MODELLING

In this project, CEAKER is a synthetically combined receptor used as the diagnostic tool system. TGFBR1, which is originally found in human body, directly interacts with Carcinoembryonic Antigen (CEA). CEA itself functions as an intercellular adhesion molecule and is upregulated in a wide variety of human cancers[1], including lung cancer. CEA-binding domain mainly lodged in TGFBR1’s extracellular environment. Hence, the domain, boarden between 1th-126th amino acid, was chosen from TGFBR1. Furthermore, Transcriptional Coactivator with PDZ-binding motif (TAZ) consists of Tar receptor, Tar HAMP domain, and EnvZ histidine kinase domain. It presents as an oncogene that is used for the early detection of lung cancer[2]. Therefore, our team selected Taz domains involving 1st-40th and 167th-484th amino acid as part of CEAKEER.

Figure 1. The selected Taz domain expands from 1-40th amino acids and 167-484th amino acids. Project’s modification of binding domain is located between 40th-167th amino acids.

Constructing 3D models is needed at first steps to visualise the protein structures. By all means, it could help us to interpret the modelling structure and predict the ligand binding site. Thus, I-Tasser Server[3] (https://zhanglab.ccmb.med.umich.edu/I-TASSER/) and Phyre 2.0[4]. (http://www.sbg.bio.ic.ac.uk/phyre2/) used as a suite of tools available to comply the modelling steps. I-Tasser server’s results are as followed:

C-Score = -2.19
Estimated TM-Score = 0.46 ± 0.15
Estimated RMSD = 12.5 ± 4.3 Å

Figure 2. 3D models of CEAKER via Phyre 2.0. Image coloured by rainbow N > C terminus. Model dimension (Å): X 123,484 Y: 55,936 Z:57,998.

Transmembrane proteins in general is poorly defined as the inherent properties of lipid embedded proteins[5]. Because of that, it is important to properly determine its protein orientation. Our team using TMHMM Server v.2.0[6] (http://www.cbs.dtu.dk/services/TMHMM/) and OPM database[7] (https://opm.phar.umich.edu/ppm_server)to predict CEAKER protein orientation in the Eschericia coli membrane.

Figure 3. The graph above shows CEAKER’s protein orientation. Y-axis displays the possibility of nth amino acid on protein located somewhere between transmembrane (red part), intracellular (blue line), and extracellular (pink line).

Figure 4. 3D models of CEAKER on membrane.

After that, we analyzing the interaction of TGFBR1-CEA and CEAKER-CEA using server ClusPro 2.0[8][9](https://cluspro.bu.edu/). This is important to ensure functional ligand-receptor system.

Figure 5. 3D models of the interaction between CEAKER and CEA.

Coefficient Weights

Table 1. Comparation of E Parameter of TGFBR1 and CEAKEER Binding to CEA.

According to the results above, we assume that CEA would bind to both TGFBR1 and CEAKER. It is determined by the higher energy score of interaction[10] between both CEAKER-CEA than TGFBR1-CEA (Table 1). CEAKER has the ability to bind the CEA thus offering the possibility to visualize the presence of lung cancer by its fluorescence.

REFERENCES

[1] Li, Y., Jiao, Z., Cao, H., & Pakala, S. B. (2010). Carcinoembryonic Antigen Interacts with TGF-Receptor and Inhibits TGF-Signalling in Colorectal Cancers. Cancer Research, 70(20), 8159-68. doi: 10.1158/0008-5472.CAN-10-1073.

[2] Sardo, Federica Lo., Strano, Sabrina., & Blandino, Giovanni. (2018). YAP and TAZ in Lung Cancer: Oncogenic Role and Clinical Target. Cancers (Basel),
10(5), 137. doi:10.3390/cancers10050137.

[3] Yang, J., & Zhang, Y. (2015). Protein Structure and Function Prediction Using I-TASSER. Current protocols in bioinformatics, 52, 5.8.1–5.8.15. doi:10.1002/0471250953.bi0508s52.

[4] Kelley, L. A., Mezulis, S., Yates, C. M., Wass, M. N., & Sternberg, M. J. (2015). The Phyre2 Web Portal for Protein Modeling, Prediction and Analysis. Nature protocols, 10(6), 845–858. doi:10.1038/nprot.2015.053.

[5] Griffin, N. M. & Schnitzer, J. E. (2010). Overcoming Key Technological Challenges in Using Mass Spectrometry for Mapping Cell Surfaces in Tissues. Mol Cell Proteomics, 10(2), R110.000935. doi: 10.1074/mcp.R110.000935.

[6] Krogh, A., Larsson, B., von Heijne, G., & Sonnhammer, E. L. (2001). Predicting Transmembrane Protein Topology with a Hidden Markov Model: Application to Complete Genomes. Journal of Molecular Biology, 305(3), 567-580. doi: 10.1006/jmbi.2000.4315.

[7] Lomize, M. A., Pogozheva, I. D., Joo, H., Mosberg, H. I., & Lomize, A. L. (2012). OPM Database and PPM Web Server: Resources for Positioning of Proteins in Membranes. Nucleic acids research, 40 (Database issue), D370–D376. doi:10.1093/nar/gkr703.

[8] Vajda, S., Yueh, C., Beglov, D., Bohnuud, T., Mottarella, S.E., Xia, B., Hall, D.R., & Kozakov, D. (2017). New additions to the ClusPro Server Motivated by CAPRI. Proteins: Structure, Function, and Bioinformatics, 85(3):435-444. doi: 10.1002/prot.25219.

[9] Kozakov, D., Hall, D.R., Xia, B., Porter, K.A., Padhorny, D., Yueh, C., Beglov, D., & Vajda, S. (2017). The Cluspro Web Server for Protein-Protein Docking. Nature Protocols, 12(2), 255-278. doi: 10.1038/nprot.2016.169.

[10] Kozakov, D., Beglov, D., Bohnuud, T., Mottarella, S., Xia, B., Hall, D.R., & Vajda, S. (2013). How Good is Automated Protein Docking? Proteins: Structure, Function, and Bioinformatics, 81(12), 2159-66. doi: 10.1002/prot.24403.

Team:Sriwijaya/Model

MODELLING

After that, we analyzing the interaction of TGFBR1-CEA and CEAKER-CEA using server ClusPro 2.0[8][9](https://cluspro.bu.edu/). This is important to ensure functional ligand-receptor system.