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Team:Peking/Project/SensorMining
From 2013.igem.org
Biosensor Mining
In order to comprehensively profile aromatics in environment, our toolkit should be equipped with biosensors responding to various aromatic components. Abundant with protein informations, large protein databases, like uniprot, are ideal gold mines to look for new biobriks. Peking iGEM team has developed a four step sieving method to screen out feasible and well characterized aromatic sensors from the protein database uniprot. This method consists of several computer programs to process massive data and a manual adjustment step to guarantee a reliable result.
Figure 1. The flow chart of sieving aromatic sensors. Step 1, narrowing down the scope of proteins into transcription factors (TFs) in specific bacteria species; step 2, screening out aromatics related transcription factors; step 3, examining whether the selected transcription factors are well studied; step 4, manual adjustment to verify the feasibility of the selected transcription factors.
First, we narrowed down the scope of proteins into transcription factors in specific bacteria species. We chose Pseudomonas putida, pseudomonas sp and pseudomonas nitroreducens as our source organisms for they live in aromatic rich environments and chose E coli and bacillus subtilis for their clear genetic contexts. We downloaded all 21,096 entries of transcription regulation related proteins of these five bacteria species from the protein data base uniprot.
Second, we screened out aromatics related transcription factors by analyzing the downloaded entries with a computer program. The computer program searched all the entries with a list of keywords (aromatic, benzene, phenol, phenyl, naphthalene, benzoic, benzaldehyde, tolyl, toluene, xylene, styrene) and rated the proteins. Once a keyword appeared in a protein’s entry, the program added one point to its rate. 912 proteins rated more than 0 point remained after this step.
Third, we used another computer program to examine whether the transcription factors remained after step two are well studied. The computer program excluded unnamed proteins that have open reading frame numbers only. Because proteins that have been characterized in E coli are more likely to work well in our host species, the computer program then searched the names of the remaining proteins together with the keyword “E coli” in google scholar and added k/10 point to its rate ( k is the number of papers in the result). 60 proteins rated more than 10 points remained after this step.
Finally, we carried on a manual adjustment on the selected 60 proteins to verify their feasibility. Proteins that regulate aromatic degradation pathways without actually responding to aromatic compounds and those originated from two component systems are excluded. 17 proteins are manually selected at last (Table 1).
Table 1. Proteins selected after manual adjustment
| Protein names | Sources | Reported Typical Inducers | Scores |
|---|---|---|---|
| XylS | Pseudomonas putida (Arthrobacter siderocapsulatus) | Benzoic acid | 259 |
| XylR | Pseudomonas putida (Arthrobacter siderocapsulatus) | m-Xylene | 219 |
| tyrR | Escherichia coli (strain K12) | tyrosine | 160 |
| nahR | Pseudomonas putida (Arthrobacter siderocapsulatus) | Salicylic acid | 106 |
| CapR | Pseudomonas putida (Arthrobacter siderocapsulatus) | phenol | 80 |
| hcaR | Escherichia coli (strain K12) | 3-Phenyl-propionic acid | 56 |
| dmpR | Pseudomonas sp. (strain CF600). | phenol | 43 |
| pobR | Pseudomonas putida(Arthrobacter siderocapsulatus) | p-Hydroxybenzoic acid | 29 |
| CymR | Pseudomonas putida (Arthrobacter siderocapsulatus) | 4-Isopropyl benzoate | 23 |
| Paax | Escherichia coli (strain K12) | phenylacedtyl-CoA | 20 |
| hpaR | Pseudomonas putida (Arthrobacter siderocapsulatus) | (3-Hydroxy-phenyl)-acetic acid | 18 |
| mhpR | Escherichia coli (strain K12) | (3-Hydroxy-phenyl)-propionic acid | 18 |
| phhR | Pseudomonas putida (Arthrobacter siderocapsulatus) | phenylalanine | 16 |
| bphS | Pseudomonas sp. (strain CF600). | 2-hydroxy-6-oxo-6-phenylhexa-2,4-dienoic acid | 16 |
| HbpR | Pseudomonas nitroreducens | 2-Hydroxybiphenyl | 12 |
| phcR | Pseudomonas putida (Arthrobacter siderocapsulatus) | phenol | 11 |
| yodB | Bacillus subtilis (strain 168) | 2-methyl hydroquinone | 11 |
Peking iGEM team has successfully screened out a set of feasible aromatic sensors using the four step sieving method. Because of its good transferability and massive data processing ability, we also believe that this method will be useful in other kinds of biobriks mining in this information explosion age.
Figure 2. Sieving conditions and sieving results of each step. Numbers of selected proteins after each step are showing on the left surface of the pyramid. Sieving conditions are showing on the right.
