Difference between revisions of "Team:Rice/Model"

 
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<h1 style = "background-color:#9bcfa7!important;"> Motivation <h1>             
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                    Existing RNA thermometers operate around 37°C and do not exhibit significant conformational changes between 25°C and 30°C. As most plants will die
+
<p>Existing RNA thermometers are designed to switch at around 37°C and do not exhibit significant conformational changes between 25°C and 30°C. As most plants cannot be expected to grow at 37°C, it was necessary to design thermometers that experienced conformational changes between 25°C and 30°C. We chose to design thermometers by finding optimal candidates using a genetic algorithm and RNA folding software, as the complexity of RNA folding thermodynamics otherwise makes it difficult to manually mutate existing thermometers to meet our performance criteria or devise them <i>de novo</i>. As the figures below show, <i>A. thaliana</i> grows best between 18–23°C and <i>P. putida </i> is typically grown in lab at 30°C. Along with the need for heterologous gene expression only at higher, stress-inducing temperatures to mediate a stress-mitigating response, we reasoned the need to design RNA thermometers that are optimized to melt at 30°C.  </p>
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                    at 37°C, it was necessary to design thermometers that experienced conformational change between 25°C. We chose to design thermometers by finding optimal  
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                    candidates using a genetic algorithm, as the complexity of RNA folding made it difficult to rationally design thermometers.
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<img class="img-responsive" style="width:100%" src="https://static.igem.org/mediawiki/2019/e/e0/T--Rice--arabidopsisgrowingtemp.svg"/>
 
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   <img class="img-responsive" style="width:100%" src="https://static.igem.org/mediawiki/2019/d/dd/T--Rice--putidagrowingtemp.svg"/>     
 
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</div><h2 style = "background-color:#26849cff!important;"> RNA thermometer design </h2> </br>
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<p>In essence, RNA thermometers are a form of temperature dependent translational regualtion. At low temperatures, there is a higher probability of more base pairs forming what is known as a "stem-loop structure". At higher temperatures, the thermometers "melt", meaning there is a decreased likilook of base pairs forming. </p>
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</div><h1 style = "background-color:#1f9ec1!important;"> How RNA Thermometers Regulate Translation</h2> </br>
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    <p>In essence, RNA thermometers are a form of temperature dependent translational regulation. At low temperatures, there is a higher probability of more base pairs forming what is known as a "stem-loop structure". At higher temperatures, the thermometers "melt", meaning there is a decreased likelihood of base pairs forming. </p>
 
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  <img class="img-responsive" style="width:100%" src="https://static.igem.org/mediawiki/2019/8/8c/T--Rice--thermometerlocky.svg"/>   
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<p>The stem-loop structure of the thermometer was created by taking the complement of the ribosome binding site(RBS) and surrounding the nucleotides and introducing mutations into the sequence. The resulting altered sequence is known as the variable region. Mutating this region serves a two prong purpose. First off, it ensures that the RBS will be locked into the structure of the thermometer, rendering it inaccessible to the ribosome at low temperatures. Secondly, it allows us to optimize for the targeted melting temperature. </p>
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 +
 
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</div><h1 style = "background-color:#f6c374!important;">RNA Thermometer Design</h2> </br>
 +
        </div>
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<p>The stem-loop structure of the thermometer was created by taking the complement of the ribosome binding site (RBS) and surrounding the nucleotides and introducing mutations into the sequence. The resulting altered sequence is known as the variable region. Mutating this region serves a two prong purpose. First off, it ensures that the RBS will be locked into the structure of the thermometer, rendering it inaccessible to the ribosome at low temperatures. Secondly, it allows us to optimize for the targeted melting temperature. </p>
 
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            <h2>Percentage base pair optimization algorithm</h2>
 
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            <ol>
 
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                <li>
 
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                    <p>
 
−
                        For each base of the complement of the context containing the RBS, create three other
 
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                        permutations which have that base mutated. All of these permutations combined form the initial
 
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                        population. The baseline is defined as the full sequence containing the context before the
 
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                        variable region, the variable region which is the complement of the RBS-containing context, and
 
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                        the RBS-containing context.
 
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                    </p>
 
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                </li>
 
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                <li>
 
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                    <p>
 
−
                        Calculate the base pairing probabilities at the two given temperatures for each test sequence
 
−
                        using the NuPack command <code>pairs -T TEMP -pseudo -material rna sequencename.</code>
 
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                    </p>
 
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                </li>
 
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                <li>
 
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                    <p>
 
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                        The output of the command consists in part of a list of every base and the base(s) which it has
 
−
                        a > 0.001 probability of base pairing with by position number. Find the base pairings where one
 
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                        of the bases is in the RBS-containing region and add up the probabilities corresponding to that
 
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                        base pairing. The nature of this NuPack command should prevent duplicates. Then, subtract the
 
−
                        probability that these bases are unpaired.
 
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                    </p>
 
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                </li>
 
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                <li>
 
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                    <p>
 
−
                        Subtract the number of base pairings at 30°C from the number of base pairings at 25°C. We want
 
−
                        to maximize the number of RBS-base pairings that disappear as the temperature increases from
 
−
                        25°C to 30°C (for reference, the command I use for the minimum free energy calculation is
 
−
                        <code>mfe -T TEMP -pseudo -material rna sequencename</code>)
 
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                    </p>
 
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                </li>
 
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                <li>
 
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                    <p>
 
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                        Using the library DEAP, select 50 individuals through the tournament selection method.
 
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                    </p>
 
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                </li>
 
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            </ol>
 
 
         </div>
 
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Latest revision as of 03:52, 22 October 2019

Motivation

Existing RNA thermometers are designed to switch at around 37°C and do not exhibit significant conformational changes between 25°C and 30°C. As most plants cannot be expected to grow at 37°C, it was necessary to design thermometers that experienced conformational changes between 25°C and 30°C. We chose to design thermometers by finding optimal candidates using a genetic algorithm and RNA folding software, as the complexity of RNA folding thermodynamics otherwise makes it difficult to manually mutate existing thermometers to meet our performance criteria or devise them de novo. As the figures below show, A. thaliana grows best between 18–23°C and P. putida is typically grown in lab at 30°C. Along with the need for heterologous gene expression only at higher, stress-inducing temperatures to mediate a stress-mitigating response, we reasoned the need to design RNA thermometers that are optimized to melt at 30°C.

How RNA Thermometers Regulate Translation


In essence, RNA thermometers are a form of temperature dependent translational regulation. At low temperatures, there is a higher probability of more base pairs forming what is known as a "stem-loop structure". At higher temperatures, the thermometers "melt", meaning there is a decreased likelihood of base pairs forming.

RNA Thermometer Design