Team:UI Indonesia/Model

MODEL

EnvZ/HBEGF Receptor Modelling

The chimeric receptor that we are working on is based from two different integral membrane proteins, EnvZ and HBEGF. EnvZ and HBEGF are basically a fully functional protein receptor that can be found in Escherichia coli and human respectively. Our chimeric receptor will structurally consist of EnvZ structure as the base structure and HBEGF as part of periplasmic domain as well as the Diphtheria Toxin Binding Region.

In order to model the EnvZ/HEBGF chimeric receptor, we should gather information about the amino acid sequence from both of the EnvZ and HBEGF. We use information from online database, Uniprot¹, and gather the FASTA (P0AEJ4).

To determine on which residue of EnvZ we should insert HBEGF in, we run secondary structure prediction of EnvZ via NetSurfP². Then we predict its topography using TOPCONS webserver³. The topography data from both TOPCONS³ and Uniport¹ database give a very similar result. Thus, using the topography data and the secondary structure information, we predict the sequence of EnvZ graphically as shown below.

On EnvZ, that consist of 450 amino acids, we now know that the periplasmic region is around 36^th-159^th residue. Therefore, if we want to insert the HBEGF, we have to insert it on this particular region. In order to determine the specific region, we do some literature research and found that the region around 80^th-146^th residue is good to be swapped with insert.⁴ On HBEGF, we determine the region for chimeric by doing literature research.⁵ We then come out with 27 residues from HBEGF including the important residue for Diphtheria Toxin (DT) binding, the 141^th residue.¹

We replace 67 residues on EnvZ and swapped it with 27 residues form HBEGF, thus making it a chimeric receptor with 410 amino acids.

After designing the chimeric FASTA, for characterization and protein purification, His-tag is used, thus the insertion of His-tag to the sequence is essential. Therefore, we conduct secondary structure prediction via NetSurfP² to assess the coiled structure and accessibility of the protein. To assess the coiled structure, we choose the C terminal side, 410^th residue, to insert His-tag because it has a high probability to form coiled structure.⁶ We also assess the accessibility of the protein by predicting its exposure relative to the chimeric protein surface. The result is shown by the table below.

Amino Acid	N	RSA	Accesibility	ASA	Probability of Coil
K	408	0.736873388	Exposed	152	0.984208047
E	409	0.832562804	Exposed	145	0.992103398
G	410	0.924472272	Exposed	73	0.999899507

RSA: relative solvent accessibility; ASA: accessible surface area

To model the functional receptor protein, one has to assess the topography of the receptor protein. We assess the topography via TOPCONS³ and PHYRE2⁷. Both of them have a very similar outcome which has been summarized into the picture below.

To model the 3D structure of the chimeric protein, we use homology modeling and submit our chimeric FASTA to I-TASSER.⁸ Unfortunately, the result of the periplasmic domain is not very favorable. This could happen because the periplasmic domain has not been determined yet, both in secondary and tertiary structure. In collaboration with researcher Didik Huswo, we conduct the periplasmic domain by ab initio via QUARK9, then merge it to form a full 3D model by CHIMERA¹⁰. Then we predict the orientation via OPM MEMBRANE¹¹. The model is shown by the picture below via pyMOL¹².

As a native receptor, EnvZ form dimer to do its function. As well as the native, the chimeric receptor is also predicted to form dimer based on its predicted dimerization region. Therefore, we model the dimer via dimer classification on Cluspro¹³ and determine the best dimer conformation based on literatures. We then do protein embedding on membrane via OPM MEMBRANE¹³. The final result shown as the following.

To validate the model, we use PROCHECK¹⁴. We provide the Ramachandran Plot, Main Chain Parameters, and Overall G score as shown below.

Stereochemical Parameter	No. of data pts	Parameter value	Typical value	Band width	No. of band widths from mean	Status
Percentage residues in A,B,L	355	86.2	88.2	10	-0.2	INSIDE
Bad contacts/100 residues	0	0	1	10	-0.1	INSIDE
Zeta angle SD	378	1.9	3.1	1.6	-0.7	INSIDE
H-bond energy SD	284	0.7	0.7	0.2	0	INSIDE
Overall G-factor	410	-0.1	-0.2	0.3	0.3	INSIDE

Diphtheria Toxin Modeling

What is Diphtheria Toxin?

Diphtheria toxin belongs to the bifunctional A–B toxins (figure below). Portion “A” mediates the enzymatic activity responsible for halting protein synthesis in the target cell while portion “B” binds to a cell receptor and mediates the translocation of the A chain into the cytosol.¹⁵

Toxin type	Source	Toxin	Target	Mechanism	Effects
A-B type toxins	Clostridium diphtheriae	Diphtheria toxin	Many cell types	Inhibits release of inhibitory neurotransmitters ADP ribosylation of EF-2	Myopathy, polyneuropathy

Acidication of the endocytic vesicle induces a conformational change in the enclosed holotoxin, enabling the A subunit to traverse the membrane and reach its cyto- plasmic target. The A subunit of diphtheria toxin catalyzes ADP ribo- sylation of the elongation factor-2 (EF-2), inactivating it.¹⁵

Leader sequence is cleaved off by the bacterial leader peptidase; the A and B subunits are generated from the precursor protein by a ‘trypsin-like enzyme’. Once in the cytoplasm of a targeted eukaryotic cell, the A chain, responsible for ADP-ribosyl transfer, is disconnected from the B chain, responsible for receptor binding and membrane insertion. (b) The B chain binds to a speci c receptor on the eukaryotic cell. After endocytosis, acidi cation in the endosome induces inser- tion of the B chain into the endosomal membrane and translocation of subunit A into the cytosol, where it catalyzes the ADP ribosylation of EF-2. As a result, protein synthesis is inhibited and the targeted cell dies. (Courtesy of Menno Kok and Jean-Claude Pechère.)¹⁵

How Do We Counter Diphtheria Toxin?

First, we must take a look of Diphtheria Toxin (DiphTox) synthesis when it is already develop the ADP-Ribosyl Transfer which has explained before.

Iron regulation of diphtheria toxin synthesis. High iron concentrations in the environment repress the synthesis of diphtheria toxin. When bound to iron, DtxR-Fe binds to the operator (Op) of the toxgene and acts as a tran- scriptional repressor of the toxgene.(Courtesy of Menno Kok and Jean-Claude Pechère.)¹⁵

In response to synthesis of diphteria toxin, one defined step to get rids of diphteria toxin is to eliminate the synthesis process with some alternatives are listed below.

The measurement of the rate which several batches of toxin catalyzes the ADP ribosylation of transferase II in vitro. We find that their specific enzyme activities vary considerably according to the portion of “nicked” toxin for the toxin to intact from Fragments A and B which is the main fragment of diphtheria toxin (DT). Intact toxin is converted to nicked toxin during a short inc

The essential point is that a bond that already cleaved somewhere between the active site and the part of the molecule can enable proteolysis by addition of trypsin from the parent cells, or presumably by other enzymes.¹⁷ According to synthesis process from previous explanation, one can infer that cleavage can enable iron molecules to repress the transcription of DT.

Validation Method

For DiphTox modelling, we simply determine the root sum of square (R²) for each regressed data within each part. If the R² result is close to 1, the regressed data which expresses the Trends in concentration data or the results of expressions on independent variables such as time are valid and can be used for our modelling

R-squared is a statistical measure of how close the data are to the fitted regression line. It is also known as the coefficient of determination, or the coefficient of multiple determination for multiple regression. The definition of R-squared is fairly straight-forward and it is the percentage of the response variable variation that is explained by a linear model. In general, the higher the R-squared, the better the model fits your data.

Below is the structural modelling of our diphtheria toxin. It consists of 56 amino acid.

Kinetic Modeling

Better improvement and developments are related to a better understanding of the living system which we want to engineer. With this approach, we strive to predict the performance of our ligand-receptor binding, transmembrane interaction, and inner membrane biochemical reaction of our project. Regarding the quantitative behavior and prediction of those aspects mentioned before, we analyze the protein with the application of mathematics and engineering. Focusing on kinetics study and assumptions related to it, we can estimate overall performance such as rates and effects of variables that affect the diphtheria toxin (Diphtox) detection performance.

As the output of this modeling, evaluation of designed protein's kinetic regarding their mechanism and principal from literature. Mass transportation principles, 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 determination of process specification of biochemical reaction protein. This approach will be followed by feasibility & analysis for the protein to be applicated for the synthesized protein’s function and enlighten some parts of our project.

Principal and Mechanism Overview

As we mentioned before, we develop an effective application of diphtheria toxin detection from human saliva with addition of chimeric protein through spectrophotometry to see color change to green which indicates there are DiphTox detected within body fluid sample of human patients. Visual detection is widely used and claimed to be effective in real time detection especially in pre-phase of diphtheria.

Beforehand, to understand to what extent our detection time & system effectiveness, we are focusing on engineered receptor and binding sector. Our focuses are mentioned in scheme below.

Receptor-Ligand Binding

Calculation of Reaction Rates

Reaction rates calculation of ligand-receptor system involves association & dissociation rates based on manipulated variable such as concentration in response to time. Therefore, these following equations are applied. The surface concentration Gamma (G), for a protein can be calculated by the following formula: 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 mols^-2

Response unit, which is the symbol for reaction rate's response that is 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 formulas below. We note that we use the initial suggestion of Free Ligand Concentration [L] to estimate the free ligand-bound [L] to the receptor using complex concentration. k_on indicates association constant and k_off indicates dissociation constant.

After the estimation, we can calculate the overall binding rates with the formula described below. This formula is derived from principal of mass-transport based reaction rates of reaction's constituent.

HB-EGF Receptor Activity

Binding of immobilized human HB-EGF with diphtheria toxin response unit versus time. It can be estimated those response from variation of diphtheria toxin concentration.¹⁸

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.¹⁸

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

Variables	Value
Initial Concentration (μM)	1
Mr	58.34
Molecular Mass	70,000
Binding Constant	2.8 x 10-8
Assumed Complex Concentration (μM)	1
Association Constant (1/M.s)	6.9 x 104
Dissociation Constant (1/s)	1.9 x 10-3

Time (s)	Response Unit	Gamma	Surface Density	Binding Sites	Association Rates (M/s)
23	17.767	3.04537E-07	1.77667E-05	2.5381E-10	7.88E+02
26	33.3333	5.71363E-07	3.33333E-05	4.7619E-10	4.20E+02
29	51.667	8.85613E-07	5.16667E-05	7.38095E-10	2.71E+02
32	53.983	9.25323E-07	5.39833E-05	7.7119E-10	2.59E+02
35	62.667	1.07416E-06	6.26667E-05	8.95238E-10	2.23E+02
38	77.333	1.32556E-06	7.73333E-05	1.10476E-09	1.81E+02
41	86.667	1.48554E-06	8.66667E-05	1.2381E-09	1.62E+02
44	98.333	1.68552E-06	9.83333E-05	1.40476E-09	1.42E+02
47	113.333	1.94264E-06	0.000113333	1.61905E-09	1.24E+02
50	124.667	2.1369E-06	0.000124667	1.78095E-09	1.12E+02
53	129.333	2.21689E-06	0.000129333	1.84762E-09	1.08E+02
56	139.000	2.38258E-06	0.000139	1.98571E-09	1.01E+02

Dissociation Rates

Time (s)	Response Unit	Gamma	Surface Density	Binding Sites	Dissociation Rates (M/s)
70	160	2.74254E-06	0.00016	2.28571E-09	-8.75E+01
90	152.6666667	2.61684E-06	0.000152667	2.18095E-09	-9.17E+01
110	148	2.53685E-06	0.000148	2.11429E-09	-9.46E+01
130	143.3333333	2.45686E-06	0.000143333	2.04762E-09	-9.77E+01
150	134.3333333	2.30259E-06	0.000134333	1.91905E-09	-1.04E+02
170	131.6666667	2.25688E-06	0.000131667	1.88095E-09	-1.06E+02
190	123.3333333	2.11404E-06	0.000123333	1.7619E-09	-1.14E+02
210	119.6666667	2.05119E-06	0.000119667	1.70952E-09	-1.17E+02

Response unit generated by the association and dissociation rates in response to time used from literature assumed from DT concentration of 666.67 ( which is defined as the average concentration of DT varied within the literature. This consideration is selected due to the lowest 95% confidence level regressed to estimate the association and dissociation constant under this concentration assumed. This results in the highest accuracy constant obtained from data fitting in the literature due to low data distribution potential. On the other hand, data validation can

Rate 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:

HB-EGF Receptor Properties

Variable	Value
Assumed Time	60 sec
Mr	58.34
Molecular Mass	70,000
Binding Constant	2.8 x 10-8
Association Constant	6.9 x 104
Dissociation Constant	1.9 x -3

Association and Dissociation Response Unit and Concentration of Natural Diphtheria Toxin and HB-EHG Binding

Association R.U.	Dissociation R.U.	Concentration (M)
116	120	0.4
120	126	0.5
146	149	0.6
159	155	0.7
171	168	0.8
187	185	1

Concentration	Dissociation R.U.	Gamma	Surface Density	Binding Sites	[R] (M)	[L] (Assumed)	Complex Concentration	Dissociation Rates (M/s)
	153.2	2.62599E-06	0.000153	2.18857E-09	2.18857E-05	0.001156757	3.61E-14	9.14E+01
0.67	Association R.U.	Gamma	Surface Density	Binding Sites	[R] (M)	[L] (Assumed)	Complex Concentration	Association Rates (M/s)
	155.1	2.65855E-06	0.000155	2.21571E-09	2.21571E-05	0.001142587	3.52E-14	9.03E+01

The programmed excel will calculate the reaction rates just by inserting desired concentration of our receptor with Response Unit determined before by interpolation.

Effects of pH

Based on thoughput literature review, to estimate the effect of pH to DT binding to vero cells can be studied from Counts per Minute (CPM)¹⁹below

Kinetics of ¹²⁵I-labeled diphteria toxin- Vero cell association. ¹²⁵I-toxin plus unlabelled toxin (6 μM/mL) was added to the cells. At the triplicate samples were processed and counted. Error bars show standard error of the mean which, if no smaller than the symbol, A 37^oC: ^120-I-toxin, circle;¹²⁵I-toxin+unlabelled toxin, square.¹⁹

Based on the graph above, above, it can be assumed that the best temperature for diphtheria binding is 4°C. Assumed that the chosen temperature is 4^oC, 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 assumptions are based on journal.

Time (hour)	dC/dt (μM/min)
0.01	888.8888889
1	5142.857143
2	8800
3	12100
4	14400
5	15333.33333
6	15667
7	15667
8	16000

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

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:

Validation for Model Parameter

Variable	Value	95% Confidence Interval
K	1.895	0.207

Precision

Variable	Value
R2	0.9917904
Radj	0.9906176
RMSD	164.3159
Variance	3.12E+05

General Variance

Variable	Value
Sample size	9
Model variance	2
Independent variable	1
Iterations	7

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 assays, 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.²⁰

Effects of Temperature

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 sensor chip, RU, resonance units. This figure shows a representative experiment.²¹

EnVz Binding

Potential mechanisms for the phosphorylation-mediated activation of OmpR regulated tran- scription. Scheme A: Standard mechanism of RR transcriptional activation.Schemes B and C: Mechanisms of OmpR transcriptional activation proposed previously.Blue ovals and starbursts represent OmpR receiver domains in inactive and active conformational states, respectively. Red arrows represent OmpR DNA binding domains and DNA is depicted in green. Pink circles and yellow circles represent phosphoryl group donors and phosphoryl groups, respectively²⁴

The Escherichia coli EnvZ/OmpR two-component system (TCS) is an extensively studied signal transduction system that has been implicated in the regulation of over 100 genes in response to changes in the osmotic milieu of the cell.In this TCS, the sensory histidine kinase (HK)

EnvZ, autophosphorylates a conserved histidine residue and the phosphoryl group is subsequently transferred to a specific aspartic acid in the N-terminal domain of the response regulator (RR), OmpR. Regulation of the transcriptional activity of OmpR is mediated by strict control of the cellular level of phosphorylated OmpR (OmpR~P) through the kinase and potentially the OmpR phosphatase activities of EnvZ ²⁴

The plotted result has expressed by graph below

This finding suggests either that the assay is causing hydrolysis of OmpR~P, resulting in a smaller amount of OmpR~P being observed than is present in the cell, or that EnvZ-mediated phosphatase activity is not the only mechanism regulating OmpR phosphorylation in the cell. That also show some indication that the effects of stimuli on OmpR phosphorylation are directly related to the sensory HK EnvZ. The low level of OmpR phosphorylation observed in the absence of a cognate HK is likely due to nonspecific phosphorylation from small-molecule phosphodonors or from noncognate HK

Scheme Analysis

The above findings argue against previously proposed models for the role of phosphorylation in OmpR–DNA interactions (Schemes B and C). What remains to be determined, however, is whether data for OmpR support the standard model of phosphorylation-mediated dimerization enhancing DNA binding Scheme A). The ITC and SV- analytical ultracentrifugation (AUC) studies presented above indicate that there is no detectable Binding of monomeric OmpR to DNA. If the model in Scheme C is correct, then OmpR dimers must be able to form in solution and subsequently become stabilized by the presence of a DNA target sequence

Intramembrane OmpR Phosphorylation

Alternative Network design for TCS network B. SK denotes the unphosphorylated sensor kinase, and RR the unphosphorylated response regulator; phosphorylated forms of these proteins are denoted as SK~P and RR~P, respectively.C. Reaction scheme for a generic two-component system. For simplicity, the release of inorganic phosphate is not depicted and the mechanistic details of the alternative phosphatase (Ph) reactions are omitted. The left-hand side of the panel represents a monofunctional design, in which SK~P phosphorylates the RR and the dephosphorylation of the RR~P is catalysed by an alternative phosphatase. The right-hand side of the panel represents a bifunctional design, in which the SK~P phosphorylates the RR and the SK dephosphorylates the RR~P. Schematic representation of graded (monostable, solid line) and hysteretic (bistable, dashed line) responses.²¹

To estimate the quantity changes every substance involved within the phosphorylation, we need to define every non-elementary reaction represented by step-by-step interactions between molecules. Here we define those non-elementary reactions which are modeled by us.

For instances, the modeled reactions need specific rate constant based on literature we reviewed. We consider using the bifunctional phosphorylation due to continuous OmpR phosphorylated by EnVz~P followed by dephosphorylation of RR~P by EnVz-HBEGF chimeric. This scheme is the most realistic scheme due to the bistability of our chimeric receptor to effect the effector of phosphorylation reaction. The T7 Promoter will be affected due to this bistability and result in continuous signal induction to produce gfp for bioluminescence.

The bistable term means that every defined time to structurally defined molecules possessing two distinct functional groups to bring about new reactivity and/or selectivity in a reaction of interest.

From the concept explained above, we can simulate the mass action type reaction with 1^st order. The simulation is processed with SIMBIOLOGY function in MATLAB software. The model network is attached below.

SK defined by EnVz as “sensor kinase” and RR defined by OmpR as “Receptor Regulator” Environmental signals may modulate the phosphorylation state of sensor kinases (SK). The phosphorylated SK transfers phosphate (P) to its cognate response regulator (RR), which causes physiological response to the signal. Frequently, the SK is bifunctional and, when unphosphorylated, it is also capable of dephosphorylating the RR. The phosphatase activity may also be modulated by environmental signals.

Using the EnvZ/ OmpR system as an example, we constructed mathematical models to examine the steady-state and kinetic properties of the network. Mathematical modelling reveals that the Two Component System (TCS) can show bistable behaviour for a given range of parameter values if unphosphorylated SK and RR form a dead-end complex that prevents SK autophosphorylation.²¹

For non-elementary reactions mentioned above, we will conduct the mass action kinetic simulation with each specimen to estimate the effect of reaction to concentration according to time. The mass action rules (left) and the equations generated by mass action with steady state approaches (right) are attached and listed below

After we propose the mass action equation above, we input the constant value of each non-elementary reaction steps with value obtained from Igoshin et al.²¹ The input processes are implemented with each reaction as we attach below¹⁵

Assuming that EnVz has 10 molecular units defined by Avogadro Number, molarity of ENVz can be determined by step explained below:

The OmpR has 3500 molecules so the molarity can be estimated to be 6 mikroM based on simulation attached below. Phosphorilated OmpR has 10% concentration of total OmpR which result in good initial phosphorilation result.²⁰

Receptor-Ligand Docking

The need to model the structural modelling of our chimeric receptor and the diphtheria toxin is basically for their usage in molecular docking. The molecular docking become important because it could predict on how our molecule will bind to each other. This docking also very important our project because we are currently working on a chimeric receptor which is a fusion protein that no one ever built. So, it is really important for us to at least know that this chimeric receptor would bind to the diphtheria toxin.

For the molecular docking, we use Cluspro²² to calculate the total energy of the 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 the formula below) being lowered down.

E = 0.4E_rep + -0.40E_att + 600E_elecDARS

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 interactions. 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 changes due to the removal of the water molecules from the interface.

The analysis is shown by the table below.

Receptor	Cluster	Representative	Weighted Score
Chimeric	0	Center	-1226.3
		Lowest Energy	-1412.7
HBEGF wt	0	Center	-1190.8
		Lowest Energy	-1353.7

The data shown above represent the predicted energy of the system made by our system. From the data, we could conclude that the chimeric receptor could bind to the diphtheria toxin as good as or even better than the HBEGF. This could happen because the system's total energy represents a number that is more negative than the chimeric, both on the center and lowest energy calculation. It means that our chimeric model could bind to diphtheria toxin.

To convince us about our system docking mechanism, we do further analyses regarding the docking by visualizing it on Molecular Operating Environment (MOE)²³. We calculate the bonding between the ligand and the receptor. We compare between chimeric system and the native system. The result of the chimeric system is shown by visualization below.

The bond analysis is of the chimeric system is shown by the table below.

Ligand	Residue	Type	Score	Distance
H 7125	LEU 64	H-don	33.40%	2.06
H 6867	GLU 79	H-don	17.90%	2.61
H 7186	GLU 79	H-don	83.00%	1.92
H 6782	GLU 84	H-don	38.50%	1.38
H 6799	GLU 84	H-don	26.50%	1.91
O 7072	GLN 69	H-acc	44.40%	2.57
O 7174	ARG 78	H-acc	15.50%	2.84
O 6866	LYS 83	H-acc	99.50%	2.28
O 6837	ARG 86	H-acc	21.40%	2.72

The result of the native (wildtype) system is shown by visualization below.

The bond analysis is of the chimeric system is shown by the table below.

Ligand	Residue	Type	Score	Distance
H 3509	ASP 114	H-don	22.80%	0.96
H 4059	CYS 116	H-don	25.70%	2.75
H 3605	LYS 111	H-acc	17.10%	3.13
O 3615	LYS 111	H-acc	66.00%	2.34
O 3531	LYS 113	H-acc	56.20%	2.71
O 3591	ARG 128	H-acc	14.50%	1.97
O 3785	ARG 128	H-acc	34.80%	2.5
O 3786	ARG 142	H-acc	20.60%	2.63

Based on the visualization and bonding data we can assess the docking mechanism on both the chimeric receptor and native receptor (HBEGF). We could assess it from the score of each bonding. If we assess the bonding score of chimeric receptor and native receptor, it is clear that bonding score of chimeric receptor is superior than native receptor on which the maximum value is 99,5%. We assume that this could happen because of the accessibility of the amino acids of each protein. This result also coherent with the result we predict on Cluspro²². Thus, these results allow us to conclude that the chimeric receptor that we construct is valid enough to done such a system that could bind to diphtheria toxin.

References