Difference between revisions of "Team:Aalto-Helsinki/Future"
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<ul id="sidenav" class="nav nav-stacked"><!-- nav-pills if we want rounded corners --> | <ul id="sidenav" class="nav nav-stacked"><!-- nav-pills if we want rounded corners --> | ||
<li><a href="#" data-scroll="pathway"><h3>Propane pathway</h3></a></li> | <li><a href="#" data-scroll="pathway"><h3>Propane pathway</h3></a></li> | ||
+ | <li><a href="#" data-scroll="homolog"><h3>Searching homologs for CAR</h3></a></li> | ||
<li><a href="#" data-scroll="cyano"><h3>Propane out of sunlight, water and thin air</h3></a></li> | <li><a href="#" data-scroll="cyano"><h3>Propane out of sunlight, water and thin air</h3></a></li> | ||
<li><a href="#" data-scroll="safety"><h3>Improving safety</h3></a></li> | <li><a href="#" data-scroll="safety"><h3>Improving safety</h3></a></li> | ||
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</section> | </section> | ||
+ | <section id="homolog" data-anchor="homolog"> | ||
+ | <h2> Searching homologs for CAR </h2> | ||
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+ | <p>As <a href = https://2015.igem.org/Team:Aalto-Helsinki/Modeling_propane>our kinetic model</a> identified, CAR (Carboxylic acid reductase from <i>Mycobacterium marinum</i>) is the most noteworthy bottleneck of the propane pathway after FadB2 and ADO. How can we then make the propane production more productive if CAR interferes? One method is to search homologs, which have the same ancestor gene and hopefully also have the same function as CAR, but possible have better performance on the kinetic level. We are focusing our efforts to find homologs, because enzyme analogs, <i>ie.</i> enzymes which have same functions but are evolutionarily from different species, are more difficult to identify because our methods cannot recognize different protein sequences to have the same functionality.</p> | ||
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+ | <p>To understand which might be homologs for CAR, created phylogenetic trees using protein sequences, because codon mutations occur less than point mutations. So even though two gene may seem different comparing their nucleic acid sequences, they actually might have the same amino acid sequences. Created two phylogenetic trees using two different methods: UPGMA (Unweighted Pair Group Method with Arithmetic Mean) and Bayesian MCMC (Markov chain Monte Carlo). UPGMA is faster but more crude than Bayesian MCMC. An UPGMA tree was done with Geneious v8.1.7 and for a Bayesian MCMC tree used <a href = http://beast.bio.ed.ac.uk/beast>BEAST v1.8.2</a>, BEAUti v1.8.2 <a href = http://mbe.oxfordjournals.org/content/29/8/1969>[1]</a>, <a href = http://beast.bio.ed.ac.uk/tracer>Tracer v1.6</a>, TreeAnnotator v1.8.2 ja FigTree v1.4.2. Needed multiple protein alignment was done with MUSCLE (with 16 iterations) in Geneious.</p> | ||
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+ | <p>Used diverse resources to find potential CAR homologs, which have the same function. InterPro had <a href = http://www.ebi.ac.uk/interpro/protein/B2HN69>an entry</a> for our CAR and used the site’s “Similar protein” -link to look for similar proteins, which are likely to be homologs. The proteins, which have AMP-dependent synthetase/ligase, acyl carrier protein-like and thioester reductase-like domains, were chosen, because CAR has them, and because mutations on domains change enzyme functions the most. Searched through UniProt-database using keywords, such as “short fatty acid coa ligase” and “Carboxylic acid reductase”. The proteins, which have similar GO-classes and a comparable description as CAR, were picked. Many similar proteins were found by protein BLAST (Blosum62). BLAST results, which E value were 0 and identity with our CAR sequence were over 80 %, were chosen. Blastp recognized from the protein sequence of CAR superfamilies, <i>ie.</i> protein families which are similar on the sequence level. They are adenylate forming domain Class I, phosphopantetheine attachment site and Rossmann-fold NAD(P)(+)-binding proteins, which descriptions match with the domain descriptions of the InterPro entry.</p> | ||
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+ | <b>PICS</b> | ||
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+ | <p>Our built phylogenetic trees are shown in Figure 1 and Figure 2. They are very similar with each other, despite that they were calculated with different algorithms. One major difference is that the oxidoreductase from <i>Sciscionella marina</i> is considered to be evolutionarily closer to our CAR than the enzyme from <i>Rhodococcus wratislaviensis</i> in the Bayesian MCMC tree, but the closer enzymes to CAR are grouped alike in the both trees. In the both figures the branch length to the thioester reductase-like protein of <i>Cryptosporangium arvum</i> is quite big. Therefore, we should consider the enzymes, which are closer to our CAR from the enzyme of <i>Cryptosporangium arvum</i>, as potential homologs which can replace CAR in the propane pathway as they have higher chance of having the same function. However, done research on found enzymes are low, especially the kinetic research. BRENDA, an enzyme database, only had kinetic values for a CAR homolog from <i>Nocardia iowensis</i>, so we really cannot tell if their performance is better. Nonetheless, if it is found in the future that one homolog has better kinetic values than CAR, it can be used to produce more propane.</p> | ||
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+ | </section> | ||
<section id="cyano" data-anchor="cyano"> | <section id="cyano" data-anchor="cyano"> |
Revision as of 15:26, 17 September 2015