
| − | <img class="img-fluid center" | + | <a target="_blank" |
| − | + | href="https://2019.igem.org/wiki/images/a/aa/T--TU_Darmstadt--rmsd_casp.png"><img | |
| − | + | class="img-fluid center" | |
| − | < | + | src="https://2019.igem.org/wiki/images/a/aa/T--TU_Darmstadt--rmsd_casp.png" |
| − | <b>Figure 8: </b> At t = 40,000 ps already the RMSD has arived at a | + | style="width:100%"></a> |
| − | + | <div class="caption"> | |
| − | + | <p> | |
| − | + | <b>Figure 8: </b> At t = 40,000 ps already the RMSD has arived at a | |
| − | + | stable value, while at the same time | |
| − | + | the gyration (<b>Fig. 10</b>) radius decreases over time | |
| − | + | continuously. | |
| + | This | ||
| + | information suggests the protein | ||
| + | might be folding and potentially developing secondary structures not | ||
| + | present previously. | ||
| + | </p> | ||
| + | </div> | ||
</div> | </div> | ||
<div class="figurcolumn column" style="width: 50%; float: left; padding: 1em;"> | <div class="figurcolumn column" style="width: 50%; float: left; padding: 1em;"> | ||
| − | <img class="img-fluid center" | + | <a target="_blank" |
| − | + | href="https://2019.igem.org/wiki/images/9/94/T--TU_Darmstadt--gyration_s14771.png"><img | |
| − | + | class="img-fluid center" | |
| − | < | + | src="https://2019.igem.org/wiki/images/9/94/T--TU_Darmstadt--gyration_s14771.png" |
| − | <b>Figure 9: </b> The prominent fluctuations of the residues from ranges | + | style="width:100%"></a> |
| − | + | <div class="caption"> | |
| − | + | <p> | |
| − | + | <b>Figure 9: </b> The prominent fluctuations of the residues from | |
| − | + | ranges | |
| − | + | 105 to 115 might | |
| − | + | indicate a binding site or another form of functional structure. The | |
| − | + | radius of gyration, just as | |
| + | the RMSD (<b>Fig. 7</b>), stabilizes around a simulation time of of | ||
| + | 110,000 ps | ||
| + | and converges towards a value of | ||
| + | 16.7 Å. | ||
| + | </p> | ||
| + | </div> | ||
</div> | </div> | ||
<div class="figurcolumn column" style="width: 50%; float: right; padding: 1em;"> | <div class="figurcolumn column" style="width: 50%; float: right; padding: 1em;"> | ||
| − | <img class="img-fluid center" | + | <a target="_blank" |
| − | + | href="https://2019.igem.org/wiki/images/0/03/T--TU_Darmstadt--gyration_casp.png"><img | |
| − | + | class="img-fluid center" | |
| − | < | + | src="https://2019.igem.org/wiki/images/0/03/T--TU_Darmstadt--gyration_casp.png" |
| − | <b>Figure 10: </b> As from t = 40,000 ps the radius of gyration | + | style="width:100%"></a> |
| − | + | <div class="caption"> | |
| − | + | <p> | |
| − | + | <b>Figure 10: </b> As from t = 40,000 ps the radius of gyration | |
| − | + | decreases constantly. At the end of the simulation the gyration | |
| + | radius | ||
| + | reaches a value of 17 Å. | ||
| + | This behavior indicates folding of the protein structure. | ||
| + | </p> | ||
| + | </div> | ||
</div> | </div> | ||
<div class="figurcolumn column" style="width: 50%; float: left; padding: 1em;"> | <div class="figurcolumn column" style="width: 50%; float: left; padding: 1em;"> | ||
| − | <img class="img-fluid center" | + | <a target="_blank" |
| − | + | href="https://2019.igem.org/wiki/images/f/f4/T--TU_Darmstadt--rmsf_s14771.png"> | |
| − | + | <img class="img-fluid center" | |
| − | < | + | src="https://2019.igem.org/wiki/images/f/f4/T--TU_Darmstadt--rmsf_s14771.png" |
| − | <b>Figure 11: </b> The fluctuations | + | style="width:100%"></a> |
| − | + | <div class="caption"> | |
| − | + | <p> | |
| − | + | <b>Figure 11: </b> The fluctuations | |
| − | + | (RMSF) of most residues appear insignificant compared to the first, | |
| − | + | the | |
| − | + | last residues and | |
| − | + | the residues close to residue 110 . Typically the N- and C-terminus | |
| − | + | tend | |
| + | to fluctuate more intensively due to the lack of | ||
| + | stabilizing structures. The prominent fluctuations in the range of | ||
| + | residue 105 to 115 | ||
| + | can indicate a binding site or another form of functional structure. | ||
| + | </p> | ||
| + | </div> | ||
</div> | </div> | ||
<div class="figurcolumn column" style="width: 50%; float: right; padding: 1em;"> | <div class="figurcolumn column" style="width: 50%; float: right; padding: 1em;"> | ||
| − | <img class="img-fluid center" | + | <a target="_blank" |
| − | + | href="https://2019.igem.org/wiki/images/a/aa/T--TU_Darmstadt--rmsf_casp.png"> | |
| − | + | <img class="img-fluid center" | |
| − | < | + | src="https://2019.igem.org/wiki/images/a/aa/T--TU_Darmstadt--rmsf_casp.png" |
| − | <b>Figure 12: </b> The prominent fluctuations of the residues from | + | style="width:100%"></a> |
| − | + | <div class="caption"> | |
| − | + | <p> | |
| − | + | <b>Figure 12: </b> The prominent fluctuations of the residues from | |
| − | + | ranges 105 to 115 might | |
| − | + | indicate a binding site or another form of functional structure. The | |
| − | + | radius of gyration, just as | |
| − | + | the RMSD (<b>Fig. 8</b>), stabilizes around a simulation time of of | |
| + | 110,000 ps | ||
| + | and converges towards a value of | ||
| + | 16.7 Å. | ||
| + | </p> | ||
| + | </div> | ||
</div> | </div> | ||
</div> | </div> | ||
| Line 909: | Line 1,031: | ||
protein. Convergence of those quantities can be interpreted as a stable state of | protein. Convergence of those quantities can be interpreted as a stable state of | ||
the protein | the protein | ||
| − | structure. As it can be seen in | + | structure. As it can be seen in <b>Fig. 7</b> and <b>Fig. 9</b> both the RMSD |
| + | and | ||
| + | the radius of | ||
gyration | gyration | ||
stabilize at the same time as the simulation reaches 110,000 ps (110 ns), | stabilize at the same time as the simulation reaches 110,000 ps (110 ns), | ||
suggesting a now | suggesting a now | ||
| − | stabilized structure of candidate S_14771 solvated in water. Another indicator | + | stabilized structure of candidate <i>S_14771</i> solvated in water. Another |
| + | indicator | ||
of a | of a | ||
functional protein is the RMSF. Instead of being averaged over all atoms, the | functional protein is the RMSF. Instead of being averaged over all atoms, the | ||
| Line 919: | Line 1,044: | ||
averaged over time with respect to each amino acid. It provides insights in both | averaged over time with respect to each amino acid. It provides insights in both | ||
protein | protein | ||
| − | stability and functionality. Fig | + | stability and functionality. <b>Fig. 11</b> reveals the RMSF of residues 105 to |
| + | 115 to | ||
be | be | ||
significantly higher than that of other residues. This hints at the presence of | significantly higher than that of other residues. This hints at the presence of | ||
| Line 926: | Line 1,052: | ||
describing our structure prediction approaches, the N- | describing our structure prediction approaches, the N- | ||
and C-terminal regions tend to fluctuate more strongly as a result of the | and C-terminal regions tend to fluctuate more strongly as a result of the | ||
| − | absence of | + | absence of stabilizing structures. |
| − | + | ||
</p> | </p> | ||
<p> | <p> | ||
| − | RMSD and gyration of radius calculations of candidate CASP12 ( | + | RMSD and gyration of radius calculations of candidate <i>CASP12</i> (<b>Fig. |
| + | 8</b> | ||
| + | and <b>Fig. 10</b>) | ||
provide evidence of folding. | provide evidence of folding. | ||
However, the RMSF values show values significantly higher, an | However, the RMSF values show values significantly higher, an | ||
| Line 937: | Line 1,064: | ||
residue 105 to | residue 105 to | ||
115. This insight consolidates the theory that residues 105 to 115 might be a | 115. This insight consolidates the theory that residues 105 to 115 might be a | ||
| − | part of a | + | part of a functional unit. |
| − | + | ||
</p> | </p> | ||
<p> | <p> | ||
| − | We were unsure whether candidate CASP12 can be considered a plausible structure | + | We were unsure whether candidate <i>CASP12</i> can be considered a plausible |
| + | structure | ||
and | and | ||
how to interpret the findings concerning the prominent fluctuations. Therefore, | how to interpret the findings concerning the prominent fluctuations. Therefore, | ||
| Line 951: | Line 1,078: | ||
<p> | <p> | ||
To analyze our system further Principle Component Analysis (PCA) was performed | To analyze our system further Principle Component Analysis (PCA) was performed | ||
| − | using GROMACS. | + | using <i>GROMACS</i>. By applying PCA to a MD simulation of a protein it is |
| + | possible | ||
| + | to extract the most relevant motions of the protein. | ||
</p> | </p> | ||
<div class="figurcolumn column" style="width: 50%; float: left; padding: 1em;"> | <div class="figurcolumn column" style="width: 50%; float: left; padding: 1em;"> | ||
| − | <img class="img-fluid center" | + | <a target="_blank" |
| − | + | href="https://2019.igem.org/wiki/images/d/db/T--TU_Darmstadt--modes_s14771.gif"><img | |
| − | + | class="img-fluid center" | |
| − | <p><b>Animation | + | src="https://2019.igem.org/wiki/images/d/db/T--TU_Darmstadt--modes_s14771.gif" |
| − | + | style="width:100%"></a> | |
| − | + | <div class="caption"> | |
| − | + | <p><b>Animation 5: </b> A Principle Component Analysis of a less (blue) and | |
| − | + | a | |
| − | + | more relevant (red) principal movements showing the most prominent | |
| + | movements of the | ||
| + | Cα-chain | ||
| + | of candidate <i>S_14771</i>. Both principal components show movement of | ||
| + | the | ||
| + | β6/β7 | ||
| + | loop consisting of residues 105 to 115 towards the active site. Thus we | ||
| + | can | ||
| + | assume that the closing β6/β7 loop is involved in the | ||
| + | reaction | ||
| + | mechanism. </p> | ||
| + | </div> | ||
</div> | </div> | ||
<div class="figurcolumn column" style="width: 50%; float: right; padding: 1em;"> | <div class="figurcolumn column" style="width: 50%; float: right; padding: 1em;"> | ||
| − | <img class="img-fluid center" | + | <a target="_blank" |
| + | href="https://2019.igem.org/wiki/images/1/17/T--TU_Darmstadt--modes_casp.gif"><img class="img-fluid center" | ||
src="https://2019.igem.org/wiki/images/1/17/T--TU_Darmstadt--modes_casp.gif" | src="https://2019.igem.org/wiki/images/1/17/T--TU_Darmstadt--modes_casp.gif" | ||
| − | style="width:100%"> | + | style="width:100%"></a> |
| − | <p><b>Animation | + | <div class="caption"> |
| − | + | <p><b>Animation 6: </b> The principal movements of candidate <i>CASP</i> | |
| − | + | appear | |
| − | + | similar to each | |
| − | + | other | |
| + | and no strong single movement can be specified. This makes the most | ||
| + | relevant | ||
| + | (red) | ||
| + | and less relevant (blue) principal component indistinguishable from one | ||
| + | another. Moreover the | ||
| + | active | ||
| + | site amino acids do not appear to be in close proximity, which would | ||
| + | make a | ||
| + | reaction catalyzed by candidate <i>CASP12</i> impossible. </p> | ||
| + | </div> | ||
</div> | </div> | ||
<p> | <p> | ||
| − | The results from the Principle Component Analysis of candidate S_14771 | + | The results from the Principle Component Analysis of candidate <i>S_14771</i> |
| − | ( | + | (<b>Animation 5</b>) show a movement of the residues 105 to 115 towards the |
| − | site, supporting our theory that residues 105 to 115 are important for the | + | active |
| − | reaction mechanism. Since the | + | site, |
| − | movement of the sortase, | + | <sup id="cite_ref-12" class="reference"> |
| + | <a href="#cite_note-12">[12] </a> | ||
| + | </sup> | ||
| + | supporting our theory that residues 105 to 115 are important for the | ||
| + | reaction mechanism. Since the the most relevant vibrational | ||
| + | movement of the sortase (red), is directed towards the active site, it is | ||
| + | possible | ||
that the β6/β7 loop either closes the binding site of the ligand | that the β6/β7 loop either closes the binding site of the ligand | ||
peptides or even transports one peptide towards the other. | peptides or even transports one peptide towards the other. | ||
| Line 987: | Line 1,144: | ||
<p> | <p> | ||
| − | Animation | + | <b>Animation 6</b> shows the results of the Principle Component Analysis of |
| − | CASP12. As the RMSF calculations suggested ( | + | candidate <i>CASP12</i>. As the RMSF calculations suggested (<b>Fig. 12</b>), |
| + | the whole protein seems to | ||
be moving randomly with no directed movement. | be moving randomly with no directed movement. | ||
| − | In addition the active site | + | In addition the active site residues |
| − | < | + | <sup id="cite_ref-12" class="reference"> |
| + | <a href="#cite_note-12">[12] </a> | ||
| + | </sup> | ||
| + | are spread across the protein confirming our assumption that the | ||
protein is not in a stable or plausible conformation. | protein is not in a stable or plausible conformation. | ||
</p> | </p> | ||
| Line 997: | Line 1,158: | ||
<h2>Conclusion</h2> | <h2>Conclusion</h2> | ||
<p> | <p> | ||
| − | We gained evidence that at least | + | We gained evidence that at least one of our Sortase A7M models is a valid and |
stable candidate by performing various methods to analyse the structural | stable candidate by performing various methods to analyse the structural | ||
| − | stability and validity of our two Sortase A7M candidates. The candidate S_14771 | + | stability and validity of our two Sortase A7M candidates. The candidate |
| + | <i>S_14771</i> | ||
that was generated using <i>RosettaCM</i> appears to be a fitting candidate not | that was generated using <i>RosettaCM</i> appears to be a fitting candidate not | ||
only due to successful analyses, but also since the residues of the active site | only due to successful analyses, but also since the residues of the active site | ||
| − | < | + | <sup id="cite_ref-12" class="reference"> |
| + | <a href="#cite_note-12">[12] </a> | ||
| + | </sup> | ||
| + | are close enough to each other to catalyze a ligation reaction. | ||
Our model created through deep learning excelled only in terms of RMSD and | Our model created through deep learning excelled only in terms of RMSD and | ||
gyration radius calculations. Not only the RMSF and Principle Component Analysis | gyration radius calculations. Not only the RMSF and Principle Component Analysis | ||
| − | but also the conformation of the active site have proven candidate CASP12 to be | + | but also the conformation of the active site have proven candidate <i>CASP12</i> |
| + | to be | ||
of no use for further calculations as it does not portray a valid conformation | of no use for further calculations as it does not portray a valid conformation | ||
of Sortase A7M. | of Sortase A7M. | ||
</p> | </p> | ||
</p> | </p> | ||
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| − | |||
| − | |||
| − | |||
| − | |||
| − | |||
| − | |||
| − | |||
| − | |||
| − | |||
| − | |||
| − | |||
| − | |||
| − | |||
| − | |||
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| − | |||
| − | |||
| − | |||
</div> | </div> | ||
</div> | </div> | ||
| Line 1,036: | Line 1,182: | ||
</div> | </div> | ||
</div> | </div> | ||
| + | |||
| + | |||
<div class="tab my-3"> | <div class="tab my-3"> | ||
<button class="btn btn-block" id="tab1" data-toggle="collapse" data-target="#body4" | <button class="btn btn-block" id="tab1" data-toggle="collapse" data-target="#body4" | ||
| Line 1,058: | Line 1,206: | ||
The procedure of choice | The procedure of choice | ||
for the introduction of a ligand into the binding site of a protein is called | for the introduction of a ligand into the binding site of a protein is called | ||
| − | + | docking. In the | |
following sections, we will present the protocol and methods we used as well as | following sections, we will present the protocol and methods we used as well as | ||
the results they yielded. | the results they yielded. | ||
| Line 1,071: | Line 1,219: | ||
affinity. | affinity. | ||
To determine the best possible binding conformation of the protein-ligand | To determine the best possible binding conformation of the protein-ligand | ||
| − | complex, we use FlexPepDock, an algorithm provided by the the RosettaCommons | + | complex, we use <i>FlexPepDock</i>, |
| + | <sup id="cite_ref-13" class="reference"> | ||
| + | <a href="#cite_note-13">[13] </a> | ||
| + | </sup> | ||
| + | an algorithm provided by the the <i>RosettaCommons</i> | ||
software package. | software package. | ||
</p> | </p> | ||
| Line 1,078: | Line 1,230: | ||
<p> | <p> | ||
The ab-initio FlexPepDock protocol consists of multiple steps and is documented | The ab-initio FlexPepDock protocol consists of multiple steps and is documented | ||
| − | on the RosettaCommons <a href="">online documentation</a>. We modified the | + | on the <i>RosettaCommons</i> <a |
| + | href="https://www.rosettacommons.org/docs/latest/application_documentation/docking/flex-pep-dock" | ||
| + | target="_blank">online documentation</a>. | ||
| + | We modified the | ||
protocol as the one provided did not work with our approach. | protocol as the one provided did not work with our approach. | ||
The modified protocol has the following form: | The modified protocol has the following form: | ||
| Line 1,085: | Line 1,240: | ||
<li>secondary structure determination</li> | <li>secondary structure determination</li> | ||
<li>complex creation</li> | <li>complex creation</li> | ||
| − | <li>FlexPepDock refinement</li> | + | <li><i>FlexPepDock</i> refinement</li> |
</ol> | </ol> | ||
<br> | <br> | ||
<p> | <p> | ||
| − | To determine the secondary structure of the peptide, fragment files (3- and | + | To determine the secondary structure of the peptide, fragment files (3-, 5- and |
| − | + | 9-mers) | |
| + | had to be generated and a PSIPRED secondary structure prediction | ||
| + | <sup id="cite_ref-15" class="reference"> | ||
| + | <a href="#cite_note-15">[15] </a> | ||
| + | </sup> | ||
| + | had to | ||
be performed. As the peptides had a sequence length less than 20 amino acids, we | be performed. As the peptides had a sequence length less than 20 amino acids, we | ||
were not able to use the online services such as <a | were not able to use the online services such as <a | ||
| − | href="http://robetta.bakerlab.org/">Robetta</a> and the <a | + | href="http://robetta.bakerlab.org/" target="_blank">Robetta</a> and the <a |
| − | href="http://bioinf.cs.ucl.ac.uk/psipred/">PSIPRED online service</a>. | + | href="http://bioinf.cs.ucl.ac.uk/psipred/" target="_blank">PSIPRED online service</a>. |
| − | Instead we used the Rosetta <a | + | Instead we used the Rosetta <a target="_blank" |
href="https://www.rosettacommons.org/docs/latest/application_documentation/utilities/app-fragment-picker">FragmentPicker | href="https://www.rosettacommons.org/docs/latest/application_documentation/utilities/app-fragment-picker">FragmentPicker | ||
application</a> and the PSIPRED <a | application</a> and the PSIPRED <a | ||
| − | href="https://github.com/psipred/psipred">command line tool</a>. | + | href="https://github.com/psipred/psipred" target="_blank">command line tool</a>. |
The generated structures serve as the input for the refinement protocol. | The generated structures serve as the input for the refinement protocol. | ||
<br> | <br> | ||
| Line 1,110: | Line 1,270: | ||
<br> | <br> | ||
<p> | <p> | ||
| − | The peptide structure was created through ab-initio modeling. | + | The peptide structure was created through <i>ab-initio</i> modeling. |
Initial creation of the peptide was followed by insertion of the peptide into | Initial creation of the peptide was followed by insertion of the peptide into | ||
the sortase binding site. This lead to a coarse model of the peptide sortase | the sortase binding site. This lead to a coarse model of the peptide sortase | ||
complex. Here we used insight gained from the molecular dynamics simulation to | complex. Here we used insight gained from the molecular dynamics simulation to | ||
place the peptide close to the binding site. | place the peptide close to the binding site. | ||
| − | < | + | This operation was performed using Biotite. |
| + | <sup id="cite_ref-10" class="reference"> | ||
| + | <a href="#cite_note-10">[10] </a> | ||
| + | </sup> | ||
<br> | <br> | ||
| − | In the final step the FlexPepDock refinement protocol is executed and 50,000 | + | In the final step the <i>FlexPepDock</i> refinement protocol is executed and |
| + | 50,000 | ||
complex structures are generated. We used the inputs as described in | complex structures are generated. We used the inputs as described in | ||
| − | + | <span id="cite_ref-13" class="reference"> | |
| + | <a href="#cite_note-13">[13] </a> | ||
| + | </span>, written by the authors of the <i>FlexPepDock</i> documentation. | ||
<br> | <br> | ||
To get a better overview over our data we performed a clustering in python, | To get a better overview over our data we performed a clustering in python, | ||
| − | using the scikit-learn package. We clustered the structures with respect to: | + | using the scikit-learn |
| + | <sup id="cite_ref-14" class="reference"> | ||
| + | <a href="#cite_note-14">[14] </a> | ||
| + | </sup> | ||
| + | package. We clustered the structures with respect to: | ||
</p> | </p> | ||
<ul> | <ul> | ||
| Line 1,137: | Line 1,307: | ||
<br> | <br> | ||
<p> | <p> | ||
| − | Here clustering is used to group the docking results and thereby | + | Here clustering is used to group the docking results and thereby decrease the |
samlple size. | samlple size. | ||
From the 50,000 results we picked the results with the 500 best total scores, | From the 50,000 results we picked the results with the 500 best total scores, | ||
| Line 1,155: | Line 1,325: | ||
</p> | </p> | ||
| − | <div class="figurcolumn column" style="width: 50%; float: center; padding: 1em;"> | + | <div class="figurcolumn column" |
| − | <img class="img-fluid center" | + | style="width: 50%; float: center; padding: 1em; margin: 0 auto;"> |
| + | <a target="_blank" | ||
| + | href="https://2019.igem.org/wiki/images/7/78/T--TU_Darmstadt--dock_lpetgg.png"><img class="img-fluid center" | ||
src="https://2019.igem.org/wiki/images/7/78/T--TU_Darmstadt--dock_lpetgg.png" | src="https://2019.igem.org/wiki/images/7/78/T--TU_Darmstadt--dock_lpetgg.png" | ||
| − | style="width:100%"> | + | style="width:100%"></a> |
| − | <p><b>Figure 13: </b> The three best scoring structures (total score, interface | + | <div class="caption"> |
| − | + | <p><b>Figure 13: </b> The three best scoring structures (total score, | |
| − | + | interface | |
| − | + | score, reweighted score) of the LPETGG-tag are shown. Only two results | |
| − | + | are | |
| − | + | visible as the best reweighted score candidate is identical to the best | |
| + | interface score candidate. The reacting section of the LPETGG-tag namely | ||
| + | glycine is colored yellow as is the active site. The glycin of both | ||
| + | ligand | ||
| + | peptides is facing the active site. </p> | ||
| + | </div> | ||
</div> | </div> | ||
<p> | <p> | ||
| Line 1,174: | Line 1,351: | ||
<div class="row"> | <div class="row"> | ||
<div class="figurcolumn column" style="width: 50%; float: left; padding: 1em;"> | <div class="figurcolumn column" style="width: 50%; float: left; padding: 1em;"> | ||
| − | <img class="img-fluid center" | + | <a target="_blank" |
| + | href="https://2019.igem.org/wiki/images/8/8d/T--TU_Darmstadt--dock_polyg.png"><img class="img-fluid center" | ||
src="https://2019.igem.org/wiki/images/8/8d/T--TU_Darmstadt--dock_polyg.png" | src="https://2019.igem.org/wiki/images/8/8d/T--TU_Darmstadt--dock_polyg.png" | ||
| − | style="width:100%"> | + | style="width:100%"></a> |
| − | <p><b>Figure 14: </b>The three best scoring structures (total score, | + | <div class="caption"> |
| − | + | <p><b>Figure 14: </b>The three best scoring structures (total score, | |
| − | + | interface score, reweighted score) of the poly-g peptide are shown. | |
| − | + | Only | |
| − | + | two results are visible as the best reweighted score candidate is | |
| − | + | identical to the best interface score candidate. Instead of facing | |
| + | the | ||
| + | active site (yellow) the reacting glycines (yellow) appear to | ||
| + | interact | ||
| + | with the β6/β7 loop of the sortase. </p> | ||
| + | </div> | ||
</div> | </div> | ||
<div class="figurcolumn column" style="width: 50%; float: right; padding: 1em;"> | <div class="figurcolumn column" style="width: 50%; float: right; padding: 1em;"> | ||
| − | <img class="img-fluid center" | + | <a target="_blank" |
| + | href="https://2019.igem.org/wiki/images/9/92/T--TU_Darmstadt--dock_mpolyg.png"><img class="img-fluid center" | ||
src="https://2019.igem.org/wiki/images/9/92/T--TU_Darmstadt--dock_mpolyg.png" | src="https://2019.igem.org/wiki/images/9/92/T--TU_Darmstadt--dock_mpolyg.png" | ||
| − | style="width:100%"> | + | style="width:100%"></a> |
| − | <p><b>Figure 15: </b>The three best scoring structures (total score, | + | <div class="caption"> |
| − | + | <p><b>Figure 15: </b>The three best scoring structures (total score, | |
| − | + | interface score, reweighted score) of the M-polyG peptide are | |
| − | + | shown. | |
| − | + | Only two results are visible as the best reweighted score candidate | |
| − | + | is | |
| − | + | identical to the best interface score candidate. | |
| − | + | Concerning the M-polyG peptide no uniform directional orientation | |
| − | + | can | |
| + | be observed. | ||
| + | The structure with the best interface score (light blue) is | ||
| + | oriendted | ||
| + | towards the β6/β7 loop while the structure with the | ||
| + | best total/reweighted | ||
| + | (dark blue) is oriented towards the β-sheets.</p> | ||
| + | </div> | ||
</div> | </div> | ||
</div> | </div> | ||
<!-- see more button instead oben halt und so --> | <!-- see more button instead oben halt und so --> | ||
<p> | <p> | ||
| − | + | <b>Fig. 13</b> shows the docking result of the LPETGG peptide to | |
| − | < | + | |
the sortase. The results shown are the best scoring structures of the clustering | the sortase. The results shown are the best scoring structures of the clustering | ||
with respect to the total score, interface score and reweighted score. As the | with respect to the total score, interface score and reweighted score. As the | ||
best scoring structure is the same for the total score and the reweighted score | best scoring structure is the same for the total score and the reweighted score | ||
| − | only two peptides are shown. This also applies to | + | only two peptides are shown. This also applies to <b>Fig. 14</b>and <b>Fig. |
| + | 15</b>. For both | ||
results the reacting glycin residues (yellow) are facing the active site. | results the reacting glycin residues (yellow) are facing the active site. | ||
Additionally, the same residues are in close proximity to the active site. | Additionally, the same residues are in close proximity to the active site. | ||
</p> | </p> | ||
<p> | <p> | ||
| − | + | <b>Fig. 14</b> and <b>Fig. 15</b> show the docking of the both polyG and | |
| + | M-polyG. While polyG | ||
results align well and seem to be interacting with the β6/β7 loop | results align well and seem to be interacting with the β6/β7 loop | ||
rather than with the active site, this does not seem to be the case for M-polyG. | rather than with the active site, this does not seem to be the case for M-polyG. | ||
| Line 1,222: | Line 1,414: | ||
<div class="row"> | <div class="row"> | ||
<div class="figurcolumn column" style="width: 50%; float: left; padding: 1em;"> | <div class="figurcolumn column" style="width: 50%; float: left; padding: 1em;"> | ||
| − | <img class="img-fluid center" | + | <a target="_blank" href="https://2019.igem.org/wiki/images/7/76/T--TU_Darmstadt--dock_zoom_active.png"><img class="img-fluid center" |
src="https://2019.igem.org/wiki/images/7/76/T--TU_Darmstadt--dock_zoom_active.png" | src="https://2019.igem.org/wiki/images/7/76/T--TU_Darmstadt--dock_zoom_active.png" | ||
| − | style="width:100%"> | + | style="width:100%"></a> |
| − | <p><b>Figure 16: </b>The close up of the M-polyG peptide (best | + | <div class="caption"> |
| − | + | <p><b>Figure 16: </b>The close up of the M-polyG peptide (best | |
| − | + | total/reweighted score) indicates an interaction of methionine with | |
| + | arginine<sub>139</sub> and cysteine<sub>126</sub>. </p> | ||
| + | </div> | ||
</div> | </div> | ||
<div class="figurcolumn column" style="width: 50%; float: right; padding: 1em;"> | <div class="figurcolumn column" style="width: 50%; float: right; padding: 1em;"> | ||
| − | <img class="img-fluid center" | + | <a target="_blank" |
| + | href="https://2019.igem.org/wiki/images/4/48/T--TU_Darmstadt--dock_zoom_loop.png"><img class="img-fluid center" | ||
src="https://2019.igem.org/wiki/images/4/48/T--TU_Darmstadt--dock_zoom_loop.png" | src="https://2019.igem.org/wiki/images/4/48/T--TU_Darmstadt--dock_zoom_loop.png" | ||
| − | style="width:100%"> | + | style="width:100%"></a> |
| − | <p><b>Figure 17: </b> Methionine of the result with the best interface score | + | <div class="caption"> |
| − | + | <p><b>Figure 17: </b> Methionine of the result with the best interface | |
| − | + | score | |
| − | + | interacted with the β6/β7 loop rather than the active | |
| + | site. Still the reactive glycine residues appear to be bound to the | ||
| + | β6/β7 loop. </p> | ||
| + | </div> | ||
</div> | </div> | ||
</div> | </div> | ||
<p> | <p> | ||
| − | As can be seen in | + | As can be seen in <b>Fig. 16</b> visualizing the result of the the docking |
| − | total/reweighted score) suggests an interaction of methionine and two of the | + | simulation |
| + | (total/reweighted score) suggests an interaction of methionine and two of the | ||
active sites namely arginine<sub>139</sub> and cysteine<sub>126</sub>. | active sites namely arginine<sub>139</sub> and cysteine<sub>126</sub>. | ||
| − | < | + | <b>Fig. 17</b> shows the interaction of M-polyG with the β6/β7 |
| − | + | loop. | |
| − | + | ||
| − | + | ||
| − | + | ||
The glycines still interact with the β6/β7 loop. | The glycines still interact with the β6/β7 loop. | ||
Instead of binding above the β6/β7 loop, which is the case for | Instead of binding above the β6/β7 loop, which is the case for | ||
| − | polyG as illustrated in | + | polyG as illustrated in <b>Fig. 14</b>, |
the interaction seems to be influenced by methionine. By interacting with the | the interaction seems to be influenced by methionine. By interacting with the | ||
residues in the β-helix | residues in the β-helix | ||
| Line 1,276: | Line 1,472: | ||
structures regarding the total score, the interface score and the reweighted | structures regarding the total score, the interface score and the reweighted | ||
score using PyMOL. | score using PyMOL. | ||
| + | <sup id="cite_ref-7" class="reference"> | ||
| + | <a href="#cite_note-7">[7] </a> | ||
| + | </sup> | ||
Since the best structures with respect to total score and reweighted score were | Since the best structures with respect to total score and reweighted score were | ||
the same for all simulations, | the same for all simulations, | ||
| Line 1,281: | Line 1,480: | ||
to all the peptides to simulate | to all the peptides to simulate | ||
the modification of the VLPs with a small peptide. | the modification of the VLPs with a small peptide. | ||
| − | |||
</p> | </p> | ||
<p> | <p> | ||
| Line 1,299: | Line 1,497: | ||
the interface score shows the M-polyG facing the mobile β6/β7 | the interface score shows the M-polyG facing the mobile β6/β7 | ||
loop. | loop. | ||
| − | In contrast to the polyG peptide | + | In contrast to the polyG peptide lacking the methionine, the M-polyG peptide |
is pulled down below the β6/β7 loop by the methionine interacting | is pulled down below the β6/β7 loop by the methionine interacting | ||
with one of the β-sheets leading to the active site. This is not the case | with one of the β-sheets leading to the active site. This is not the case | ||
| Line 1,330: | Line 1,528: | ||
<div class="flex-center"> | <div class="flex-center"> | ||
| − | + | <p> | |
| + | For our project it was key to understand and characterize Sortase A7M. | ||
| + | As there is no annotated 3D structure for this specific Sortase, an <i>in | ||
| + | silico</i> | ||
| + | structure determination | ||
| + | was performed. This problem was tackled using two different approaches. The Deep | ||
| + | Learning approach did not yield a promising model as later analysis also | ||
| + | confirmed. | ||
| + | However, comparative modeling with <i>Rosetta</i> produced valid structures. We | ||
| + | used | ||
| + | the best structure, candidate <i>S_14771</i>, for extensive characterization. | ||
| + | We evaluated the model with regard to its secondary structure using Ramachandran | ||
| + | plots which suggested plausible secondary structures. | ||
| + | </p> | ||
| − | + | <p> | |
| − | + | Molecular Dynamics simulations were used to investigate stability and dynamic | |
| − | + | properties of the candidate. | |
| + | The RMSD and radius of gyration stabilized over the course of the simulation, a | ||
| + | first indicator of an equilibrated structure. | ||
| + | Interestingly, RMSF analysis showed strong fluctuations of residues 105 to 115. | ||
| + | We further investigated this by performing | ||
| + | Principle Component Analysis. Doing so, we extracted the principle movements of | ||
| + | the model. We could observe movement | ||
| + | of the β6/β7 loop towards the active site, suggesting the presence | ||
| + | of a binding site. | ||
| + | Consequently, we performed docking simulations. | ||
| + | </p> | ||
| − | + | <p> | |
| − | + | FlexPepDock was used to conduct the docking simulations with target peptides. | |
| + | Each run yielded 50,000 structures. | ||
| + | In multiple steps we reduced the amount of complexes to 100 clusters with | ||
| + | respect to total, reweighted and interface score. | ||
| + | We extracted the best scoring complexes and investigated interactions. | ||
| + | </p> | ||
| − | + | <p> | |
| − | + | For LPETGG we observed a uniform binding to the active site, fullfilling our | |
| − | + | expectation. | |
| − | + | Strikingly, polyG appeared to bind to the β6/β7 loop in a uniform | |
| − | + | manner. | |
| − | + | As it is known from literature polyG is a functioning ligand of sortase. | |
| + | Supported by literature and our data, <b>we postulate the following | ||
| + | mechanism</b>: | ||
| + | the β6/β7 loop transports bound polyG towards the active site of | ||
| + | Sortase A7M, thereby lowering the activation energy of the linking reaction. | ||
| + | </p> | ||
| − | + | <p> | |
| − | + | As the theory is neither backed up by nor contradicts experimental data, further | |
| − | + | research is required. | |
| − | + | </p> | |
| − | + | ||
| − | + | ||
| − | + | ||
| − | + | ||
| − | + | ||
| − | + | ||
| − | + | ||
| − | + | ||
</div> | </div> | ||
| Line 1,366: | Line 1,589: | ||
</div> | </div> | ||
| + | <h2>Acknowledgements</h2> | ||
| + | <p>We would like to thank the working group of Prof. Dr. Kay Hamacher. Especially Benjamin Mayer, | ||
| + | Maximilian | ||
| + | Dombrowsky and Patrick Kunzmann for their generous advice and support.</p> | ||
| + | <p>Furthermore, we would like to thank the | ||
| + | <a target="_blank" href="https://2019.igem.org/Team:TU_Darmstadt/Collaborations"> LAB3 </a> for providing us with | ||
| + | the computing power necessary to | ||
| + | execute our Modeling.</p> | ||
| + | <h2>References</h2> | ||
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<a id="back-to-top" href="#" class="back-to-top" role="button" title="Return to the top" | <a id="back-to-top" href="#" class="back-to-top" role="button" title="Return to the top" | ||
Latest revision as of 18:34, 21 October 2019
Modeling
Introduction
In synthetic biology, theoretical models are often used to gain insights, to predict and
to improve
experiments. In our project we are modifying Virus-like particles (VLPs) by attaching
proteins to the
surface of the P22 capsid through a linker. The linking is catalyzed using
the enzyme Sortase A7M, which is a calcium-independent mutant of the
wild type
Sortase A from Staphylococcus aureus. We performed
modeling to predict the unknown structure of the
Sortase A7M to improve the linking between proteins and therefore to optimize the
modification
efficiency of our platform.
Two different modeling approaches were used to determine the structure of the
Sortase A7M.
We compared
machine learning approaches to traditional, comparative Monte-Carlo based modeling
methods. The
results were evaluated using an energy-scoring function and molecular dynamics (MD)
simulations. The
most promising Sortase A7M structures were used to perform a
docking simulation to
investigate binding with linkers.
In silico modeling and simulation of proteins requires a 3D structure, which can be obtained from the RCSB Protein Data Bank. However, if no 3D structures are annotated, as is the case with Sortase A7M, the structure has to be determined by other means. The structure prediction of Sortase A7M was done using two different approaches.
Deep Learning
Background
Machine Learning is a class of algorithms that aim to determine a function between two datasets. This is commonly done by presenting the algorithm with training data, as well as a scoring function to measure its success at processing the input data. During training a feedback loop is used to allow the algorithm to automatically find a function that fits the data. In contrast, classical algorithms are often hardcoded to solve a specific problem and only allow for limited flexibility.
A neural network consists of neurons, which are commonly referred to as nodes. They process input using weights, which are adjusted during its training. Nodes in neural networks are linked together: one neuron processes the input of other neurons, loosely mimicking the structure of biological brains. While one usually has a fixed amount of input and output neurons, limited by the data one wishes to classify, adding layers of hidden neurons can improve the classification. This is often referred to as deep learning and has led to revolutions in applications like speech and image recognition.
Using Machine Learning to predict protein structures has many advantages, compared to conventional methods, especially for iGEM teams who often only have limited access to resources. After training a neural network, which is a computationally expensive process and is often done in centralized data centers, it can be used to predict the structure of a wide variety of proteins. [1] Using pretrained models, novel protein structures can be obtained within seconds [2] compared to conventional methods taking several hours or days. [3]
Until earlier this year the use of Machine Learning in the prediction of protein structures has been restricted to applications within human-written algorithms. [2] AlQuarishi demonstrated a complete deep learning approach that is able to make predictions within 1-2 Å of other approaches, [2] while only using a fraction of the computational power. This enables accurate structural prediction with less powerful, as well as less expensive, hardware and thus significantly reduces the cost of structural modeling.
Procedure
We used AlQuarashi’s approach in combination with his pretrained model, which was trained on the ProteinNet database containing all structures released prior to the start of CASP12 (12th Critical Assessment of Techniques for Protein Structure Prediction – 2016) our generated candidate structure CASP12 is named after. The results were tested against the CASP12 datasets and reached distance root-mean-square deviation (RMSD) values between 10 and 13 Å. The RMSD is defined as root-mean-square deviation of all atom positions compared to a template structure. It is defined as:
where xi is a vector of the atomic coordinates of the i-th atom. All proteins in the CASP datasets were not published until after the competition, and thus represent an assessment with only little bias. [4] We used these pretrained datasets to make structural predictions for our Sortase A7M. The predicted structure was then relaxed in a molecular dynamics simulation.
In the following, the specific steps for obtaining a tertiary structure predicted by AlQuarashi’s model are listed.
- We used the amino acid sequence of the Sortase A7M in the FASTA format to predict the tertiary structure of the amino acid backbone using AlQuarishi’s implementation of his end-to-end differentiable learning of protein structure with the pretrained preCASP ProteinNet database. The output file was a .tertiary file which contains a sequential 3x3 Matrix with atomic coordinates from each amino acid backbone starting at the N-Terminus. The raw backbone is depicted in Animation 1.
- As the standard format for protein structure information is the PDB (Protein Data Bank) file format, we wrote a python script to combine the structural information from the FASTA and .tertiary files into a single PDB file. For ease of use we used the Biotite Python Module.
- Using Rosetta's fixed backbone design program 'fixbb' with the 'hpatch', the optimal position of the side-chains was determined and added to the PDB file. The fixed backbone tool adds the corresponding side-chains and optimizes their conformation. The hpatch database ensures that hydrophilic side-chains are to be preferred on the surface of the protein as our sortase is present in an aqueous environment. The resulting structure is depicted in Animation 2.
In order to evaluate the structure obtained, we constructed a Ramachandran plot by calculating the dihedral angles of the amino acid backbone, as depicted in Fig. 1. These can then be compared to the typical dihedral angles for specific secondary structures such as α-Helices and β-Sheets. The typical angles from a randomly sampled dataset are depicted in Fig. 2.
Figure 1: The dihedral angles of amino acids can be calculated to create a Ramachandran plot.
Figure 2: The dihedral angles over a range of randomly sampled proteins.
Results
Animation 1: The raw PDB File converted from the .tertiary file.
Animation 2: The PDB-File after Step 3.
For analysis the Strucure was viewed in PyMOL . As can be seen in Animation 3 below, no secondary structures could be recognized by PyMOL. Thus, a Ramachandran plot was used to evaluate the dihedral angles of the backbone. This is depicted in Fig. 3. It was found that the angles do not match with the typical angles for α-helices and β-sheets.
Animation 3: The cartoon view in PyMOL.
Figure 3: Ramachandran plot of the predicted structure.
During training the predictions in AlQuarashi’s Model were optimized for their RMSD, which is the root-mean-square deviation of the distance between the atoms of the prediction and reference structure. Thus, even though the predictions are expected to have a similar shape to the physical structure, they may not be in the energy minimum.
Rosetta Comparative Modeling
Background
In our second approach we used the RosettaCommons comparative modeling (RosettaCM), which is based on homology modeling [5] . Homology modeling is a protein modeling method, which requires one or more template structures as a base for the protein to be modeled. The protein sequences are aligned with the sequence of the target protein. Unaligned sections are modeled using fragment or protein libraries, which leads to creating protein structures based on different sequence homologues of the protein of interest. Ab-initio or de novo modeling on the other hand attempts to find protein structures solely based on physicochemical principles applied to the primary sequence, which can be compared to the refolding of a denatured protein.
RosettaCM combines ab-initio modeling with homology modeling. The homologous structures, for which a resolved 3D structure with sufficiently similar sequence exists, are generated using homology modeling. Afterwards the unaligned sequences are modeled de novo. By combining the two methods, RosettaCM represents a precise and resource efficient tool for protein structure prediction. Rosetta applications rely on the Monte-Carlo Optimization, which is a probabilistic approach to finding a local minimum in the energy landscape of protein conformations. The underlying equation serving as the foundation of the statistical Monte-Carlo [6] method is the Metropolis acceptance criterion:
where kB is the Boltzmann constant, ΔE the difference
in
energy of the two states and T the temperature. The term kBT can
also
be written as a single factor β.
During the statistical protein folding based on the Monte-Carlo method, the initial structure is changed by small random perturbations of the atom locations. Whether the structure is accepted, or not, is decided by the Metropolis acceptance criterion. If ΔE < 0, the structure is accepted, otherwise the newly proposed structure is accepted with probability p as described in the Metropolis acceptance criterion.
Procedure
The RosettaCM protocol requires evolutionary related structures and sequences, as well as fragment files of the target structure. The fragment files serve as a structure template for the proteins and consist of peptide fragments of sizes 3 and 9 amino acids. We gathered five evolutionary related structures from the PDB with the accession numbers:
The five PDB entries represent different structures of sortases from Staphylococcus aureus. Fragment files can be created with the Robetta online server or with the Rosetta FragmentPicker application.
The RosettaCM procedure is best described in the following steps: [5]
- sequence and structural alignment of templates
- fragment insertion in unaligned sections
- replacement of random segment with segment from a different template structure
- energy minimization
- all-atom optimization
The alignment can be performed with various tools. We used MAFFT to generate the multiple sequence alignments. Prior to using the alignments as an input, they were converted to the grishin alignment format, as RosettaCM requires the alignments to be in said format. The minimization is performed using the Rosetta centroid energy function. For the centroid function to be applied, the protein is converted to the centroid representation. A protein in centroid representation consists of the backbone atoms N, Cα, OCarbonyl and an atom of varying size representing the side chain. The advantage of using the centroid representation is that the energy landscape can be traversed easier due to the smoother nature of the centroid energy landscape. Finally, the generated structure undergoes a second minimization in an all-atom model by means of Monte-Carlo optimization. This is similar to the energy minimization, but without the amino acids being represented as centroids of their functional groups. Structures computed through all-atom optimizations can reach atomic resolutions, [5] which is crucial for a model meant to be used to estimate atomic interactions.
Results
The run yielded 15,000 structures, which have been compared using the Rosetta scoring functions (talaris2013). From the 15,000 structures generated, we inspected the ten best scoring structures.
As can be seen in Fig. 4, the most prominent differences can be found in the regions close to the N- and C-terminus. As fluctuations in those regions are not untypical, we decided to use the best scoring structure, candidate S_14771 (Animation 4), as the input for the simulations to follow.
Figure 4: The structural alignment of the ten best scoring sortase structures displaying minor differences with the exception of the C- and N-terminal regions. N- and C-terminal regions tend to show strong fluctuations, thus it is unsurprising to find the terminal regions to be unaligned.
Animation 4: Sortase A7M candidate S_14771 created through RosettaCM.
In order to evaluate the secondary structure of the Sortase A7M candidate S_14771 a Ramachandran plot was created and compared to the five sortases used as input for the comparative modeling. Comparisons were also drawn with the sortase predicted by Deep Learning, as well as a database of randomly sampled proteins. Ramachandran plots of dihedral angles (Fig. 5) can be a first indicator whether the structures computed are valid.
Figure 5: The Ramachandran plot of randomly sampled proteins [1] and the input structures of the comparative modeling show similar secondary structures. Secondary structure analysis of both sortase candidates reveals absence of secondary structures for candidate CASP12. This is not the case with candidate S_14771 as the Ramachandran plot shows all relevant structures.
The Ramachandran plot (Fig. 5) showing α-helices and β-sheets is a strong indicator of a successful structure determination, as those secondary structures are crucial for the functionality of sortases.
Conclusion
We used machine learning methods, as well as Monte-carlo simulations
to
determine the structure of the mutated transpeptidase
Sortase A7M.
The machine
learning approach using AlQuarishi's Deep Neural Network yielded a
structure which seemed to
not have any secondary structures. To exclude the possibility of an
error in the
PyMOL visualization software by Schroedinger,
[7]
a Ramachandran plot
(Fig. 3)
was created. The plot shows that no typical secondary structures are
present, which is a strong indicator of a failed approach to
determine a
structure.
The approach, using Rosetta Comparative Modeling,
yielded
15,000
structures scored with the talaris2013 scoring function. The ten
best structures
were aligned and exhibited almost identical secondary structures
(Fig. 4).
The greatest structural differences are present in the N- and
C-terminal
regions. Since terminal regions tend to fluctuate more strongly than
non-terminal segments of the protein, we deemed those fluctuations
non-relevant
for the proteins functionality.
Being the best scoring candidate, structure S_14771 was
analyzed
structurally
using a Ramachandran plot (Fig. 5). The plot shows all
the
relevant and
typical structures sortases exhibit and serves as an indicator for
a
successful structure prediction.
In the steps to follow, a molecular dynamics (MD)
simulation will be performed on both structures. Even though
structure CASP12
does not seem to be a valid structure, refolding processes during a
MD
simulation might lead to a relaxation of the protein and allow for a
promising
prediction of the Sortase A7M structure.
Background
The structure predictions made so far were based on statistical methods with physical constraints. The Deep Learning algorithm uses a neural network trained to find a function associating the amino acid sequence and the final 3D positions of the atoms within the protein. On the other hand, predictions were made with Rosetta using the Monte-Carlo Method. Here random movement of individual atoms occurs, and the energy is estimated after each step.
Even though both methods use physical constraints to find plausible protein structures, neither of them actually simulates the behavior of these molecules within a physical force field. Moreover, both methods do not necessarily output fully relaxed protein structures and simulate water implicitly by preferring hydrophilic parts of the proteins to be on the outside. Thus, we conducted a molecular dynamics (MD) simulation to verify the plausibility of our protein structure and allow equilibration. The molecular dynamics simulation provides the opportunity to simulate water as discrete molecules, creating a solvated protein. This step is crucial to validate the structures, as the interaction with water is one of the primary mechanism for protein folding. Since neither candidate CASP12 nor S_14771 have been modeled with explicit water an according MD simulation is imperative, to verify the correctness of the candidates conformation. This of course is much more expensive in terms of computational ressources. As the protein has to be placed in a simulation box and said box is filled with water molecules. This is called solvation and is visualized for candidate S_14771 in Fig. 6.
Figure 6: Sortase A7M in a force field surrounded by discrete water molecules and ions.
We used GROMACS (GROningen MAchine for Chemical Simulations) as the tool for our molecular dynamic simulations. GROMACS solves Newtons equations of motion for individual atoms [8] . While this classical simulation is much more accurate than predictions made by the other methods, approximations are used nonetheless: Forces are cut after a certain radius and the system size is quite small. [8] Additionally, atoms are assumed to be classical particles, which is not the case, as quantum mechanics plays a role in particle-particle interactions. Still, this simulation is very computationally expensive. Therefore, only time periods less than one second could be simulated.
Methods
To perform the molecular dynamics simulations we mostly followed the GROMACS Lysozyme tutorial as it serves our purpose perfectly. We created our simulation box to be of dodecahedral shape and a 0.7 nm distance of the solute to the box borders. We used periodic boundry conditions and a Na+ Cl- concentration of 0.012 mol/L. The main difference of our approach was that we used the CHARMM36 [9] force field instead of the OPLS-AA/L force field and have adjusted our molecular dynamics parameters accordingly. The simulation was performed on a NVIDIA GTX 760 graphics card allowing us to simulate approximately 1 ns per hour.
To analyse the MD simulation we used the Python programming language and the Biotite package [10] as well as GROMACS analysis tools as covar and anaeig. The first analyses are a root-mean-square deviation (RMSD), a root-mean-square fluctuation (RMSF) and a gyration radius analysis. RMSD calculations have been described in the structure prediction section. To compute the RMSF the movement distance of each residue is computed as a root-mean-square over time as:
where v(t)i is the position of atom i at time t. The radius of gyration is a quantity used to describe the expansion or compression of a particle. The final analysis performed on the MD simulation is called Principle Component Analysis (PCA). By applying PCA to a protein it is possible to gain insights into the relevant vibrational motions and thereby the physical mechanism of the protein. [11]
Results
First indicators
The first possible indicators of a stable protein structure are converging root-mean-square deviation (RMSD), small root-mean-square fluctuation (RMSF) values as well as converging radii of gyration. Using the Python software package and the module Biotite we calculated these quantities and plotted the results for both candidate S_14771 and candidate CASP12.
Figure 7: The RMSD is one of three main indicators of a stable protein structure of the MD simulation of S_14771 over the period of 200,000 ps. As time progressed the RMSD increased with a smaller slope. The value stabilizes at a time of 110,000 ps and fluctuated around the value of 6 Å.
Figure 8: At t = 40,000 ps already the RMSD has arived at a stable value, while at the same time the gyration (Fig. 10) radius decreases over time continuously. This information suggests the protein might be folding and potentially developing secondary structures not present previously.
Figure 9: The prominent fluctuations of the residues from ranges 105 to 115 might indicate a binding site or another form of functional structure. The radius of gyration, just as the RMSD (Fig. 7), stabilizes around a simulation time of of 110,000 ps and converges towards a value of 16.7 Å.
Figure 10: As from t = 40,000 ps the radius of gyration decreases constantly. At the end of the simulation the gyration radius reaches a value of 17 Å. This behavior indicates folding of the protein structure.
Figure 11: The fluctuations (RMSF) of most residues appear insignificant compared to the first, the last residues and the residues close to residue 110 . Typically the N- and C-terminus tend to fluctuate more intensively due to the lack of stabilizing structures. The prominent fluctuations in the range of residue 105 to 115 can indicate a binding site or another form of functional structure.
Figure 12: The prominent fluctuations of the residues from ranges 105 to 115 might indicate a binding site or another form of functional structure. The radius of gyration, just as the RMSD (Fig. 8), stabilizes around a simulation time of of 110,000 ps and converges towards a value of 16.7 Å.
Typical RMSDs and radii of gyration converge towards a value dependent on the size of the protein. Convergence of those quantities can be interpreted as a stable state of the protein structure. As it can be seen in Fig. 7 and Fig. 9 both the RMSD and the radius of gyration stabilize at the same time as the simulation reaches 110,000 ps (110 ns), suggesting a now stabilized structure of candidate S_14771 solvated in water. Another indicator of a functional protein is the RMSF. Instead of being averaged over all atoms, the RMSF is averaged over time with respect to each amino acid. It provides insights in both protein stability and functionality. Fig. 11 reveals the RMSF of residues 105 to 115 to be significantly higher than that of other residues. This hints at the presence of a functional unit along these residues. As commented on in the section describing our structure prediction approaches, the N- and C-terminal regions tend to fluctuate more strongly as a result of the absence of stabilizing structures.
RMSD and gyration of radius calculations of candidate CASP12 (Fig. 8 and Fig. 10) provide evidence of folding. However, the RMSF values show values significantly higher, an effect possibly caused by instability or refolding. Nevertheless, the strongest fluctuations, disregarding the terminal regions, can be seen in the region of residue 105 to 115. This insight consolidates the theory that residues 105 to 115 might be a part of a functional unit.
We were unsure whether candidate CASP12 can be considered a plausible structure and how to interpret the findings concerning the prominent fluctuations. Therefore, we decided to perform a Principle Component Analysis.
Principle Component Analysis
To analyze our system further Principle Component Analysis (PCA) was performed using GROMACS. By applying PCA to a MD simulation of a protein it is possible to extract the most relevant motions of the protein.
Animation 5: A Principle Component Analysis of a less (blue) and a more relevant (red) principal movements showing the most prominent movements of the Cα-chain of candidate S_14771. Both principal components show movement of the β6/β7 loop consisting of residues 105 to 115 towards the active site. Thus we can assume that the closing β6/β7 loop is involved in the reaction mechanism.
Animation 6: The principal movements of candidate CASP appear similar to each other and no strong single movement can be specified. This makes the most relevant (red) and less relevant (blue) principal component indistinguishable from one another. Moreover the active site amino acids do not appear to be in close proximity, which would make a reaction catalyzed by candidate CASP12 impossible.
The results from the Principle Component Analysis of candidate S_14771 (Animation 5) show a movement of the residues 105 to 115 towards the active site, [12] supporting our theory that residues 105 to 115 are important for the reaction mechanism. Since the the most relevant vibrational movement of the sortase (red), is directed towards the active site, it is possible that the β6/β7 loop either closes the binding site of the ligand peptides or even transports one peptide towards the other.
Animation 6 shows the results of the Principle Component Analysis of candidate CASP12. As the RMSF calculations suggested (Fig. 12), the whole protein seems to be moving randomly with no directed movement. In addition the active site residues [12] are spread across the protein confirming our assumption that the protein is not in a stable or plausible conformation.
Conclusion
We gained evidence that at least one of our Sortase A7M models is a valid and stable candidate by performing various methods to analyse the structural stability and validity of our two Sortase A7M candidates. The candidate S_14771 that was generated using RosettaCM appears to be a fitting candidate not only due to successful analyses, but also since the residues of the active site [12] are close enough to each other to catalyze a ligation reaction. Our model created through deep learning excelled only in terms of RMSD and gyration radius calculations. Not only the RMSF and Principle Component Analysis but also the conformation of the active site have proven candidate CASP12 to be of no use for further calculations as it does not portray a valid conformation of Sortase A7M.
Now that the binding site of the Sortase had been found, the peptide ligand needed to be inserted into the binding site to create a peptide-protein complex. The procedure of choice for the introduction of a ligand into the binding site of a protein is called docking. In the following sections, we will present the protocol and methods we used as well as the results they yielded.
Background
Enzymes are one of the most relevant macromolecules in biology. Their functionality is determined through the way they interact with their ligands. Although enzymes are highly specific concerning the ligands they interact with, similar compounds can often bind to the same enzyme albeit with different affinity. To determine the best possible binding conformation of the protein-ligand complex, we use FlexPepDock, [13] an algorithm provided by the the RosettaCommons software package.
Procedure
The ab-initio FlexPepDock protocol consists of multiple steps and is documented on the RosettaCommons online documentation. We modified the protocol as the one provided did not work with our approach. The modified protocol has the following form:
- secondary structure determination
- complex creation
- FlexPepDock refinement
To determine the secondary structure of the peptide, fragment files (3-, 5- and
9-mers)
had to be generated and a PSIPRED secondary structure prediction
[15]
had to
be performed. As the peptides had a sequence length less than 20 amino acids, we
were not able to use the online services such as Robetta and the PSIPRED online service.
Instead we used the Rosetta FragmentPicker
application and the PSIPRED command line tool.
The generated structures serve as the input for the refinement protocol.
The generation of the peptide-protein complex can be divided into three steps:
- peptide creation
- peptide relaxation
- coarse complex creation
The peptide structure was created through ab-initio modeling.
Initial creation of the peptide was followed by insertion of the peptide into
the sortase binding site. This lead to a coarse model of the peptide sortase
complex. Here we used insight gained from the molecular dynamics simulation to
place the peptide close to the binding site.
This operation was performed using Biotite.
[10]
In the final step the FlexPepDock refinement protocol is executed and
50,000
complex structures are generated. We used the inputs as described in
[13]
, written by the authors of the FlexPepDock documentation.
To get a better overview over our data we performed a clustering in python,
using the scikit-learn
[14]
package. We clustered the structures with respect to:
- total score: the total score of the docking provided by the Rosetta scoring function
- interface score: the sum of the energy of the residues in the interfacing region
- reweighted score: a score calculated by double weighting the contribution of the residues in the interfacing region
- root-mean-square deviation: the root-mean-square deviation of the peptides in relation to the structure with the highest score
- peptide direction: the direction the peptide is facing
Here clustering is used to group the docking results and thereby decrease the samlple size. From the 50,000 results we picked the results with the 500 best total scores, the 500 best interface scores and the 500 best reweighted scores. As we aimed to create an unbiased set for clustering, the abscence of duplicates in the set was ensured. We decreased the sample size to 100 groups representing the best scoring structures from the three categories.
Results
For sequences MGGGGPPPPPP(M-polyG), GGGGPPPPPP(polyG) and PPPPPPLPETGG(LPETGG) 50,000 structures have been created and clustered. After the clustering the sample consisted of 100 structures of docked complexes.
Figure 13: The three best scoring structures (total score, interface score, reweighted score) of the LPETGG-tag are shown. Only two results are visible as the best reweighted score candidate is identical to the best interface score candidate. The reacting section of the LPETGG-tag namely glycine is colored yellow as is the active site. The glycin of both ligand peptides is facing the active site.
Analysis of the scores has shown a similar score for all the three dockings. The best scoring results of the LPETGG docking show a tendency of the glycines to face the active site while also being in close proximity to the active site.
Figure 14: The three best scoring structures (total score, interface score, reweighted score) of the poly-g peptide are shown. Only two results are visible as the best reweighted score candidate is identical to the best interface score candidate. Instead of facing the active site (yellow) the reacting glycines (yellow) appear to interact with the β6/β7 loop of the sortase.
Figure 15: The three best scoring structures (total score, interface score, reweighted score) of the M-polyG peptide are shown. Only two results are visible as the best reweighted score candidate is identical to the best interface score candidate. Concerning the M-polyG peptide no uniform directional orientation can be observed. The structure with the best interface score (light blue) is oriendted towards the β6/β7 loop while the structure with the best total/reweighted (dark blue) is oriented towards the β-sheets.
Fig. 13 shows the docking result of the LPETGG peptide to the sortase. The results shown are the best scoring structures of the clustering with respect to the total score, interface score and reweighted score. As the best scoring structure is the same for the total score and the reweighted score only two peptides are shown. This also applies to Fig. 14and Fig. 15. For both results the reacting glycin residues (yellow) are facing the active site. Additionally, the same residues are in close proximity to the active site.
Fig. 14 and Fig. 15 show the docking of the both polyG and M-polyG. While polyG results align well and seem to be interacting with the β6/β7 loop rather than with the active site, this does not seem to be the case for M-polyG. Instead of both structures interacting with the β6/β7 loop or active site one (best interaction score; dark blue) interacts with the β6/β7 loop and the other (best reweighted/total score; light blue-gray) appears to interact with the active site.
Figure 16: The close up of the M-polyG peptide (best total/reweighted score) indicates an interaction of methionine with arginine139 and cysteine126.
Figure 17: Methionine of the result with the best interface score interacted with the β6/β7 loop rather than the active site. Still the reactive glycine residues appear to be bound to the β6/β7 loop.
As can be seen in Fig. 16 visualizing the result of the the docking simulation (total/reweighted score) suggests an interaction of methionine and two of the active sites namely arginine139 and cysteine126. Fig. 17 shows the interaction of M-polyG with the β6/β7 loop. The glycines still interact with the β6/β7 loop. Instead of binding above the β6/β7 loop, which is the case for polyG as illustrated in Fig. 14, the interaction seems to be influenced by methionine. By interacting with the residues in the β-helix methionine could potentially hinder binding of glycine to the β6/β7 loop by partial immobilization of the peptide. Overall peptide binding and orientation is less uniform compared polyG without the leading methionine, which could be an indicator of lesser binding affinity of M-PolyG towards the β6/β7 loop.
Conclusion
To computationally investigate binding affinities of the polyG and M-polyG as well as the LPETGG tags we performed docking simulations using the Rosetta FlexPepDock application. We used a modified version of the recommended protocol as the modified version was easier to automate and served our purpose better than the standard protocol. From the calculated scores only, we could not see a difference in binding affinities. Thus, we inspected the best scoring structures regarding the total score, the interface score and the reweighted score using PyMOL. [7] Since the best structures with respect to total score and reweighted score were the same for all simulations, only two structures have been inspected per run. A polyproline tag was appended to all the peptides to simulate the modification of the VLPs with a small peptide.
As expected, the results showed that for LPETGG, the glycines of both peptides oriented towards the active site. This is unsurprising as peptides with the sequence LPXTGG are known to be substrate of the Sortase. It was more surprising to see the polyG tag oriented away from the active site since polyG also is a known substrate of the sortase. Both polyG peptides were facing the β6/β7 loop (residues 105 to 115) uniformly and appeared to be interacting with it. The M-polyG peptides did not show a uniform orientation or interaction scheme. On one hand the visualization of the best result concerning the total and reweighted score has shown interaction of methionine with the cysteine126 and arginine139, two residues of the active site. On the other hand, the visualization of the best result with respect to the interface score shows the M-polyG facing the mobile β6/β7 loop. In contrast to the polyG peptide lacking the methionine, the M-polyG peptide is pulled down below the β6/β7 loop by the methionine interacting with one of the β-sheets leading to the active site. This is not the case with the polgG results, which lie aligned in one plane with the β6/β7 loop.
For our project it was key to understand and characterize Sortase A7M. As there is no annotated 3D structure for this specific Sortase, an in silico structure determination was performed. This problem was tackled using two different approaches. The Deep Learning approach did not yield a promising model as later analysis also confirmed. However, comparative modeling with Rosetta produced valid structures. We used the best structure, candidate S_14771, for extensive characterization. We evaluated the model with regard to its secondary structure using Ramachandran plots which suggested plausible secondary structures.
Molecular Dynamics simulations were used to investigate stability and dynamic properties of the candidate. The RMSD and radius of gyration stabilized over the course of the simulation, a first indicator of an equilibrated structure. Interestingly, RMSF analysis showed strong fluctuations of residues 105 to 115. We further investigated this by performing Principle Component Analysis. Doing so, we extracted the principle movements of the model. We could observe movement of the β6/β7 loop towards the active site, suggesting the presence of a binding site. Consequently, we performed docking simulations.
FlexPepDock was used to conduct the docking simulations with target peptides. Each run yielded 50,000 structures. In multiple steps we reduced the amount of complexes to 100 clusters with respect to total, reweighted and interface score. We extracted the best scoring complexes and investigated interactions.
For LPETGG we observed a uniform binding to the active site, fullfilling our expectation. Strikingly, polyG appeared to bind to the β6/β7 loop in a uniform manner. As it is known from literature polyG is a functioning ligand of sortase. Supported by literature and our data, we postulate the following mechanism: the β6/β7 loop transports bound polyG towards the active site of Sortase A7M, thereby lowering the activation energy of the linking reaction.
As the theory is neither backed up by nor contradicts experimental data, further research is required.
Acknowledgements
We would like to thank the working group of Prof. Dr. Kay Hamacher. Especially Benjamin Mayer, Maximilian Dombrowsky and Patrick Kunzmann for their generous advice and support.
Furthermore, we would like to thank the LAB3 for providing us with the computing power necessary to execute our Modeling.
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- ↑ Raveh ., et. al. Rosetta FlexPepDock ab-initio: Simultaneous Folding, Docking and Refinement of Peptides onto Their Receptors. Plos One. 2011. [13]
- ↑ Buitinck L. et. al., API design for machine learning software: experiences from the scikit-learn project. arXiv. 2013. [14]
- ↑ McGuffin L.J., et. al. The PSIPRED protein structure prediction server. 2000. 4: 404-405 [15]
