Goal for the Fifth InterLab

The goal of the iGEM InterLab Study is to identify and correct the sources of systematic variability in synthetic biology measurements, so that eventually, measurements that are taken in different labs will be no more variable than measurements taken within the same lab. Until we reach this point, synthetic biology will not be able to achieve its full potential as an engineering discipline, as labs will not be able to reliably build upon others’ work.

In the previous interlab studies, it was shown that by measuring GFP expression in absolute fluorescence units calibrated against a known concentration of fluorescent molecule can greatly reduce the variability in measurements between labs. However, when taking bulk measurements of a population of cells (such as with a plate reader), there is still a large source of variability in these measurements: the number of cells in the sample.

Because the fluorescence value measured by a plate reader is an aggregate measurement of an entire population of cells, we need to divide the total fluorescence by the number of cells in order to determine the mean expression level of GFP per cell. Usually this is done by measuring the absorbance of light at 600nm, from which the “optical density (OD)” of the sample is computed as an approximation of the number of cells. OD measurements are subject to high variability between labs, however, and it is unclear how good of an approximation an OD measurement actually is. If a more direct method is used to determine the cell count in each sample, then potentially another source of variability can be removed from the measurements.

This year, teams participating in the interlab study helped iGEM to answer the following question: Can we reduce lab-to-lab variability in fluorescence measurements by normalizing to absolute cell count or colony-forming units (CFUs) instead of OD?

In order to compute the cell count in the different teams samples, two orthogonal approaches were be used:

1. Converting between absorbance of cells to absorbance of a known concentration of beads.

Absorbance measurements use the way that a sample of cells in liquid scatter light in order to approximate the concentration of cells in the sample. In this year’s Measurement Kit, teams were provided with a sample containing silica beads that are roughly the same size and shape as a typical E. coli cell, so that it should scatter light in a similar way. Because the concentration of the beads is known, each lab’s absorbance measurements can be converted into a universal, standard “equivalent concentration of beads” measurement.

2. Counting colony-forming units (CFUs) from the sample.

A simple way to determine the number of cells in a sample of liquid media is to pour some out on a plate and see how many colonies grow on the plate. Since each colony begins as a single cell (for cells that do not stick together), we can determine how many live cells were in the volume of media that we plated out and obtain a cell concentration for our sample as a whole. Each team will have to determine the number of CFUs in positive and negative control samples in order to compute a conversion factor from absorbance to CFU.

By using these two approaches, Interlab Measurement Study will be able to determine how much they agree with each other, and whether using one (or both) can help to reduce lab-to-lab variability in measurements. If it can, then together we will have brought synthetic biology one step closer to becoming a true, reliable engineering discipline.

Calibration Reference

Calibration 1:OD600 Reference point - LUDOX Protocol

LUDOX CL-X (45% colloidal silica suspension) was used as a single point reference to obtain a conversion factor to transform our absorbance (Abs600) data from our plate reader into a comparable OD600 measurement as would be obtained in a spectrophotometer. Such conversion is necessary because plate reader measurements of absorbance are volume dependent; the depth of the fluid in the well defines the path length of the light passing through the sample, which can vary slightly from well to well. In a standard spectrophotometer, the path length is fixed and is defined by the width of the cuvette, which is constant. Therefore this conversion calculation can transform Abs600 measurements from a plate reader (i.e., absorbance at 600nm, the basic output of most instruments) into comparable OD600 measurements. The LUDOX solution is only weakly scattering and so will give a low absorbance value.

[ IMPORTANT NOTE : many plate readers have an automatic path length correction feature. This adjustment compromises the accuracy of measurement in highly light scattering solutions, such as dense cultures of cells. YOU MUST THEREFORE TURN OFF PATHLENGTH CORRECTION if it can be disabled on your instrument . Our Instrument did not have any pathlength correction].

Materials

1ml LUDOX CL-X (provided in kit)

ddH2 0 (provided by team)

96 well plate, black with clear flat bottom preferred (provided by team)

The docking simulation of “Thioredoxin-Fusion protein”

1.  Since the structure of Thioredoxin has been studied, we can lock down the active site of thioredoxin by use Uniprot. The team found that there are two active site , which are NO. 33 and NO.36 of the sequence.

2.  By using NCBI BLAST, the team compared the sequence of the fusion protein with Thioredoxin. The team confirmed that the active sites of fusion protein corresponding to the ones of Thioredoxin are No.33 and 36 , both are Cysteine, C.

3.  The team later constructed a fusion protein 3D model and then labelled the active sites by using PyMOL. By creating the model, the team could learn why thioredoxin is helpful toward protein folding since the active sites of Thioredoxin are not facing away from MSMEG5998.

This 3D model shows the surface of the fusion protein, which allows us to grasp the concept of what our protein looks like. The region labeled in red is the possible binding site of Thioredoxin, which maybe can assist the fusion protein itself or other proteins folding.

The structure of the fusion protein (MSMEG5998 part)

1.  While the structure of MSMEG5998 remains unknown, the team still manage to predict the model by using similar protein to create a model, the software tool we used is Swiss Model[3] [4].

2.  When deciding the model of MEMEG5998, the team used the Swiss Model by comparing the amino acid sequence among the database of protein sequence. There are two main factors lead to two different models, which are by coverage or by identity. The team choose the highest coverage protein sequence to be our model, named” MSMEG5998 Swiss model”.

3.  The sequence of the MSMEG5998 by using Swiss model is compared with that of fusion protein by using Uniprot. The team then discovered three similar groups being labeled below, which are likely active sites.

4.  The three possible loci corresponding to the fusion protein sequence are:

i.  189，Arginine，R

ii.  214，Glutamine，Q

iii. 246，Alanine，A

Since the pdb. files presented by raptorX were unable to visualize hydrogen bonds of the compound, thus the team used PMViewer v1.5.7 to add on hydrogen bonds and negative charge. (the following pictures are compounds before and after enhancements)

Further enhancements to the compound before docking simulation on MSMEG5998

Under PMViewer, the appearance of the protein before enhancements.

The fusion protein after enhancements, which adds hydrogen and charge to the protein. This process allows the structure and the binding process as real as possible.

Adding ligand to the docking simulation of MSMEG5998-Aflatoxin B2

Search PubChem to locate the ligand, which in this case is AflatoxinB2, and then download the SDF format.

The docking of MSMEG5998 to Aflatoxin B2

1.  The settings for Aflatoxin B2 before docking: Minimize the energy, in order to acquire a stabilized compound which is easier to go through the docking simulation.

2.  Select the docking function to proceed.

Autodocking area

The possible autodocking area are limited to the three active sites of MSMEG5998 mentioned earlier, which can increase the model’s accuracy. After autodocking, we visualize the result by using PyMOL to create a 3D docking model. The three active sites for docking are tested, and compared to one another. The team finally come up with one ideal active site, which is 214,glutamine,Q.

The docking was processed by Autodock (please visit our software tools page, the cube area is the area our team choose to process the docking stimulation, the results are in the picture below.

This is a side view of the protein macromolecule. The MSMEG5998 active site 214 is presented in red, while the blue compound represents Aflatoxin.

Discussion and Conclusion

1.  By using protein modeling techniques, the team predicted a fusion protein with multifunction while one doesn’t inhibit the other, or creating structural failure. Which later on helped us in the wet lab experiment to proceed.

2.  With the software tools, the team is able to predict an enhanced fusion protein (MSMEG5998 combined with Thioredoxin) that performs better than the original protein (MSMEG5998).

3.  With the cooperation of the wet lab projects, the team is able to confirm the results of the prediction.(Click the button to visit our project’s result.)

4.   Future goals:

i.  unfortunately, there is a time limit to our project. However, the team would like to continue our modeling project and also put the theory into practice, trying to see whether active site 214 is the actually binding site with Aflatoxin. The team would conduct experiments of point mutation on site 214, to see if the binding affinity changes or not, in order to explain why this site 214 is crucial toward Aflatoxin degradation.

ii.  After conducting the two main modeling project, our team successfully predicts the function of our fusion protein; however, the long term goal is that the team envisions our aflatoxin-degrading protein put in to massive and commercialized production. Therefore, our team would want to measure the productivity of our protein, in order to seek for the ideal producing conditions and reach the maximum efficiency.(Click the button to see some of the results from the experiment our team has conducted.)

References