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| <article> | | <article> |
| The output generated in the matching step is the layout of the scaffold as well as one or more states of the amino acid which enable interaction with the ligand. This information is stored as a “.pdb” file and becomes part of the input for the design step. </br> | | The output generated in the matching step is the layout of the scaffold as well as one or more states of the amino acid which enable interaction with the ligand. This information is stored as a “.pdb” file and becomes part of the input for the design step. </br> |
− | <p><b>Our results for this step </b></p> | + | <h4>Our results for this step </h4> |
− | We used the “1j1u”-scaffold from PDB for our matching step. The “1j1u.pdb”-file contains the Tyrosyl-tRNA-synthetase, which is labeld under “Chain A”, the orthogonol tRNA under “Chain B” and the natural ligand Tyrosyl. For our project, we deleted the natural ligand and “Chain B”, because it was not neccerary to change their structure or sequence and it was a way to save computer time and power. | + | We used the <a href="http://www.rcsb.org/pdb/explore.do?structureId=1j1u">“1J1U”</a>-scaffold from PDB for our matching step. The “1J1U.pdb”-file contains the Tyrosyl-tRNA-synthetase, which is labeld under “Chain A”, the orthogonol tRNA under “Chain B” and the natural ligand Tyrosyl. For our project, we deleted the natural ligand and “Chain B”, because it was not neccerary to change their structure or sequence and it was a way to save computer time and power. |
| | | |
− | We designed the ligands manually by using Avogadro, and for the .cst-file, we choose the default matching algorithm for simulations of both amino acids. | + | We designed the ligands manually by using <a href="https://avogadro.cc/">Avogadro</a>, and for the .cst-file, we choose the default matching algorithm for simulations of both amino acids. |
| </article> | | </article> |
| </div> | | </div> |
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| <h4> Design Step</h4> | | <h4> Design Step</h4> |
| <article> | | <article> |
− | <p>The design step applies an algorithm such that the binding pocket and the near environment are mutated and the remaining scaffold is repacked. Additionally, a badness-of-fit score is generated which indicates how well the mutation fits the amino acid. For every file from the matching step, a model with a score and a “.pdb-file” will be generated, specifying where the sequence can be located, and the 3D-structure can be analyzed. Notably, the amino acid structure can be extracted separately. | + | <p>The design step applies an algorithm such that the binding pocket and the near environment are mutated and the remaining scaffold is |
| + | repacked. Additionally, a badness-of-fit score is generated which indicates how well the mutation fits the amino acid. For every file from the |
| + | matching step, a model with a score and a “.pdb-file” will be generated, specifying where the sequence can be located, and the 3D-structure can be |
| + | analyzed. Notably, the amino acid structure can be extracted separately. |
| The following section describes the structure of the design step. For further details on each step, click the technical details button. </br> | | The following section describes the structure of the design step. For further details on each step, click the technical details button. </br> |
| 1. Optimizing the catalytic interactions </br> | | 1. Optimizing the catalytic interactions </br> |
− | For the first alternative, the file can be generated either by the Rosetta standard or a manually created .”res”- file. For more details, we refer to the Rosetta documentation. (link:https://www.rosettacommons.org/manuals/archive/rosetta3.5_user_guide/d1/d97/resfiles.html). </br> | + | For the first alternative, the file can be generated either by the Rosetta standard or a manually created .”res”- file. For more details, we refer to the |
− | For the latter alternative, residues are automatically categorized by their location of the Calpha.</p> | + | <a href="https://www.rosettacommons.org/manuals/archive/rosetta3.5_user_guide/d1/d97/resfiles.html">Rosetta documentation</a>. </br> |
| + | For the latter alternative, residues are automatically categorized by their location of the Cα;lpha;.</p> |
| <a class="hidden-expand">TECHNICAL DETAILS</a></article> | | <a class="hidden-expand">TECHNICAL DETAILS</a></article> |
| <article class="hidden-block"> | | <article class="hidden-block"> |
| Residues are catagorized as follows: </br> | | Residues are catagorized as follows: </br> |
| <ul> | | <ul> |
− | <li> residues that have their Calpha within a distance cut1 angstroms of any ligand heavyatom will be set to designable | + | <li> residues that have their Cα within a distance cut1 angstroms of any ligand heavyatom will be set to designable |
− | <li> res that have Calpha within a distance cut2 of any ligand heavyatom and the Cbeta closer to that ligand atom than the Calpha will be set to designable. cut2 has to be larger than cut1 | + | <li> res that have Cα within a distance cut2 of any ligand heavyatom and the Cβ closer to that ligand atom than the Calpha will be set to designable. cut2 has to be larger than cut1 |
− | <li> res that have Calpha within a certain distance cut3 of any ligand heavyatom will be set to repackable. cut3 has to be larger than cut2 | + | <li> res that have Cα within a certain distance cut3 of any ligand heavyatom will be set to repackable. cut3 has to be larger than cut2 |
− | <li>res that have Calpha within a distance cut4 of any ligand heavy atom and the Cbeta closer to that ligand atom will be set to repackable. cut4 has to be larger than cut3 | + | <li>res that have Cα within a distance cut4 of any ligand heavy atom and the Cβ closer to that ligand atom will be set to repackable. cut4 has to be larger than cut3 |
| <li> all residues not in any of the above 4 groups are kept static. | | <li> all residues not in any of the above 4 groups are kept static. |
| </ul> </br> | | </ul> </br> |
| </article> | | </article> |
| <article> <p>2. Cycles of sequence design and minimazation within constrains </br> | | <article> <p>2. Cycles of sequence design and minimazation within constrains </br> |
− | To optimize the structure we used applied an iterative optimization algorithm. This algorithm mutates all residues from the backbone, which are not part of the catalytic center, to alanine, and a small energy function refraction will place the ligand in an optimal position to the backbone. </br> | + | To optimize the structure we used applied an iterative optimization algorithm. This algorithm mutates all residues from the backbone, which are not part |
| + | #of the catalytic center, to alanine, and a small energy function refraction will place the ligand in an optimal position to the backbone. </br> |
| </p> <a class="hidden-expand">TECHNICAL DETAILS</a></article> | | </p> <a class="hidden-expand">TECHNICAL DETAILS</a></article> |
| <article class="hidden-block"> | | <article class="hidden-block"> |
− | For this approach, bb_min and chi_min allow for backbone flexibility and the rotation of the torsions. An alternative for this minimization step is the Monte Carlo rigid body ligand sampling. For further information on this method, we refer to the ROSETTA documentation (https://www.rosettacommons.org/manuals/archive/rosetta3.5_user_guide/d6/dbc/enzyme_design.html). </br> | + | For this approach, bb_min and chi_min allow for backbone flexibility and the rotation of the torsions. An alternative for this minimization step is the |
| + | Monte Carlo rigid body ligand sampling. For further information on this method, we refer to the <a href="https://www.rosettacommons.org/manuals/archive/rosetta3.5_user_guide/d6/dbc/enzyme_design.html">ROSETTA documentation</a>. |
| + | </br> |
| </article> | | </article> |
− | <p><b>Design step inputs </b></p> | + | <h4>Design step inputs </h4> |
| The following input files are relevant for the design procedure: | | The following input files are relevant for the design procedure: |
| <ul> | | <ul> |
Results
Results in silico
We used this algrithm to simulate the evolution of the tyrosyl-tRNA with the amino acids Nitrophenylalanine and CBT-ASP
We obtained 13 synthetase sequences for CBT-ASP, and 43 sequences for NPA, which fit well into the binding site according to the ROSETTA score.
Results in vivo
In order to test the functionality and specificity of our modeled aaRS, we translated a selection of the most promising amino acid sequences into DNA sequences optimized for E.coli and ordered them via gene synthesis. We then used a positive-negative selection system for characterization. The experiment proceeds as follows:
Due to problems with regards to the protein- and salt-concentration, we retransformed the gensyntheses which had been cloned into pSB1C3. In a next step, these syntheses were cotransformed in E.coli(BL21) with our positive selection plasmid.
With regards to CBT2, only the original colony could be transformed. From CBT4 and CBT5, were used each originally isolated clone and its retransformed counterpart.
Due to the IPTC-induced promoter, we used variants without IPTG, and with 5 mM, 10 mM, and 15 mM added IPTS for all plasmids for the kanamycine resistance.
We chose additional variants with regards to the antibiotics; one variant each of kanamycine, kanamycine and chloramphenicole, and kanamycine, chloramphenicole and tetracycline. The number of resulting colonies for each variant are summarized in figure X. Our in vivo results show that our in silico designed enzymes did not lead to a loss of functioning.
We ordered seven synthetase sequences of NPA via IDT, and four synthetase sequences of CBT-Asparagine by courtesy of Genscript, where we had previously
won a grant of 500€. Due to problems on the part of IDT, the sequences for NPA could not be synthetized. We still provide the sequences for further use below.
All further descriptions therefore refer to the synthetase sequences for CBT-Asparagine.
Unfortunately, we were not able to amplify the four sequences or clone them into the detection system pSB3T5. A test digestion revealed that the length of the
sequence in the plasmid puk57, which was the plasmid delivered by Genscript, did not correspond to the sequence length ordered. Therefore, we disregarded synthetase candidate number 1
for further tests.
For the remaining three sequences, we instead utilized the positive-negative selection plasmids for validation of our syntheses.
After the first, positive selection cycle, colonies formed only if the non-canonical amino acid was present.
Thus, through the positive selection, we could show that the synthetase did not lead to the loss of functioning of the enzyme.
To show specificity, we conducted a negative selection as well. We managed to clone the synthetases into the negative selection plasmid,
but were not able to verify this selection cycle. Therefore, further tests are needed to validate the specificity of the synthetases.
References
Liu, W., Brock, A., Chen, S., Chen, S., Schultz, P. G. ,(2007). Genetic incorporation of unnatural amino acids into proteins in mammalian cells. Nature methods, 4(3), 239-244.
Richter, F., Leaver-Fay, A., Khare, S. D., Bjelic, S., Baker, D. (2011). De novo enzyme design using Rosetta3. PloS one, 6(5): e19230.
Simons, K. T., Kooperberg, C., Huang, E., Baker, D. (1997). Assembly of protein tertiary structures from fragments with similar local sequences using simulated annealing and Bayesian scoring functions. Journal of molecular biology, 268(1), 209-225.