Part:BBa_K3757002

c_blank

Cyclotide MCoTI-II with Trypsin inhibitor activity from Momordica cochinchinensis.

Sequence and Features

Assembly Compatibility:

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COMPATIBLE WITH RFC[10]
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COMPATIBLE WITH RFC[12]
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COMPATIBLE WITH RFC[21]
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COMPATIBLE WITH RFC[23]
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COMPATIBLE WITH RFC[25]
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COMPATIBLE WITH RFC[1000]

MCoTI-II is a squash trypsin inhibitor cyclotide, also known as cyclic knottins, and originates from the plant species Momordica cochinchinensis¹. Cyclotides are a class of plant cyclic peptides with a length of around 30 amino acids (aa). They comprise three disulfide bridges, which together with their cyclic backbone form the characteristic cyclic cystine knot motif making the cyclotide structure very rigid. This motif is responsible for cyclotides’ exceptional stability towards proteases and heat. The regions between their six cysteine residues are called loops 1 to 6. Cyclotides are classified into three categories by their sequence: Möbius, bracelet and trypsin inhibitor cyclotides with the latter having a low sequence conservation compared to the other two classes.² In plants, these peptides are expressed as precursors, which are finally localized to the vacuole. There, the actual cyclotide sequences are recognized by specialized cyclizing asparaginyl endopeptidases (AEPs), which catalyze the backbone cyclization.³ MCoTI-II itself is 34 aa in length, with the sequence GGVCPKILKKCRRDSDCPGACICRGNGYCGSGSD, and is folded into a cystine stabilized triple-stranded beta-sheet. Its natural function as a trypsin inhibitor is based on its inhibitory loop 1. Thereby, it acts as a defensive agent against herbivores.¹ MCoTI-II does not disrupt cell membranes and does not display antibacterial, nor hemolytic activity.⁴

Cyclotides like MCoTI-II have been used for grafting approaches in the past. Thereby, grafting means the insertion into or the replacement of one of the cyclotide’s loops with another peptide sequence (figure 1). This enables combining the cyclotide’s stability with the inserted peptide’s biological activity⁴. Regarding MCoTI-II, successful grafting into loops 1, 3, 5 and 6 has been demonstrated⁵.

Figure 1: Illustration of the grafting approach. One or multiple loops of the cyclotide MCoTI-II are replaced by another peptide sequence.

This biological part is a grafted construct of MCoTI-II designed and tested by iGEM team Tuebingen 2021. Two different antimicrobial peptides (AMPs) were used and either grafted into loop 1 or loop 6 of the cyclotide. In addition, a His6-tag (BBa_M50428) was grafted into loop 5 for affinity purification and immunodetection. Furthermore, flexible GS-linkers were included at the interface of the cyclotide’s native sequence and the grafted peptide. For further information on the project, please visit project description wiki page of team Tuebingen 2021. The different grafted constructs used by iGEM Tuebingen are listed in table 1.

Table 1: The grafted cyclotide constructs iGEM team Tuebingen 2021 worked with in the wetlab; the vectors in which the constructs were cloned are shown as well.
BBa_K3757002	c_blank	MCoTI-II loop 5 His6	X
BBa_K3757003	c_CHEN_1	MCoTI-II loop 1 CHEN, loop 5 His6	X	X
BBa_K3757004	c_CHEN_6	MCoTI-II loop 6 CHEN, loop 5 His6	X	X
BBa_K3757005	c_KR-12_1	MCoTI-II loop 1 KR-12, loop 5 His6	X
BBa_K3757006	c_KR-12_6	MCoTI-II loop 6 KR-12, loop 5 His6	X

The construct c_blank is MCoTI-II with a His6-tag grafted into loop 5. Its sequence and peptide segments are displayed in figure 2.

Figure 2: Peptide sequence and features of the cyclotide grafting construct c_blank.

The grafted construct was cloned into an Oak1 precursor peptide (BBa_K3757007) and expressed in N. benthamiana, regulated by a CaMV 35S promoter (BBa_K788000) and a 35S terminator (BBa_K1159307), C-terminally tagged with a c-myc-tag (BBa_K3757008). This is summarized as the composite part BBa_K3757007. This composite part was cloned into a vector with the genes encoding CtAEP1 (BBa_K3757001) and GFP (BBa_K3669012) as a reporter gene, forming a 3in1 vector (BBa_K3757011). Also, as a control, a 2in1 vector was constructed, which misses the CtAEP1 encoding gene. The resulting vector can then be used for transient transfection of N. benthamiana leaves to express the stabilized AMPs. For further information, visit experiments wiki page of team Tuebingen 2021.

Modeling

Building the Structures

To predict the final structures with which we would run our simulations later on, we used AlphaFold⁸. After predicting the structures, we wrote a script that utilizes the Python package Modeller¹⁰ to cyclize the structures, meaning it joins their termini. The final cyclized structures were compared to the wild-type version of MCoTI-II. We calculated the root-mean-square-deviation (RMSD), a measure as to how far the atoms of two structures are spaced apart at average, given in Ångström, using PyMOL⁹ and its “align” command. The RMSD of our final structure of c_blank compared to MCoTI-II is 1.320 Å.

Molecular Dynamics Simulations

Molecular Dynamics simulation is the process of computing the interactions of different atoms/molecules over a certain timeframe and then visualizing/analyzing the resulting data. The interactions between different atoms are computed using force fields, which are databases with definitions of how strong and with what kind of force certain atoms interact with each other. It is possible to compute all-atom systems, where all atoms are computed as individual entities, which we did.

Our first assumption was that the AMPs interact with the bacterial membrane, as they were characterized to do so by the literature. They are effective against gram-positive and gram-negative bacteria.⁶
The second assumption our model includes is the use of a model gram-negative bacterial membrane containing a 3:1 ratio of zwitterionic to anionic phospholipids. The zwitterionic phospholipid is POPE (palmitoyloleoylphosphatidylethanolamine) and the anionic one is POPG (palmitoyloleoylphosphatidylglycerol).¹¹.

We used the Molecular Dynamics simulation suite GROMACS^12,13, which is used for preparing and running simulations. All our all-atom systems were built by our partner team IISER Kolkata as part of our partnership. After we provided them with the structures of our constructs built above, they used them to build the membrane systems and prepared the scripts for running those. After some slight modifications to make the simulations run-ready, we ran them using our supercomputer access. To analyze our simulations we used “Visual Molecular Dynamics”¹⁴ (VMD in short), Grace, as well as GROMACS itself.

The following parameters were analyzed regarding our all-atom simulations:

Root Mean Square Deviation (RMSD): as we expected the membrane to be more stable than the protein, we expected the protein to be stabilized once binding occurs (blue line in figure 3)
Solvent Accessible Surface (SASA): we expected the SASA to decrease once the protein binds to the membrane, as part of its surface gets blocked (orange line in figure 3)
Hydrogen bonds (H-Bonds): we expected the number of hydrogen bonds to increase once the protein and membrane come into contact (black line in figure 3)

To make it easier to compare the fluctuation of the parameters at different time points, figure 3 shows the running averages (of 50 datapoints) of the mentioned parameters within one graph for the construct c_blank.

Figure 3: Parameters of c_blank. The change of Root Mean Square Deviation (RMSD) (blue line), Solvent Accessible Surface (SASA) (orange line) and number of formed hydrogen bonds (H-Bonds) (black line) are shown over time.

As the values are quite close to 0 and therefore correlation is low, it can be said that no other parameter is as representative as the number of hydrogen bonds generated.

In case of RMSD, this might be due to the fact that its values were already low even when the protein hadn’t bound to the membrane. Furthermore, the membrane itself is flexible as well, not holding the protein in one place as initially thought.

For SASA, a possible explanation could be that the protein wasn’t close enough to the molecules of the membrane to effectively reduce the amount of surface reachable by water molecules.

To compare the constructs to each other, we computed the arithmetic mean of the number of hydrogen bonds created over the simulation:

Table 2: Average number of hydrogen bonds over the course of the simulation.
Construct	Average number of hydrogen bonds
c_KR-12_1_CHEN_6	5.87306347
KR-12	5.0225
c_blank	3.626
CHEN	5.82

c_blank, our scaffold, has the lowest score and therefore interacts the least with the membrane.

Molecular Docking

Molecular Docking is the process of finding the optimal docking position of a ligand to a receptor (usually an enzyme) in a 3-dimensional space. It is sometimes used to screen a library of ligands against each other to find the best-fitting ligand for an enzyme. The score of a certain docking position of a ligand is called the binding affinity and is calculated using an energy function in kcal/mol. The position with the lowest binding energy is the one with the highest binding affinity and therefore the optimal calculated position. To build our model, we assumed the following things:

Our AMPs, namely KR-12 and CHEN, are both positively charged. Therefore, they should bind to the membrane by leveraging the opposing charge, meaning using anionic phospholipids.
The anionic phospholipid POPG, also known as palmitoyloleoylphosphatidylglycerol, is an anionic phospholipid that is abundant in bacterial membranes¹¹.

To run our molecular docking, we needed the structures of our ligands (AMPs, grafted constructs) and receptor (POPG). We used the same ligand structures as in all our simulations and got a POPG membrane structure from the GROMACS download site¹⁵ from which we extracted a single POPG molecule.

We used PyRx¹⁶ to prepare the ligands and the receptor by automatically letting the program add hydrogens, choose polar hydrogens, add charges, and do necessary further preprocessing. This didn’t work for one structure and so we prepared it using AutoDockTools^17,18.

To calculate the docking of our constructs, we used AutoDock Vina¹⁹, a tool for running Molecular Docking. We ran the simulations on the supercomputer BinAC.

The calculated binding affinity for c_blank is -1.9 kcal/mol. AutoDock Vina produces multiple modes of how the ligand could be docked to the receptor (we chose to produce 5 per ligand). We tried to choose the ones with the highest binding affinities for our model, while also filtering out impossible configurations. This occurred as it seems because Vina cannot dock circular molecules and therefore breaks the bond between the termini. This wouldn’t happen in a natural system.

The binding mode of c_blank can be found in figure 4. CHEN side chains are colored pink, KR-12 side chains are colored cyan, POPG is colored orange and the peptide itself is colored gray with dark-blue side chains. The visualizations and analysis of the bonds were created using the Protein-Ligand Interaction Profiler²⁰.

File:T--Tuebingen--empty complex.mp4

Expression in Nicotiana benthamiana

Green fluorescence from infiltrated N. benthamiana leaves could be observed, which shows successful infiltration and transient expression of genes from this vector (figure 5).

Figure 5: Green fluorescence showing GFP expression in a tobacco leaf transfected with our 3in1_c_blank construct by agroinfiltration observed by fluorescence microscopy.

References

¹Heitz, A., Hernandez, J. F., Gagnon, J., Hong, T. T., Pham, T. T., Nguyen, T. M., Le-Nguyen, D., & Chiche, L. (2001). Solution structure of the squash trypsin inhibitor MCoTI-II. A new family for cyclic knottins. Biochemistry, 40(27), 7973–7983. https://doi.org/10.1021/bi0106639

²Craik, D. J., & Conibear, A. C. (2011). The chemistry of cyclotides. The Journal of Organic Chemistry, 76(12), 4805–4817. https://doi.org/10.1021/jo200520v

³Conlan, B. F., Gillon, A. D., Barbeta, B. L., & Anderson, M. A. (2011). Subcellular targeting and biosynthesis of cyclotides in plant cells. American Journal of Botany, 98(12), 2018–2026. https://doi.org/10.3732/ajb.1100382

⁴Koehbach, J., Gani, J., Hilpert, K., & Craik, D. J. (2021). Comparison of a Short Linear Antimicrobial Peptide with Its Disulfide-Cyclized and Cyclotide-Grafted Variants against Clinically Relevant Pathogens. Microorganisms, 9(6), 1249. https://doi.org/10.3390/microorganisms9061249

⁵Craik, D. J., & Du, J. (2017). Cyclotides as drug design scaffolds. Current Opinion in Chemical Biology, 38, 8–16. https://doi.org/10.1016/j.cbpa.2017.01.018

⁶ Dong, W., Dong, Z., Mao, X., Sun, Y., Li, F., & Shang, D. (2016). Structure-activity analysis and biological studies of chensinin-1b analogues. Acta Biomaterialia, 37, 59–68. https://doi.org/10.1016/j.actbio.2016.04.003

⁷Yang, Jianyi; Zhang, Yang (2015): I-TASSER server: new development for protein structure and function predictions. In: Nucleic acids research 43 (W1), W174-81. DOI: 10.1093/nar/gkv342

⁸Jumper, John; Evans, Richard; Pritzel, Alexander; Green, Tim; Figurnov, Michael; Ronneberger, Olaf et al. (2021): Highly accurate protein structure prediction with AlphaFold. In: Nature. DOI: 10.1038/s41586-021-03819-2

⁹The PyMOL Molecular Graphics System, Version 2.0 Schrödinger, LLC.

¹⁰ Sali, A.; Blundell, T. L. (1993): Comparative protein modelling by satisfaction of spatial restraints. In: Journal of Molecular Biology 234 (3), S. 779–815. DOI: 10.1006/jmbi.1993.1626

¹¹ Lee, Juho; Jung, Sang Won; Cho, Art E. (2016): Molecular Insights into the Adsorption Mechanism of Human β-Defensin-3 on Bacterial Membranes. In: Langmuir 32 (7), S. 1782–1790. DOI: 10.1021/acs.langmuir.5b04113

¹²Abraham, Mark James; Murtola, Teemu; Schulz, Roland; Páll, Szilárd; Smith, Jeremy C.; Hess, Berk; Lindahl, Erik (2015): GROMACS: High performance molecular simulations through multi-level parallelism from laptops to supercomputers. In: SoftwareX 1-2, S. 19–25. DOI: 10.1016/j.softx.2015.06.001

¹³Páll, Szilárd; Abraham, Mark James; Kutzner, Carsten; Hess, Berk; Lindahl, Erik (2015): Tackling Exascale Software Challenges in Molecular Dynamics Simulations with GROMACS. In: Stefano Markidis und Erwin Laure (Hg.): Solving Software Challenges for Exascale, Bd. 8759. Cham: Springer International Publishing (Lecture Notes in Computer Science), S. 3–27.

¹⁴Schulten, W. H. (1996). VMD - Visual Molecular Dynamics. Journal of Molecular Graphics, 33-38

¹⁵Kukol, Andreas (2009): Lipid Models for United-Atom Molecular Dynamics Simulations of Proteins. In: Journal of chemical theory and computation 5 (3), S. 615–626. DOI: 10.1021/ct8003468

¹⁶Dallakyan, Sargis; Olson, Arthur J. (2015): Small-molecule library screening by docking with PyRx. In: Methods in molecular biology (Clifton, N.J.) 1263, S. 243–250. DOI: 10.1007/978-1-4939-2269-7_19.

¹⁷Morris, Garrett M.; Huey, Ruth; Lindstrom, William; Sanner, Michel F.; Belew, Richard K.; Goodsell, David S.; Olson, Arthur J. (2009): AutoDock4 and AutoDockTools4: Automated docking with selective receptor flexibility. In: Journal of computational chemistry 30 (16), S. 2785–2791. DOI: 10.1002/jcc.21256.

¹⁸ Sanner, M. F. (1999). Python: a programming language for software integration and development. J Mol Graph Model, 17(1), 57-61.

¹⁹Trott, O., & Olson, A. J. (2010). AutoDock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. Journal of computational chemistry, 31(2), 455-461.

²⁰Adasme, Melissa F.; Linnemann, Katja L.; Bolz, Sarah Naomi; Kaiser, Florian; Salentin, Sebastian; Haupt, V. Joachim; Schroeder, Michael (2021): PLIP 2021: expanding the scope of the protein-ligand interaction profiler to DNA and RNA. In: Nucleic acids research 49 (W1), W530-W534. DOI: 10.1093/nar/gkab294.