Latest revision as of 03:05, 18 October 2018

MODELLING

Structural Modelling

Chimera Combination

Our first steps in modelling the subsequent parts of Finding Diphthy iGEM-UI 2018 in silico is by constructing all 3D models via I-Tasser server.^1,2,3 The extension of the product file is .pdb, that could be read by the server. The chimera molecules which we need to predict their modelling are HB-EGF/TAR (Heparin Binding Epidermal Growth Factor- TAR chemotaxis), CheA signalling protein, Che-Y signalling protein, LuxAB dimerized luciferase subunits, and eYFP (enhanced yellow fluorescent protein), as well as DiphTox (modified diphtheria exotoxin). Since CheA and CheY are required to be linked with LuxB or eYFP, we have cited one of the universal linker, that is ‘GGGSGGGGSGGGGSG’ peptides, according to Sun S et al.
Our signalling part of the project is referred these sequences of all chimera combinations.

LuxB-CheY
LuxB-CheA
CheY-eYFP
CheA-eYFP

In choosing the best combination, we use FoldX option via YASARA molecules viewer to calculate the ∆G of each molecule, searching for the smallest free energy (regarding its stability in vivo). All those sequences are also submitted to I-Tasser server for projecting their 3D models qualitatively. The following results would conclude that our cytoplasmic signalling combinations are CheY-eYFP and LuxB-CheA.

Table 1. Specific Gibbs Energy within Each Protein Combination.

Combination	∆G
LuxB-CheY	58.15 kcal/mol
LuxB-CheA	1355.46 kcal/mol
CheY-eYFP	36.48 kcal/mol
CheA-eYFP	36.48 kcal/mol

Characterisation or purification of those proteins would promote the usage of His-tag; therefore, insertion of His-tag inside the sequence is essential. To ensure the slightest change of tertiary structures of each protein, we would need to find out the secondary structure and surface accesibility via NetSurfP ver. 1.1 analyser http://www.cbs.dtu.dk/services/NetSurfP/ We would insert His-tag sequence in either no available specific protein domain or the coiled secondary structure of protein to minimize any interruptions. Here is our DiphTox data from NetSurfP server.Result from the NetSurf server, we choose C-terminus side, because it most likely turns/coils around (indicated by has high number on the most right column is closest to 1), and it is freely exposed (indicated by most left column has E alphabet)

Table 2. Coiling probability of DipThox’s specific domain.

Class assignment	Amino acid	Amino acid number	Probability for Coil
B	I	54	0.223
E	K	55	0.669
E	S	56	0.994

Performing structural similarity between original molecule and the one inserted with His-tag sequence have been done by MUSTANG server that built in via YASARA molecule viewer.⁵ The output would be distance calculation between interacting atoms called RMSD (Root-mean-square deviation). Following tables are summaries of the molecular similarity analysis. From the data that described above, all the combinations are acceptable, except LuxA, since its possible combination has high RMSD. The threshold is relative, but several literatures define the RMSD value of 2 as threshold for structure similarity.^5,6,7

Table 1. RMSD Calculation within Several Protein Linked with His-tag.

Similarities between	RMSD
LuxA with LuxA + His	2.203 Å
LuxC with LuxC + His	0.985 Å
LuxD with LuxD + His	0.1777 Å
LuxE with LuxE + His	0.800
CheY-eYFP with CheY-eYFP+his	0.108 Å
eYFP with eYFP + His	0.315 Å
CheA with CheA + His	0.134 Å

HB-EGF/Tar Receptor Modelling

In HB-EGF, the part that serves as binding domain for diphtheria exotoxin predominantly located in the extracellular environment. Therefore, the domain, expands between 20^th – 160^th amino acid, was selected from natural HB-EGF protein. On the other hand, the Tar domain that are functions to establish intracellular chemotactic signalling includes NdeI cutting-site (around 257^th amino acid) until the utmost C-terminal of the protein (the 553^rd amino acid).8-11 By those factors, our team also selected Tar domains involving the 1st – 33^rd and 191^st – 553^rd amino acid as part of chimeric protein.

Figure 1. The selected segment of Tar protein. The functional intracellular domain of Tar is shown as yellow box, blue box is transmembrane domain and orange box is periplasmic domain. Selected Tar domain expands from 1st -33^rd amino acids and 191^st -553^rd amino acids. Modification of binding domain is located between 33^rd – 191^st amino acids

Our team have predicted the HB-EGF/Tar protein orientation in the Escherichia coli membrane. For this purpose, server TMHMM and OPM Membrane, are utilized to predict protein orientation.^12,13 Conceptual hypothesis about the chimera protein is that it should begin its orientation of C-terminus in cytoplasm, then continued to fold into transmembrane and extracellular sites, as well as re-folding towards cytoplasm.

Figure 2. The graph above explains the result of HB-EGF/Tar orientation, which began from C-terminus (left) to N-terminus (right).¹² Y-axis pictured the possibility of n^th amino acid on protein located somewhere between transmembrane (red part), intracellular (blue line), and extracellular (pink line). There is also a diagram located above the graph that represent the most possible location of each domain (with elongated box).

From the results, it could be concluded that the protein was oriented as expected in the hypothesis. Therefore, the usage of chimera protein is predicted to be functional anatomically.

Figure 3.Molecular comparation of HB-EGF native protein (left) with the HB-EGF/Tar fusion (right).^13,14 The pink-coloured domain is intracellularly located as the N-terminus, yellow-coloured domain for the transmembrane one. Then, purple-coloured could be a sign as the extracellular domain, finally folding into transmembrane and back to cytoplasm with orange-coloured and cyan-coloured domain respectively.

Figure 4.3d models of HBEGF-TAR chimera on membrane.

After deciding sequence combination of amino acids in modelled chimera HB-EGF/Tar protein, analyzing the interaction of both fusion protein and diphtheria exotoxin (Figure 5) is extremely important to ensure functional ligand-receptor system. The basic concept of interaction modelling is that the protein will be bound to each other well if it causes the ‘environment’ energy (termed by E parameter; calculated by formula below) being lowered down. In this part, our team sent the respective sequence to ClusPro website for further analyzing.¹⁵

Figure 5. 3d model of proposed diphtox

E = 0.4E_rep + -0.40E_att + 600E_elec + 1.00E_DARS

Note: E_rep and E_attr denote as repulsive and attractive contributions to the van der Waals interaction energy. Additionally, E_elec means an electrostatic energy that occur during both protein interaction. E_DARS is a pairwise structure-based potential constructed by the Decoys of the Reference State (DARS) approach, and it primarily represents desolvation contributions, i.e., the free energy change due to the removal of the water molecules from the interface.¹⁵

Table 4. Comparation of E parameter of native and chimera protein of HB-EGF interacted with DiphTox.

HB-EGF
Protein

Median Energy (kcal/mol)

Lowest Energy (kcal/mol)

Native
-944.3

-994.3

Chimera
858.2

934.4

Figure 6. HB-EGF/Tar receptor-DiphTox 3D interaction modelling result.

Figure 7.HB-EGF natural receptor and DiphTox 3D interaction modelling result.

The result of interaction modelling is quantified as energy score based on the formula above. Referring to figure 6 and 7, we might expect that the DiphTox (cyan) would bind to both native and chimeric HB-EGF receptor that are both located in the extracellular (green). It is indicated by higher energy score of interaction between chimeric HB-EGF/Tar receptor-DiphTox than that of to HB-EGF natural receptor-DiphTox (Table 4). This means that the chimeric receptor could bind towards DiphTox as good (or even better) than the original one.

Beside the cell’s ability to detect toxin, our team also need to ensure the signaling machine works well. Our team also modelled the interaction between LuxA dan LuxB (that we fused with CheA). From figure 8 and 9, we might expect that both proteins are still able to interact normally after combining them with FRET unit (CheA or CheY protein).

Table 5. Comparation of E parameter of native and His-tagged protein of LuxB-CheA.

LuxAB	Median Energy (kcal/mol)	Lowest Energy (kcal/mol)
Native	-1515.4.3	-1553.2
Chimera	-1220.9	-1290.7

Figure 8.LuxA and LuxB-CheA (LuxAB-CheA) 3D interaction modelling result.

Figure 9..LuxA and LuxB 3D interaction modelling result.

Reference:

J Yang, R Yan, A Roy, D Xu, J Poisson, Y Zhang. The I-TASSER Suite: Protein structure and function prediction. Nature Methods, 12: 7-8 (2015)
A Roy, A Kucukural, Y Zhang. I-TASSER: a unified platform for automated protein structure and function prediction. Nature Protocols, 5: 725-738 (2010)
Y Zhang. I-TASSER server for protein 3D structure prediction. BMC Bioinformatics, vol 9, 40 (2008)
Sun, S., Yang, X., Wang, Y., Shen, X., 2016. In Vivo Analysis of Protein–Protein Interactions with Bioluminescence Resonance Energy Transfer (BRET): Progress and Prospects. International Journal of Molecular Sciences 17, 1704. https://doi.org/10.3390/ijms17101704
MUSTANG: A multiple structural alignment algorithm Konagurthu AS, Whisstock JC, Stuckey PJ, Lesk AM (2006) Proteins 64,559-574
Bordogna, A., Pandini, A., Bonati, L., 2010. Predicting the accuracy of protein-ligand docking on homology models. Journal of Computational Chemistry 32, 81–98. https://doi.org/10.1002/jcc.21601
Carugo, O., 2003. How root-mean-square distance (r.m.s.d.) values depend on the resolution of protein structures that are compared. Journal of Applied Crystallography 36, 125–128. https://doi.org/10.1107/s0021889802020502
Kanchan, K., Linder, J., Winkler, K., Hantke, K., Schultz, A. and Schultz, J. (2009). Transmembrane Signaling in Chimeras of the Escherichia coli Aspartate and Serine Chemotaxis Receptors and Bacterial Class III Adenylyl Cyclases. Journal of Biological Chemistry, 285(3), pp.2090-2099.
Ward, S., Delgado, A., Gunsalus, R. and Manson, M. (2002). A NarX-Tar chimera mediates repellent chemotaxis to nitrate and nitrite. Molecular Microbiology, 44(3), pp.709-719.
Melchers, L. S., Regensburg-Tuïnk, T. J., Bourret, R. B., Sedee, N. J., Schilperoort, R. A. and Hooykaas, P. J. (1989). Membrane topology and functional analysis of the sensory protein VirA of Agrobacterium tumefaciens. The EMBO Journal, 8(7), pp.1919-1925.
Weerasuriya, S., Schneider, B. M. and Manson, M. D. (1998). Chimeric Chemoreceptors in Escherichia coli: Signaling Properties of Tar-Tap and Tap-Tar Hybrids. Journal of Bacteriology, 180(4), pp.914-920.
Cbs.dtu.dk. (2018). TMHMM Server, v. 2.0. [online] Available at: http://www.cbs.dtu.dk/services/TMHMM/ [Accessed 22 Jul. 2018].
Lomize M.A., Pogozheva I,D, Joo H., Mosberg H.I., Lomize A.L. OPM database and PPM web server: resources for positioning of proteins in membranes. Nucleic Acids Res., 2012, 40(Database issue):D370-6
Kelley LA et al. (2015). The Phyre2 web portal for protein modeling, prediction and analysis. Nature Protocols 10, pp.845-858
Kozakov D, Hall DR, Xia B, Porter KA, Padhorny D, Yueh C, Beglov D, Vajda S. The ClusPro web server for protein-protein docking. Nature Protocols.2017 Feb;12(2):255-278 ; pdf
Kozakov D, Beglov D, Bohnuud T, Mottarella S, Xia B, Hall DR, Vajda, S. How good is automated protein docking? Proteins: Structure, Function, and Bioinformatics, 2013 Aug ; pdf
Kozakov D, Brenke R, Comeau SR, Vajda S. PIPER: An FFT-based protein docking program with pairwise potentials. Proteins. 2006 Aug 24; pdf
Comeau SR, Gatchell DW, Vajda S, Camacho CJ. ClusPro: an automated docking and discrimination method for the prediction of protein complexes.Bioinformatics. 2004 Jan 1; pdf
Comeau SR, Gatchell DW, Vajda S, Camacho CJ. ClusPro: a fully automated algorithm for protein-protein docking Nucleic Acids Research. 2004 Jul 1; pdf
Högbom, M., Eklund, M., Nygren, P. Å., & Nordlund, P. (2003). Structural basis for recognition by an in vitro evolved affibody. Proceedings of the National Academy of Sciences, 100(6), 3191-3196.
2015.igem.org. (2018). Team:Stockholm/Description - 2015.igem.org. [online] Available at: https://2015.igem.org/Team:Stockholm/Description [Accessed 22 Jul. 2018].

Diphteria Toxin

To ensure the DiphTox has no toxicity, we performed literature search. First, we need to know every domain that construct the whole Diphteria Toxin (DT). We were using online databases such as Uniprot (https://www.uniprot.org/uniprot/Q5PY51) and Interpro (https://www.ebi.ac.uk/interpro/protein/Q5PY51).[1,2] We found that DT consists of three domains (Figure 1), which each of them has specific function.[3]

Figure 1..Diphteria toxin domain.

DT is classified into AB toxin which A fragment consists of C (catalytic) domain and B fragment consists of T (translocation) and R (recognition) domain (Figure 2).[3]

Figure 2.Diphteria toxin as AB toxin.

To cause cell intoxication, first DT binds to proHBEGF on surface membrane. EGF like domain on proHBEGF receptor binds the R domain of DT. Receptor bound toxin is concentrated in clathrin coated pits and internalized into clathrin coated vesicle. With the increase of vacuolar ATPase activity, the pH inside vesicle is decreased. The decrease of pH will change the conformational structure of DT, especially T domain. T domain function is to translocate C domain to cytosol. There are many hypotheses about the mechanism of C domain translocation from lumen to cytosolic space. After C domain is delivered to cytosol, it undergoes structural change into enzymatically active conformation and catalyzes NADdependent ADP ribosilation of EF-2, leading to cellular death (apoptosis) via protein synthesis inhibition. One publication said that only A substance can lead to cellular death. The mechanism of intoxication is summarized in figure 3.[4,5]

Figure 3.. Intoxication of DT.

From this theory, we conclude that A fragment or C domain is the main cause of cytotoxic activity. T and R domain function is only for C domain delivery into cytosol. In order to maximize the safety, we want to take the toxin as small as we can but still retain the binding activity of DT. Publication by John M. Rolf and Leon Eidels said that last 54 amino acids (482- 535) are sufficient enough to make HT and HBEGF bind. These 54 amino acids is part of R domain which has no cytotoxicity effect.[6]

Reference

https://www.uniprot.org/uniprot/Q5PY51
https://www.ebi.ac.uk/interpro/protein/Q5PY51
Gillet D, Barbier J. Diphtheria toxin. The Comprehensive Sourcebook of Bacterial Protein Toxins. 2015;:111-132.
Murphy J. Mechanism of Diphtheria Toxin Catalytic Domain Delivery to the Eukaryotic Cell Cytosol and the Cellular Factors that Directly Participate in the Process. Toxins. 2011;3(3):294-308.
Yamaizumi, M.; Mekada, E.; Uchida, T.; Okada, Y. One molecule of diphtheria toxin fragment A introduced into a cell can kill the cell. Cell 1978, 15, 245–250.
Rolf J, Eidels L. Characterization of the diphtheria toxin receptor-binding domain.Molecular Microbiology. 1993;7(4):585-591.

Kinetical Modelling

In synthesizing any designed system of engineered protein, one must able to oversee and predict regarding its performance and behaviour. In analysing the protein, one can apply principles of mathematics and engineering in doing so. Reaction kinetics study can be largely described as the study of reactions regarding their performance such as rates, effects of various variables, etc. In this report, the kinetics of the designed protein will be evaluated based on existing literature. Mass transportation principle, such as Langmuir-Hill or Michaelis-Menten equation, will be loosely applied and adequately correlated for the resulting models. Mathematical models and non-linear regression will play roles for the protein. Out team hopes that this mathematical modelling analysis for the protein will shed a new perspective for the synthesized protein’s function and enlighten some parts of our project.

Qualitative Overview

Figure 1. The figure above describes the event of diphtheria toxin initial binding to the HB-EGF receptors in human epithelial cells. Attached toxin will inhibit protein synthesis and result in apoptosis.

Figure 2. The figure above describes the difference between the chimeric protein performance’s regarding of its reaction with diphtheria toxin.

Thus, in overseeing the entire performance, it can be divided into three parts:

HB-EGF binding to the diphtheria toxin

TAR protein’s binding performance

Reaction rates of the auto-phosphorylation process

Quantitative Overview

In calculating reaction rates of either association or dissociation rate, the following mathematical equations are applied:

The surface concentration Gamma (G), for a protein can be calculated by the following formula (3): One Response Unit (RU) in the Blacore 2000/3000 machine corresponds to a surface coverage of 10^-6 g m^-2 for a typical protein. A 100 kDa protein generating a response of 1000 RU, corresponds to a surface coverage of 10^-8 moles m^-2.

Formulas:

Response unit, which is the symbol for reaction rates’ response that are given directly by the response machine will first be converted into gamma (surface concentration), which will then be converted into concentration of complex with the following formula:

Therefore, reaction rates can be calculated with following formula:

The formula is synthesized by following the mass-transportation-based reaction rates, which is based on chemical kinetics of the reaction’s constituent.

HB-EGF Receptor Activity

Part I: Association and Dissociation Rates Based on Time

This part of analysis is mainly referenced to an article by Brooke et.al. [1], in which they model binding of immobilized HB-EGF with diphtheria toxin response unit versus time, which will be shown below:

Figure 3.Binding of DT to immobilized hHB-EGF. The first arrow represents the start of the injection of DT over the immobilized hHB-EGF. The arrow with a ball represents the end of the DT injection and the beginning of the flow running buffer. From the bottom curve to the top curve, the respective binding curves for DT of 400 nM, 500 nM, 600 nM, 700 nM, 800 nM and 1 μM concentrations are shown. The number ①, ②, and ③ represent the baseline of running buffer, the association phase of the binding of DT to hHB-EGF, and the dissociation phase of the release of DT from hHB-EGF, respectively, RU, resonance units. This figure shows a representative experiment (use Materials and Methods).

Therefore, the prediction of both association rates and dissociation rates are shown below, by comparing our chimeric protein’s kinetics with literature, the x axis represents time, while the y axis represents response in Response Unit (RU).

Association Rates

Table 1.HB-EGF Receptor Properties

Table 2.Calculation Table of Diphtheria Toxin and HB-EGF Association Rate

Therefore, the data regression will be plotted as below:

Figure 4.Association Response of Diphtheria Toxin towards HB-EGF Receptor.

Figure 5.Reaction Rate of Diphtheria Toxin towards HB-EGF Receptor.

Dissociation Rates

Table 3.HB-EGF Receptor Properties

Table 4.Calculation Table of Diphtheria Toxin and HB-EGF Dissociation Rate

Therefore, the data regression will be plotted as below:

Figure 6.Dissociation Response of Diphtheria Toxin towards HB-EGF Receptor.

Figure 7.Reaction Rate of Diphtheria Toxin towards HB-EGF Receptor.

Part II: Rates Based on Concentration

The figure/literature used is still the same as the association/dissociation rates above; therefore, by remodelling and regressing the data based on the literature, one can predict the association and dissociation rates on certain times, with the concentration of the toxin as the independent variable, which is shown at the interactive Excel with this interface:

Table 5.HB-EGF Receptor Properties

Table 6.Association and Dissociation Constants of HB-EGF Receptor.

Table 7.Association and Dissociation Response Unit and Concentration of Natural Diphtheria Toxin and HB-EGF Binding.

Table 8.Calculation of Binding Reaction Rate of HB-EGF and Diphtheria Toxin Based on Concentration.

Table 8.Calculation of Binding Reaction Rate of HB-EGF and Diphtheria Toxin.

Figure 8.The Regression Graph for Response Unit vs Toxin Concentration

The programmed Excel will calculate the reaction rates just by inserting desired concentration.

Part III: Effects of pH and Temperature

Effects of pH

Based on throughout literature review, the closest thing for binding of diphtheria toxin is for Vero Cells [2], which is shown below:

Based on graph above, it can be assumed that the best temperature for diphtheria binding is 4°C. Assumed that the chosen temperature is 4°C, the regression model for said temperature for graph of time versus reaction rate, is shown below (at toxin concentration of 0.06 μM). All of the data and assumption are based on journal.

Table 10.Concentration Counts of Diphtheria Toxin and HB-EGF Complex versus Time

The proposed model will follow arbitrary model based on Hill-Langmuir model, which is:

Our team formulated models (based on utilization of Polymath 6.0 software) based on that with trial and error fit to the data, and the best fitted model would be:

Figure 10.The Estimated Graphs for DT-Vero Cells Binding (Auth’s Personal Data)

Model

Table 11.Result of Polymath 6.0

Non-linear regression settings

Max # iterations = 64

Precision

Table 12.Result of Polymath 6.0 (cont’d).

General

Table 13Result of Polymath 6.0 (cont’d).

The comparison table is shown as below:

Table 14Comparison Data between Literature and Model Regression of Concentration Counts of Diphtheria Toxin and HB-EGF Complex versus Time.

Effects of Temperature

Figure 11.Effect of pH on diphtheria toxin cytotoxicity and association of 125I-labeled diphtheria toxin with cells. Maintenance medium was replaced with complete Hanks’ 199 plus 25 mM HEPES buffer titrated to the pH indicated. For cytotoxicity assay, cells were incubated 3 h with 5 ng/mL of toxin, washed three times with normal media, and incubated a further 48 h. Cytotoxicity (█) was determined as previously described (6). For effects on association, titrated media was added to cells, followed by 125I-toxin (0.03 μg/mL) or 125I-toxin plus unlabelled toxin (0.03 μg/mL). after 2 hours at 37℃ (○) or 12 h at 4℃ (𝛥) cells were washed and radioactivity was assayed as usual.

Figure 12.Dissociation of DT from immobilized hHB-EGF in running buffers of decreasing pH. The results for DT of 600 nM concentration in running buffers of pH 6.9, 6.4 and 5.8 are shown. The association phase (pH 7.4) of these curves is not shown. The origin represents the end of the DT injection and is the time at which the running buffer of specific pH has started flowing over the sensorchip, RU, resonance untis. This figure shows a representative ecperiment (see Materials and Methods).

Based on the graphs above, one can infer that:

In smaller pH (more acidic), the rates of dissociation is faster; nevertheless, once the pH is below 5.3, there will be no change in the rate since the intoxicated cells have already broken (lysis).

In the human epithelial tissues, the intoxicated cells will not be destroyed although the pH is 5.3 as the environment temperature is close to 370C.

For research insight, the recommended temperature for testing binding rates is 25°C with range of pH between 5.6-5.9.

Can’t be mathematically modelled since the data is not sufficient for accurate modelling. The decision is made by engineering rule of thumb, in which data less than 5, is not reliable enough to be modelled into linear or non-linear regression, since the potential deviation is large.

Tar Binding

The Tar binding model would be based on two research articles with different models, which are from [3] and [4]. Modelling based on [3] is as below:

Figure 13.Time versus Response for TAR16 Binding to TAR*16 [3]

Association Rate

Table 15.Properties of TAR Receptor

Table 16.TAR Dissociation and Association Constant and Concentration Receptor

Table 17.Calculation Table of TAR Binding Association Rate

Figure 15.Association Response of Tar Binding.

Figure 16.Association Reaction Rate of Tar Binding.

Dissociation Rate

Table 18.Properties of TAR Ligand (where TAR is ligand, and TAR16 is the receptor).

Table 19.TAR Receptor Dissociation and Association Constant and Concentration

Table 20.Calculation Table of TAR Binding Dissociation Rate

Figure 17.Dissociation Response of Tar Binding.

Figure 18.Dissociation Reaction Rate of TAR Binding

Alternative Modelling

Modelling based on [4] is described as below:

Figure 19.Time versus Frequency henge for TAR to Tat Binding Process. [4]

This model use different unit than before, in which the response is measured by the frequency change in the biosensor, therefore, the initial equation that are used is:

where,
Δf = the observed frequency change (Hz)
Δm = the change in mass per unit area (in g/cm2)
Cf = the sensitivity factor for the crystal used (i.e. 56.6 Hz μg-1 cm2 for a 5 MHz AT-cut quartz crystal at room temperature.)

After finding the mass per unit area, the response unit can be calculated, and from that, the calculation is the same as before.

Association Rates

Table 21.Properties of Tar Ligand (where TAR is ligand, and TAR16 is the receptor).

Table 22.Tar Receptor Dissociation and Association Constant and Concentration

Table 23.Tar Binding Association Rates Calculation Table.

Figure 20.Association Frequency Change of Tar Binding.

Figure 21.Association Reaction Rate of Tar Binding.

The similar method of finding reaction rates as association rates is applied on the calculation of dissociation rates:

Dissociation Rates

Table 24.Tar Binding Dissociation Rates Calculation Table.

Figure 22.Dissociation Response of Tar Binding.

Figure 23.Dissociation Reaction Rate of Tar Binding.

Tar Binding Rates Based on Concentration

As for the same with HB-EGF binding above, we formulate Excel so it can predict the reaction rates based on the input of concentration, as shown below:

CheA Autophosphorylation

The source of the model is from [5]. The literature elaborates on the quantitative modelling of CheA autophosphorylation process, in which graph below left, models on the ratio of concentration of CheA-P at a certain time (P-CheA_t or can be modelled as (〖dC〗_(〖P-CheA〗_t ))) to the maximum concentration of CheA-P that can be achieved. The graph on the right, shows the ratio of concentration of CheA-P at a certain time (P-CheA_t or can be modelled as 〖dC〗_(〖P-CheA〗_t )/t) to the initial concentration of CheA-P:

Figure 24.Ratio Between Concentration of CheA-P at a Certain Time to Concentration of CheA-P at a Threshold Tike (Left: Maximum Right: Minimum) versus Time [5]

With the similar approach to the modelling of association rate of Diphtheria toxin and HB-EGF, the overview and thus prediction of the autophosphorylation process can be modelled, as below:

Table 25.CheA Properties and Variables Needed.

Table 26.Ratio of The Change of CheA-P Concentration Against Time to the Maximum Concentration of CheA-P Achieved versus Time

Based on derived Langmuir-Hill model as explained above, the model used for above data is:

Figure 25.The Estimated Graphs of Ratio between Concentration of CheA-P at a Certain Time to Maximum Concentration of CheA-P versus Time (Auth’s Pers Data).

Table 27.Result of Polymath 6.0/h6>

Nonlinear regression settings
Max # iterations = 64
Precision

Table 28.Result of Polymath 6.0

General

Table 29.Result of Polymath 6.0

Source data points and calculated data points.

Table 30.Comparison of Data between Literature and Model Regression of Ratio of The Change of CheA-P Concentration Against Time to the Maximum Concentration of CheA-P Achieved versus Time.

ANALYSIS
General Analysis
Most of the base literature and reference model for all of the kinetics, be it DT and HB-EGF, Tar-agent, or autophosphorylation process, are based on the fundamental understanding of macro and micro mass-transfer in accordance of engineering’s perspective, in which:

Which means that the rate of mass transfer (in this case): toxin/agent transport to be bound by the receptor, is affected by the agent’s own mass constituent (for example: concentration, or mass ratio) times its multiplier constant (sometimes called rate-limiting step), therefore, if one seeks to model the equation above, the first step to do is to linearize the differential equation by integrating it.
The resulting graph, if it is in linear progression, will result in f(x) = ln(x) graph, like below:

Figure 26.The graph of f(x) = ln(x).

The resulting shape signifies that mass transportation will progress quickly in the early stage and decline slowly in the later stage, which can be explained by basic science and engineering, in which a group of mass will transport faster when there are bulk of them, signifying a push force between them, causing them to move faster, and then will gradually decline when the mass is mostly transferred, since there are no other force to move them, until it finally hits hypothetical saturated condition when it finally hits its peak.

As for the reaction rates, the model will follow the engineering principle of chemical kinetics, in which the reaction rates will be maximum in the beginning, since the binding process happens maximum in the beginning, and gradually becomes much slower as the binding process becomes progressively slower too.

Other base modelling for the mass-transport is Langmuir-Hill model, in which the binding of a ligand to a receptor/macromolecule will be faster in the presence of another ligand in close proximity. The resulting graph will have similar shape to the f(x) = ln(x) graph since the models basically have similar fundamental base, which can be found in the autophosphorylation graph.

In reality, especially in micro-molecular protein complex, of course, there are difference and complex’s own signature characteristics, regarding their rules in mass transportation, that will be explained in detail as follows:

HB-EGF Binding

Figure 27.Association Response of Diphtheria Toxin to HB-EGF.

Figure 28.Reaction Rate of Diphtheria Toxin to HB-EGF.

Figure 29.Dissociation Response of Diphtheria Toxin to HB-EGF.

Figure 30.Reaction Rate of Diphtheria Toxin to HB-EGF.

Both response unit and reaction rates for both association and dissociation reaction roughly follows the same mass transfer principle, in which the response unit (the existence of the mass) will grow faster in the early stage and then will gradually become slower in the later stage. The reaction rates will follow directly from the response unit, therefore, the reaction rates will reach its peak in the beginning and begins to falter until it reaches the maximum. The literature base [1] confirms and give another insight to the analysis, in which the Diphtheria toxin-association is environment dependent, while the release of Diphtheria Toxin (Dissociation) is rate-limiting step. The paper also elaborates that there might another additional factor regarding of interaction of toxin to HB-EGF.

The final regression of the data ultimately utilizes polynomial equation since it gives the largest R² which means the smallest deviation, so it can be utilized for future interpolation or extrapolation purpose.

Effects of pH and Temperature on HB-EGF Binding
Effects of pH

Figure 31.The Estimated Graphs for DT-Vero Cells Binding (Auth’s Personal Data)

Since the base literature on the effects of pH to the HB-EGF binding is limited, the consensus of the best temperature for the binding process will be elaborated on our own experimentation, meanwhile the literature result in temperature of 4°C for binding process graph is shown above, in which the shape of the graph follows Langmuir-Hill principle. The final regression of the data utilizes the modified version of Langmuir-Hill equation, in which the team combines both trial and error and iteration method to achieve the highest R².

TAR-Binding Kinetics
Effects of Temperature
Since the literature reference of pH influence on the binding process is also limited, this is the analysis and our consensus regarding of the pH effects.

In smaller pH (more acidic), the rates of dissociation is faster; nevertheless, once the pH is below 5.3, there will be no change in the rate since the intoxicated cells have already broken (lysis).

In the human epithelial tissues, the intoxicated cells will not be destroyed although the pH is 5.3 as the environment temperature is close to 37°C.

For research insight, the recommended temperature for testing binding rates is 25°C with range of pH between 5.6-5.9.

Can’t be mathematically modelled since the data is not sufficient for accurate modelling. The decision is made by engineering rule of thumb, in which data less than 5, is not reliable enough to be modelled into linear or non-linear regression, since the potential deviation is large.

Can’t be mathematically modelled since the data is not sufficient for accurate modelling. The decision is made by engineering rule of thumb, in which data less than 5, is not reliable enough to be modelled into linear or non-linear regression, since the potential deviation is large.

The Tar binding kinetics use the same principle and method of calculation as the DT-HBEGF binding reaction, therefore the graph:

Figure 32.Association Response of Tar Binding.

Figure 33.Reaction Rate of Tar Binding.

Figure 34.Dissociation Response of Tar Binding.

Figure 35.Reaction Rate of Tar Binding.

Figure 36.Association Frequency Change of TAR Binding

Figure 37.Association Reaction Rate of Tar Binding.

Figure 38.Dissociation Response of Tar Binding.

Figure 39.Dissociation Reaction Rate of Tar Binding.

The alternative modelling is done to further prove that the TAR-binding kinetics toward ligands will also follow the mass-transfer principle. The polynomial regression is used since it give the biggest R² (smallest deviation)

Auto-phosphorylation of CheA

Figure 40.The Estimated Graphs of Ratio between Concentration of CheA-P at a Certain Time to Maximum Concentration of CheA-P versus Time (Auth’s Pers Data)

This graph also closely follow the Langmuir Hill principle, even though the formula linearization and conception is not as simple as the aforementioned process, since autophosphorylation process has more elaborate steps, which is:

The process above has more elaborate formulation of model in which the final model will roughly constitute of this equation:
x = time
x = initial time
K = total constant of reactions

When one sees the formula, it can be roughly estimated to form a similar ln graph with similar shapes to the Langmuir-Hill approximation too, therefore the final and simplified model of the autophosphorylation process will be fitted to modified Langmuir-Hill equation by trial and error and iteration to achieve the highest R².

Reference :

Brooke, J. S., Cha, J. H., & Eidels, L. (1998). Diphtheria toxin: receptor interaction: association, dissociation, and effect of pH. Biochemical and biophysical research communications, 248(2), 297-302.

Middlebrook, J. L., Dorland, R. B., & Leppla, S. H. (1978). Association of diphtheria toxin with Vero cells. Demonstration of a receptor. Journal of Biological Chemistry, 253(20), 7325-7330.

Nair, T. M., Myszka, D. G., & Davis, D. R. (2000). Surface plasmon resonance kinetic studies of the HIV TAR RNA kissing hairpin complex and its stabilization by 2-thiouridine modification. Nucleic Acids Research, 28(9), 1935–1940

Tassew, N., & Thompson, M. (2003). Kinetic characterization of TAR RNA–Tat peptide and neomycin interactions by acoustic wave biosensor. Biophysical chemistry, 106(3), 241-252.

Tawa, P., & Stewart, R. C. (1994). Kinetics of CheA autophosphorylation and dephosphorylation reactions. Biochemistry, 33(25), 7917-7924

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-       <a class="navbar-brand" href="https://2018.igem.org/Team:UI_Indonesia/">Home</a>
+       <a class="navbar-brand" href="https://2018.igem.org/Team:UI_Indonesia">Home</a>
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-           <a href="asd.html">Project<span class="caret"></span></a>
+           <a href="https://2018.igem.org/Team:UI_Indonesia/Project#">Project<span class="caret"></span></a>
            <ul class="dropdown-menu">
              <li><a href="https://2018.igem.org/Team:UI_Indonesia/Project#">Overview</a></li>
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-		<li class="navbar-safety"><a href="https://2018.igem.org/Team:UI_Indonesia/Safety">Safety</a></li>
+    <li class="navbar-safety"><a href="https://2018.igem.org/Team:UI_Indonesia/Safety">Safety</a></li>
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+    <li class="dropdown navbar-interlab">
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-			<li><a href="https://2018.igem.org/Team:UI_Indonesia/InterLab#results">Results and Discussions</a></li>
+      <li><a href="https://2018.igem.org/Team:UI_Indonesia/InterLab#results">Results and Discussions</a></li>
-			<li><a href="https://2018.igem.org/Team:UI_Indonesia/InterLab#conclusions">Conclusions</a></li>
+      <li><a href="https://2018.igem.org/Team:UI_Indonesia/InterLab#conclusions">Conclusions</a></li>
            </ul>
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-		<li class="navbar-model"><a href="https://2018.igem.org/Team:UI_Indonesia/Model">Model</a></li>
+    <li class="navbar-model current"><a href="https://2018.igem.org/Team:UI_Indonesia/Model">Model</a></li>
-		<li class="dropdown navbar-humanpractice">
+    <li class="dropdown navbar-humanpractice">
-           <a href="https://2018.igem.org/Team:UI_Indonesia/HumanPractices">Human Practices<span class="caret"></span></a>
+           <a href="https://2018.igem.org/Team:UI_Indonesia/Human_Practices">Human Practices<span class="caret"></span></a>
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-             <li><a href="https://2018.igem.org/Team:UI_Indonesia/HumanPractices#collapse1">Campus Campaign</a></li>
+             <li><a href="https://2018.igem.org/Team:UI_Indonesia/Human_Practices">Integrated Human Practice</a></li>
-             <li><a href="https://2018.igem.org/Team:UI_Indonesia/HumanPractices#collapse2">Social Work</a></li>
+             <li><a href="https://2018.igem.org/Team:UI_Indonesia/Public_Engagement">Education and Public Engagement</a></li>
-             <li><a href="https://2018.igem.org/Team:UI_Indonesia/HumanPractices#collapse3">Biosafety & Biosecurity Training</a></li>
+             <li><a href="https://2018.igem.org/Team:UI_Indonesia/Human_Practices#catalogue">Human Practice Catalogue</a></li>
-			<li><a href="https://2018.igem.org/Team:UI_Indonesia/HumanPractices#collapse4">Human Practice Catalogue</a></li>
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+    <li class="navbar-team"><a href="https://2018.igem.org/Team:UI_Indonesia/Team">Team</a></li>
-		<li class="navbar-collaborations"><a href="https://2018.igem.org/Team:UI_Indonesia/Collaborations">Collaborations</a></li>
+    <li class="navbar-collaborations"><a href="https://2018.igem.org/Team:UI_Indonesia/Collaborations">Collaborations</a></li>
-		<li class="navbar-attributions"><a href="https://2018.igem.org/Team:UI_Indonesia/Attributions">Attributions</a></li>
+    <li class="navbar-attributions"><a href="https://2018.igem.org/Team:UI_Indonesia/Attributions">Attributions</a></li>
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-<!-------------------------TEST------------------------------------------->
+<!-- Container (Introduction Section) -->
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-	<div class="w3-content w3-container w3-padding-64 headerTestDiv"">
-		<div class="headerTest">Improve</div>
-		<div class="header2ndTest">Overview</div>
-	</div>
-	<!-------------------Accordion--->
-	<div class="w3-content w3-container" style="padding:0px;">
-		<div class="panel-group" id="accordion">
-			<div class="panel panel-default">
-			  <div class="panel-heading">
-				<h4 class="panel-title">
-				  <a data-toggle="collapse" data-parent="#accordion" href="#collapse1">Introduction</a>
-				</h4>
-			  </div>
-			  <div id="collapse1" class="panel-collapse collapse">
-				<div class="panel-body">
-					<h5>As a contribution of iGEM UI 2018 team to improve existing parts in iGEM Registry, we decided to assemble “Improved Blue Fluorescent Protein (BFP)” (<a href="http://parts.igem.org/Part:BBa_K2607002">BBa_K2607002</a>, <b>Figure 1</b>) as improvement of B0034-BFP (<a href="http://parts.igem.org/Part:BBa_K592024">BBa_K592024</a>). Our established part consists of these following additional features:</h5>
-					<br>
-					<div class="w3-row w3-center">
-					<img src="https://static.igem.org/mediawiki/2018/9/99/T--UI_Indonesia--Figure1Improve.png" class="w3-image" width="700">
-					<h6><b>Figure 1.</b> Original designed “Improved BFP” (BBa_K2607002) gBlock we ordered from Integrated DNA Technologies, Inc. (IDT) with length of 1151 basepairs (bp). Note that extra 42 bases in the upstream and 41 bases in the downstream are BioBrick prefix and suffix and for polymerase chain reaction (PCR) amplification purpose. The part itself consists of 1068 bp. Range number in red indicates the position of each component in the gBlock. Rbs = ribosome binding site.</h6></div>
-					<h5><ul>
+<h1 align="center"> Structural Modelling </h1>
-					<li>lacI regulated promoter (<a href="http://parts.igem.org/Part:BBa_R0010">BBa_R0010</a>) in the upstream</li>
+<h3 align="center"> Chimera Combination </h3>
-					<li>Double terminator (<a href="http://parts.igem.org/Part:BBa_B0010">BBa_B0010</a> and <a href="http://parts.igem.org/Part:BBa_B0012">BBa_B0012</a>) in the downstream</li>
-					<li>SalI restriction site between the promoter and ribosome binding site</li>
-					<li>NdeI restriction site between the ribosome binding site and coding sequence</li>
-					<li>Modified blue fluorescence protein coding sequence to eliminate SalI restriction site in the middle without altering amino acid sequence.</li></ul></h5>
-					<h5>This part can be used as reporter when combined with other parts. Because this part has its own promoter, ribosome binding site, and terminator, addition of these components is basically unnecessary. In addition, appropriate restriction enzyme (NdeI or SalI) can also be used to replace the promoter or ribosome binding site with the desired one.</h5>
-				</div>
+<div class="w3-content w3-container w3-padding-64">
-			  </div>
+  <h5>Our first steps in modelling the subsequent parts of <i>Finding Diphthy</i> iGEM-UI 2018 <i>in silico</i> is by
-			</div>
+  constructing all 3D models via <i>I-Tasser</i> server.<sup>1,2,3</sup> The extension of the product file is <i>.pdb</i>, that
-		<div class="panel panel-default">
+  could be read by the server. The chimera molecules which we need to predict their modelling are HB-EGF/TAR
-			<div class="panel-heading">
+  (<i>Heparin Binding Epidermal Growth Factor-</i> TAR chemotaxis), CheA signalling protein, Che-Y signalling protein,
-				<h4 class="panel-title">
+  LuxAB dimerized luciferase subunits, and eYFP (<i>enhanced yellow fluorescent protein</i>), as well as DiphTox
-				<a data-toggle="collapse" data-parent="#accordion" href="#collapse2">Methods</a>
+  (modified diphtheria exotoxin). Since CheA and CheY are required to be linked with LuxB or eYFP,
-				</h4>
+  we have cited one of the universal linker, that is ‘GGGSGGGGSGGGGSG’ peptides, according to <i>Sun S et al</i>.
-			</div>
+  <br>
-			<div id="collapse2" class="panel-collapse collapse">
+Our signalling part of the project is referred these sequences of all chimera combinations.
-				<div class="panel-body">
+<br>
-					<h5><b>Figure 2</b> shows the original plan of how we conducted the experiment with our established and existing parts.</h5>
+<br>
-					<br>
+<ul>
-					<div class="w3-row w3-center">
+<li>LuxB-CheY
-					<img src="https://static.igem.org/mediawiki/2018/f/f6/T--UI_Indonesia--Figure2Improve.png" class="w3-image" width="700">
+<li>LuxB-CheA
-					<h6><b>Figure 2.</b> Original plan workflow with newly-established (BBa_K2607002) and existing (BBa_K592024) parts of BFP.</h6></div>
+<li>CheY-eYFP
-					<h5><b><i>Part I: Gel Electrophoresis Confirmation</i></b></h5>
+<li>CheA-eYFP
-					<h5>Upon receiving our “Improved BFP” gBlock from IDT, we performed PCR to amplify it until sufficient quantity (about 100 ng/uL). We then created two restriction digestion reactions, one with NdeI restriction enzyme and one with SalI restriction enzyme. Each reaction comprised of 100 uL PCR-amplified “Improved BFP” gBlock, 6 uL restriction enzyme, 15 uL CutSmart<sup>®</sup> restriction buffer, and 29 uL nuclease-free water. The reaction was subsequently incubated for four hours at 37<sup>o</sup>C incubator. The digestion products were then run for electrophoresis in 1% agarose gel with tris-acetic acid-EDTA (TAE) buffer 1x, power supply 50 volt for 70 minutes. Visualization was performed with Bio-Rad Gel Doc<sup>TM</sup> XR+ Gel Documentation System.</h5>
+</ul>
-					<br>
+<br>
-					<h5><b><i>Part II: Ultraviolet (UV) Check</i></b></h5>
-					<h5>First, we acquired BBa_K592024 by transforming wild-type <i>Escherichia coli</i> K-12 with plasmid in well 17C of Distribution Kit Plate 1, isolating the plasmid, and performing restriction with EcoRI and PstI restricton enzymes. Our “Improved BFP” and BBa_K592024 were separately cloned into plasmid pQE80L using EcoRI and PstI according to our laboratory protocol. The resultant ligation products were subsequently transformed into wild-type <i>E. coli</i> K-12, along with empty pQE80L as control. Transformation products were spread into Luria-Bertani (LB) agar medium containing ampicillin with ratio 1000:1. On the following day, the fluorescence was assessed qualitatively under ultraviolet (UV) light.</h5>
-					<br>
-					<h5><b><i>Part III: Fluorescence Assay</i></b></h5>
-					<h5>Fluorescence assay was conducted according to 5<sup>th</sup> InterLab protocol with some modifications. From each transformation plates, two colonies were picked and let to be grown in LB medium with ampicillin (1000:1). Two colonies of <i>E. coli</i> containing empty plasmid pQE80L were also inoculated for control. After 18 hours, each overnight culture was diluted until optical density at 600 nm (OD<sub>600</sub>) of 0.02 and final volume of 12 mL in 50 mL centrifuge tube covered with foil. At t = 0 hour, 1 mL from each tube was sampled to be measured for its OD<sub>600</sub> and fluorescence. The measurement was conducted with GloMax<sup>®</sup> - Multi Detection System with “UV” settings: 365 nm excitation and 410-460 nm emission. The data obtained were recorded and analyzed in Excel. The rest of dilutions were incubated in shaker under 37<sup>o</sup>C, 220 rotations per minute (rpm). We repeated the same procedure after t = 6 hour and overnight (t = 14 hour) incubation.</h5>
-				</div>
-			</div>
-		</div>
-		<div class="panel panel-default">
-			<div class="panel-heading">
-				<h4 class="panel-title">
-				<a data-toggle="collapse" data-parent="#accordion" href="#collapse3">Results and Discussions</a>
-				</h4>
-			</div>
-			<div id="collapse3" class="panel-collapse collapse">
-				<div class="panel-body">
-					<h5><b><i>Part I: Gel Electrophoresis Confirmation</i></b></h5>
-					<br>
-					<div class="w3-row w3-center">
-					<img src="https://static.igem.org/mediawiki/2018/b/bb/T--UI_Indonesia--Figure3Improve.png" class="w3-image">
-					<h6><b>Figure 3.</b> Gel electrophoresis of restriction of “Improved BFP” gBlock with 1% agarose gel, TAE 1x, 50 V, and 70 minutes. Lane 1 = BioLabs, Inc. 100 bp DNA ladder (#N3231L) with size range 100-1517 bp. Lane 2 = uncut PCR-amplified “Improved BFP” gBlock (expected size 1151 bp). Lane 3 = “Improved BFP” gBlock cut with NdeI. Lane 4 = “Improved BFP” gBlock cut with SalI.</h6>
-					</div>
-					<h5>Restriction result of PCR-amplified “Improved BFP” gBlock is shown in <b>Figure 3</b>. At lane 2 consisting of uncut “Improved BFP” gBlock (size 1151 bp), only single band is observed. The band is located between 1000-1200 bp marker at lane 1, which is consistent to the gBlock original size. Upon restriction with NdeI, the expected bands are 271 bp and 880 bp in size, while restriction with SalI will yield expected bands of 249 bp and 902 bp. At lane 3 and 4, there are three bands observed, indicating that the gBlocks were partially digested with respective enzymes, NdeI and SalI. The heaviest bands in lane 3 and 4 were parallel with the band in lane 2, indicating that the bands were uncut "Improved BFP" gBlocks. The middle bands in lane 3 and 4 were similar in size (around 900 bp in size), representing the larger fragments of digestion products with their respective enzymes. The lightest bands in lane 3 and 4 were also similar in size, located between 200-300 bp marker at lane 1. They represent the smaller fragments of digestion products. The smaller fragment upon NdeI digestion is 271 bp, while upon SalI digestion is 249 bp. The lightest band in lane 3 migrated slightly slower than lightest band in lane 4, denoting larger size of smaller fragment in lane 3 than lane 4, which is consistent to the expected restriction results.</h5>
-					<br>
-					<h5><b><i> Part II: Ultraviolet (UV) Check</i></b></h5>
-					<br>
-					<div class="w3-row w3-center">
-					<img src="https://static.igem.org/mediawiki/2018/a/a1/T--UI_Indonesia--Figure4Improve.png" class="w3-image" width="700">
-					<h6><b>Figure 4</b>. Colony of transformed wild-type <i>Escherichia coli</i> K-12 with empty pQE80L (yellow arrow), pQE80L with “Improved BFP” (green arrow), and pQE80L with BBa_K592024 (orange arrow) in plasmid pQE80L under ultraviolet (UV) illumination.</h6>
-					</div>
-					<h5>Transformation results of wild-type <i>E. coli</i> K-12 with empty pQE80L, pQE80L with “Improved BFP”, and pQE80L with BBa_K592024 under ultraviolet (UV) light is shown in <b>Figure 4</b>. Qualitatively, higher fluorescence was observed in transformed <i>E. coli</i> with “Improved BFP” compared with others. The probable explanation is that our “Improved BFP” has its own promoter. When our part is cloned into pQE80L with EcoRI and PstI, there are two promoters in the upstream of BFP coding region, theoretically leading to more BFP mRNA and protein being produced. However, to further investigate this hypothesis, we performed quantitative BFP fluorescence measurement from three samples (<i>E. coli</i> K-12 with empty pQE80L, pQE80L + “Improved BFP", and pQE80L + BBa_K592024), as will be explained in Part III.</h5>
-					<br>
-					<h5><b><i> Part III: Fluorescence Assay</i></b></h5>
-					<div class="w3-row w3-center">
-					<h6><b>Table 1</b>. Fluorescence and OD<sub>600</sub> measurement of transformed <i>E. coli</i> K-12 with one of three plasmids: empty pQE80L, pQE80L with Improved BFP”, and pQE80L with BBa_K592024. Replicate 1 and 2 were from first colony of respective sampled transformed <i>E. coli</i>, while replicate 3 and 4 were from second colony. Fl = fluorescence in arbitrary unit.</h6>
-					<img src="https://static.igem.org/mediawiki/2018/d/d5/T--UI_Indonesia--Table1Improve.jpeg" class="w3-image" width="700">
-					</div>
-					<br>
-					<div class="w3-row w3-center">
-					<img src="https://static.igem.org/mediawiki/2018/f/fa/T--UI_Indonesia--Figure5Improve.png" class="w3-image">
-					<h6><b>Figure 5</b>. Fluorescence/OD<sub>600</sub> measurement (in mean arbitrary units ± standard deviation) of transformed <i>E. coli</i> with one of three plasmids (empty pQE80L, pQE80L with “Improved BFP”, and pQE80L with BBa_K592024) at t = 0 hour, 6 hour, and 14 hour. * and ** indicate statistically significant differences (p = 0.005 and < 0.001, respectively).</h6>
-					</div>
-					<h5>Complete data of fluorescence assay is shown in <b>Table 1</b>, while the overall result is visualized in <b>Figure 5</b>. At t = 0, 6, and 14 hour, although not shown in Figure 5, fluorescence/OD<sub>600</sub> of transformed <i>E. coli</i> with pQE80L + “Improved BFP” and pQE80L + BBa_K592024 were significantly higher than negative control (transformed <i>E. coli</i> with empty pQE80L). At t = 6 hour, fluorescence/OD<sub>600</sub> of <i>E. coli</i> with “Improved BFP” was significantly higher than <i>E. coli</i> with BBa_K592024 (p = 0.005). Similar result was found after overnight incubation (p < 0.001). These results support previous qualitative observation of fluorescence and may be explained by ‘double promoter’ of “Improved BFP” in pQE80L. In addition, from Figure 5 it is also observed that fluorescence/OD<sub>600</sub> was decreasing along with time. The probable explanation of this result is higher bacterial concentration in medium over time, resulting in larger denominator and reducing fluorescence/OD<sub>600</sub>. Moreover, it is also probable that the bacteria underwent cessation, causing decline in BFP production and resulting in lower fluorescence/OD<sub>600</sub>. Nevertheless, from this experiment, it can be inferred that <i>E. coli</i> transformed with “Improved BFP” retains significantly higher fluorescence/OD<sub>600</sub> than BBa_K592024 under plasmid pQE80L expression system.</h5>
-					</div>
-				</div>
-			</div>
-		</div>
-		<div class="panel panel-default">
-			<div class="panel-heading">
-				<h4 class="panel-title">
-				<a data-toggle="collapse" data-parent="#accordion" href="#collapse4">Conclusions</a>
-				</h4>
-			</div>
-			<div id="collapse4" class="panel-collapse collapse">
-				<div class="panel-body">
-					<h5>To conclude, through experiment with “Improved BFP”, we demonstrated that our improved part can be cut between promoter and ribosome binding site, also between ribosome binding site and coding region with SalI and NdeI restriction enzyme, respectively. Therefore, appropriate restriction enzyme can be utilized to replace promoter or ribosome binding site with the desired ones. When we conducted experiment to compare “Improved BFP” and previous part (BBa_K592024) under pQE80L expression system, it was observed that “Improved BFP” yield more fluorescence/OD<sub>600</sub>, probably due to intrinsic promoter it owned.   </h5>
-				</div>
-			</div>
-		</div>
-	  </div>
-	</div>
-   <script src="https://ajax.googleapis.com/ajax/libs/jquery/3.3.1/jquery.min.js"></script>
+   <h5>In choosing the best combination, we use <i>FoldX</i> option via <i>YASARA molecules viewer</i>
+  to calculate the ∆G of each molecule, searching for the smallest free energy
+  (regarding its stability in <i>vivo</i>). All those sequences are also submitted to
+  <i>I-Tasser</i> server for projecting their 3D models qualitatively. The following
+  results would conclude that our <i>cytoplasmic</i> signalling combinations are
+  CheY-eYFP and LuxB-CheA. </h5>
+  <br><br>
+  <div align="center"><!-------TABLE 1-------TABLE 1-------TABLE 1------->
+  <h6><b>Table 1.</b> Specific Gibbs Energy within Each Protein Combination.</h6>
+  <table >
+  <tr>
+    <th width="120px">Combination</th>
+    <th width="120px"><p align="center">∆G</p></th>
+  </tr><tr>
+    <td><b>LuxB-CheY</b></td>
+    <td><p align="right">58.15 kcal/mol</p></td>
+  </tr><tr>
+    <td><b>LuxB-CheA</b></td>
+    <td><p align="right">1355.46 kcal/mol</p></td>
+  </tr><tr>
+    <td><b>CheY-eYFP</b></td>
+    <td><p align="right">36.48 kcal/mol</p></td>
+  </tr><tr>
+    <td><b>CheA-eYFP</b></td>
+    <td><p align="right">36.48 kcal/mol</p></td>
+  </tr>
+  </table>
+  </div>
+  <!-------PAGE 2----------PAGE 2----------PAGE 2----------PAGE 2------->
+  <br><br>
+  <h5>Characterisation or purification of those proteins would promote the usage
+  of <i>His-tag</i>; therefore, insertion of His-tag inside the sequence is essential.
+  To ensure the slightest change of tertiary structures of each protein,
+  we would need to find out the secondary structure and surface accesibility
+  via <i>NetSurfP ver. 1.1</i> analyser <a href="http://www.cbs.dtu.dk/services/NetSurfP/" style="color:blue">http://www.cbs.dtu.dk/services/NetSurfP/</a>
+  We would insert <i>His-tag</i> sequence in either no available specific protein
+  domain or the coiled secondary structure of protein to minimize any
+  interruptions. Here is our DiphTox data from <i>NetSurfP</i> server.Result from the <i>NetSurf server</i>, we choose C-terminus side,
+  because it most likely turns/coils around (indicated by
+  has high number on the most right column is closest to 1),
+  and it is freely exposed (indicated by most left column has E alphabet)</h5>
+  <br><br>
+  <div align="center"><!-------TABLE 2-------TABLE 2-------TABLE 2------->
+  <h6><b>Table 2.</b> Coiling probability of DipThox’s specific domain.</h6>
+  <table >
+  <tr>
+    <th>Class assignment</th>
+    <th>Amino acid</th>
+    <th><p align="right">Amino acid<br>number</p></th>
+    <th><p align="right">Probability<br>for Coil</p></th>
+  </tr><tr>
+    <td><b>B</b></td>
+    <td>I</td>
+    <td><p align="right">54</p></td>
+    <td><p align="right">0.223</p></td>
+  </tr><tr>
+    <td><b>E</b></td>
+    <td>K</td>
+    <td><p align="right">55</p></td>
+    <td><p align="right">0.669</p></td>
+  </tr><tr>
+    <td><b>E</b></td>
+    <td>S</td>
+    <td><p align="right">56</p></td>
+    <td><p align="right">0.994</p></td>
+  </tr>
+  </table>
+  </div>
+  <br><br>
+  <h5>Performing structural similarity between original molecule and
+  the one inserted with <i>His-tag</i> sequence have been done by <i>MUSTANG</i>
+  server that built in via <i>YASARA molecule viewer.<sup>5<sup></i> The output would be
+  distance calculation between interacting atoms called RMSD
+  (Root-mean-square deviation). Following tables are summaries of the
+  molecular similarity analysis. From the data that described above, all the combinations are acceptable,
+  except LuxA, since its possible combination has high RMSD. The threshold
+  is relative, but several literatures define the RMSD value of 2 as
+  threshold for structure similarity.<sup>5,6,7</sup></h5>
+  <br><br>
+  <div align="center">
+  <h6><b>Table 1.</b> RMSD Calculation within Several Protein Linked with His-tag.</h6>
+  <table ><!-------TABLE 3-------TABLE 3-------TABLE 3------->
+  <tr>
+    <th width="300px"><p align="center">Similarities between</th>
+    <th width="120px"><p align="center">RMSD</p></th>
+  </tr><tr>
+    <td><b>LuxA with LuxA + His</b></td>
+    <td>2.203 Å</td>
+  </tr><tr>
+    <td><b>LuxC with LuxC + His</b></td>
+    <td>0.985 Å</td>
+  </tr><tr>
+    <td><b>LuxD with LuxD + His</b></td>
+    <td>0.1777 Å</td>
+  </tr><tr>
+    <td><b>LuxE with LuxE + His</b></td>
+    <td>0.800 </td>
+  </tr><tr>
+    <td><b>CheY-eYFP with CheY-eYFP+his</b></td>
+    <td>0.108 Å</td>
+  </tr><tr>
+    <td><b>eYFP with eYFP + His</b></td>
+    <td>0.315 Å</td>
+  </tr><tr>
+    <td><b>CheA with CheA + His</b></td>
+    <td>0.134 Å</td>
+  </tr>
+  </table>
+  </div>
+  <br><br>
+<h3> HB-EGF/Tar Receptor Modelling </h3>
+<br><br>
+  <h5>In HB-EGF, the part that serves as binding domain for diphtheria exotoxin
+  predominantly located in the extracellular environment. Therefore,
+  the domain, expands between 20<sup>th</sup> – 160<sup>th</sup> amino acid, was selected from
+  natural HB-EGF protein. On the other hand, the Tar domain that are
+  functions to establish intracellular chemotactic signalling includes
+  NdeI cutting-site (around 257<sup>th</sup> amino acid) until the utmost C-terminal
+  of the protein (the 553<sup>rd</sup> amino acid).8-11 By those factors, our team also
+  selected Tar domains involving the 1st – 33<sup>rd</sup> and 191<sup>st</sup> –
+<sup>rd</sup> amino acid as part of chimeric protein.</h5><br>
+<div align ="center">
+  <img src = "https://static.igem.org/mediawiki/2018/5/5d/T--UI_Indonesia--fig1.jpg"></img><!----Figure 1 Image----->
+  <br>
+  <h6><b>Figure 1.</b> The selected segment of Tar protein. The functional
+  intracellular domain of Tar is shown as yellow box, blue box is
+  transmembrane domain and orange box is periplasmic domain. Selected Tar
+  domain expands from 1st -33<sup>rd</sup> amino acids and 191<sup>st</sup> -553<sup>rd</sup> amino acids.
+  Modification of binding domain is located between 33<sup>rd</sup> – 191<sup>st</sup> amino acids</h6>
+</div>
+  <br><br>
+  <h5>Our team have predicted the HB-EGF/Tar protein orientation in the
+  <i>Escherichia coli</i> membrane. For this purpose, server <i>TMHMM</i> and <i>OPM Membrane</i>,
+  are utilized to predict protein orientation.<sup>12,13</sup> Conceptual hypothesis
+  about the chimera protein is that it should begin its orientation of
+  C-terminus in cytoplasm, then continued to fold into transmembrane and
+  extracellular sites, as well as re-folding towards cytoplasm.<h5>
+  <!-------PAGE 3----------PAGE 3----------PAGE 3----------PAGE 3------->
+  <br><br>
+  <img src = "https://static.igem.org/mediawiki/2018/e/e8/T--UI_Indonesia--fig2.jpg"></img>
+  <h6><b>Figure 2.</b> The graph above explains the result of HB-EGF/Tar
+  orientation, which began from C-terminus (left) to N-terminus (right).<sup>12</sup>
+  Y-axis pictured the possibility of n<sup>th</sup> amino acid on protein located somewhere
+  between transmembrane (red part), intracellular (blue line), and
+  extracellular (pink line). There is also a diagram located above the graph
+  that represent the most possible location of each domain (with elongated box).</h6>
+  <br>
+  <h5>From the results, it could be concluded that the protein was oriented
+  as expected in the hypothesis. Therefore, the usage of chimera protein is
+  predicted to be functional anatomically. </h5>
+  <br>
+  <img></img><!----Figure 2 Image----->
+  <br>
+<div class="row">
+  <div class="column">
+<img src = "https://static.igem.org/mediawiki/2018/3/32/T--UI_Indonesia--model3a.jpg"></img>
+  </div>
+  <div class="column">
+<img src = "https://static.igem.org/mediawiki/2018/c/cc/T--UI_Indonesia--model3b.jpg"></img>
+  </div>
+</div>
+<br>
+<br>
+<br>
+<br>
+<br>
+<br>
+<br>
+<br>
+<br>
+<br>
+<br>
+<br>
+<br>
+<br>
+  <br>
+  <h6><b>Figure 3.</b>Molecular comparation of HB-EGF native protein (left)
+  with the HB-EGF/Tar fusion (right).<sup>13,14</sup> The pink-coloured
+  domain is intracellularly located as the N-terminus, yellow-coloured
+  domain for the transmembrane one. Then, purple-coloured could be a sign
+  as the extracellular domain, finally folding into transmembrane and back
+  to cytoplasm with orange-coloured and cyan-coloured domain respectively.</h6>
+  <!-------PAGE 4----------PAGE 4----------PAGE 4----------PAGE 4------->
+  <br>
+  <img src = "https://static.igem.org/mediawiki/2018/f/f5/T--UI_Indonesia--modelGIF2.jpg"></img>
+  <br><h6><b>Figure 4.</b>3d models of HBEGF-TAR chimera on membrane.</h6>
+  <h5>After deciding sequence combination of amino acids in modelled chimera
+  HB-EGF/Tar protein, analyzing the interaction of both fusion protein and
+  diphtheria exotoxin <b>(Figure 5)</b> is extremely important to ensure functional
+  ligand-receptor system. The basic concept of interaction modelling is
+  that the protein will be bound to each other well if it causes the
+  ‘environment’ energy (termed by E parameter; calculated by formula below)
+  being lowered down. In this part, our team sent the respective sequence to
+  ClusPro website for further analyzing.<sup>15</sup></h5>
+  <br>
+  <img src = "https://static.igem.org/mediawiki/2018/3/33/T--UI_Indonesia--modelGIF1.jpg"></img>
+  <br>
+  <h6><b>Figure 5.</b> 3d model of proposed diphtox</h6>
+  <div><h4 align="center">
+  E = 0.4E<sub>rep</sub> + -0.40E<sub>att</sub> + 600E<sub>elec</sub> + 1.00E<sub>DARS</sub>
+  </h4></div>
+  <h6>Note: E<sub>rep</sub> and E<sub>attr</sub> denote as repulsive and attractive contributions
+  to the <i>van der Waals</i> interaction energy. Additionally, E<sub>elec</sub> means an
+  electrostatic energy that occur during both protein interaction. E<sub>DARS</sub>
+  is a pairwise structure-based potential constructed by the Decoys of
+  the Reference State (DARS) approach, and it primarily represents
+  desolvation contributions, i.e., the free energy change due to the
+  removal of the water molecules from the interface.<sup>15</sup><h6>
+  <br>
+  <div align="center">
+  <h6><b>Table 4.</b> Comparation of E parameter of native and chimera protein of HB-EGF interacted with DiphTox.</h6>
+  <table ><!-------TABLE 4-------TABLE 4-------TABLE 4------->
+  <tr>
+    <th><p align="center">HB-EGF<br>Protein</th>
+    <th><p align="center">Median Energy (kcal/mol)</p></th>
+    <th><p align="center">Lowest Energy (kcal/mol)</p></th>
+  </tr><tr>
+    <td><b>Native</b></td>
+    <td><p align="center">-944.3</p></td>
+    <td><p align="center">-994.3</p></td>
+  </tr><tr>
+    <td><b>Chimera</b></td>
+    <td><p align="center">858.2</p></td>
+    <td><p align="center">934.4</p></td>
+  </tr>
+  </table>
+  </div>
+  <br>
+  <img src = "https://static.igem.org/mediawiki/2018/a/ac/T--UI_Indonesia--Figure6Model.png"></img>
+  <br>
+  <h6><b>Figure 6.</b> HB-EGF/Tar receptor-DiphTox 3D interaction modelling result.</h6>
+  <br>
+  <img src = "https://static.igem.org/mediawiki/2018/7/71/T--UI_Indonesia--Figure7Model.png"></img>
+  <br>
+  <h6><b>Figure 7.</b>HB-EGF natural receptor and DiphTox 3D interaction modelling result.</h6>
+  <br>
+  <br>
+  <h5>The result of interaction modelling is quantified as energy score based on
+  the formula above. Referring to <b>figure 6</b> and <b>7</b>, we might expect that the
+  DiphTox (cyan) would bind to both native and chimeric HB-EGF receptor that
+  are both located in the extracellular (green). It is indicated by higher
+  energy score of interaction between chimeric HB-EGF/Tar receptor-DiphTox
+  than that of to HB-EGF natural receptor-DiphTox (<b>Table 4</b>). This means
+  that the chimeric receptor could bind towards DiphTox as good
+  (or even better) than the original one. </h5>
+  <br><br>
+  <h5>Beside the cell’s ability to detect toxin, our team also need to ensure
+  the signaling machine works well. Our team also modelled the interaction
+  between LuxA dan LuxB (that we fused with CheA). From <b>figure 8</b> and <b>9</b>,
+  we might expect that both proteins are still able to interact normally
+  after combining them with FRET unit (CheA or CheY protein). </h5>
+  <!-------PAGE 5----------PAGE 5----------PAGE 5----------PAGE 5------->
+  <br>
+  <div align="center">
+  <h6><b>Table 5.</b> Comparation of E parameter of native and His-tagged protein of LuxB-CheA.</h6>
+  <table ><!-------TABLE 5-------TABLE 5-------TABLE 5------->
+  <tr>
+    <th><p align="center">LuxAB</th>
+    <th><p align="center">Median Energy (kcal/mol)</p></th>
+    <th><p align="center">Lowest Energy (kcal/mol)</p></th>
+  </tr><tr>
+    <td><b>Native</b></td>
+    <td><p align="center">-1515.4.3</p></td>
+    <td><p align="center">-1553.2</p></td>
+  </tr><tr>
+    <td><b>Chimera</b></td>
+    <td><p align="center">-1220.9</p></td>
+    <td><p align="center">-1290.7</p></td>
+  </tr>
+  </table>
+  </div>
+  <br>
+  <img src="https://static.igem.org/mediawiki/2018/d/d5/T--UI_Indonesia--Figure8Model.png"></img>
+<!----Figure 6 Image----->
+  <h6><b>Figure 8.</b>LuxA and LuxB-CheA (LuxAB-CheA) 3D interaction modelling result.</h6>
+  <img src ="https://static.igem.org/mediawiki/2018/2/2d/T--UI_Indonesia--Figure9Model.png"></img>
+<!----Figure 7 Image----->
+  <h6><b>Figure 9.</b>.LuxA and LuxB 3D interaction modelling result.</h6>
+  <br>
+  <br>
+<h5><b>Reference:</b></h5>
+  <br>
+<ol align="justify">
+    <li>J Yang, R Yan, A Roy, D Xu, J Poisson, Y Zhang. The I-TASSER Suite: Protein structure and function prediction. Nature Methods, 12: 7-8 (2015)</li>
+    <li>A Roy, A Kucukural, Y Zhang. I-TASSER: a unified platform for automated protein structure and function prediction. Nature Protocols, 5: 725-738 (2010)</li>
+    <li>Y Zhang. I-TASSER server for protein 3D structure prediction. BMC Bioinformatics, vol 9, 40 (2008)</li>
+    <li>Sun, S., Yang, X., Wang, Y., Shen, X., 2016. In Vivo Analysis of Protein–Protein Interactions with Bioluminescence Resonance Energy Transfer (BRET): Progress and Prospects. International Journal of Molecular Sciences 17, 1704. https://doi.org/10.3390/ijms17101704</li>
+    <li>MUSTANG: A multiple structural alignment algorithm Konagurthu AS, Whisstock JC, Stuckey PJ, Lesk AM (2006) Proteins 64,559-574</li>
+    <li>Bordogna, A., Pandini, A., Bonati, L., 2010. Predicting the accuracy of protein-ligand docking on homology models. Journal of Computational Chemistry 32, 81–98. https://doi.org/10.1002/jcc.21601</li>
+    <li>Carugo, O., 2003. How root-mean-square distance (r.m.s.d.) values depend on the resolution of protein structures that are compared. Journal of Applied Crystallography 36, 125–128. https://doi.org/10.1107/s0021889802020502</li>
+    <li>Kanchan, K., Linder, J., Winkler, K., Hantke, K., Schultz, A. and Schultz, J. (2009). Transmembrane Signaling in Chimeras of the Escherichia coli Aspartate and Serine Chemotaxis Receptors and Bacterial Class III Adenylyl Cyclases. <i>Journal of Biological Chemistry</i>, 285(3), pp.2090-2099.</li>
+    <li>Ward, S., Delgado, A., Gunsalus, R. and Manson, M. (2002). A NarX-Tar chimera mediates repellent chemotaxis to nitrate and nitrite. <i>Molecular Microbiology</i>, 44(3), pp.709-719.</li>
+    <li>Melchers, L. S., Regensburg-Tuïnk, T. J., Bourret, R. B., Sedee, N. J., Schilperoort, R. A. and Hooykaas, P. J. (1989). Membrane topology and functional analysis of the sensory protein VirA of Agrobacterium tumefaciens. The <i>EMBO Journal</i>, 8(7), pp.1919-1925.</li>
+    <li>Weerasuriya, S., Schneider, B. M. and Manson, M. D. (1998). Chimeric Chemoreceptors in <i>Escherichia coli</i>: Signaling Properties of Tar-Tap and Tap-Tar Hybrids. <i>Journal of Bacteriology</i>, 180(4), pp.914-920.</li>
+    <li>Cbs.dtu.dk. (2018). <i>TMHMM Server</i>, v. 2.0. [online] Available at: <a href="http://www.cbs.dtu.dk/services/TMHMM/" style="color:blue">http://www.cbs.dtu.dk/services/TMHMM/</a> [Accessed 22 Jul. 2018].</li>
+    <li>Lomize M.A., Pogozheva I,D, Joo H., Mosberg H.I., Lomize A.L. OPM database and PPM web server: resources for positioning of proteins in membranes. Nucleic Acids Res., 2012, 40(Database issue):D370-6</li>
+    <li>Kelley LA et al. (2015). The Phyre2 web portal for protein modeling, prediction and analysis. <i>Nature Protocols</i> 10, pp.845-858 </li>
+    <li>Kozakov D, Hall DR, Xia B, Porter KA, Padhorny D, Yueh C, Beglov D, Vajda S. The ClusPro web server for protein-protein docking. <i>Nature Protocols</i>.2017 Feb;12(2):255-278 ; pdf </li>
+    <li>Kozakov D, Beglov D, Bohnuud T, Mottarella S, Xia B, Hall DR, Vajda, S. How good is automated protein docking? <i>Proteins: Structure, Function, and Bioinformatics</i>, 2013 Aug ;  pdf </li>
+    <li>Kozakov D, Brenke R, Comeau SR, Vajda S. PIPER: An FFT-based protein docking program with pairwise potentials. <i>Proteins</i>. 2006 Aug 24;  pdf </li>
+    <li>Comeau SR, Gatchell DW, Vajda S, Camacho CJ. ClusPro: an automated docking and discrimination method for the prediction of protein complexes.<i>Bioinformatics</i>. 2004 Jan 1;  pdf </li>
+    <li>Comeau SR, Gatchell DW, Vajda S, Camacho CJ. ClusPro: a fully automated algorithm for protein-protein docking <i>Nucleic Acids Research</i>. 2004 Jul 1;  pdf </li>
+    <li>Högbom, M., Eklund, M., Nygren, P. Å., & Nordlund, P. (2003). Structural basis for recognition by an in vitro evolved affibody. Proceedings of the National Academy of Sciences, 100(6), 3191-3196.</li>
+    <li>2015.igem.org. (2018). <i>Team:Stockholm/Description - 2015.igem.org</i>. [online] Available at: https://2015.igem.org/Team:Stockholm/Description [Accessed 22 Jul. 2018].</li>
+  </ol></div>
+  <!-------PAGE 6----------PAGE 6----------PAGE 6----------PAGE 6------->
+  <br>
+<div class="w3-content w3-container w3-padding-64">
+<h1 align="center"> Diphteria Toxin </h1>
+<br>
+<h5>To ensure the DiphTox has no toxicity, we performed literature search. First, we need to know every domain that construct the whole Diphteria Toxin (DT). We were using online databases such as Uniprot (<a href="https://www.uniprot.org/uniprot/Q5PY51" style="color:blue">https://www.uniprot.org/uniprot/Q5PY51</a>) and Interpro (<a href="https://www.ebi.ac.uk/interpro/protein/Q5PY51" style="color:blue">https://www.ebi.ac.uk/interpro/protein/Q5PY51</a>).[1,2] We found that DT consists of three domains (Figure 1), which each of them has specific function.[3]</h5>
+<br>
+<br>
+<img src ="https://static.igem.org/mediawiki/2018/9/90/T--UI_indonesia--diphteria_toxin_domain.png"></img>
+<h6><b>Figure 1.</b>.Diphteria toxin domain.</h6>
+<br>
+<br>
+<h5>DT is classified into AB toxin which A fragment consists of C (catalytic) domain and B fragment consists of T (translocation) and R (recognition) domain (<b>Figure 2</b>).[3]
+<br>
+<img src ="https://static.igem.org/mediawiki/2018/7/73/T--UI_indonesia--diphteriatoxinasAB.png"></img>
+<h6><b>Figure 2.</b>Diphteria toxin as AB toxin.</h6>
+<br>
+<h5>To cause cell intoxication, first DT binds to proHBEGF on surface membrane. EGF like domain on proHBEGF receptor binds the R domain of DT. Receptor bound toxin is concentrated in clathrin coated pits and internalized into clathrin coated vesicle. With the increase of vacuolar ATPase activity, the pH inside vesicle is decreased. The decrease of pH will change the conformational structure of DT, especially T domain. T domain function is to translocate C domain to cytosol. There are many hypotheses about the mechanism of C domain translocation from lumen to cytosolic space. After C domain is delivered to cytosol, it undergoes structural change into enzymatically active conformation and catalyzes NADdependent ADP ribosilation of EF-2, leading to cellular death (apoptosis) via protein synthesis inhibition. One publication said that only A substance can lead to cellular death. The mechanism of intoxication is summarized in <b>figure 3.</b>[4,5]
+<br>
+<img src ="https://static.igem.org/mediawiki/2018/7/7d/T--UI_indonesia--intoxication.png"></img>
+<h6><b>Figure 3.</b>. Intoxication of DT.</h6>
+<br>
+<h5>From this theory, we conclude that A fragment or C domain is the main cause of cytotoxic activity. T and R domain function is only for C domain delivery into cytosol. In order to maximize the safety, we want to take the toxin as small as we can but still retain the binding activity of DT. Publication by John M. Rolf and Leon Eidels said that last 54 amino acids (482-
+) are sufficient enough to make HT and HBEGF bind. These 54 amino acids is part of R domain which has no cytotoxicity effect.[6]
+<br>
+<h5><b>Reference</b>
+<ol>
+<li> <a href="https://www.uniprot.org/uniprot/Q5PY51" style="color:blue">https://www.uniprot.org/uniprot/Q5PY51</a>
+<li> <a href="https://www.ebi.ac.uk/interpro/protein/Q5PY51" style="color:blue">https://www.ebi.ac.uk/interpro/protein/Q5PY51</a>
+<li> Gillet D, Barbier J. Diphtheria toxin. The Comprehensive Sourcebook of Bacterial Protein Toxins. 2015;:111-132.
+<li> Murphy J. Mechanism of Diphtheria Toxin Catalytic Domain Delivery to the Eukaryotic Cell Cytosol and the Cellular Factors that Directly Participate in the Process. Toxins. 2011;3(3):294-308.
+<li> Yamaizumi, M.; Mekada, E.; Uchida, T.; Okada, Y. One molecule of diphtheria toxin fragment A introduced into a cell can kill the cell. Cell 1978, 15, 245–250.
+<li> Rolf J, Eidels L. Characterization of the diphtheria toxin receptor-binding domain.Molecular Microbiology. 1993;7(4):585-591.
+<br>
+<br>
+<h1 align="center"> Kinetical Modelling </h1>
+<div class="w3-content w3-container w3-padding-64">
+  <h5>In synthesizing any designed system of engineered protein, one must able to oversee and predict regarding its performance and behaviour. In analysing the protein, one can apply principles of mathematics and engineering in doing so. Reaction kinetics study can be largely described as the study of reactions regarding their performance such as rates, effects of various variables, etc. In this report, the kinetics of the designed protein will be evaluated based on existing literature. Mass transportation principle, such as Langmuir-Hill or Michaelis-Menten equation, will be loosely applied and adequately correlated for the resulting models. Mathematical models and non-linear regression will play roles for the protein. Out team hopes that this mathematical modelling analysis for the protein will shed a new perspective for the synthesized protein’s function and enlighten some parts of our project.</h5><br>
+  <h5><b>Qualitative Overview</b></h5>
+  <div align="center">
+    <img src ="https://static.igem.org/mediawiki/2018/2/27/T--UI_Indonesia--modelling1.png" width="500px"></img>
+    <h6><b>Figure 1.</b> The figure above describes the event of diphtheria toxin initial binding to the HB-EGF receptors in human epithelial cells. Attached toxin will inhibit protein synthesis and result in apoptosis.</h6>
+    <br>
+  </div>
+  <div align="center">
+    <img src ="https://static.igem.org/mediawiki/2018/a/ad/T--UI_Indonesia--modelling2.png" width="500px"></img>
+    <h6><b>Figure 2.</b> The figure above describes the difference between the chimeric protein performance’s regarding of its reaction with diphtheria toxin.</h6>
+    <br>
+  </div>
+  <h5>Thus, in overseeing the entire performance, it can be divided into three parts:</h5>
+  <ul>
+    <li>HB-EGF binding to the diphtheria toxin</li>
+    <li>TAR protein’s binding performance</li>
+    <li>Reaction rates of the auto-phosphorylation process</li>
+  </ul>
+  <br>
+  <h5><b>Quantitative Overview</b></h5>
+  <h5>In calculating reaction rates of either association or dissociation rate, the following mathematical equations are applied:</h5><br>
+  <h5><i>The surface concentration Gamma (G), for a protein can be calculated by the following formula (3): One Response Unit (RU) in the Blacore 2000/3000 machine corresponds to a surface coverage of 10<sup>-6</sup> g m<sup>-2</sup> for a typical protein. A 100 kDa protein generating a response of 1000 RU, corresponds to a surface coverage of 10<sup>-8</sup> moles m<sup>-2</sup>.</i></h5><br>
+  <h5><b>Formulas:</b></h5>
+  <div align="center">
+    <img src ="https://static.igem.org/mediawiki/2018/a/a7/T--UI_Indonesia--modelling3.png" width="500"></img>
+    <br>
+  </div>
+  <h5>Response unit, which is the symbol for reaction rates’ response that are given directly by the response machine will first be converted into gamma (surface concentration), which will then be converted into concentration of complex with the following formula:</h5><br>
+  <div align="center">
+    <img src ="https://static.igem.org/mediawiki/2018/e/eb/T--UI_Indonesia--modelling4.png" width="500"></img>
+    <br>
+  </div>
+  <h5>Therefore, reaction rates can be calculated with following formula:</h5><br>
+  <div align="center">
+    <img src ="https://static.igem.org/mediawiki/2018/b/be/T--UI_Indonesia--modelling5.png" width="500"></img>
+    <br>
+  </div>
+  <h5>The formula is synthesized by following the mass-transportation-based reaction rates, which is based on chemical kinetics of the reaction’s constituent.</h5>
+  <br>
+  <h5><b>HB-EGF Receptor Activity</b></h5>
+  <h5><i>Part I: Association and Dissociation Rates Based on Time</i></h5>
+  <h5>This part of analysis is mainly referenced to an article by Brooke et.al. [1], in which they model binding of immobilized HB-EGF with diphtheria toxin response unit versus time, which will be shown below:</h5><br>
+  <div align="center">
+    <img src ="https://static.igem.org/mediawiki/2018/c/c1/T--UI_Indonesia--modelling6.png" width="800"></img>
+    <h6><b>Figure 3.</b>Binding of DT to immobilized hHB-EGF. The first arrow represents the start of the injection of DT over the immobilized hHB-EGF. The arrow with a ball represents the end of the DT injection and the beginning of the flow running buffer. From the bottom curve to the top curve, the respective binding curves for DT of 400 nM, 500 nM, 600 nM, 700 nM, 800 nM and 1 μM concentrations are shown. The number ①, ②, and ③ represent the baseline of running buffer, the association phase of the binding of DT to hHB-EGF, and the dissociation phase of the release of DT from hHB-EGF, respectively, RU, resonance units. This figure shows a representative experiment (use Materials and Methods).</h6>
+    <br>
+  </div>
+  <h5>Therefore, the prediction of both association rates and dissociation rates are shown below, by comparing our chimeric protein’s kinetics with literature, the x axis represents time, while the y axis represents response in Response Unit (RU).</h5>
+  <br>
+  <h5>
+      <ol>
+        <li>Association Rates</li>
+        <br>
+          <div align="center">
+            <h6><b>Table 1.</b>HB-EGF Receptor Properties</h6>
+            <img src ="https://static.igem.org/mediawiki/2018/1/1f/T--UI_Indonesia--modelling7.png" width="400"></img>
+            <br>
+            <h6><b>Table 2.</b>Calculation Table of Diphtheria Toxin and HB-EGF Association Rate</h6>
+            <img src ="https://static.igem.org/mediawiki/2018/6/65/T--UI_Indonesia--modelling8.png" width="800"></img>
+            <br>
+            <h5>Therefore, the data regression will be plotted as below:</h5>
+            <img src ="https://static.igem.org/mediawiki/2018/d/d0/T--UI_Indonesia--modelling9.png" width="500"></img>
+            <h6><b>Figure 4.</b>Association Response of Diphtheria Toxin towards HB-EGF Receptor.</h6>
+            <br>
+            <img src ="https://static.igem.org/mediawiki/2018/e/ec/T--UI_Indonesia--modelling10.png" width="500"></img>
+            <h6><b>Figure 5.</b>Reaction Rate of Diphtheria Toxin towards HB-EGF Receptor.</h6>
+            <br>
+          </div>
+        <li>Dissociation Rates</li>
+        <br>
+          <div align="center">
+            <h6><b>Table 3.</b>HB-EGF Receptor Properties</h6>
+            <img src ="https://static.igem.org/mediawiki/2018/0/08/T--UI_Indonesia--modelling11.png" width="400"></img>
+            <br>
+            <h6><b>Table 4.</b>Calculation Table of Diphtheria Toxin and HB-EGF Dissociation Rate</h6>
+            <img src ="https://static.igem.org/mediawiki/2018/3/3f/T--UI_Indonesia--modelling12.png" width="800"></img>
+            <br>
+            <h5>Therefore, the data regression will be plotted as below:</h5>
+            <img src ="https://static.igem.org/mediawiki/2018/4/4f/T--UI_Indonesia--modelling13.png" width="500"></img>
+            <h6><b>Figure 6.</b>Dissociation Response of Diphtheria Toxin towards HB-EGF Receptor.</h6>
+            <br>
+            <img src ="https://static.igem.org/mediawiki/2018/2/21/T--UI_Indonesia--modelling14.png" width="500"></img>
+            <h6><b>Figure 7.</b>Reaction Rate of Diphtheria Toxin towards HB-EGF Receptor.</h6>
+            <br>
+          </div>
+      </ol>
+  </h5>
+  <h5><i>Part II: Rates Based on Concentration</i></h5>
+  <h5>The figure/literature used is still the same as the association/dissociation rates above; therefore, by remodelling and regressing the data based on the literature, one can predict the association and dissociation rates on certain times, with the concentration of the toxin as the independent variable, which is shown at the interactive Excel with this interface:</h5>
+  <br>
+    <div align="center">
+      <h6><b>Table 5.</b>HB-EGF Receptor Properties</h6>
+      <img src ="https://static.igem.org/mediawiki/2018/d/d4/T--UI_Indonesia--modelling15.png" width="300"></img>
+      <br>
+      <h6><b>Table 6.</b>Association and Dissociation Constants of HB-EGF Receptor.</h6>
+      <img src ="https://static.igem.org/mediawiki/2018/1/11/T--UI_Indonesia--modelling16.png" width="300"></img>
+      <br>
+      <h6><b>Table 7.</b>Association and Dissociation Response Unit and Concentration of Natural Diphtheria Toxin and HB-EGF Binding.</h6>
+      <img src ="https://static.igem.org/mediawiki/2018/c/ce/T--UI_Indonesia--modelling17.png" width="400"></img>
+      <br>
+      <h6><b>Table 8.</b>Calculation of Binding Reaction Rate of HB-EGF and Diphtheria Toxin Based on Concentration.</h6>
+      <img src ="https://static.igem.org/mediawiki/2018/5/5c/T--UI_Indonesia--modelling18.png" width="700"></img>
+      <br>
+      <h6><b>Table 8.</b>Calculation of Binding Reaction Rate of HB-EGF and Diphtheria Toxin.</h6>
+      <img src ="https://static.igem.org/mediawiki/2018/e/e7/T--UI_Indonesia--modelling19.png" width="900"></img>
+      <br>
+      <img src ="https://static.igem.org/mediawiki/2018/1/10/T--UI_Indonesia--modelling20.png" width="500"></img>
+      <h6><b>Figure 8.</b>The Regression Graph for Response Unit vs Toxin Concentration</h6>
+      <img src ="https://static.igem.org/mediawiki/2018/5/52/T--UI_Indonesia--modelling21.png" width="500"></img>
+      <h5>The programmed Excel will calculate the reaction rates just by inserting desired concentration.</h5>
+    </div>
+  <br>
+  <h5><i>Part III: Effects of pH and Temperature</i></h5>
+  <h5>
+    <ol>
+      <li>Effects of pH</li>
+      <br>
+      <h5>Based on throughout literature review, the closest thing for binding of diphtheria toxin is for Vero Cells [2], which is shown below:</h5>
+      <br>
+          <div align="center">
+            <img src ="https://static.igem.org/mediawiki/2018/b/ba/T--UI_Indonesia--modelling22.png" width="700"></img>
+            <br>
+            <h5>Based on graph above, it can be assumed that the best temperature for diphtheria binding is 4°C. Assumed that the chosen temperature is 4°C, the regression model for said temperature for graph of time versus reaction rate, is shown below (at toxin concentration of 0.06 μM). All of the data and assumption are based on journal.</h5>
+            <br>
+            <h6><b>Table 10.</b>Concentration Counts of Diphtheria Toxin and HB-EGF Complex versus Time</h6>
+            <img src ="https://static.igem.org/mediawiki/2018/9/91/T--UI_Indonesia--modelling23.png" width="450"></img>
+            <br>
+            <h5>The proposed model will follow arbitrary model based on Hill-Langmuir model, which is:</h5>
+            <br>
+            <img src ="https://static.igem.org/mediawiki/2018/c/c3/T--UI_Indonesia--modelling24.png" width="600"></img>
+            <br>
+            <h5>Our team formulated models (based on utilization of <i>Polymath 6.0</i> software) based on that with trial and error fit to the data, and the best fitted model would be:</h5>
+            <img src ="https://static.igem.org/mediawiki/2018/5/52/T--UI_Indonesia--modelling25.png" width="600"></img>
+            <br>
+            <img src ="https://static.igem.org/mediawiki/2018/0/00/T--UI_Indonesia--modelling26.png" width="600"></img>
+            <h6><b>Figure 10.</b>The Estimated Graphs for DT-Vero Cells Binding (Auth’s Personal Data)</h6>
+            <br>
+            <br>
+            <h5><b>Model</b></h5>
+            <br>
+            <img src ="https://static.igem.org/mediawiki/2018/e/e8/T--UI_Indonesia--modelling27before.png" width="200" align="left"></img>
+            <br><br>
+            <div align="center">
+              <h6><b>Table 11.</b>Result of Polymath 6.0</h6>
+              <img src ="https://static.igem.org/mediawiki/2018/a/af/T--UI_Indonesia--modelling27.png" width="700"></img>
+            </div>
+            <br>
+            <h5><b>Non-linear regression settings</b></h5>
+            <br>
+            <h5>Max # iterations = 64</h5>
+            <br>
+            <h5><b>Precision</b></h5>
+            <div align="center">
+              <h6><b>Table 12.</b>Result of Polymath 6.0 (cont’d).</h6>
+              <img src ="https://static.igem.org/mediawiki/2018/3/33/T--UI_Indonesia--modelling28.png" width="300"></img>
+            </div>
+            <br>
+            <h5><b>General</b></h5>
+            <div align="center">
+              <h6><b>Table 13</b>Result of Polymath 6.0 (cont’d).</h6>
+              <img src ="https://static.igem.org/mediawiki/2018/5/52/T--UI_Indonesia--modelling29.png" width="300"></img>
+            </div>
+            <br>
+            <h5><b>The comparison table is shown as below:</b></h5>
+            <div align="center">
+              <h6><b>Table 14</b>Comparison Data between Literature and Model Regression of Concentration Counts of Diphtheria Toxin and HB-EGF Complex versus Time.</h6>
+              <img src ="https://static.igem.org/mediawiki/2018/d/df/T--UI_Indonesia--modelling30.png" width="400"></img>
+            </div>
+          </div>
+          <br>
+      <li>Effects of Temperature</li>
+      <br>
+      <div align="center">
+        <img src ="https://static.igem.org/mediawiki/2018/d/db/T--UI_Indonesia--modelling31.png" width="400"></img>
+        <h6><b>Figure 11.</b>Effect of pH on diphtheria toxin cytotoxicity and association of 125I-labeled diphtheria toxin with cells. Maintenance medium was replaced with complete Hanks’ 199 plus 25 mM HEPES buffer titrated to the pH indicated. For cytotoxicity assay, cells were incubated 3 h with 5 ng/mL of toxin, washed three times with normal media, and incubated a further 48 h. Cytotoxicity (█) was determined as previously described (6). For effects on association, titrated media was added to cells, followed by 125I-toxin (0.03 μg/mL) or 125I-toxin plus unlabelled toxin (0.03 μg/mL). after 2 hours at 37℃ (○) or 12 h at 4℃ (𝛥) cells were washed and radioactivity was assayed as usual.</h6>
+        <br>
+        <img src ="https://static.igem.org/mediawiki/2018/7/71/T--UI_Indonesia--modelling32.png" width="400"></img>
+        <h6><b>Figure 12.</b>Dissociation of DT from immobilized hHB-EGF in running buffers of decreasing pH. The results for DT of 600 nM concentration in running buffers of pH 6.9, 6.4 and 5.8 are shown. The association phase (pH 7.4) of these curves is not shown. The origin represents the end of the DT injection and is the time at which the running buffer of specific pH has started flowing over the sensorchip, RU, resonance untis. This figure shows a representative ecperiment (see Materials and Methods).</h6>
+        <br>
+      </div>
+      <h5>Based on the graphs above, one can infer that:</h5>
+        <ul>
+          <li>In smaller pH (more acidic), the rates of dissociation is faster; nevertheless, once the pH is below 5.3, there will be no change in the rate since the intoxicated cells have already broken (lysis).</li>
+          <li>In the human epithelial tissues, the intoxicated cells will not be destroyed although the pH is 5.3 as the environment temperature is close to 370C.</li>
+          <li>For research insight, the recommended temperature for testing binding rates is 25°C with range of pH between 5.6-5.9.</li>
+          <li>Can’t be mathematically modelled since the data is not sufficient for accurate modelling. The decision is made by engineering rule of thumb, in which data less than 5, is not reliable enough to be modelled into linear or non-linear regression, since the potential deviation is large.</li>
+        </ul>
+    </ol>
+  </h5>
+  <br>
+  <h5><b>Tar Binding</b></h5>
+  <h5>The Tar binding model would be based on two research articles with different models, which are from [3] and [4]. Modelling based on [3] is as below:</h5>
+  <div align="center">
+    <img src ="https://static.igem.org/mediawiki/2018/6/6f/T--UI_Indonesia--modelling33.png" width="400"></img>
+    <h6><b>Figure 13.</b>Time versus Response for TAR16 Binding to TAR*16 [3]</h6>
+    <br>
+  </div>
+  <h5>
+    <ol>
+      <li>Association Rate</li>
+      <div align="center">
+        <h6><b>Table 15.</b>Properties of TAR Receptor</h6>
+        <img src ="https://static.igem.org/mediawiki/2018/9/9c/T--UI_Indonesia--modelling34.png" width="300"></img>
+        <br>
+        <h6><b>Table 16.</b>TAR Dissociation and Association Constant and Concentration Receptor</h6>
+        <img src ="https://static.igem.org/mediawiki/2018/1/15/T--UI_Indonesia--modelling35.png" width="350"></img>
+        <br>
+        <h6><b>Table 17.</b>Calculation Table of TAR Binding Association Rate</h6>
+        <img src ="https://static.igem.org/mediawiki/2018/e/e4/T--UI_Indonesia--modelling36.png" width="800"></img>
+        <br>
+        <img src ="https://static.igem.org/mediawiki/2018/c/cf/T--UI_Indonesia--modelling37.png" width="500"></img>
+        <h6><b>Figure 15.</b>Association Response of Tar Binding.</h6>
+        <br>
+        <img src ="https://static.igem.org/mediawiki/2018/2/28/T--UI_Indonesia--modelling38.png" width="500"></img>
+        <h6><b>Figure 16.</b>Association Reaction Rate of Tar Binding.</h6>
+      </div>
+      <br>
+      <li>Dissociation Rate</li>
+      <div align="center">
+        <h6><b>Table 18.</b>Properties of TAR Ligand (where TAR is ligand, and TAR16 is the receptor).</h6>
+        <img src ="https://static.igem.org/mediawiki/2018/5/5c/T--UI_Indonesia--modelling39.png" width="300"></img>
+        <br>
+        <h6><b>Table 19.</b>TAR Receptor Dissociation and Association Constant and Concentration</h6>
+        <img src ="https://static.igem.org/mediawiki/2018/b/b7/T--UI_Indonesia--modelling40.png" width="300"></img>
+        <br>
+        <h6><b>Table 20.</b>Calculation Table of TAR Binding Dissociation Rate</h6>
+        <img src ="https://static.igem.org/mediawiki/2018/5/51/T--UI_Indonesia--modelling41.png" width="800"></img>
+        <br>
+        <img src ="https://static.igem.org/mediawiki/2018/3/3d/T--UI_Indonesia--modelling42.png" width="500"></img>
+        <h6><b>Figure 17.</b>Dissociation Response of Tar Binding.</h6>
+        <br>
+        <img src ="https://static.igem.org/mediawiki/2018/7/73/T--UI_Indonesia--modelling43.png" width="500"></img>
+        <h6><b>Figure 18.</b>Dissociation Reaction Rate of TAR Binding</h6>
+      </div>
+      <br>
+      <li>Alternative Modelling</li>
+      <h5>Modelling based on [4] is described as below:</h5>
+      <div align="center">
+        <img src ="https://static.igem.org/mediawiki/2018/1/12/T--UI_Indonesia--modelling44.png" width="400"></img>
+        <h6><b>Figure 19.</b>Time versus Frequency henge for TAR to Tat Binding Process. [4]</h6>
+      </div>
+      <h5>This model use different unit than before, in which the response is measured by the frequency change in the biosensor, therefore, the initial equation that are used is:</h5>
+      <div align="center">
+        <img src="https://static.igem.org/mediawiki/2018/b/be/T--UI_Indonesia--modelling45.png">
+      </div>
+      <h5>
+        where,
+        <br>
+        Δf  = the observed frequency change (Hz)<br>
+        Δm  = the change in mass per unit area (in g/cm2)<br>
+        Cf  = the sensitivity factor for the crystal used (i.e. 56.6 Hz μg-1 cm2 for a 5 MHz AT-cut quartz crystal at room temperature.)<br>
+        <br>
+        After finding the mass per unit area, the response unit can be calculated, and from that, the calculation is the same as before.
+        <br>
+        <ol type="a">
+          <li>Association Rates</li>
+          <div align="center">
+            <h6><b>Table 21.</b>Properties of Tar Ligand (where TAR is ligand, and TAR16 is the receptor).</h6>
+            <img src ="https://static.igem.org/mediawiki/2018/1/10/T--UI_Indonesia--modelling46.png" width="300"></img>
+            <br>
+            <h6><b>Table 22.</b>Tar Receptor Dissociation and Association Constant and Concentration</h6>
+            <img src ="https://static.igem.org/mediawiki/2018/3/3f/T--UI_Indonesia--modelling47.png" width="300"></img>
+            <br>
+            <h6><b>Table 23.</b>Tar Binding Association Rates Calculation Table.</h6>
+            <img src ="https://static.igem.org/mediawiki/2018/1/1f/T--UI_Indonesia--modelling48.png" width="800"></img>
+            <br>
+            <img src ="https://static.igem.org/mediawiki/2018/c/c0/T--UI_Indonesia--modelling49.png" width="500"></img>
+            <h6><b>Figure 20.</b>Association Frequency Change of Tar Binding.</h6>
+            <br>
+            <img src ="https://static.igem.org/mediawiki/2018/d/dc/T--UI_Indonesia--modelling50.png" width="500"></img>
+            <h6><b>Figure 21.</b>Association Reaction Rate of Tar Binding.</h6>
+          </div>
+          <h5>The similar method of finding reaction rates as association rates is applied on the calculation of dissociation rates: </h5>
+          <br>
+          <li>Dissociation Rates</li>
+          <div align="center">
+            <h6><b>Table 24.</b>Tar Binding Dissociation Rates Calculation Table.</h6>
+            <img src ="https://static.igem.org/mediawiki/2018/b/b4/T--UI_Indonesia--modelling51.png" width="800"></img>
+            <br>
+            <img src ="https://static.igem.org/mediawiki/2018/0/0a/T--UI_Indonesia--modelling52.png" width="500"></img>
+            <h6><b>Figure 22.</b>Dissociation Response of Tar Binding.</h6>
+            <br>
+            <img src ="https://static.igem.org/mediawiki/2018/9/91/T--UI_Indonesia--modelling53.png" width="500"></img>
+            <h6><b>Figure 23.</b>Dissociation Reaction Rate of Tar Binding.</h6>
+          </div>
+        </ol>
+        <h5><b>Tar Binding Rates Based on Concentration</b></h5>
+        <h5>As for the same with HB-EGF binding above, we formulate Excel so it can predict the reaction rates based on the input of concentration, as shown below:</h5>
+        <div align="center">
+          <img src ="https://static.igem.org/mediawiki/2018/6/64/T--UI_Indonesia--modelling54.png" width="700"></img>
+        </div>
+        <br>
+        <h5><b>CheA Autophosphorylation</b></h5>
+        <h5>The source of the model is from [5]. The literature elaborates on the quantitative modelling of CheA autophosphorylation process, in which graph below left, models on the ratio of concentration of CheA-P at a certain time (P-CheA<sub>t</sub> or can be modelled as (〖dC〗_(〖P-CheA〗_t ))) to the maximum concentration of CheA-P that can be achieved. The graph on the right, shows the ratio of concentration of CheA-P at a certain time (P-CheA<sub>t</sub> or can be modelled as 〖dC〗_(〖P-CheA〗_t )/t) to the initial concentration of CheA-P:</h5>
+        <br>
+        <div align="center">
+          <img src ="https://static.igem.org/mediawiki/2018/4/4c/T--UI_Indonesia--modelling55.png" width="600"></img>
+          <h6><b>Figure 24.</b>Ratio Between Concentration of CheA-P at a Certain Time to Concentration of CheA-P at a Threshold Tike (Left: Maximum Right: Minimum) versus Time [5]</h6>
+        </div>
+        <h5>With the similar approach to the modelling of association rate of Diphtheria toxin and HB-EGF, the overview and thus prediction of the autophosphorylation process can be modelled, as below:</h5>
+        <br>
+        <div align="center">
+        <h6><b>Table 25.</b>CheA Properties and Variables Needed.</h6>
+        <img src ="https://static.igem.org/mediawiki/2018/1/10/T--UI_Indonesia--modelling56.png" width="600"></img>
+        <br>
+        <h6><b>Table 26.</b>Ratio of The Change of CheA-P Concentration Against Time to the Maximum Concentration of CheA-P Achieved versus Time</h6>
+        <img src ="https://static.igem.org/mediawiki/2018/9/9e/T--UI_Indonesia--modelling57.png" width="200"></img>
+        <br>
+        <h5>Based on derived Langmuir-Hill model as explained above, the model used for above data is:</h5>
+        <br>
+        <img src ="https://static.igem.org/mediawiki/2018/6/60/T--UI_Indonesia--modelling58.png" width="600"></img>
+        <br>
+        <img src ="https://static.igem.org/mediawiki/2018/b/b1/T--UI_Indonesia--modelling59.png" width="700"></img>
+        <h6><b>Figure 25.</b>The Estimated Graphs of Ratio between Concentration of CheA-P at a Certain Time to Maximum Concentration of CheA-P versus Time (Auth’s Pers Data).</h6>
+        <br>
+        <h6><b>Table 27.</b>Result of Polymath 6.0/h6>
+        <img src ="https://static.igem.org/mediawiki/2018/1/17/T--UI_Indonesia--modelling60.png" width="600"></img>
+        </div>
+        <br>
+        <b>Nonlinear regression settings</b>
+        <br>
+        Max # iterations = 64
+        <br>
+        <b>Precision</b>
+        <div align="center">
+          <h6><b>Table 28.</b>Result of Polymath 6.0</h6>
+          <img src ="https://static.igem.org/mediawiki/2018/4/47/T--UI_Indonesia--modelling61.png" width="300"></img>
+        </div>
+        <br>
+        <b>General</b>
+        <br>
+        <div align="center">
+          <h6><b>Table 29.</b>Result of Polymath 6.0</h6>
+          <img src ="https://static.igem.org/mediawiki/2018/5/5c/T--UI_Indonesia--modelling62.png" width="300"></img>
+        </div>
+        Source data points and calculated data points.
+        <br>
+        <div align="center">
+          <h6><b>Table 30.</b>Comparison of Data between Literature and Model Regression of Ratio of The Change of CheA-P Concentration Against Time to the Maximum Concentration of CheA-P Achieved versus Time.</h6>
+          <img src ="https://static.igem.org/mediawiki/2018/4/4b/T--UI_Indonesia--modelling63.png" width="600"></img>
+        </div>
+      </h5>
+    </ol>
+  <br>
+  <h5>
+    <b>ANALYSIS</b>
+    <br>
+    <b>General Analysis</b>
+    <br>
+    Most of the base literature and reference model for all of the kinetics, be it DT and HB-EGF, Tar-agent, or autophosphorylation process, are based on the fundamental understanding of macro and micro mass-transfer in accordance of engineering’s perspective, in which:
+    <br>
+    <img src ="https://static.igem.org/mediawiki/2018/3/33/T--UI_Indonesia--modelling64.png" width="600"></img>
+    <br>
+    Which means that the rate of mass transfer (in this case): toxin/agent transport to be bound by the receptor, is affected by the agent’s own mass constituent (for example: concentration, or mass ratio) times its multiplier constant (sometimes called rate-limiting step), therefore, if one seeks to model the equation above, the first step to do is to linearize the differential equation by integrating it.
+    <br>
+    <img src ="https://static.igem.org/mediawiki/2018/6/62/T--UI_Indonesia--modelling65.png" width="600"></img>
+    The resulting graph, if it is in linear progression, will result in f(x) = ln(x) graph, like below:
+    <br>
+    <div align="center">
+      <img src ="https://static.igem.org/mediawiki/2018/1/1a/T--UI_Indonesia--modelling66.png" width="500"></img>
+      <h6><b>Figure 26.</b>The graph of f(x) = ln(x).</h6>
+    </div>
+    <br>
+    <br>
+    The resulting shape signifies that mass transportation will progress quickly in the early stage and decline slowly in the later stage, which can be explained by basic science and engineering, in which a group of mass will transport faster when there are bulk of them, signifying a push force between them, causing them to move faster, and then will gradually decline when the mass is mostly transferred, since there are no other force to move them, until it finally hits hypothetical saturated condition when it finally hits its peak.
+    <br>
+    <br>
+    As for the reaction rates, the model will follow the engineering principle of chemical kinetics, in which the reaction rates will be maximum in the beginning, since the binding process happens maximum in the beginning, and gradually becomes much slower as the binding process becomes progressively slower too.
+    <br>
+    <br>
+    Other base modelling for the mass-transport is Langmuir-Hill model, in which the binding of a ligand to a receptor/macromolecule will be faster in the presence of another ligand in close proximity. The resulting graph will have similar shape to the f(x) = ln(x) graph since the models basically have similar fundamental base, which can be found in the autophosphorylation graph.
+    <br>
+    <img src ="https://static.igem.org/mediawiki/2018/7/73/T--UI_Indonesia--modelling67.png" width="600"></img>
+    <br>
+    In reality, especially in micro-molecular protein complex, of course, there are difference and complex’s own signature characteristics, regarding their rules in mass transportation, that will be explained in detail as follows:
+    <br>
+    <ol type="a">
+      <li>HB-EGF Binding</li>
+      <div align="center">
+        <img src ="https://static.igem.org/mediawiki/2018/4/47/T--UI_Indonesia--modelling68.png" width="500"></img>
+        <h6><b>Figure 27.</b>Association Response of Diphtheria Toxin to HB-EGF.</h6>
+        <br>
+        <img src ="https://static.igem.org/mediawiki/2018/e/e6/T--UI_Indonesia--modelling69.png" width="500"></img>
+        <h6><b>Figure 28.</b>Reaction Rate of Diphtheria Toxin to HB-EGF.</h6>
+        <br>
+        <img src ="https://static.igem.org/mediawiki/2018/e/ea/T--UI_Indonesia--modelling70.png" width="500"></img>
+        <h6><b>Figure 29.</b>Dissociation Response of Diphtheria Toxin to HB-EGF.</h6>
+        <br>
+        <img src ="https://static.igem.org/mediawiki/2018/5/50/T--UI_Indonesia--modelling71.png" width="500"></img>
+        <h6><b>Figure 30.</b>Reaction Rate of Diphtheria Toxin to HB-EGF.</h6>
+      </div>
+      <br>
+      <br>
+      Both response unit and reaction rates for both association and dissociation reaction roughly follows the same mass transfer principle, in which the response unit (the existence of the mass) will grow faster in the early stage and then will gradually become slower in the later stage. The reaction rates will follow directly from the response unit, therefore, the reaction rates will reach its peak in the beginning and begins to falter until it reaches the maximum. The literature base [1] confirms and give another insight to the analysis, in which the Diphtheria toxin-association is environment dependent, while the release of Diphtheria Toxin (Dissociation) is rate-limiting step. The paper also elaborates that there might another additional factor regarding of interaction of toxin to HB-EGF.
+      <br>
+      <br>
+      The final regression of the data ultimately utilizes polynomial equation since it gives the largest R<sup>2</sup> which means the smallest deviation, so it can be utilized for future interpolation or extrapolation purpose.
+      <br>
+      <br>
+      <b>Effects of pH and Temperature on HB-EGF Binding</b>
+      <br>
+      <i>Effects of pH</i>
+      <div align="center">
+        <img src ="https://static.igem.org/mediawiki/2018/4/4f/T--UI_Indonesia--modelling72.png" width="600"></img>
+        <h6><b>Figure 31.</b>The Estimated Graphs for DT-Vero Cells Binding (Auth’s Personal Data)</h6>
+      </div>
+      <br>
+      <br>
+      Since the base literature on the effects of pH to the HB-EGF binding is limited, the consensus of the best temperature for the binding process will be elaborated on our own experimentation, meanwhile the literature result in temperature of 4°C for binding process graph is shown above, in which the shape of the graph follows Langmuir-Hill principle. The final regression of the data utilizes the modified version of Langmuir-Hill equation, in which the team combines both trial and error and iteration method to achieve the highest R<sup>2</sup>.
+      <br>
+      <br>
+      <li>TAR-Binding Kinetics</li>
+      <i>Effects of Temperature</i>
+      <br>
+      Since the literature reference of pH influence on the binding process is also limited, this is the analysis and our consensus regarding of the pH effects.
+      <br>
+      <ul>
+        <li>In smaller pH (more acidic), the rates of dissociation is faster; nevertheless, once the pH is below 5.3, there will be no change in the rate since the intoxicated cells have already broken (lysis).</li>
+        <li>In the human epithelial tissues, the intoxicated cells will not be destroyed although the pH is 5.3 as the environment temperature is close to 37°C.</li>
+        <li>For research insight, the recommended temperature for testing binding rates is 25°C with range of pH between 5.6-5.9.</li>
+      </ul>
+      <br>
+      Can’t be mathematically modelled since the data is not sufficient for accurate modelling. The decision is made by engineering rule of thumb, in which data less than 5, is not reliable enough to be modelled into linear or non-linear regression, since the potential deviation is large.
+      <br>
+      <br>
+      Can’t be mathematically modelled since the data is not sufficient for accurate modelling. The decision is made by engineering rule of thumb, in which data less than 5, is not reliable enough to be modelled into linear or non-linear regression, since the potential deviation is large.
+      <br>
+      <br>
+      The Tar binding kinetics use the same principle and method of calculation as the DT-HBEGF binding reaction, therefore the graph:
+      <br>
+      <div align="center">
+        <img src ="https://static.igem.org/mediawiki/2018/e/e0/T--UI_Indonesia--modelling73.png" width="500"></img>
+        <h6><b>Figure 32.</b>Association Response of Tar Binding.</h6>
+        <br>
+        <img src ="https://static.igem.org/mediawiki/2018/a/ae/T--UI_Indonesia--modelling74.png" width="500"></img>
+        <h6><b>Figure 33.</b>Reaction Rate of Tar Binding.</h6>
+        <br>
+        <img src ="https://static.igem.org/mediawiki/2018/9/91/T--UI_Indonesia--modelling75.png" width="500"></img>
+        <h6><b>Figure 34.</b>Dissociation Response of Tar Binding.</h6>
+        <br>
+        <img src ="https://static.igem.org/mediawiki/2018/4/43/T--UI_Indonesia--modelling76.png" width="500"></img>
+        <h6><b>Figure 35.</b>Reaction Rate of Tar Binding.</h6>
+        <br>
+        <img src ="https://static.igem.org/mediawiki/2018/d/d9/T--UI_Indonesia--modelling77.png" width="500"></img>
+        <h6><b>Figure 36.</b>Association Frequency Change of TAR Binding</h6>
+        <br>
+        <img src ="https://static.igem.org/mediawiki/2018/f/f1/T--UI_Indonesia--modelling78.png" width="500"></img>
+        <h6><b>Figure 37.</b>Association Reaction Rate of Tar Binding.</h6>
+        <br>
+        <img src ="https://static.igem.org/mediawiki/2018/7/7d/T--UI_Indonesia--modelling79.png" width="500"></img>
+        <h6><b>Figure 38.</b>Dissociation Response of Tar Binding.</h6>
+        <br>
+        <img src ="https://static.igem.org/mediawiki/2018/1/1b/T--UI_Indonesia--modelling80.png" width="500"></img>
+        <h6><b>Figure 39.</b>Dissociation Reaction Rate of Tar Binding.</h6>
+      </div>
+      <br>
+      The alternative modelling is done to further prove that the TAR-binding kinetics toward ligands will also follow the mass-transfer principle. The polynomial regression is used since it give the biggest R<sup>2</sup> (smallest deviation)
+      <br>
+      <br>
+      <li>Auto-phosphorylation of CheA</li>
+      <br>
+      <div align="center">
+        <img src ="https://static.igem.org/mediawiki/2018/d/df/T--UI_Indonesia--modelling81.png" width="600"></img>
+        <h6><b>Figure 40.</b>The Estimated Graphs of Ratio between Concentration of CheA-P at a Certain Time to Maximum Concentration of CheA-P versus Time (Auth’s Pers Data)</h6>
+      </div>
+      <br>
+      This graph also closely follow the Langmuir Hill principle, even though the formula linearization and conception is not as simple as the aforementioned process, since autophosphorylation process has more elaborate steps, which is:
+      <br>
+      <img src ="https://static.igem.org/mediawiki/2018/d/da/T--UI_Indonesia--modelling82.png" width="600"></img>
+      <br>
+      The process above has more elaborate formulation of model in which the final model will roughly constitute of this equation:
+      <br>
+      <img src ="https://static.igem.org/mediawiki/2018/c/ce/T--UI_Indonesia--modelling83.png" width="600"></img>
+      x = time<br>
+      x = initial time<br>
+      K = total constant of reactions<br>
+      <br>
+      When one sees the formula, it can be roughly estimated to form a similar ln graph with similar shapes to the Langmuir-Hill approximation too, therefore the final and simplified model of the autophosphorylation process will be fitted to modified Langmuir-Hill equation by trial and error and iteration to achieve the highest R<sup>2</sup>.
+    </ol>
+  </h5>
+</div>
+<div class="w3-content w3-container w3-padding-64">
+  <h5><b>Reference :</b>
+  <ol align="justify">
+    <li>Brooke, J. S., Cha, J. H., & Eidels, L. (1998). Diphtheria toxin: receptor interaction: association, dissociation, and effect of pH. Biochemical and biophysical research communications, 248(2), 297-302.</li>
+    <li>Middlebrook, J. L., Dorland, R. B., & Leppla, S. H. (1978). Association of diphtheria toxin with Vero cells. Demonstration of a receptor. Journal of Biological Chemistry, 253(20), 7325-7330.</li>
+    <li>Nair, T. M., Myszka, D. G., & Davis, D. R. (2000). Surface plasmon resonance kinetic studies of the HIV TAR RNA kissing hairpin complex and its stabilization by 2-thiouridine modification. Nucleic Acids Research, 28(9), 1935–1940</li>
+    <li>Tassew, N., & Thompson, M. (2003). Kinetic characterization of TAR RNA–Tat peptide and neomycin interactions by acoustic wave biosensor. Biophysical chemistry, 106(3), 241-252.</li>
+    <li>Tawa, P., & Stewart, R. C. (1994). Kinetics of CheA autophosphorylation and dephosphorylation reactions. Biochemistry, 33(25), 7917-7924</li>
+  </ol></div></h5>
+  <br>
+</div>
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Difference between revisions of "Team:UI Indonesia/Model"

Latest revision as of 03:05, 18 October 2018

Structural Modelling

Chimera Combination

Table 1. Specific Gibbs Energy within Each Protein Combination.

Table 2. Coiling probability of DipThox’s specific domain.

Table 1. RMSD Calculation within Several Protein Linked with His-tag.

HB-EGF/Tar Receptor Modelling

From the results, it could be concluded that the protein was oriented as expected in the hypothesis. Therefore, the usage of chimera protein is predicted to be functional anatomically.

Figure 4.3d models of HBEGF-TAR chimera on membrane.

Figure 5. 3d model of proposed diphtox

E = 0.4Erep + -0.40Eatt + 600Eelec + 1.00EDARS

Table 4. Comparation of E parameter of native and chimera protein of HB-EGF interacted with DiphTox. HB-EGFProtein Median Energy (kcal/mol) Lowest Energy (kcal/mol) Native -944.3 -994.3 Chimera 858.2 934.4

Table 4. Comparation of E parameter of native and chimera protein of HB-EGF interacted with DiphTox.

Figure 6. HB-EGF/Tar receptor-DiphTox 3D interaction modelling result.

Figure 7.HB-EGF natural receptor and DiphTox 3D interaction modelling result.

Table 5. Comparation of E parameter of native and His-tagged protein of LuxB-CheA.

Figure 8.LuxA and LuxB-CheA (LuxAB-CheA) 3D interaction modelling result.

Figure 9..LuxA and LuxB 3D interaction modelling result.

Reference:

Diphteria Toxin

Figure 1..Diphteria toxin domain.

DT is classified into AB toxin which A fragment consists of C (catalytic) domain and B fragment consists of T (translocation) and R (recognition) domain (Figure 2).[3]

Figure 2.Diphteria toxin as AB toxin.

Figure 3.. Intoxication of DT.

Kinetical Modelling

Qualitative Overview

Figure 1. The figure above describes the event of diphtheria toxin initial binding to the HB-EGF receptors in human epithelial cells. Attached toxin will inhibit protein synthesis and result in apoptosis.

Figure 2. The figure above describes the difference between the chimeric protein performance’s regarding of its reaction with diphtheria toxin.

Thus, in overseeing the entire performance, it can be divided into three parts:

Quantitative Overview

In calculating reaction rates of either association or dissociation rate, the following mathematical equations are applied:

Formulas:

Response unit, which is the symbol for reaction rates’ response that are given directly by the response machine will first be converted into gamma (surface concentration), which will then be converted into concentration of complex with the following formula:

Therefore, reaction rates can be calculated with following formula:

The formula is synthesized by following the mass-transportation-based reaction rates, which is based on chemical kinetics of the reaction’s constituent.

HB-EGF Receptor Activity

Part I: Association and Dissociation Rates Based on Time

This part of analysis is mainly referenced to an article by Brooke et.al. [1], in which they model binding of immobilized HB-EGF with diphtheria toxin response unit versus time, which will be shown below:

Therefore, the prediction of both association rates and dissociation rates are shown below, by comparing our chimeric protein’s kinetics with literature, the x axis represents time, while the y axis represents response in Response Unit (RU).

Table 1.HB-EGF Receptor Properties

Table 2.Calculation Table of Diphtheria Toxin and HB-EGF Association Rate

Therefore, the data regression will be plotted as below:

Figure 4.Association Response of Diphtheria Toxin towards HB-EGF Receptor.

Figure 5.Reaction Rate of Diphtheria Toxin towards HB-EGF Receptor.

Table 3.HB-EGF Receptor Properties

Table 4.Calculation Table of Diphtheria Toxin and HB-EGF Dissociation Rate

Therefore, the data regression will be plotted as below:

Figure 6.Dissociation Response of Diphtheria Toxin towards HB-EGF Receptor.

Figure 7.Reaction Rate of Diphtheria Toxin towards HB-EGF Receptor.

Part II: Rates Based on Concentration

Table 5.HB-EGF Receptor Properties

Table 6.Association and Dissociation Constants of HB-EGF Receptor.

Table 7.Association and Dissociation Response Unit and Concentration of Natural Diphtheria Toxin and HB-EGF Binding.

Table 8.Calculation of Binding Reaction Rate of HB-EGF and Diphtheria Toxin Based on Concentration.

Table 8.Calculation of Binding Reaction Rate of HB-EGF and Diphtheria Toxin.

Figure 8.The Regression Graph for Response Unit vs Toxin Concentration

The programmed Excel will calculate the reaction rates just by inserting desired concentration.

Part III: Effects of pH and Temperature

Based on throughout literature review, the closest thing for binding of diphtheria toxin is for Vero Cells [2], which is shown below:

Table 10.Concentration Counts of Diphtheria Toxin and HB-EGF Complex versus Time

The proposed model will follow arbitrary model based on Hill-Langmuir model, which is:

Our team formulated models (based on utilization of Polymath 6.0 software) based on that with trial and error fit to the data, and the best fitted model would be:

Figure 10.The Estimated Graphs for DT-Vero Cells Binding (Auth’s Personal Data)

Model

Table 11.Result of Polymath 6.0

Non-linear regression settings

Max # iterations = 64

Precision

Table 12.Result of Polymath 6.0 (cont’d).

General

Table 13Result of Polymath 6.0 (cont’d).

The comparison table is shown as below:

Table 14Comparison Data between Literature and Model Regression of Concentration Counts of Diphtheria Toxin and HB-EGF Complex versus Time.

Based on the graphs above, one can infer that:

Tar Binding

The Tar binding model would be based on two research articles with different models, which are from [3] and [4]. Modelling based on [3] is as below:

Figure 13.Time versus Response for TAR16 Binding to TAR*16 [3]

Table 15.Properties of TAR Receptor

Table 16.TAR Dissociation and Association Constant and Concentration Receptor

E = 0.4E_rep + -0.40E_att + 600E_elec + 1.00E_DARS

Table 4. Comparation of E parameter of native and chimera protein of HB-EGF interacted with DiphTox.

HB-EGF
Protein

Median Energy (kcal/mol)

Lowest Energy (kcal/mol)

Native
-944.3

-994.3

Chimera
858.2

934.4