Team:TU-Delft/Modeling/StructuralModeling

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<h6>Figure 1 Chemical compounds present in the breath of a TB-patient<sup>[1]</sup>. Methyl nicotinate (a), methyl phenylacetate (b), methyl p-anisate (c) and o-phenylanisole (d)</h6>
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<h6>Figure 1 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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<p>By reprogramming the binding niche of the niacin receptor, also known as GPR109A, an olfactory receptor for methyl nicotinate could be engineered.</p>  
<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.<sup>[2]</sup> By mutating more amino acids in the binding cavity, a binding niche for methyl phenylacetate could be constructed.</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>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. <sup>[2]</sup> The protein modeling 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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<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] The protein modeling 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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<p>Below is the modeling approach described, applied to all olfactory receptors used in our research, unless written otherwise.</p>
<p>Below is the modeling approach described, applied to all olfactory receptors used in our research, unless written otherwise.</p>
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<p>1. Firstly, the in silico simulations have to be redone in YASARA in order to produce similar results as reported. <sup>[2]</sup> 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 the homology build macro.
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<p>1. Firstly, the in silico 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 the homology build macro.
Define the binding cavities of the receptor and see if it is similar. Run a 10 ns MD without any ligand and see if the binding niche shrinks in volume.</p>  
Define the binding cavities of the receptor and see if it is similar. Run a 10 ns MD without any ligand and see if the binding niche shrinks in volume.</p>  

Revision as of 13:31, 23 September 2012

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Structural Modeling



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.

The main chemical compound 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).


Figure 1 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)


By reprogramming the binding niche of the niacin receptor, also known as GPR109A, an olfactory receptor for methyl nicotinate could be engineered.

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.

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] The protein modeling 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.



Modeling approach


Below is the modeling approach described, applied to all olfactory receptors used in our research, unless written otherwise.

1. Firstly, the in silico 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 the homology build macro. Define the binding cavities of the receptor and see if it is similar. Run a 10 ns MD without any ligand and see if the binding niche shrinks in volume.

By docking the amyl butyrate in the binding niche of the hOR2AG1 olfactory receptor, and providing the same results as described, gives a firm 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.

2. The rGPR109A and hOR1G1 receptors with their rI7 flanks were modeled in the same manner as with the hOR2AG1 protein. The original rGPR109A, hGPR109A, hOR1G1 and I7 receptors underwent the same procedure. Also, the hOR2AG1 receptor model was used for further analyzes. After undergoing step 1, other ligands were used for all proteins. For the rGPR109A/rI7, rGPR109A and hGPR109A models the following ligands were used (figure 2).


Figure 2 Configurations of used ligands for GPR109A derivatives. Niacin (A1 = C, A2 = N, A3 = OH), methyl nicotinate (A1 = C, A2 = N, A3 = OMe) and methyl isonicotinate (A1 = N, A2 = C, A3 = OMe)

The ligands for hOR1G1/rI7 and hOR1G1 are shown in figure 3.

Figure 3 Configurations of used ligands for OR1G1 derivatives. Isoamyl acetate (A1 = CMe, A2 = C), buthyl acetate (A1 = C, A2 = C) and isoamyl propionate

The I7 model will have the ligands hexanal and octanal (figure 4) docked in its binding niche.

Figure 4 Configurations of used ligands for I7 receptor. Hexanal (A = C), nonanal (A = CMe) and octanal (A = CEt)


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.

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.



Results


In order to test if this is the case, different small molecules that are a derivative of the original ligands should be docked in the correct receptor. By testing them in silico and experimentally, we will be able to check if the novel ligand-binding niche is indeed as specific as we would like it to be.



Future follow-ups


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.

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.

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] 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

[4] Kurtz AJ, Lawless HT, Acree TE (2010) The Cross-Adaptation of Green and Citrus Odorants Chem. Percept. 3:149–155