Composite

Part:BBa_K3088001:Design

Designed by: Syailendra Karuna Sugito, Elvan Wiyarta, Eko Ngadiono, Refael Alfa Budiman, Aditya Parawangsa   Group: iGEM19_UI_Indonesia   (2019-10-07)


HBEGF/ENVZ Chimeric Complete


Assembly Compatibility:
  • 10
    COMPATIBLE WITH RFC[10]
  • 12
    COMPATIBLE WITH RFC[12]
  • 21
    COMPATIBLE WITH RFC[21]
  • 23
    COMPATIBLE WITH RFC[23]
  • 25
    COMPATIBLE WITH RFC[25]
  • 1000
    COMPATIBLE WITH RFC[1000]

Design Notes

Sequencing of this part (BBa_K3088001) combined with part (BBa_K769001) into pSB1C3 is confirmed and showed as follow:

AAAGATGGACTGACTGAATGCCTCAAATGTTCTTTACGATGCCATTGGGATATATCAACGGTGGTATATCCAGTGATTTTTTTCTCCATTTTAGCTTCCT TAGCTCCTGAAAATCTCGATAACTCAAAAAATACGCCCGGTAGTGATCTTATTTCATTATGGTGAAAGTTGGAACCTCTTACGTGCCCGATCAACTCGAG TGCCACCTGACGTCTAAGAAACCATTATTATCATGACATTAACCTATAAAAATAGGCGTATCACGAGGCAGAATTTCAGATAAAAAAAATCCTTAGCTTT CGCTAAGGATGATTTCTGGAAGTGCCATTCCGCCTGACCTAGTTCAAGTGTCCGAGAAGAATTCGCGGCCGCTTCTAGAGAGGTACTGACTCATAAAAAA TTTATTTGCTTTGTGAGCGGATAACAATTATAATAACGATCGCGAAAGAGGAGAAATACGCGATCGAAGATGCGTCGTCTGCGTTTTAGTCCACGCAGCT CGTTTGCACGTACGCTTTTGTTAATCGTTACATTGCTTTTTGCTTCCCTGGTCACCACTTACTTAGTTGTATTAAACTTCGCAATCTTGCCAAGTCTGCA ACAGTTTAACAAGGTATTGGCTTACGAGGTTCGCATGCTTATGACCGACAAACTTCAATTGGAAGACGGGACTCAACTGGTTGTACCACCCGCATTTCGC CGTGAGAAGTACGTTAAGGAATTGCGCGCCCCCAGTTGCATCTGTCACCCCGGATACCATGGAGAGCGCTGCCACGGCTTGTCACTGGTGCCGTTGACAG AGATCCATCAGGGTGACTTTAGCCCGCTTTTCCGTTATACGTTGGCAATTATGTTACTTGCCATCGGTGGCGCATGGTTATTTATTCGCATTCAGAACCG TCCGTTGGTCGATTTAGAGCACGCTGCACTTCAAGTTGGTAAGGGCATCATCCCGCCACCATTGCGCGAGTACGGGGCCTCGGAAGTACGTAGTGTTACT CGTGCCTTCAATCATATGGCCGCCGGTGTTAAGCAGTTAGCAGACGACCGCACTCTGTTGATGGCTGGCGGGTCTCATGATTTGCGNACCCCCTGACNCG TATCCGCCTGGCTACTGAAATGATGTCGGAACAGGATGGAACTTGGCCGAGTCCTCAATAAAGACTTGAGGAATGAACGCTTTATTGAACGTTCTCCACT TTTGCTACCGCCCGAAANCCTTTGAANGGGACNNNATGGGGTTGGGGANAATTTTGCCAAANNGGGTCAACCGGAANNNAAAGGGCTTCCCCNNTTTTGG GGGAAAANNNNNCCCTTTTTTGNNNGGCCCTAAGGTTTAACCNNCCCCCGAAAAGGGGGTAAGGGNCCCNNNCCCCNCCNCCCGTTTTGGNNGGGAGGAC
GNCCCCCCCCNA

Figure 1. This the illustration of the plasmid (pSB1C3) containing part BBa K3088001 and BBa_K769001.

I. 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 function 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 (https://www.uniprot.org/uniprot/Q99075 and https://www.uniprot.org/uniprot/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.

Figure 2. On EnvZ, that consist of 450 amino acids, we now know that the periplasmic region is around 36th-159th 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 researches and found that the region around 80th-146th 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 141th residue.1
Figure 3. We replace 67 residues on EnvZ and swapped it with 27 residues form HBEGF, thus making it to be a chimeric receptor with 410 amino acids.
Figure 4. This is the model of our chimeric protein

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

Figure 5. 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 PHYRE277. Both of them have a very similar outcome which been summarize into the picture below.

Figure 6.
Figure 7.
Figure 8.

To model the 3D structure of the chimeric protein, we use homology modelling and submit our chimeric FASTA to I-TASSER.8 Unfortunately, the result for 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 full 3D model by CHIMERA10. Then we predict the orientation via OPM MEMBRANE. The model is shown by the picture below via pyMOL12.

Figure 9. This is the monomeric structure of our chimeric

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 MEMBRANE11. The final result shown as the following.

Figure 10. This is the dimeric structure of our chimeric

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

Figure 11.
Figure 12.
Figure 13.

II. 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 build. 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 Cluspro15 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 formula below) being lowered down.


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


Note: Erep and Eattr denote as repulsive and attractive contributions to the van der Waals interaction energy. Additionally, Eelec means an electrostatic energy that occur during both protein interactions. EDARS 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.

Figure 14.

The data shown above represent the predicted energy of the system that 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 rather than the HBEGF. This could happen because the system 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 a further analyses regarding the docking by visualize it on Molecular Operating Environment (MOE)16. 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.

Figure 15.

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

Figure 16.

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

Figure 17.

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

Figure 18.

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 Cluspro15. Thus, these result 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

  1. The UniProt Consortium. UniProt: a worldwide hub of protein knowledge. Nucleic Acids Research. 2019 Jan 9; 47(1): 506-15.
  2. Klausen MS, Jespersen MC, Nielsen H, et all. NetSurfP-2.0: Improved prediction of protein structural features by integrated deep learning. Proteins. 2019 Feb; 87: 520– 7.
  3. Tsirigos KD, Peters C, Shu N, et all. The TOPCONS web server for consensus prediction of membrane protein topology and signal peptides. Nucleic Acids Res. 2015 Jul 1; 43(1): 401-7.
  4. iGEM Stockholm. ABBBA: affibody-based bacterial biomarker assay [internet]. iGEM; 2015 [cited on 2019 Aug 31]. Available from: http://2015.igem.org/Team:Stockholm
  5. Mitamura T, Higashiyama S, Taniguchi N, et all. Diphteria toxin binds to the epidermal growth factor (EGF)-like domain of human heparin-binding EGF-like growth factor/diphtheria toxin eceptor and inhibits specifically its mitogenic activity. Biological Chemistry. 1995 Jan 20; 270 (3): 1-5.
  6. iGEM Universitas Indonesia. Finding Diphthy [internet]. iGEM; 2018 [cited on 2019 Aug 31]. Available from: http://2018.igem.org/Team:UI_Indonesia/Project#
  7. Kelley LA, Mezulis S, Yates CM, et all. The Phyre2 web portal for protein modeling, prediction and analysis. Nature Protocols. 2015; 10: 845-58.
  8. J Yang, Y Zhang. I-TASSER server: new development for protein structure and function predictions. Nucleic Acids Research. 2015; 43: 174-81.
  9. D. Xu and Y. Zhang. Toward optimal fragment generations for ab initio protein structure assembly. Proteins. 2013; 81: 229-39.
  10. Pettersen EF, Goddard TD, Huang CC, et all. UCSF Chimera--a visualization system for exploratory research and analysis. J Comput Chem. 2004 Oct;25 (13): 1605-12.
  11. Lomize M, Pogozheva I, Joo H, et all. OPM database and PPM web server: resources for positioning of proteins in membranes. Nucleic Acids Res. 2012; 40: 370-6.
  12. The PyMOL Molecular Graphics System, Version 2.0 Schrödinger, LLC.
  13. Yueh C, Hall DR, Xia B, et all. ClusPro-DC: dimer classification by the cluspro server for protein–protein docking. Journal of Molecular Biology. 2017 Feb; 429(3): 372-81.
  14. Laskowski RA, MacArthur MW, Moss DS, et all. PROCHECK - a program to check the stereochemical quality of protein structures. J App Cryst. 1993; 26: 283-91.
  15. Vajda S, Yueh C, Beglov D, et all. New additions to the ClusPro server motivated by CAPRI. Proteins: Structure, Function, and Bioinformatics. 2017 Mar; 85(3): 435-44.
  16. Molecular Operating Environment (MOE), 2014.09 ed., 1010 Sherbooke St. West, Suite #910, Montreal, QC, Canada, H3A 2R7: Chemical Computing Group.