Difference between revisions of "Team:Heidelberg/Toolbox"

Line 471: Line 471:
 
                                             <h1 style="text-align: center !important; color:##393939 !important; font-weight: 400 !important;padding: 0px !important; margin: 0px !important">
 
                                             <h1 style="text-align: center !important; color:##393939 !important; font-weight: 400 !important;padding: 0px !important; margin: 0px !important">
 
                                             1. Optimize parental sequence / gene pool</h1>
 
                                             1. Optimize parental sequence / gene pool</h1>
−
                                       
+
                                            <div class="content col-md-12 col-lg-12" style="padding-top: 10px">With the <a href="https://2017.igem.org/wiki/index.php?title=Team:Heidelberg/Software">AiGEM</a> (Artificial Intelligence for Genetic Evolution Mimicking) software suite we provide powerful software tools for <i>in silico</i> evolution. Applying our genetic algorithm, <a href="https://2017.igem.org/wiki/index.php?title=Team:Heidelberg/Software/GAIA">GAIA</a>, we <a href="https://2017.igem.org/wiki/index.php?title=Team:Heidelberg/Validation#gusgal">fully in silico evolved a \(\beta\)-glucuronidase towards \(\beta\)-galactosidase function</a> and <a href="https://2017.igem.org/wiki/index.php?title=Team:Heidelberg/Validation#lac">derived \(\beta\)-lactamase variants exceeding wildtype activity</a>.<br>
 +
We thus strongly recommend the application of GAIA prior to <i>in vivo</i> evolution. Through optimization of the starting sequence and the acompanying reduction of the required library size the time until phage pool convergence can be tremendously reduced.<br>
 +
GAIA is written in python and readily available on our <a href="https://github.com/igemsoftware2017/AiGEM_TeamHeidelberg2017/blob/master/GAIA/GAIA_README.md">github repo</a>.</div>
  
  

Revision as of 00:32, 2 November 2017

Welcome to our
Toolbox Guide!

To enable rapid design and simple cloning of APs, we created a software tool, so that cloning can easily be planned. All parts that can be chosen are available in pSB1C3 backbones from the registry. Just follow the instructions and create your own fully customized AP.

Ready to PREDCEL?

Have you already chosen a protein to be evolved?

Which approach do you pursue by using our evolutionary toolbox?

Are you sure your protein is safe to be evolved?

Do you know which equipment is required to safely work with phages?

Did you calculate all required parameters on our Integrated Modeling page?

Did you already engage with your local community to discuss your plans?

Did you consider possible consequences of your planned PREDCEL experiment?

Now it´s on you!

When you talk about PREDCEL with your colleagues, friends and local communities, you can spread the idea of making directed evolution experiments safe and to use it responsibly for addressing urgent human needs

1. Optimize parental sequence / gene pool

With the AiGEM (Artificial Intelligence for Genetic Evolution Mimicking) software suite we provide powerful software tools for in silico evolution. Applying our genetic algorithm, GAIA, we fully in silico evolved a \(\beta\)-glucuronidase towards \(\beta\)-galactosidase function and derived \(\beta\)-lactamase variants exceeding wildtype activity.
We thus strongly recommend the application of GAIA prior to in vivo evolution. Through optimization of the starting sequence and the acompanying reduction of the required library size the time until phage pool convergence can be tremendously reduced.
GAIA is written in python and readily available on our github repo.

2. Get phage

3. Add selection pressure

For any method of directed evolution to culminate in an improved protein, a means of applying selective pressure is needed. Thus, adding components capable of enforcing selection in PACE and PREDCEL is one of the most crucial steps in construct design. For directed evolution experiments selective pressure may need to be fine-tuned by using different ORIs, promoters, ribosome binding sites and backbones. In this section of the toolbox guide, all those components will be set up for you to suit your particular project. To get an idea of how selection could be enforced in your projects, take a look at our examples:

MAWS 2.0 - in silico riboswitch design for selective pressure.

A simple and sure-fire way to couple gene expression to a product of enzyme catalysis is to introduce a riboswitch for that particular product. This is a short RNA sequence either trapping or releasing the ribosome binding site in the presence of a small molecule. Back in 2015 the Heidelberg iGEM Team developed a software suite (MAWS - making aptamers without SELEX) to predict such riboswitches for arbitrary small molecule targets. This year we use and provide new-and-improved distribution of MAWS. We predict and validate synthetic riboswitches generated using MAWS 2.0 to couple gene expression to enzyme activity. This establishes riboswitches predicted in silico as a valuable component of any directed evolution project seeking to evolve novel biocatalytic properties.

MAWS 2.0

OptoSELECT - Light-dependent selective pressure

The generation of proteins with radically altered or highly specific new activities is a major goal in the field of directed evolution. Modulation of selection stringency is indispensable for the optimization of proteins, which in their native form exhibit a lack of activity for the required action. To further extend the possibilities of PACE/PREDCEL based protein optimization and to evolve novel activities without the use of intermediate evolutionary steps, we provide an optogenetic modulator of selection stringency. The variation of stringency is based on the blue light-dependent transcription factor EL222 and a bidirectional promoter system that can induce or repress the expression of geneIII upon blue light irradiation in a non-toxic, rapidly delivered, and reversible manner.

OptoSELECT
Plasmid

Plasmid

1. First, choose your preferred backbone from the pull down menu. We provide three different origins of replication that differ in their copy number and therefore affect the selection stringency, your AP will have. Be aware that the origin of replication should be compatible to the origin of replication of all other plasmids you plan to use. In most of the cases it is best to combine your origin of replication with an ampicillin resistance, but we provide other antibiotic resistances for special applications as well.
2. Second, decide which RBS you need for control of geneIII translation. We offer six different RBS with different strengths. For further information look...
3. Next, decide, which reporter you would like to choose. A set of different fluorescent and luminescent reporters is available

Resistance & Ori

RBS + geneIII

RBS + Reporter

Activator & Promoter
4. When all standard parts are defined, the promoter and associated regulatory sequences must be set. Decide between the given options. Enter either an own sequence (as text or upload the file) or use one of the provided standard promoters that were already used by our team. Notice that the length of this part should not be below 200 bp. Ensure, that your sequence does not include a RBS.

Additional sequence

5. Finally, decide which additional gene you would like to express on your AP. Most circuits that are used in PACE, need at least one additional protein, like split-proteins, chaperone or interacting factors. Again, it is possible to enter the desired sequence or choose one of the provided genes. Make sure, you enter a whole expression cassette, with promter, RBS, CDS and terminator.
Create Plasmid

4. Induce mutagenesis

5. Optimize conditions

PACE and PREDCEL require well chosen conditions to yield the best results. Use our Interactive Webtools to calculate parameters like Glucose Concentration, Arabinose Concentration and Medium Concumption online, or obtain our phage titer Model and use it to reduce the risk of washout.
When trying new conditions for a PACE experiment check if the setup is vulnerable to contaminations To make monitoring the experiment by sequencing less expensive and to save you time, we provide a tool that calculates the number of sequences that needs to be sequenced to find mutations with a certain probability.