Fire, by Giuseppe Arcimboldo.
1566
Oil on wood, 67 x 51 cm
Kunsthistorisches Museum, Vienna
The allegory of Fire combines objects that are more or less directly related to fire in a bizarre profile head. The cheek is formed by a large firestone, the neck and chin are formed by a burning candle and an oil lamp, the nose and ear are contoured by firesteels; a blond moustache is formed by a crossed bundle of wood shavings for kindling, the eye is an extinguished candle stub, the forehead area is a wound-up fuse, the hair of the head forms a crown of blazing logs. The breast is composed of fire weapons: mortar and canon barrels together with the respective gunpowder shovel and a pistol barrel.
In protein engineering studies, mutating a residue to increase thermostability without affecting the activity of the protein/enzyme is a major consideration for researchers. The laborious method is list the number of possible mutations and then finding out the stability and activity for each mutant, one after another.
This method becomes more time consuming if the protein is a membrane proteins and especially determining their 3D structure. I like to call membrane proteins as “diva” proteins. The reason being that they are high maintenance and tend to be picky about what conditions require for them to crystallize. It has been reported that when thermostability is introduced in membrane proteins, their solubility increases, thus increasing the chances of getting a good crystal for diffraction. [1]
Not everyone could avail high-throughput mutation experiments to screen for thermostable membrane proteins. Here is where Bioinformatics based analysis comes to help in faster screening and selecting a few mutants among the hundreds that can be tested experimentally. In the recent issue of Biophysical Journal, Sauer et al have come up with two methods to identify potential “thermoadaptive” sequences. [2]
The first method or global method, involves generating a heatmap of amino acid frequency differences between the thermophilic and mesophilic sequences. So, residues that are either most represented or less represented are identified.
The second method or pairwise method, involves pairwise comparison of thermophilic and mesophilic sequences and identify the differences.
A unique observation was that the the selected list of amino acids did not overlap from either of the methods and the global method identified potential mutants in the N-terminal domain of the test case and the pairwise method identified the potential C-terminal mutants only. This could be a case of thermostabilization for the protein tested, i.e., BsTetL – Tetracycline transporter from Bacillus subtilis.
The caveat is that there should be enough available sequences for identification of potential mutants, in any protein family. This does not, on the outset, seem like a limitation. Since, we have abundant protein sequences available and steadily increasing.
The main selling point is the speed of identifying the mutations given a particular target sequence, and its applicability in membrane protein crystallization. However, their success rate was 26-30%. Here, success indicates both thermostable mutant and maintaining the tetracycline resistance activity.
References:
- Mancusso R, Karpowich NK, Czyzewski BK, & Wang DN (2011). Simple screening method for improving membrane protein thermostability. Methods (San Diego, Calif.), 55 (4), 324-9 PMID: 21840396
- Sauer DB, Karpowich NK, Song JM, & Wang DN (2015). Rapid Bioinformatic Identification of Thermostabilizing Mutations. Biophysical journal, 109 (7), 1420-8 PMID: 26445442
Getting to know Structural Bioinformatics 