Difference between revisions of "Team:Aalto-Helsinki/Future"

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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>
 
<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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<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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   <figcaption style="margin-bottom:2%;margin-top:2%;"><p><b>Figure 2:</b> The Bayesian MCMC phylogenetic tree. The node labels are posterior values, which describe how accurate a branch point is. Because almost all values are over 0.9, we can believe its accuracy depending on initial values. Used Blosum62 as substitution model. ESS (effective sample size) was 6404.1033 for posterior. Click the image to enlarge it.</p></figcaption>
 
   <figcaption style="margin-bottom:2%;margin-top:2%;"><p><b>Figure 2:</b> The Bayesian MCMC phylogenetic tree. The node labels are posterior values, which describe how accurate a branch point is. Because almost all values are over 0.9, we can believe its accuracy depending on initial values. Used Blosum62 as substitution model. ESS (effective sample size) was 6404.1033 for posterior. Click the image to enlarge it.</p></figcaption>
 
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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>
  
  

Revision as of 17:57, 17 September 2015

Future

Propane pathway

There is still plenty of room for improvement in the propane pathway. The identified bottlenecks, enzymes CAR and ADO could be studied further to find out whether there are more efficient enzymes with the same function available in the nature. If not, it might be worth the effort to try and engineer the existing CAR and ADO to be more efficient, as has already been once done for ADO. The idea of having different enzymes of the pathway close together, by fusion to each other or by using different kinds of scaffolds, including our amphiphilic proteins, could also be studied further.

In out pathway model, we also identified the propane output to be sensitive to NADPH/NADH concentration. Therefore, it might be that NADPH/NADH is a limiting factor, if its generation is insufficient. This is something worth studying, and if NADPH/NADH regeneration is indeed verified to be a bottleneck, then it could be studied whether this regeneration could somehow be enhanced.

Even though it was not possible within our timeframe, one could try knocking out more endogenous aldehyde reductases and alcohol dehydrogenases that compete with ADO for butyraldehyde. This approach has been tried by Pauli Kallio and his associates with success: knocking out two endogenous aldehyde reductases Ahr and YqhD resulted in significant improvement in propane output.

Searching homologs for CAR

As our kinetic model identified, CAR (Carboxylic acid reductase from Mycobacterium marinum) 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, ie. 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.

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 BEAST v1.8.2, BEAUti v1.8.2 [1], Tracer v1.6, TreeAnnotator v1.8.2 ja FigTree v1.4.2. Needed multiple protein alignment was done with MUSCLE (with 16 iterations) in Geneious.

Used diverse resources to find potential CAR homologs, which have the same function. InterPro had an entry 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, ie. 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.

Figure 1: The UPGMA phylogenetis tree and sources for the proteins. The node labels describe how evolutionarily distant the proteins are: the higher a value is the more distant they are. The search method tells how we found the proteins and the ID its respective identification code. The enzyme from the human is picked to be an outsider group for calculations. The human enzyme actually functions to the opposite direction than our CAR; it oxidases butyraldehyde to butyric acid. Underlined enzyme is our CAR. The used substitution model for UPGMA was Jukes-Cantor. Click the image to enlarge it.

Figure 2: The Bayesian MCMC phylogenetic tree. The node labels are posterior values, which describe how accurate a branch point is. Because almost all values are over 0.9, we can believe its accuracy depending on initial values. Used Blosum62 as substitution model. ESS (effective sample size) was 6404.1033 for posterior. Click the image to enlarge it.

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 Sciscionella marina is considered to be evolutionarily closer to our CAR than the enzyme from Rhodococcus wratislaviensis 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 Cryptosporangium arvum is quite big. Therefore, we should consider the enzymes, which are closer to our CAR from the enzyme of Cryptosporangium arvum, 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 Nocardia iowensis, 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.

Propane out of sunlight, water and thin air

One significant benefit of the pathway is that it can operate in the presence of ​oxygen. This is required to incorporate the pathway in oxygenic, photosynthetic organisms like cyanobacteria. Cyanobacterial propane production could have a tremendous effect on the way energy is produced and consumed in the society. Fuel production would essentially require only sunlight, water and CO\(_2\), and would thus be completely renewable.

Improving safety

As propane is a highly flammable liquid, large-scale microbial production could pose a fire and/or explosion hazard. The production would most likely happen in closed containers with nothing to ignite the gas, easing the problem. However, leaks are always possible: the propane or the bacteria themselves could leak from the microbial container or pipelines to enclosed spaces where ignition is possible. By replacing air, propane could also cause a suffocation danger. To help avoid these problems, it would be beneficial if the propane could be detected. However, propane itself cannot be seen and it has no odour, making detection difficult. Gas molecules with an odour (e.g. ethyl mercaptan) could be added to the purified product and production container. However, this does not allow us to detect the gas if the bacteria leak to produce propane in an enclosed space where no such safety measures are taken.

To help detect microbial propane production, it might be thus reasonable to have the propane-producing bacteria also produce a certain scent when propane production is active. This could be achieved by for instance incorporating a banana smell generator device to the same bacterium which is producing propane. Another, perhaps even better option would be to modify the bacteria so that they need to be given certain nutrients not widely available in the environment to survive.