Team:Bielefeld-CeBiTec/Model

Modeling

Short Summary

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. In practice, our modeling consisted of the following steps:
Step Software/Method Meaning
1. Ligand Preparation Manually via Avogadro Due to the novelty of our amino acid, no information on the ligand is available in databases. Therefore, all information has to be provided manually and then generate a conformer ensemble, containing for example all energetically useful arrangements of atoms within the molecule.
2. Scaffold categorization ROSETTA protocol The scaffold describes the rough layout of the synthetase. We downloaded the scaffold 1j1u, the aaRS of Methalonococcus janischii as a template, and then relaxed its structure to improve the outcome of the ROSETTA algorithm.
3. Set simulation constrains Manually via ROSETTA Constrains with regards to possible mutations of the synthetase ensure that the generated sequences fit to the amino acid. For example, we constrained the distance between certain atoms and their angle to a range optimal for hydrogen bonds.
4. Enzyme Matching ROSETTA protocol ROSETTA combines information about the ligand and constrains to find possible hydrogen bonding partners and propose the shape of the scaffold within the set constraints.
5. Enzyme Design ROSETTA protocol An algorithm uses the information from the previous step and information on the ligand to simulate the mutation process and generate sequences for optimized scaffolds with corresponding scores as measures of fit.
6. Evaluate results in silico Manually We evaluate the visual output and the score values and order the sequences with the most promising results via gene synthesis.
7. Evaluate results in vivo Manually The synthetases are validated in the lab with the corresponding ncAA via a positive-negative selection system.
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

Introduction

Overview

As part of our iGEM project, we are faced with the challenge of adapting the tRNA synthetase to non-canonical amino acids. For this purpose, modelled possible candidates for synthetases as a preparation for carrying out a positive-negative selection according to Schulz [] in the laboratory. Due to the rapid development in the field of protein and molecular structure analysis, there has been an increase in the availability of molecular 3D structure data. These data are organized in publicly available databases which provide a foundation for the modeling and simulation of chemical-biological processes in bioinformatics. As our non-canonical amino acid has been synthetized by ourselves, no such comprehensive information is available, yet. However, information of similarly structured amino acids can potentially serve as a basis for our modeling. As evaluating an expanded genetic code is a complex task, the practical laboratory work of our project is supplemented by a theoretical approach, involving modeling, simulation, and evaluation on the computerin silico. Specifically, we focused on simulation to designaimed at designing an aaRS tRNA synthetase for the new non-canonical amino acid CBT-ASP. Additionally to CBT, we also simulated the evolution process for the non-canonical amino acid NPA as a validation of our modeling procedure, altough as synthases for this ncAA are known and thus comparable to our in silico result, we can evaluate our modeling procedure. (Vielleicht hier ein wenig schöner) For this purposeOur core challenge was to evolve, the binding pocket must be evolved in a manner which effectively charges the tRNA with the amino acid, thus also recognizing this amino acid specifically.

Method

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"

ROSETTA Enzyme Design

Overview

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. The enzyme design algorithm basically is summarized in Fig. B

Figure (2): Flowchart Enzym Design Protocol

Matching Step

The meaning of the matching step is to match the amino acids which constrains to the ligand, following specific constrains which ensure that the result is sensible and feasible. For this, ROSETTA analyzes the structural formula of the non- canonical amino acid and offers the possible hydrogen binding partners.
Matching step inputs
For the matching step, the following input-files are needed:
  • a “.params”-file specifying information about the ligand
  • a “.pdb”-file providing a rough scaffold layout
  • a “.cst”-file to define the bindings between ligand and scaffold
  • a “.pos”-file to define the positions of the amino acids of the scaffold
  • a “.flags” file to control all inputs. These files are necessary, as they describe the ligand and backbone and specify the parameters of the algorithm
To read about each file in further detail, please click the technical detail button below:
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Matching step outputs
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.
Our results for this step
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 designed the ligands manually by usingin Avogadro, and for the .cst-file, we choose the default matching algorithm for simulations of both amino acids.

Design Step

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)
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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.
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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.
Design step outputs
The output for the design step is a “.pdb”-file containing the mutated scaffold and a “.score”-file. For every PDB-file, a line in the score-file is generated, so it is easy to evaluate the given structure. The first score in the file is the total score of the model. After that, the number of hydrogen bonds in the protein as a whole and in the constraints is listed, followed by the number of dismissed polars in the catalytic residues as well in the whole protein and in the constraints. See the technical details below for a full overview of the output information
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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 algrithm 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.
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.
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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

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