Difference between revisions of "Team:Bielefeld-CeBiTec/Model"

On this page, we describe our main modeling project, which was integral for our whole project. However, besides this complex modeling, we also conducted and applied several straight-forward stochastic and statistical models to support and guide our laboratory work. These modeling projects are briefly described here; for further information, please check the corresponding link leading to the part of our project the model has been an element of.
Discriminant function model for the ICG prediction:
We conducted a discriminant function analysis for the recognition of which base – natural or unnatural – is present at a specified position of a base sequence. This model is part of our ICG model software, found here.
Calculation of an effective library size for the selection system:
We used a combination of combinatorics and statistics to calculate the optimal library size for the selection process, such that it is expected to contain all possible sequence mutations, and therefore easily all possible resulting amino acids, at least once. This calculation is part of the translational system, found here.
Comparison of mRFP production for the positive selection system (BBa_K2201373):
We modeled and visually compared the mRFP production over time for the normal signaling and the enhanced signaling circuit of the positive selection system. The system and plot can be found here.

As our project explores possibilities of an expanded genetic code via unnatural bases and non-canonical amino acids, we set out to complement our lab work via modeling of novel amino acyl tRNA synthetases (aaRS) for a non-canonical amino acids we synthetized in the lab. In order to incorporate non-canonical amino acids into proteins via the translational process, the aaRS has to attach the amino acid to the respective tRNA. Thus, we designed aaRS sequences which were meant to link our own non-canonical amino acid to a fitting tRNA. As a result, we obtained a couple of sequences of possible aaRS candidates, which we evaluated, based on a ROSETTA score, and ordered via gene synthesis. n practice, our modeling consisted of the following steps:

As a result, we obtained a couple of sequences of possible aaRS candidates, which we evaluated, based on a ROSETTA score, and ordered via gene synthesis. Figure A describes our modeling project as a whole

We used the open-source software "Rosetta" for the main part of our modeling project, which was introduced at the University of Washington by David Baker in 1997, initially in the context of protein structure prediction. Since then, Rosetta has grown to include numerous modules and is currently widely used in research. In our application, we focus on the Rosetta module called the "Rosetta Enzyme Design Protocol"

Since the non-canonical amino acid synthesized in the laboratory is completely novel, there is no corresponding tRNA synthetase which can load the tRNA, yet. For this reason, we use the enzyme design protocol to design the binding pocket in a way that allows it to form an effective and specific enzyme. The protocol consists of two main steps: matching and designing (Richter et al., 2011) The enzyme design algorithm basically is summarized in Fig. B

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.
1. Optimizing the catalytic interactions
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).
For the latter alternative, residues are automatically categorized by their location of the Calpha.
TECHNICAL DETAILS

• residues that have their Calpha within a distance cut1 angstroms of any ligand heavyatom will be set to designable
• 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
• 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
• 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
• all residues not in any of the above 4 groups are kept static.

2. Cycles of sequence design and minimazation within constrains
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.
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).

Design step inputs

The following input files are relevant for the design procedure:

• “.pdb”-file generated in the matching step
• “.cst”-file for the ligand
• “.params”-file for the ligand and the scaffold
• “.flags” to coordinate the inputs

For further information on these files, please refer to step 2 above.
We choose our synthetases because of a good total score and a good ligand score. We checked the corresponding PDB-files, and rated the ligand and the binding pocket as satisfying, so that the ligand assumedly does not collide with residues in the near environment. The total scores for CBT are not as good as the scores for NPA. However, the ligand scores are acceptable in both cases. A visual evaluation confirms that the ligand fits into the binding pocket.
Our results for this step
We used this algorithm to simulate the evolution of the tyrosyl-tRNA with the amino acids Nitrophenylalanine and CBT-ASP.
NPA simulation:
We created one .cst-file-block for the nitrogroup of NPA. Since there are two oxygen-atoms in the nitrogroup, we defined two atom nametags. As several possibilities are useful, we defined two possible constraint partners for the hydrogen bonds. The first is asparagine (N) or glutamine (Q) and the second is glycine (G). We set the possible distance to 2.8 A, as it is the optimal distance for hydrogenbonds, and a tolerance level of 0.5 A. We set the angles to 120° with a tolerance of 40°, as recommended by Florian Richter during our talk in cologne. The torsion angles were set to 180° with a tolerance of 180° and a penalty of 0, such that the torsion angles can rotate completely freely.(Richter, unpublished data)
CBT-ASP simulation:
CBT-ASP can build hydrogen bonds in two ways. The first is a weak hydrogen bond on the sulphur atom and the other possibility is a normal hydrogen bond on the nitrogen (N2) after the C-gamma. We wrote three cst-files, one for a possible bond with sulpur, one for a possible bond with nitrogen, and one for both bonds. As possible corresponding amino acids, we chose serine, threonine, tyrosine, asparagine, glutamine, and glycine.
It is recommended to write a “.flags”-file, because there are several input- parameters to be defined, but it is also possible to define them via console user interface.
For the categorization of the scaffold, we chose the automatic determination and set the following cuts: cut1: 6 A, cut2: 8 A, cut3: 10 A and cut4: 12 A, like the baker-lab commonly used.

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.

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.