Team:HKUST/Engineering

ShellBi

Project
Description Design Engineering Proof of Concept Implementation Safety Contribution Notebook
Wet Lab
Experiments Results
Dry Lab
Model
Reach
Human Practice Collaboration Communication Education Sustainable
Parts
Basic Parts Composite Parts
Team
Team Attributions

Engineering

Building our Design and Engineered Product

The complete design of our biosensor- as influenced by multiple engineering cycles and opinions of stakeholders

Design

In the simplest conception of our project, we need to generate a visible readout in response to the ion influx changes in our chassis caused by the antagonism of shellfish toxins (namely saxitoxin, STX) and veratridine (VTD). We came across the EnvZ/OmpR two-component system (TCS), which is well studied in E. coli. It modulates gene expression of OmpC and OmpF (both are proteins involved in ion transport) based on the intracellular osmolarity; and this has potential for expressing two reporter genes via the same pathway. This two component system seemed promising since the antagonism of STX and VTD leads to a substantial ionic level change in a cell, which could also be expressed as change in osmolarity. Therefore, our initial design was to express two fluorescent proteins — red and green — under the regulation of the two native promoters of OmpF (termed PompF) and OmpC (termed PompC).

As found in literature, the intracellular ionic concentration increased as a result of Veratridine treatment on cells, while at the same time, the ionic concentration (Na+ and K+) declined upon saxitoxin treatment all within the span of 10-15 minutes (Figure 1). If the effect itself is so rapid, we could potentially build a biosensor that could also provide a response in an equivalent short span. For this, we chose the 2 brightest and fastest maturing fluorescent proteins — eGFP for green readout and mCherry for red readout.

Figure 1: Change in ion influx upon addition of Veratridine 100μM (Na+ ▴ and K+ ▵) and Saxitoxin 1 μM (Na+ ▾ and K+ ▿)

Build

To save time on molecular cloning, we wanted to order both promoters and their corresponding fluorescent proteins in the same gBlock from IDT. However, mCherry contributed a high complexity score due to its high GC content (>60%) and secondary structure. For this reason, we chose to replace mCherry with mRFP1, also a fast maturing protein. Although there are other GFP proteins that are faster maturing, eGFP is more photostable and brighter, hence we chose this as our reporter.

The initial fragment we ordered included “PompC+RBS+eGFP+ T0 terminator” and a separation of a scar (BBa_K4061005), and “PompF+RBS+mRFP1+T0 terminator”

This fragment is in the parts registry as BBa_K4061070

Figure 2: TCS basal parts assembly BBa_K4061070

In order to establish control, we chose the same strong RBS for both promoter systems: Bba_B0034 (efficiency 1.0). We used a short terminator T0 (BBa_K864600) for the construct to reduce the complexity score to qualify IDT screening. The terminator has an efficiency of 97%.

After we received this gBlock from IDT, we cloned it into a pSB1C3 vector via the RFC10 standard. Screening of colonies was facilitated by the presence of fluorescence proteins as part of the construct.

Test

In order to test the circuit, we require access to the highly potent toxins, saxitoxin or related drugs, as well as veratridine. Hong Kong customs do not permit the entry of such drugs and neither is our lab equipped to handle such potent neurotoxins. Hence, we tried to replicate the ionic effects by varying the sodium concentration of the culture medium (LB).

In order to confirm whether changing the external sodium concentration induced a difference in intracellular ionic levels, we used an osmometer (see experiments).

Experiment page

Measuring intracellular osmolarity

We recognise that any fluorescent measure we take to characterize the TCS would need an indication of the exact intracellular osmolarity the system is responding to. We cultured wild type (WT) BL21 in different sodium concentrations of LB medium. After 16 hours, we obtained an equal number of cells (~1x10E9), as calculated using OD600, from each medium and briefly washed them in MilliQ. The cells were sonicated in MilliQ to suspend all osmolytes. After briefly centrifuging at full speed, we measured the osmolarity using an osmometer (Advanced 3000 osmometer). See experiments for full details.

Experiment page

Measuring Fluorescence Intensity

We measured the fluorescence intensity of the cells cultured in media of increasing [Na+] via the FlexStation 3 Multi-Mode Microplate Reader. The plates were blanked using LB and we used WT BL21 grown in different concentration media as control.
Our plate reader measured the samples at 2 different excitation and emission wavelengths — one for eGFP and the other for mRFP1:

	Excitation	Emission
eGFP	488 nm	507 nm
mRFP1	584 nm	607 nm

In order to normalise the intensity to cell number, we divide the observed fluorescence intensity (arbitrary units, AU) to the optical density (OD600) of the cells.

Learn

We expected a curve as modelled in Figure 3.

Figure 3: Fluorescence intensity against intracellular osmolarity (Theoretical)

Our result can be found under the Results page under TCS basal. In both fluorescent proteins, we observe a 2-fold increase/decrease at the extremes of osmolarity range and there is an observable trend as modelled. However, the intensities of both fluorescent proteins are highly contrasting.

Result page

PompC regulated eGFP exhibits fluorescence intensity in the range of 10000 to 20000 AU while PompF regulated mRFP1 exhibits fluorescence intensity in the range of 600 to 1200 AU. At high osmolarity, mRFP1 does show WT-like expression but this is not the case with eGFP. Even at extremely low intracellular osmolarity, the fluorescence intensity of eGFP is high. A possible reason is the leaky expression of PompC is at high plasmid copy number, as characterised by SDU Denmark as well as University of Indonesia.

One limitation is that we are not working on an ΔOmpC, ΔOmpF strain. So the cells might be regulating the intracellular conditions using native porin proteins, making the ion influx change not as tremendous and hence affecting the circuit behavior. Working on custom knockout strains, however, might cause lethality at extreme medium osmolarities, hence we prefer not to do so. If this is the case, then eventually when the circuit is tested on cells treated with shellfish toxins and antagonist drugs, we could expect a more significant and robust readout. However, we cannot test the drugs on our cells for safety reasons.

Design

Although we do observe the expected trend, the fluorescence of eGFP is overpowering mRFP1 which is not desirable since our genetically engineered cells must exhibit a clear red fluorescence upon toxin detection. In conversation with Dr. Aifeng Li, an expert on Shellfish Toxins, we realised that in order to gain acceptance of a biosensor when in competition with other chemical methods, we need to make sure to limit false positives or false negatives. Since we are working with 2 fluorophores, the undesirable signal (noise) is a big issue, even as seen in data from TCS basal.

In order to suppress this noise, we decided to encode antisense RNA against the undesired fluorophores. We encode an antisense RNA against eGFP under the PompF promoter and antisense RNA against PompC. The FP of interest is expressed along with the antisense RNA to the other FP, hence reducing noise.

We could not place an antisense RNA sequence under the promoter while it is also expressing a fluorescence protein because the protein requires an RBS and antisense RNA must not be translated. Therefore, we had to use the same promoter but 2 times, one for regulating the protein, and one for regulating the antisense RNA.

The antisense RNA contains a 36nt long sequence binding at the CDS, RBS as well as the -10 region of the corresponding promoter. The 3’ end of the antisense fragment contains an Hfq-binding domain which is shown to stabilise as well as enhance the degradation of sense-antisense RNA hybridisation. Using NUPACK, the free energies of the RNAs forming secondary structures are calculated to be -6.2 and -5.4 kcal/mol for the antisense of GFP and RFP respectively, which suggests that they would function properly without forming loops to themselves.

Figure 4: Antisense RNA components and binding regions

Build

We wished to reduce the number of clones we make so we tried to assemble as many parts as we could in one gBlock fragment. However, the antisense fragments by nature form DNA duplex with their target due to which we needed to clone the antisense fragments against both FPs, one at a time. We are currently at the stage of cloning them together. The construct is registered as BBa_K4061075.

Figure 5: TCS basal with antisense RNA for noise reduction

In order to screen for successful colonies, we would require to cPCR several colonies since even the colonies on the control plate, if any, would appear green. We add CIAP to limit the chances of self-ligation in the case of incomplete digestion from either of the 2 enzymes.

In order to collect more data on the efficiency of both, the promoters PompC and PompF as well as the antisense RNA, we built the following constructs:

Figure 6: Constitutively expressed eGFP with PompF regulated antisense RNA to eGFP

Figure 7: Constitutively expressed mRFP1 with PompC regulated antisense RNA to mRFP

Test

We measured the fluorescence intensities of the constructs constitutively expressing FP with and without antisense RNA against the fluorophore. The following constructs were measured:

• ❒ Constitutively expressed eGFP
• ❒ Constitutively expressed eGFP + PompF_antisense RNA to eGFP
• ❒ Constitutively expressed mRFP1
• ❒ Constitutively expressed mRFP + PompC_antisense RNA to mRFP1

These constructs were grown in LB media with different salt concentrations (see Experiments). We took 4 sample readings (2 biological and 2 technical replicates) and calculated their averages to compute the fluorescence intensity. This intensity was divided by the OD600 of the sample to normalise the reading per fixed number of cells.

Fluorescence Intensity of eGFP = ^{Fluorescence emission at 507 nm} ⁄ _{Absorbance OD600}

Fluorescence Intensity of mRFP1 = ^{Fluorescence emission at 607 nm} ⁄ _{Absorbance OD600}

In order to analyse the promoter’s functionality and efficiency, we subtract the fluorescence intensity of the construct with the antisense fragment, from the intensity of the constitutively expressed FP.

Promoter function = Fluorescence Intensity (Constitutive fluorophore)
        - Fluorescence Intensity (fluorophore w Antisense fragment)

Learn

We figured that the antisense fragments indeed suppress the fluorescence to a great extent. We also found that eGFP naturally has a higher fluorescence intensity than mRFP1, so perhaps the data from the initial circuit is a fair justification of the biosensor, but we may need to use a different fluorophore in order to have a robust biosensor.

The promoter OmpC which regulated the antisense RNA against mRFP1 seems to fit the expected trend. The antisense RNA seems to function better to reduce the fluorescence at higher osmolarity, than in lower osmolarities. However, the OmpF promoter driven antisense RNA against eGFP does not seem to follow a trend. In fact, the suppression seems approximately ideal at all concentration. This is surprising since OmpF must lose its activity at higher osmolarities. Even if PompF were to be leaky, the suppression would not be observed to be this excessive. See result page for more information.

Result page

However, this result contradicts the data from TCS basal construct where both PompC and PompF demonstrated an ideal trend at increasing intracellular osmolarities.

Design

In order to troubleshoot whether the observed results were a limitation of the assay or the promoter itself, we wish to isolate and test the activity of PompF alone, uninterrupted by other constructs in the cell. We could also potentially test how the different regulatory regions (F1-4) on the PompF affect the function of the promoter. PompC has already been characterised several times (BBa_R0082), however, PompF lacks significant data.

Build

We ordered primers that could PCR out the different regions of the PompF promoter. The PCR product is cloned into a pSB1C3 vector. The final constructs to be tested are:

• ❒ PompF (with negative regulator)_strong RBS_mRFP1_T0 terminator
• ❒ PompF (without negative regulator)_strong RBS_mRFP1_T0 terminator

Test

We culture the clones in media of varying osmolarities for 16 hours and observe the fluorescence intensity of mRFP1 from the cells. A negative plasmid control (WIld-type BL21 cells) is also included to account for noise.

Learn

We realised that the fluorescence intensity of mRFP1 is just naturally lower than eGFP. This was also observed when constitutive mRFP1 was cultured in optimal LB medium and the highest fluorescence was about 6500AU whereas eGFP was about 15000AU with the same plate-reader gain setting. Therefore, the sample appearing green might just be a result of eGFP overpowering the mRFP1 fluorescence. We have redesigned TCS basal construct to have a GFP mut3 which is a faster maturing protein with a lower fluorescence intensity.

After culturing the PompF without the negative regulatory region, F4, we realise that this negative regulator is indeed effective in establishing a good ‘switch’ mechanism. The cells which did not have the negative regulator region, observed a much lower decline at higher osmolarities than the construct that had the F4. The decline was also much steeper and was more sensitive to an increasing osmolarity with the F4 region.

Although with this F4 region, we are able to establish a better switch between a low and a high osmolarity, we require the PompF to be active over a wider range of intracellular osmolarity. This is because the biosensor must be able to detect samples which may contain different levels of the toxin. Moreover, the biosensor also should give the red signal upon detection of various different toxins which might have different channel-blocking efficiencies. Therefore, we need to design a promoter that could be active over a broader range of osmolarities, yet all lower than native optimal state. We still need an F4 negative regulator on PompF because a biosensor needs to have a discrete distinction between ‘red’ and ‘green’ state. Research by Koga-Ban, Y, et al (1983) demonstrates how mutations within PompF could lead to enhanced expression of OmpF (in our case reporter protein), while a concurrent reduction in OmpC (our case, associated reporter protein). This could be significant since it could help broaden the range in which PompF functions to produce the red fluorescence. This allows flexibility in reaction conditions and detection limits. See more under Implementation.

Implementation page

Final Design

Based on the results we collected throughout these DBTL cycles, we feel the best design for our circuit would in fact be with following components:

• ❒ GFP mut3 instead of eGFP
• ❒ PompF with mutations on the positive regulators

Design

In conversation with local oyster farms we realise that speed and robustness of biosensor are quintessential for their adoption. We need to model a mechanism of feedback loop to accelerate the circuit. A limitation of our current model as per our data shows that PompF regulated mRFP1 has a very narrow range of activity where the fluorescence intensity drops starting at an intracellular osmolarity of 30mOsm/kg. Whitaker, W. R. (2012) demonstrated the incorporation of a modular scaffold to enhance interaction between two proteins.

In order to make the Two Component System work faster we could employ a protein scaffold that brings together the two components- EnvZ and OmpR and enhances phosphotransfer between them. However, if the scaffold is constitutively expressed, the OmpR would always be phosphorylated and there would be non-specific gene expression. In order to prevent it, we encoded the scaffold protein only under OmpC, while engineered an auto-inhibition domain on EnvZ which causes steric hindrance around its kinase domain to prevent phosphotransfer. The native EnvZ/OmpR has not been knocked out, so we assume that the initial response is triggered by the native mechanism.

In low osmolarity, the scaffold protein will not be expressed, and the EnvZ would be inhibited. There would be a lower OmpR~P concentration leading to PompF activation. Since the EnvZ is auto-inhibited, the PompF signal may be able to sustain longer with less fluctuation. As the osmolarity increases, the OmpR~P concentration increases leading to PompC activation, which in turn expresses the scaffold, fortifying the interaction, therefore finally, inducing a positive feedback loop and speeding up the circuit.

Build

We modelled EnvZ to identify it’s kinase domain, the four-helix bundle, using SWISS-MODEL, then inserted an SH3 ligand adjacent to this four-helix bundle. The C terminus of the EnvZ contained an SH3 binding protein (see sequence in Design) attached with a 12aa inert linker protein. This makes up the auto-inhibition of EnvZ.

Design page

The OmpR is engineered with a Leucine zipper binding domain on its C terminus. A scaffold protein which contains SH3 ligand on N terminus and a Leucine zipper binding domain on the C terminus binds to the engineered segments on both EnvZ and OmpR, hence fortifying and stabilising their interaction.

In order to establish control of the system, the scaffold protein was encoded under the OmpC promoter along with other reporters and regulators.

Figure 8: Composite part (BBa_K4061080) with engineered response regulators (EnvZ and OmpR) of TCS

The final circuit contains engineered EnvZ and engineered OmpR constitutively expressed under BBa_J23100, along with PompC regulated eGFP and scaffold, and finally PompF regulated mRFP. This was cloned into pSB1C3 using 3A assembly, RFC10 standard.

Test

We will clone the engineered Two Component System and use unengineered TCS basal circuitry as control. The fluorescence intensity of both mRFP1 and eGFP is measured using fluorescence spectrometry.

Learn

The construct is currently in the process of being cloned, however, we speculate that there might be issues regarding the optimal switch between auto-inhibition and scaffold mediated interaction. This is because the SH3 ligand we used for auto-inhibition as well as on the scaffold, have the same sequence and binding affinities (Kd=0.1uM). In that case, the model would be competitive and not preferable for an easy switch. We might expect non-uniform results when the construct is grown at different media osmolarities. To avoid this, we may need to consider using a ligand for auto-inhibition that still binds SH3 binding domain but weaker than the ligand present on the scaffold.

Design

In order to tune the binding strength, we need to find sequences that bind to SH3 binding sequence with different dissociation constant (Kd). High Kd indicates weak binding, while smaller Kd is stronger binding. A higher Kd is required for the auto-inhibition ligand since we require the inhibition to preferably lift, allowing the scaffold to tether the EnvZ and OmpR together stronger (with low Kd) to allow phosphotransfer (See Design and Model page).

Design page

Model Optimisation

In a resting state, we want the state to be low osmolarity, so the scaffold, being regulated under PompC, will not be produced. In that case, the auto-inhibition would persist, favouring the production of PompF regulated mRFP1. However, with a rise in osmolarity, PompC would produce the scaffold which would preferentially bind the EnvZ (due to its stronger affinity) and allow phosphotransfer to OmpR. This would make an efficient feedback loop for the fortification and stabilisation of signals.

Build

The gene circuit remains the same, however, the sequence of amino acids on the SH3 ligand that offers auto-inhibition needs to be changed. With reference to the research done by Whitaker, W. R. et al, 2012, we might be able to use a Kd of 5uM offered by the amino acid sequence: PPPVPPRRRR. This can easily be reversed with the incoming of a scaffold protein which contains a sequence that offers a Kd of 0.1uM.

Design

We are going to use E. coli cells as the chassis for our project. However, we went through the fundamental DBTL cycle even in order to find the best chassis for our project.

We wanted to have a chassis that could

• ❒ Respond to the antagonism between PST and Veratridine
• ❒ Accommodate and regulate the Two Component System in a desirable way

Our inspiration paper observed the effects of the antagonism between Saxitoxin (a PST) and Veratridine in 3 gram-negative bacteria- Cyanobacteria (C. raciborskii AWT205 and C. raciborskii T3) and A. fischeri. Researchers observed the ionic concentration of K+ and Na+ via flame photometry. Given that the Two Component System was native to E. coli, and that we were all trained to handle this organism, we hosted all our engineered parts in E. coli. However, we remained dubious about this as a choice of our final chassis since we did not have any evidence for its response towards the antagonism between PST and veratridine. We considered using Cyanobacteria and V. fischeri as well since we had data and evidence for their response to toxins.

Learn

We consulted Professor Qinglu Zeng and Professor Stanley Lau, HKUST about the growth conditions and handling precautions for photosynthetic cyanobacteria and V. fischeri respectively. Both suggested that we do not engineer those organisms since their growth requirements would get too complicated for us to handle and given their slow replication time, troubleshooting would take us enormous time. Hence, we resorted to prove, at least, the genetic interaction in E. coli.

Meanwhile, we could attempt to find evidence that perhaps E. coli could be used for final use of biosensor, perhaps by finding similar ion channels in the organism as the ones on which the antagonism was studied.

Design

We cloned the circuit into E. coli BL21 cells as described through the engineering cycle above. In our efforts to find the most appropriate chassis for the final implementation of the biosensor, we decided to conduct BLASTp search for bacterial proteins that could be similar to the eukaryotic Voltage Gated Sodium Channels (VGSCs) which are well-known targets of PSTs such as saxitoxin and tetrodotoxin. This may allow us to find the appropriate binding protein as well which opens up room for dry lab modelling for the toxin response on the system. We did not consider using eukaryotic cells or expressing eukaryotic VGSCs in E coli because expression of eukaryotic VGSCs in E. coli results in toxicity [7]

Test

As our inspiration paper demonstrated that cyanobacteria Cylindrospermopsis raciborskii, Anabaena circinalis and V. fischeri respond to the antagonism, we initially wanted to find the channels in the cyanobacterial strains to provide more concrete evidence for its feasibility. We conducted a BLASTp global search using the whole, as well as the binding domains of human Nav1.7 channels and Nav1.5 channels to saxitoxin and tetrodotoxin using Uniprot and NCBI database with p-value 1000 as our search parameter. Initial global search found no significant similarities between the eukaryotic voltage-gated ion channels and bacterial proteins. The result only consists of other eukaryotic channels. The closest channel in the search to the cyanobacteria used in the reference paper are some channels in dinoflagellates. However, there are no significant similar proteins found in the two cyanobacteria.

Learn

We realized that the genome of the cyanobacteria Cylindrospermopsis raciborskii and Anabaena circinalis in the NCBI database are labelled as contig, which means that some proteins in their genome are not characterised yet. Despite the BLASTp being inconclusive, the cyanobacteria and marine bacteria, V. fischeri still responded to the drug antagonism. So despite the divergence of ion channels between prokaryotes and eukaryotes, there must be some conserved domain which offers a potential for the toxin binding. Since we know now it is impossible to locate the protein without the strain, we decided to locate other bacterial proteins that are affected by the antagonism.
Dr. Brett Neilan, author of our reference paper, suggested using a phylogenetic analysis as a method to trace the evolutionary relationship of VGSCs to point at an organism with the highest probability to preserve the binding site for neurotoxin in the desired channel while the proteins do not necessarily share identical domain.

Design

Phylogeny is the study of evolutionary descent of organisms, presented using a tree diagram [2]. Sometimes binding domains are conserved through generations, so a phylogenetic tree of proteins could help us find potential targets for the antagonism.

Build

We used the MEGA version X as our software choice to conduct phylogenetic and molecular evolutionary analyses by Kumar et al. [1].

To locate a suitable channel responsible for the antagonism, a global protein phylogenetic tree was first conducted to isolate the cluster with eukaryotic VGSCs. However, due to the lack of computation power and access to computer clusters, the global search was inconclusive. Therefore, after each global analysis, an analysis on the isolated proteins and some negative control with Bootstrap verification was conducted to eliminate any noise from other proteins in the global analysis. A conclusion is drawn every time and repeats the global process with some proteins eliminated by the conclusions until the best channel is isolated, which represents an engineering cycle of its own.

Figure 9: Phylogenetic tree cycle

Test

During our test process, we came up with conclusions after each iteration of the analysis and this improved our next iteration. 3 iterations were performed to reach the final decision. The details can be seen below.

Supplementary Appendix

Learn

After multiple cycles of phylogeny, we discovered that transporters are less likely to have the domain to be bound by PSTs, ion channels are more likely to be affected by PSTs and VGSCs are highly specific in their evolutionary pathway. Therefore, it indicates that ion transporters and other ion channels such as Voltage-Gated Potassium Channels are less likely to have a binding site for neurotoxins. This narrows down our research focus to only VGSCs instead of other ion transporters and ion channels.

Although bacterial VGSCs are separated with eukaryotic VGSCs, their bootstrap value is 31, which by standard is greater than 30 and thus suggests that they are similar in their structure. In addition, Bacillus halodurans VGSCs (NaChBac) surprisingly resides on eukaryotic cluster after variations in the parameters of the phylogeny, which demonstrates its potential feasibility as our channel choice since:

• ❒ It is closer to Bacillus subtilis which is easier to engineer
• ❒ The structure of NaChBac is fully characterised and many papers suggest that they can bind to neurotoxins
• ❒ There are several studies suggesting the expression of NaChBac in E. coli, so the chassis can be E. coli

Test

Although it seems that NaChBac expression in E. coli and its potential target for the antagonism could be promising, we need to check if the antagonism indeed works on NaChBac. Surprisingly we found that Veratridine, instead of activating the channel, in fact blocks it, similar to PSTs. In addition, Tetrodotoxin, which is very similar to Saxitoxin, exhibits minimal effect on blocking the channel alone [3]. This will be detrimental to our circuit, therefore, we step back on the conclusions we made and focus on ion channels that can be affected by the antagonism.

Learn

After some literature review, studies indicate bacterial VGSCs are less likely to be affected by PSTs but there is evidence that PSTs bind to Voltage-Gated Potassium Channels and Copper transporters, so the effect of PSTs and the antagonism is not specific to VGSCs. However, in another paper, there is evidence that saxitoxin inhibits the motility of E. coli [4], which also does not show any positive results in the phylogeny.

Design

After realizing the non-specific nature of PST binding to various channels, we chose a more direct approach, molecular docking to predict probable protein targets for the neurotoxins.

Build

We used AutoDock Vina [5] and CB-dock [6], which are widely known for their versatility and user-friendly features. We used the same ion channels in the molecular docking as were used to build the phylogeny. During molecular docking, the ligand is stimulated to bind to different locations randomly, then the dock conformation is evaluated using its free energy of binding. Binding affinities and binding position of the ligand are predicted. The input files of the channels are obtained through RCSB-PDB and the files for the ligands saxitoxin and veratridine are obtained through pubchem.

Model page

Test

To save time for docking multiple proteins, we will do pairwise comparison between the interaction of the tested receptor with saxitoxin and human VGSCs Nav1.7 with saxitoxin as a fast screening reference.

Learn

After some screening we found that E. coli small-conductance mechanosensitive channels (Mscs) have similar scores with Nav1.7 for the toxins. After analysis, we learn that the position of Saxitoxin on Nav1.7 matches literature findings where it binds onto the pore region of the Mscs, demonstrating its potential as an inhibitor of Mscs. The results are shown in Figure 10. Even for Veratridine (VTD), the binding score to Mscs and Nav1.7 are similar. The binding region of VTD matches the ideal binding site on Nav1.7 and fortunately also binds onto the transmembrane region of Mscs, suggesting that it can modify its gating as shown in Figure 11. Testing process and detailed results can be seen here.

Supplementary Appendix

Therefore, after switching our chassis from cyanobacteria to Bacillus halodurans, we decided to use E. coli as our choice of chassis.

Figure 10: CB-dock results for STX and Nav1.7 right: STX + MscS

Figure 11: CB-dock results for VTD and Nav1.7 right: VTD + MscS

References:

1. Kumar S, Stecher G, Li M, Knyaz C, and Tamura K (2018) MEGA X: Molecular Evolutionary Genetics Analysis across computing platforms. Molecular Biology and Evolution 35:1547-1549.

2. Baum, D. (2008) Reading a Phylogenetic Tree: The Meaning of Monophyletic Groups. Nature Education 1(1):190

3. Zhu, W., Li, T., Silva, J.R. et al. Conservation and divergence in NaChBac and NaV1.7 pharmacology reveals novel drug interaction mechanisms. Sci Rep 10, 10730 (2020).

4. Tisa, L S et al. “Effects of organic antagonists of Ca(2+), Na(+), and K(+) on chemotaxis and motility of escherichia coli.” Journal of bacteriology vol. 182,17 (2000): 4856-61.

5. Trott, O., & Olson, A. J. (2010). AutoDock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. Journal of computational chemistry, 31(2), 455-461.

6. Yang Liu, Maximilian Grimm, et al . CB-Dock: a web server for cavity detection-guided protein–ligand blind docking. Acta Pharmacologica Sinica, 2019.’

7. Powl, Andrew M., and B.A. Wallace. "Chimeric Bacterial-Human Nav1.7 Sodium Channels Expressed In E. Coli". Biophysical Journal, vol 96, no. 3, 2009, p. 13a. Elsevier BV

PROJECT

• Description
• Design
• Engineering
• Proof of Concept
• Implementation
• Safety
• Contribution
• Notebook

WETLAB

• Experiments
• Results

DRYLAB

• Model

REACH

• Human Practice
• Collaboration
• Communication
• Education
• Sustainable

PARTS

• Basic Parts
• Composite Parts

TEAM

• Team
• Attributions