Team:TU-Delft/Modeling/StructuralModeling

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<h2>Summary</h2>
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<p>In order to engineer a yeast strain that is able to detect a tuberculosis (TB) molecule, its receptor should be designed in such a way that the molecule can act as an agonist. By modeling the olfactory receptor in silico, its biophysical and biochemical properties are investigated at the molecular level.  The aim is to get a clear understanding of how a ligand binds in the receptor and how to mutate the binding niche to let it bind more specifically.</p>
 
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<p>In order to engineer a yeast strain that is able to detect a tuberculosis (TB) molecule like methyl nicotinate, its receptor should be designed in such a way that the molecule can act as an agonist. By modeling the olfactory receptor in silico, its biophysical and biochemical properties are investigated at the molecular level. The aim is to get a clear understanding of how a ligand binds in the receptor and how to mutate the binding niche to let it bind to methyl nicotinate more specifically.</p>
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The main chemical compound that we want to act as an agonist, is methyl nicotinate (figure 1a), which is very close related to the agonist of the niacin receptor, niacin (figure 2).  
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</p>
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<p>A validated model of the human niacin receptor 1 was built and a prediction criteria was derived from experimental data of mutated variants of this receptor. Also, the human OR2AG1 receptor was successfully reproduced with its ligand docked inside. An additional hydrogen-bonding of two amino acids within the receptor was found to have an considerable influence on the conformational change properties.</p>
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<p>It is expected that this prediction method could give more reliable models of odorant receptors and thus engineer many receptors for ligands like fruit odors, compounds exhaled by people with a disease, sense explosives and drugs.</p>
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<a href="http://igem.org/wiki/images/7/78/All_compounds.png" rel="lightbox" title="AllCompounds">
 
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<img src="http://igem.org/wiki/images/7/78/All_compounds.png" name="kugroup" width="570"  border="0" id="kugroup" /></a>
 
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<h6><b>Figure 1.</b> Chemical compounds present in the breath of a TB-patient.[1] Methyl nicotinate (a), methyl phenylacetate (b), methyl p-anisate (c) and o-phenylanisole (d)</h6>
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<h2>Introduction</h2>
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<p>Within the Snifferomyces project, the team of iGEM TU Delft 2012 aims to develop an olfactory device to sense volatile compounds. As an application, a few snifferomyces receptors were engineered in yeast to detect molecules in the breath of a tuberculosis (TB) patient (Figure 1a-d) and to detect banana smell. </p>
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<a href="http://2012.igem.org/wiki/images/d/df/All_compounds2.png" rel="lightbox" title="AllCompounds">
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<img src="http://2012.igem.org/wiki/images/d/df/All_compounds2.png" name="kugroup" width="570"  border="0" id="kugroup" /></a>
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<h6><b>Figure 1.</b> Chemical compounds present in the breath of a TB-patient, represented both in 2D as in 3D.[1] Methyl nicotinate (a), methyl phenylacetate (b), methyl p-anisate (c) and o-phenylanisole (d)</h6>
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<p>By reprogramming the binding niche of the niacin receptor, also known as GPR109A, an olfactory receptor for methyl nicotinate could be engineered.</p>
 
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<p>To get a more reliable diagnose whether a patient has TB, receptors for the remaining ligands (figure 1a-d) should be designed, synthesized and integrated in the membrane of the yeast-strain. Therefore, during this project, there has been an effort in designing a receptor for another ligand to improve the diagnostic result. The compound methyl phenylacetate is closely related to isoamylbenzoate, which gained a higher affinity to the hOR2AG1 receptor after reprogramming the selectivity filter.[2] By mutating more amino acids in the binding cavity, a binding niche for methyl phenylacetate could be constructed.</p>
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<p>These included the niacin receptor 1 of Rattus Norvegicus (a.k.a. rGPR109A) and the banana receptor of Mus Musculus (a.k.a. mOlfr154). By replacing the first alpha-helix, N- and C-terminals by the ones of the I7 receptor of Rattus Norvegicus, the receptor is known to be integrated in the cell membrane [3]. These two Snifferomyces receptors, niacin receptor (NR1) and banana receptor (BR4), together with their I7 flanks, were to be modeled in molecular dynamics software, named YASARA. This way, a prediction method of the receptor activity could be implemented for use on both these receptors. However, the NR1-receptor is not specifically evolved to detect one of the TB-molecules, methyl nicotinate (Figure 1a), but niacin. In theory, the BR4-receptor can be reengineered in such a way that it could let the second TB-molecule, methyl phenylacetate (Figure 1b) act as an agonist. </p>
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<p>The structural modeling of this iGEM-project was based on the method of using the crystal structure of bovine rhodopsin receptor as a template for our olfactory receptors, as described earlier.[2] In contrast to the protein modeling software used in this paper, during this project the software YASARA was used for this purpose, mainly because of its many features like Molecular Dynamics (MD) simulations, docking ligands in a specific niche, etc.</p>
 
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<h3>Modeling approach</h3>
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<h3>Prediction method</h3>
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<p>Below is the initial modeling approach described, applied to all olfactory receptors used in our research, unless written otherwise.</p>
 
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<p>1. Firstly, the <i>in silico</i> simulations have to be redone in YASARA in order to produce similar results as reported.[2] This involves the modeling of the olfactory receptor hOR2AG1 by using the crystal structure of the bovine rhodopsin and altering the alignment between the two sequences, by swapping amino acids.
 
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Define the binding cavities of the receptor and examine if it is similar. Run a 10 ns MD without any ligand and analyze if the binding niche shrinks in volume.</p>
 
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<p>By docking the ligand amyl butyrate, used in the paper, in the binding niche of the hOR2AG1 olfactory receptor, and providing similar results as described, provides a conformation that YASARA is indeed applicable to the structural modeling. Executing a 10 ns MD on the vertical configuration of amyl butyrate and analyzing how well the hydrogen bonds are preserved, should give more insight in the reliability of the program.</p>
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<p>The research group of Gerwert et al. [2] developed a method based on Molecular Dynamics (MD) simulations and experimental data, to predicted whether a molecule would act as an agonist for the hOR2AG1 receptor. Within their research, point-mutations were inserted in the gene of the hOR2AG1 receptor, in order to lower the activity hardly or dramatically. By incorporating these point-mutations in their model, they could predict whether such a mutation would indeed influence the activity of the receptor. They concluded that if a molecule would be hydrogen-bonded for >50% of 10 nanoseconds simulation time to amino acid Thr 279, and > 0% of the same time period to Ser 263 or Ser 264 in the binding niche, the receptor would be activated. This requirement was specifically targeted for molecules with an ester-configuration, just like amyl butyrate and isoamyl acetate are. </p>
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<p>2. The modeling approach for the rGPR109A and hOR1G1 receptors with their rI7 flanks is equivalent to the one of the hOR2AG1 protein. The 'wildtype' rGPR109A, hGPR109A, hOR1G1 and I7 receptors underwent the same procedure. Also, the hOR2AG1 receptor model is to be used for further simulations. After undergoing step 1, other ligands were used for all proteins.</p>
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<p>To examine whether a similar requirement could be acquired from the NR1-model, a similar analysis approach was developed by using the experimental data from the hGPR109A mutants [4]. The EC50 responses of these mutants were normalized to the response of the wildtype (WT) hOR2AG1 receptor. Some mutants were simulated together with niacin docked in their binding niche, by using Molecular Dynamics in YASARA. Instead of 10 ns simulation time, 1 ns was chosen as simulation time, mainly due to the fact that a simulation of 10 ns would take 7-8 days to complete. The results of these simulations are analyzed on hydrogen-bonding between amino acids and the ligand. That information was generated for all mutants and the WT, and thus compared to one another, to see whether there is a correlation in the hydrogen-bonded protein-ligand interaction. </p>
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<p>For the rGPR109A/rI7, rGPR109A and hGPR109A models The molecules in figure ??? represent the ligands for the above described models.</p>
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<p>In addition, all other internal hydrogen-bonds between the amino acids of the receptor were analyzed to examine whether a point-mutation has influence on these hydrogen-bonds as well, even if the point-mutation did not disrupt the binding niche directly. It was proposed that a disruption in the hydrogen-bonding between several amino acids might influence the conformational change of the receptor, thus may not activate the receptor and in turn not the G-alpha protein. Analyses of the MD results on the hGPR109A and hOR2AG1 should give an insight whether other hydrogen-bonds then the ones bound to the ligand influence the conformational change. </p>
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[PICTURE OF ALL LIGANDS]
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<a href="http://igem.org/wiki/images/4/45/Nicopic.png" rel="lightbox" title="Nicotinate derivatives">
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<img src="http://igem.org/wiki/images/4/45/Nicopic.png" name="kugroup" width="120"  border="0" id="kugroup" /></a>
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<h6><b>Figure 2.</b> Characterization of used ligands for all models. a) The ligands for GPR109A derivatives include niacin (A1 = C, A2 = N, A3 = OH),  methyl nicotinate (A1 = C, A2 = N, A3 = OMe) and methyl isonicotinate (A1 = N, A2 = C, A3 = OMe). b) The ligands for OR1G1 derivatives include isoamyl acetate  (A1 = CMe, A2 = C), buthyl acetate (A1 = C, A2 = C) and isoamyl propionate. c) The ligands for I7 derivatives include hexanal  (A = C), nonanal (A = CMe) and  octanal (A = CEt).</h6>
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<h3>Reengineering of binding niche</h3>
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<p>After docking a ligand in the cavity of the receptor, a 10 ns MD simulation is executed and the preservation of the hydrogen bonds between the ligand and related amino acids are analyzed to examine how well the ligand is docked in the receptor. These results are compared with the experimental labwork data.</p>
 
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<p>3. In all cases, depending on the conclusions of the in silico and experimentally results, specific point mutations in silico – and if time is left, also experimentally – can reconfigure the ligand-binding niche in such a way that it only specifically binds to the desired compound, i.e. only methyl nicotinate to the rGPR109A/rI7 receptor.</p>
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<p>One of the main objectives the Snifferomyces project consisted of, was to engineer a receptor that has an affinity to methyl nicotinate. Because this compound and niacin are very close related, the choose to use this receptor as an template was obvious. In order to let this receptor sense methyl nicotinate with a high affinity, the binding-niche should be reprogrammed in order to let it bind. With the information on the internal hydrogen-bonding, several residues surrounding the ligand can be mutated without causing an disruption in the conformational change. </p>
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<p>The same may hold for the BR4 receptor, which is a receptor to the ester amyl butyrate (apricot smell). The second TB-molecule, methyl phenylacetate, is also an ester. MD simulations and experimental data on the hOR2AG1 receptor [2] showed that the affinity on the ligand isoamylbenzoate (papaya smell) was increased after one point-mutation. MD simulations on the BR4-receptor with the second TB-molecule, methyl phenylacetate, should indicate whether a point-mutation for higher affinity should be necessary. </p>
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<h3>Results</h3>
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<h2>Results</h2>
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<p>The main drawback that was encountered during the execution of this part of the project were the long simulation times. YASARA has a friendly users interface and is not to hard to understand. However, the Molecular Dynamics simulations are optimized for use on a 8-core computer. This means that a simulation of 10 ns takes about 7-8 days to completely simulate on a 16-core computer, let alone an 8-core computer. Also take in account that not every simulation goes as planned, so this process takes up a lot of time.</p>
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<h3>BR4 Model</h3>
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<p>Thankfully, the SARA institute [URL] was willing to help us and assist us in setting up the environment to use their HPC Cloud Server remotely from our office at Delft University. This gave us the opportunity to execute our simulations on a configurable amount of cores. By testing what amount of cores was the fastest to use (but not necessarily the most efficient) short simulations on 4, 8, 12, 16, 18, 20 and 24 cores were performed. The outcome was in favor of the 16-core computer. This meant that a simulation of 10 ns would take 7-8 days.</p>
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<p>The BR4-model was based upon the human OR2AG1 (a.k.a. hOR2AG1) receptor, which has amyl butyrate [FIG] as an agonist. Previous research had modeled the hOR2AG1 receptor <i>in silico</i> and by using a homology modeling macro of YASARA, this model was reproduced conform the alignment done by Gerwert et.al.[2]</p>
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<a href="http://2012.igem.org/wiki/images/d/d4/AM.png" rel="lightbox" title="AM">
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<b>The modeling of the hOR2AG1 receptor</b>
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<img src="http://2012.igem.org/wiki/images/d/d4/AM.png" name="kugroup" width="570"  border="0" id="kugroup" /></a>
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<h6><b>Figure 2.</b> Reproduction of the hOR2AG1 binding-niche [2] by homology modeling.</h6>
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<p>After building the model of the hOR2AG1 receptor by the method of the paper as described earlier, the calculated volume of the binding niche was examined graphically to conclude any similarity. While the cavity was indeed similar, the outcome of the 10 ns MD simulation without any ligand did not provide the correct end configuration of the binding niche.</p>
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</p>After aligning the hOR2AG1 receptor with the BR4 receptor, and using the same swapping method as described for the NR1 model, the BR4-model was constructed. However, time did not permit us to execute simulations on the hOR2AG1 to test if the model would hold the same binding-niche characteristics. As soon as this model is conformed to the characteristics of the model from the paper, the BR4 sequence can be aligned with it to build the model.</p>
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<p>A first reason why the model did not yield the same result, is that it might be poorly build. Repeated requests by e-mail to the authors of the paper, to provide their model to compare it, proved to be fruitless. Two other models were used during the 10 ns simulation; the model of the Zhang Server [REF] and the model build by the model building macro provided by YASARA. While the latter did not provide the desired result, the first one did. However, the important amino acids that are responsible for the docking of the ligand, were shifted by 8 amino acids towards the C-terminal, providing it to not usable for the simulations.</p>
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<h3>NR1 Model</h3>
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<p>Another reason why the MD simulations did not yield the expected result, is that the authors of the paper used the protein simulation software GROMACS, in contrast to the software YASARA used within this project. Both programs use different set-ups of their force fields. While the provided information on the adjustable parameters was implemented where ever possible, it did not yield the same result. Also, take in account that YASARA provides already 10 force fields for different kinds of simulation approaches.</p>
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<p>The model of the NR1 receptor was based upon the predicted model of Zhang I-Tasser website [5]. In turn, this model of the human niacin receptor 1 (a.k.a. hGPR109A) was predicted by the Zhang research group. By aligning the construct of our NR1 amino acid sequence with hGPR109A (Figure 3), a homology model was built by swapping the different amino acids of hGPR109A by the ones of NR1. </p>
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<p>The main conclusion of the structural modeling of the hOR2AG1 olfactory receptor was that the results of the paper were not reproducible, at least not in YASARA.</p>
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<a href="http://2012.igem.org/wiki/images/8/8d/Alignment1.png" rel="lightbox" title="AM">
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<img src="http://2012.igem.org/wiki/images/8/8d/Alignment1.png" name="kugroup" width="570"  border="0" id="kugroup" /></a>
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<h6><b>Figure 3.</b> Alignment of hGPR109A and Snifferomyces receptor NR1. </h6>
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<p>Several hGPR109A mutants from the data of Offermanns et al. [4] were chosen to be used in the MD simulations, which can be seen in table 1. </p>
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<b>The modeling of the hGPR109A receptor</b>
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<p>The modeling of the hGPR109A receptor yielded more result, with a slightly different approach. Instead of 10 ns MD simulation, 1 ns MD simulations were executed because of time shortage. No initial MD simulation without ligand was executed, and ligands were docked directed in the receptor. Experimental data was obtained from research on the protein-ligand interaction between the receptor and niacin. The results described in the paper indicate that the amino acids Ser 178 and Arg 111 play a crucial role in hydrogen bonding, of which the latter plays the most important one.[REF] Both the model build by the YASARA macro and the amino acid-swapping method did not deliver a docking between the amino acids Ser 178 and Arg 111, however the model of the Zhang server did. [REF]</p>
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<h6><b>Table 1. </b><i>In silico</i> hydrogen-bond contacts of niacin with Homo sapiens GPR109A variants. The percentages represent frequency of hydrogen-bond contact occurrence.</h6>
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<a href="http://2012.igem.org/wiki/images/b/be/Table2_energy.png" rel="lightbox" title="Table 1">
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<img src="http://2012.igem.org/wiki/images/b/be/Table2_energy.png" name="kugroup" width="570"  border="0" id="kugroup" /></a>
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<h6>TMH1 stands for TransMembrane Helix 1, ECL2 stands for ExtraCellular Loop 2.</h6>
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<h6>[a] Calculated binding energy by YASARA.</h6>
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<h6>[b] Two criteria for receptor activation; temporary bonding to Ser 247 (> 25%) and fluctuating bonding to Arg 111 (<0%), and 1) Robust bonding to Ser 178 and Arg 251 (>50%) and 2) fluctuating and temporary bonding to Ser 178 (0-24%) and Arg 251 (25-49%) respectively with temporary or robust bonding between Asn 45 and Ser 287.</h6>
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<h6>[c] <i>In vivo</i> activity qualified as EC50 response <20, otherwise inactive. EC50 data from Offermans et al.[4]</h6>
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<h6>[d] Mutated residue variant compared to its relevant residue.</h6>
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<h6>[e] NR1 receptor with ligand methyl nicotinate. </h6>
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<p>The docking of the ligand niacin in the protein yielded a bonding mainly between the amino acids Arg 111, Ser 178 and Arg 251 (FIGURE ???), of which Arg 111 is bonded the strongest. The Youtube-movie below shows the ligand-protein interaction. The amino acids that make up this binding niche differ partially from the ones described in the paper. </p>
 
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<p>Table 1 shows the percentage of the total simulation time at which the amino acids are bound to the ligand. Each MD simulation was executed after an energy minimization and the specific point-mutation during a simulation time of 1 nanosecond. The active/inactive states are partly based on the classification of the responses of the hOR2AG1 mutants; every EC50 value (from the research of Offermanns et al.[4]) of 20 or higher is classified as being inactive and having a low affinity. Every response below 20 is considered active.</p>
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<iframe title="YouTube video player" class="youtube-player" type="text/html"
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width="425" height="349" src="http://www.youtube.com/embed/b3811GKJUNo"
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<h6>Movie 1 Molecular interaction between the ligand niacin and the hGPR109A receptor. Choose the 720p HD option to get the best visualisation.</h6>
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<p>In addition, in the paper hydrogen-bonds in time are also classified in groups; whenever there is a hydrogen-bond present for 1-24% of the simulation time, it is considered temporary bonds, fluctuating during 25-49% of the time, and robust bonds are considered when they are bound 50-100% of the simulation time. </p>
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###[PICTURE OF BINDING NICHE ARG 111, SER 178 AND ARG 251]
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<h6><b>Figure ???.</b> Configuration of the hGPR109A binding niche and niacin. a) Detailed representation of the cavity after 1 ns MD simulation. The ligand niacin is colored orange, residues cyan and transmembrane helices gray. b) Experimental results from research on the interaction between niacin and the human niacin receptor 1. [REF] The chart represents the activation by cells containing mutant receptors normalized to cells containing the wildtype (WT) hGPR109A protein. c) An <i>in silico</i> model of the hGPR109A receptor (gray) with the ligand niacin (orange).</h6>
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<p>The M103V, L107F, S178I, F198L, R251A, C183A, C266A, R111A and C100A mutants of the hGPR109A receptor were investigated computationally in a 1 ns simulation of a thousand iterations. After analyzing the results, a same criteria as described in hOR2AG1 research paper on classifying the hydrogen bonds was applied; Robust bonds are present in 100-50 % of the simulating time, fluctuating in 49-25% and temporary in 24-1%.</p>
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<p>Analyses of table 1 shows primarily that for a hGPR109A mutant to be active, at least the protein-ligand interaction for amino acid Ser 178 and Arg 251 have to be >50, together with Ser 247 and Arg 111 , which should have a value of >25 and >0, respectively. However, this is not the case for mutant L83V, which has a very low value for its hydrogen-bonding to Ser 178 and Arg 251. When checking for the internal hydrogen-bonds, one in particular stood in correlation with this mutant and the others. The hydrogen-bonding of Asn 45 to Ser 287 can be considered robust in comparison with the other active mutants, which are <50. This means that this bonding between transmembrane helix 1 and 7 is a sort of "fail-safe" mechanism that comes into action when both hydrogen-bondings of Ser 178 and Arg 251 to the ligand fail.</p>
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<p>The inactive mutants all have a value of <50 for Arg 251 which classifies them as being inactive, except for N86Y and R111A. The latter mutant has no Arg 111 at all, and can be considered as being inactive by definition. The hydrogen-bonding of Arg 251 to the ligand is robust, however the protein-ligand interaction of Ser 178 very low. In comparison to the data of the mutant L83V, for the mutant N86Y to be inactive, the interaction between Ser 287 and Asn 45 should be <50, which is the case. </p>
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<h6><b>Table ???.</b><i> In silico</i> hydrogen-bond contacts of niacin with hGPR109A variants. The percentages represent frequency of hydrogen-bond contact occurrence.</h6>
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<a id="youtube"></a>
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###[PICTURE OF BINDING NICHE ARG 111, SER 178 AND ARG 251]
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<a href="" rel="lightbox" title="Table 1">
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<h6>[a] Criteria for hydrogen bonding; temporary bonding to [QUALIFY] (>0%) and robust bonding to Arg 111 (>49%). [b] Mutated residue variant compared to its relevant residue. [c] Low expression of the receptor.</h6>
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<iframe width="560" height="315" src="http://www.youtube.com/embed/CIZGJ4LEo_k?rel=0" frameborder="0" allowfullscreen></iframe>
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<h6> Choose 720p HD for the best visualization. In case the place seems to be blank, zoom into the page by using Ctrl + Mouse-wheel and hit F5.</h6>  
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<p> Molecular Dynamics analyses of the NR1-receptor (see movie) shows a result similar to mutant N86Y. However, in this case the Ser 187 - Asn 45 bonding (which is also present in the NR1 receptor) is active for 38% of the time. The boundary condition for this interaction to have influence on a weak protein-ligand bonding of Ser 178 or Arg 251, could be fixed at >25%, instead of >50% as mentioned earlier. If this is because the NR1-receptor has different properties than the GPR109A on the overal scale of the receptor, further research should figure out. The interaction of methyl nicotinate to the NR1-receptor clearly shows no affinity at all, which can also be concluded from the dramatic drop in binding energy.</p>
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<h3>Reengineering of the NR1 receptor</h3>
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<p>Furthermore, another interaction of Arg 251 with its neighboring amino acid, Glu 196, was investigated. This hydrogen-bond can be considered as being very robust, whereas the bonding is always 99% or higher, except of course for mutant R251A. It seems that this mutant may have a role to play in the conformational change of the receptor, but in the absence of the amino acid the receptor is still able to be activated, but many times less than the non-mutated wildtype receptor. From this can be suggested that Arg 251 indeed may be mutated in the reengineering assay, if further investigation turns out that the bonding between Ser 287 and Asn 45 does not play a large role in compensating the absense of Arg 251. </p>
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<h3>Future follow-ups</h3>
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<h2>Discussion</h2>
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<p>If time permits, we could see if we can change the receptor in a different way to see if we can reconfigure the binding niche in such a way that other chemical compounds could bind to it, say, methyl isonicotinate instead of methyl nicotinate. Furthermore, it would be interesting if some kind of universality can be found to predict other configurations of binding niches.</p>
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<p>While the method of this research was mainly based upon the experimental and MD data from Gerwert et.al.[2], not all MD requirements as done by them were taken into account. The main differences were the use of MD simulation program (instead of GROMACS, YASARA was used) and the simulation time was a 10-fold lower. The higher the simulation time, the more the simulation time is similar to the real protein-ligand interaction time. </p>
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<p>Another interesting approach would be to engineer an olfactory receptor for one of the other three chemical compounds (figure 2) which are found in the breath of a tuberculosis patient. to make a combination of two novel yeast cells and let measurements be even more sensitive. </p>
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<p>One should also take in account that the models of the hGPR109A and hOR2AG1 receptors are an approximation of reality. By doing point-mutations on these receptors, the models can be refined. For this research only a limited amount of hGPR109A mutants were available. For example, a point-mutation in Ser 247 would give more insight on the affinity of the binding niche. In addition, the NR1 and BR4 models are derivatives of the hGPR109A and hOR2AG1, respectively. The alignments included no gaps, which means that the swapping method could give a reliable representation of the Snifferomyces receptors. </p>
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<p>To have a more accurate model of the NR1 and BR4 receptors, point-mutations on these receptors and measurement of the response to certain ligands should be implemented in further research. By running MD simulations, interesting amino acids can be selected for mutation to examine whether the expected response is generated by the receptor. </p>
 +
<p>Point-mutations on possible important amino acids like Asn 45 and Ser 287 should give an understanding whether the interaction between these two residues really act as a “fail-safe” mechanism that could still activate the G-alpha protein. Further research could give more insight into  other possible residues that could be responsible for such a mechanism.</p>
 +
 +
<p>The simulation times were a crucial part of the structural modeling in YASARA. YASARA has a friendly user's interface and has many integrated features. However, the MD simulations are optimized for use on a 8-core computer. This means that a simulation of 10 ns takes about 7-8 days to completely simulate on a 16-core computer, let alone an 8-core computer. Also, by taking in account the error probability for every simulation, this process exhausts a lot of time.</p>
 +
 +
<p>Thankfully, the <a href="https://www.sara.nl/">SARA institute</a> was willing to help us and assist us in setting up the environment to use their HPC Cloud Server remotely from our office at Delft University. This gave us the opportunity to execute our simulations on a configurable amount of cores. By testing what amount of cores was the fastest to use (but not necessarily the most efficient) short simulations on 4, 8, 12, 16, 18, 20 and 24 cores were performed. The outcome was in favor of the 16-core computer. This means that a simulation of 10 ns would take 7-8 days. In contrast, a simulation of 1 nanosecond took about 2-2.5 days.</p>.
 +
 +
<br/>
 +
<br/>
 +
<h2>Follow-up</h2>
 +
<br/>
 +
 +
<p>Some additional ideas on structural modeling had to be given a lower priority. An interesting approach would be to engineer an olfactory receptor for one of the other three chemical compounds (Figure 1b-d) which are found in the breath of a tuberculosis patient. In the same manner, the reprogramming of the binding niche could allow different derivatives of a certain compound (methyl isonicotinate instead of methyl nicotinate) allow to bind, of which a kind of universality could be investigated to predict a configuration of the binding niche to any ligand.</p>
 +
 +
<p>Another follow-up would be to investigate <i>in silico</i> how large the conformational change of the receptor is, and see how this correlates to the hydrogen bonding, RSMD and energy. This would require data on simulations done with and without a ligand. This could eventually lead to a value for the dissociation rate between the G-alpha protein and the receptor.</p>
 +
 +
<p>Because the hOR2AG1 is a receptor to a fruity molecule, there is a potential that it could sense other odors van fruits that hold an ester-configuration as well. Many of these odors are derivatives of each other, so the BR4 receptor could be reengineered in such a way that it can sense any fruity molecule.</p>
 +
 +
<br/>
 +
<br/>
<h2>References</h2>
<h2>References</h2>
-
<p>[1] Syhre M, Chambers ST (2008) The scent of Mycobacterium tuberculosis. <i>Tuberculosis. </i><b>88</b>:317–323</p>
+
<br/>
-
<p>[2] Gelis L, Wolf S, Hatt H, Neuhaus EM, Gerwert K (2012) Prediction of a Ligand-Binding Niche within a Human Olfactory Receptor by Combining Site-Directed Mutagenesis with Dynamic Homology Modeling. <i>Angew. Chem. Int. Ed. </i><b>51</b>:1274-1278</p>
+
 
-
<p>[3] Tunaru S, Lättig J, Kero J, Krause G, Offermanns S (2005) Characterization of Determinants of Ligand Binding to the Nicotinic Acid Receptor GPR109A (HM74A/PUMA-G). <i>Mol Pharmacol. </i><b>68</b>:1271-1280</p>
+
<h6>[1] Syhre M, Chambers ST (2008) The scent of Mycobacterium tuberculosis. Tuberculosis. 88:317–323</h6>
-
<p>[4] Kurtz AJ, Lawless HT, Acree TE (2010) The Cross-Adaptation of Green and Citrus Odorants <i>Chem. Percept. </i><b>3</b>:149–155</p>
+
<h6>[2] Gelis L, Wolf S, Hatt H, Neuhaus EM, Gerwert K (2012) Prediction of a Ligand-Binding Niche within a Human Olfactory Receptor by Combining Site-Directed Mutagenesis with Dynamic Homology Modeling. Angew. Chem. Int. Ed. 51:1274-1278</h6>
-
<p>J Zhang, Y Zhang. GPCR-ITASSER: A new composite algorithm for G protein-coupled receptor structure prediction and the application on human genome. 2011</p>
+
<h6>[3] Jasmina Minic, Marie-annick Persuy, Elodie Godel, Josiane Aioun, Ian Connerton, Roland Salesse, Functional expression of olfactory receptors in yeast and development of a bioassay for odorant screening, FEBS Journal (2005)</h6>
 +
<h6>[4] Tunaru S, Lättig J, Kero J, Krause G, Offermanns S (2005) Characterization of Determinants of Ligand Binding to the Nicotinic Acid Receptor GPR109A (HM74A/PUMA-G). Mol Pharmacol. 68:1271-1280</h6>
 +
<h6>[5]J Zhang, Y Zhang. GPCR-ITASSER: A new composite algorithm for G protein-coupled receptor structure prediction and the application on human genome. 2011</h6>
 +
 
 +
 

Latest revision as of 04:02, 27 October 2012

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Summary


In order to engineer a yeast strain that is able to detect a tuberculosis (TB) molecule like methyl nicotinate, its receptor should be designed in such a way that the molecule can act as an agonist. By modeling the olfactory receptor in silico, its biophysical and biochemical properties are investigated at the molecular level. The aim is to get a clear understanding of how a ligand binds in the receptor and how to mutate the binding niche to let it bind to methyl nicotinate more specifically.

A validated model of the human niacin receptor 1 was built and a prediction criteria was derived from experimental data of mutated variants of this receptor. Also, the human OR2AG1 receptor was successfully reproduced with its ligand docked inside. An additional hydrogen-bonding of two amino acids within the receptor was found to have an considerable influence on the conformational change properties.

It is expected that this prediction method could give more reliable models of odorant receptors and thus engineer many receptors for ligands like fruit odors, compounds exhaled by people with a disease, sense explosives and drugs.



Introduction


Within the Snifferomyces project, the team of iGEM TU Delft 2012 aims to develop an olfactory device to sense volatile compounds. As an application, a few snifferomyces receptors were engineered in yeast to detect molecules in the breath of a tuberculosis (TB) patient (Figure 1a-d) and to detect banana smell.



Figure 1. Chemical compounds present in the breath of a TB-patient, represented both in 2D as in 3D.[1] Methyl nicotinate (a), methyl phenylacetate (b), methyl p-anisate (c) and o-phenylanisole (d)

These included the niacin receptor 1 of Rattus Norvegicus (a.k.a. rGPR109A) and the banana receptor of Mus Musculus (a.k.a. mOlfr154). By replacing the first alpha-helix, N- and C-terminals by the ones of the I7 receptor of Rattus Norvegicus, the receptor is known to be integrated in the cell membrane [3]. These two Snifferomyces receptors, niacin receptor (NR1) and banana receptor (BR4), together with their I7 flanks, were to be modeled in molecular dynamics software, named YASARA. This way, a prediction method of the receptor activity could be implemented for use on both these receptors. However, the NR1-receptor is not specifically evolved to detect one of the TB-molecules, methyl nicotinate (Figure 1a), but niacin. In theory, the BR4-receptor can be reengineered in such a way that it could let the second TB-molecule, methyl phenylacetate (Figure 1b) act as an agonist.



Prediction method


The research group of Gerwert et al. [2] developed a method based on Molecular Dynamics (MD) simulations and experimental data, to predicted whether a molecule would act as an agonist for the hOR2AG1 receptor. Within their research, point-mutations were inserted in the gene of the hOR2AG1 receptor, in order to lower the activity hardly or dramatically. By incorporating these point-mutations in their model, they could predict whether such a mutation would indeed influence the activity of the receptor. They concluded that if a molecule would be hydrogen-bonded for >50% of 10 nanoseconds simulation time to amino acid Thr 279, and > 0% of the same time period to Ser 263 or Ser 264 in the binding niche, the receptor would be activated. This requirement was specifically targeted for molecules with an ester-configuration, just like amyl butyrate and isoamyl acetate are.

To examine whether a similar requirement could be acquired from the NR1-model, a similar analysis approach was developed by using the experimental data from the hGPR109A mutants [4]. The EC50 responses of these mutants were normalized to the response of the wildtype (WT) hOR2AG1 receptor. Some mutants were simulated together with niacin docked in their binding niche, by using Molecular Dynamics in YASARA. Instead of 10 ns simulation time, 1 ns was chosen as simulation time, mainly due to the fact that a simulation of 10 ns would take 7-8 days to complete. The results of these simulations are analyzed on hydrogen-bonding between amino acids and the ligand. That information was generated for all mutants and the WT, and thus compared to one another, to see whether there is a correlation in the hydrogen-bonded protein-ligand interaction.

In addition, all other internal hydrogen-bonds between the amino acids of the receptor were analyzed to examine whether a point-mutation has influence on these hydrogen-bonds as well, even if the point-mutation did not disrupt the binding niche directly. It was proposed that a disruption in the hydrogen-bonding between several amino acids might influence the conformational change of the receptor, thus may not activate the receptor and in turn not the G-alpha protein. Analyses of the MD results on the hGPR109A and hOR2AG1 should give an insight whether other hydrogen-bonds then the ones bound to the ligand influence the conformational change.



Reengineering of binding niche


One of the main objectives the Snifferomyces project consisted of, was to engineer a receptor that has an affinity to methyl nicotinate. Because this compound and niacin are very close related, the choose to use this receptor as an template was obvious. In order to let this receptor sense methyl nicotinate with a high affinity, the binding-niche should be reprogrammed in order to let it bind. With the information on the internal hydrogen-bonding, several residues surrounding the ligand can be mutated without causing an disruption in the conformational change.

The same may hold for the BR4 receptor, which is a receptor to the ester amyl butyrate (apricot smell). The second TB-molecule, methyl phenylacetate, is also an ester. MD simulations and experimental data on the hOR2AG1 receptor [2] showed that the affinity on the ligand isoamylbenzoate (papaya smell) was increased after one point-mutation. MD simulations on the BR4-receptor with the second TB-molecule, methyl phenylacetate, should indicate whether a point-mutation for higher affinity should be necessary.



Results


BR4 Model


The BR4-model was based upon the human OR2AG1 (a.k.a. hOR2AG1) receptor, which has amyl butyrate [FIG] as an agonist. Previous research had modeled the hOR2AG1 receptor in silico and by using a homology modeling macro of YASARA, this model was reproduced conform the alignment done by Gerwert et.al.[2]



Figure 2. Reproduction of the hOR2AG1 binding-niche [2] by homology modeling.

After aligning the hOR2AG1 receptor with the BR4 receptor, and using the same swapping method as described for the NR1 model, the BR4-model was constructed. However, time did not permit us to execute simulations on the hOR2AG1 to test if the model would hold the same binding-niche characteristics. As soon as this model is conformed to the characteristics of the model from the paper, the BR4 sequence can be aligned with it to build the model.



NR1 Model


The model of the NR1 receptor was based upon the predicted model of Zhang I-Tasser website [5]. In turn, this model of the human niacin receptor 1 (a.k.a. hGPR109A) was predicted by the Zhang research group. By aligning the construct of our NR1 amino acid sequence with hGPR109A (Figure 3), a homology model was built by swapping the different amino acids of hGPR109A by the ones of NR1.



Figure 3. Alignment of hGPR109A and Snifferomyces receptor NR1.

Several hGPR109A mutants from the data of Offermanns et al. [4] were chosen to be used in the MD simulations, which can be seen in table 1.


Table 1. In silico hydrogen-bond contacts of niacin with Homo sapiens GPR109A variants. The percentages represent frequency of hydrogen-bond contact occurrence.


TMH1 stands for TransMembrane Helix 1, ECL2 stands for ExtraCellular Loop 2.
[a] Calculated binding energy by YASARA.
[b] Two criteria for receptor activation; temporary bonding to Ser 247 (> 25%) and fluctuating bonding to Arg 111 (<0%), and 1) Robust bonding to Ser 178 and Arg 251 (>50%) and 2) fluctuating and temporary bonding to Ser 178 (0-24%) and Arg 251 (25-49%) respectively with temporary or robust bonding between Asn 45 and Ser 287.
[c] In vivo activity qualified as EC50 response <20, otherwise inactive. EC50 data from Offermans et al.[4]
[d] Mutated residue variant compared to its relevant residue.
[e] NR1 receptor with ligand methyl nicotinate.

Table 1 shows the percentage of the total simulation time at which the amino acids are bound to the ligand. Each MD simulation was executed after an energy minimization and the specific point-mutation during a simulation time of 1 nanosecond. The active/inactive states are partly based on the classification of the responses of the hOR2AG1 mutants; every EC50 value (from the research of Offermanns et al.[4]) of 20 or higher is classified as being inactive and having a low affinity. Every response below 20 is considered active.

In addition, in the paper hydrogen-bonds in time are also classified in groups; whenever there is a hydrogen-bond present for 1-24% of the simulation time, it is considered temporary bonds, fluctuating during 25-49% of the time, and robust bonds are considered when they are bound 50-100% of the simulation time.

Analyses of table 1 shows primarily that for a hGPR109A mutant to be active, at least the protein-ligand interaction for amino acid Ser 178 and Arg 251 have to be >50, together with Ser 247 and Arg 111 , which should have a value of >25 and >0, respectively. However, this is not the case for mutant L83V, which has a very low value for its hydrogen-bonding to Ser 178 and Arg 251. When checking for the internal hydrogen-bonds, one in particular stood in correlation with this mutant and the others. The hydrogen-bonding of Asn 45 to Ser 287 can be considered robust in comparison with the other active mutants, which are <50. This means that this bonding between transmembrane helix 1 and 7 is a sort of "fail-safe" mechanism that comes into action when both hydrogen-bondings of Ser 178 and Arg 251 to the ligand fail.

The inactive mutants all have a value of <50 for Arg 251 which classifies them as being inactive, except for N86Y and R111A. The latter mutant has no Arg 111 at all, and can be considered as being inactive by definition. The hydrogen-bonding of Arg 251 to the ligand is robust, however the protein-ligand interaction of Ser 178 very low. In comparison to the data of the mutant L83V, for the mutant N86Y to be inactive, the interaction between Ser 287 and Asn 45 should be <50, which is the case.


Choose 720p HD for the best visualization. In case the place seems to be blank, zoom into the page by using Ctrl + Mouse-wheel and hit F5.

Molecular Dynamics analyses of the NR1-receptor (see movie) shows a result similar to mutant N86Y. However, in this case the Ser 187 - Asn 45 bonding (which is also present in the NR1 receptor) is active for 38% of the time. The boundary condition for this interaction to have influence on a weak protein-ligand bonding of Ser 178 or Arg 251, could be fixed at >25%, instead of >50% as mentioned earlier. If this is because the NR1-receptor has different properties than the GPR109A on the overal scale of the receptor, further research should figure out. The interaction of methyl nicotinate to the NR1-receptor clearly shows no affinity at all, which can also be concluded from the dramatic drop in binding energy.



Reengineering of the NR1 receptor


Furthermore, another interaction of Arg 251 with its neighboring amino acid, Glu 196, was investigated. This hydrogen-bond can be considered as being very robust, whereas the bonding is always 99% or higher, except of course for mutant R251A. It seems that this mutant may have a role to play in the conformational change of the receptor, but in the absence of the amino acid the receptor is still able to be activated, but many times less than the non-mutated wildtype receptor. From this can be suggested that Arg 251 indeed may be mutated in the reengineering assay, if further investigation turns out that the bonding between Ser 287 and Asn 45 does not play a large role in compensating the absense of Arg 251.



Discussion


While the method of this research was mainly based upon the experimental and MD data from Gerwert et.al.[2], not all MD requirements as done by them were taken into account. The main differences were the use of MD simulation program (instead of GROMACS, YASARA was used) and the simulation time was a 10-fold lower. The higher the simulation time, the more the simulation time is similar to the real protein-ligand interaction time.

One should also take in account that the models of the hGPR109A and hOR2AG1 receptors are an approximation of reality. By doing point-mutations on these receptors, the models can be refined. For this research only a limited amount of hGPR109A mutants were available. For example, a point-mutation in Ser 247 would give more insight on the affinity of the binding niche. In addition, the NR1 and BR4 models are derivatives of the hGPR109A and hOR2AG1, respectively. The alignments included no gaps, which means that the swapping method could give a reliable representation of the Snifferomyces receptors.

To have a more accurate model of the NR1 and BR4 receptors, point-mutations on these receptors and measurement of the response to certain ligands should be implemented in further research. By running MD simulations, interesting amino acids can be selected for mutation to examine whether the expected response is generated by the receptor.

Point-mutations on possible important amino acids like Asn 45 and Ser 287 should give an understanding whether the interaction between these two residues really act as a “fail-safe” mechanism that could still activate the G-alpha protein. Further research could give more insight into other possible residues that could be responsible for such a mechanism.

The simulation times were a crucial part of the structural modeling in YASARA. YASARA has a friendly user's interface and has many integrated features. However, the MD simulations are optimized for use on a 8-core computer. This means that a simulation of 10 ns takes about 7-8 days to completely simulate on a 16-core computer, let alone an 8-core computer. Also, by taking in account the error probability for every simulation, this process exhausts a lot of time.

Thankfully, the SARA institute was willing to help us and assist us in setting up the environment to use their HPC Cloud Server remotely from our office at Delft University. This gave us the opportunity to execute our simulations on a configurable amount of cores. By testing what amount of cores was the fastest to use (but not necessarily the most efficient) short simulations on 4, 8, 12, 16, 18, 20 and 24 cores were performed. The outcome was in favor of the 16-core computer. This means that a simulation of 10 ns would take 7-8 days. In contrast, a simulation of 1 nanosecond took about 2-2.5 days.

.

Follow-up


Some additional ideas on structural modeling had to be given a lower priority. An interesting approach would be to engineer an olfactory receptor for one of the other three chemical compounds (Figure 1b-d) which are found in the breath of a tuberculosis patient. In the same manner, the reprogramming of the binding niche could allow different derivatives of a certain compound (methyl isonicotinate instead of methyl nicotinate) allow to bind, of which a kind of universality could be investigated to predict a configuration of the binding niche to any ligand.

Another follow-up would be to investigate in silico how large the conformational change of the receptor is, and see how this correlates to the hydrogen bonding, RSMD and energy. This would require data on simulations done with and without a ligand. This could eventually lead to a value for the dissociation rate between the G-alpha protein and the receptor.

Because the hOR2AG1 is a receptor to a fruity molecule, there is a potential that it could sense other odors van fruits that hold an ester-configuration as well. Many of these odors are derivatives of each other, so the BR4 receptor could be reengineered in such a way that it can sense any fruity molecule.



References


[1] Syhre M, Chambers ST (2008) The scent of Mycobacterium tuberculosis. Tuberculosis. 88:317–323
[2] Gelis L, Wolf S, Hatt H, Neuhaus EM, Gerwert K (2012) Prediction of a Ligand-Binding Niche within a Human Olfactory Receptor by Combining Site-Directed Mutagenesis with Dynamic Homology Modeling. Angew. Chem. Int. Ed. 51:1274-1278
[3] Jasmina Minic, Marie-annick Persuy, Elodie Godel, Josiane Aioun, Ian Connerton, Roland Salesse, Functional expression of olfactory receptors in yeast and development of a bioassay for odorant screening, FEBS Journal (2005)
[4] Tunaru S, Lättig J, Kero J, Krause G, Offermanns S (2005) Characterization of Determinants of Ligand Binding to the Nicotinic Acid Receptor GPR109A (HM74A/PUMA-G). Mol Pharmacol. 68:1271-1280
[5]J Zhang, Y Zhang. GPCR-ITASSER: A new composite algorithm for G protein-coupled receptor structure prediction and the application on human genome. 2011