Overview

Our goal is to design peptides to imitate CLEC5A docking with dengue virus. In order to ensure the peptides and the envelope protein (E protein) of dengue virus have an interaction, all the structure of peptides and proteins and their interactions were modeled using Rosetta. First, we utilized RosettaCM (Comparative modeling with Rosetta) to generate the structure of E protein from a local strain (PL046) based on the crystal structure (PDB: 1OAN). Second, we used the ab initio method to predict the peptide structures purely based on their sequences. Then, we utilized the clustering method to cluster the results and find the most probable structure of the peptide. Finally, we verified the interactions between these predicted peptides and the E protein based on the global protein-protein docking.

References

Combs, Steven A., Deluca, Samuel L., Deluca, Stephanie H., Lemmon, Gordon H., Nannemann, David P., Nguyen, Elizabeth D., Willis, Jordan R., Sheehan, Jonathan H. & Meiler, Jens. (2013). Small-molecule ligand docking into comparative models with Rosetta. Nature Protocols, 8(7), 1277-1298. doi: 10.1038/nprot.2013.074
Raveh, Barak., London, Nir., Zimmerman, Lior. & Schueler-Furman, Ora. (2011). Rosetta FlexPepDockab-initio: Simultaneous folding, docking and refinement of peptides onto their receptors. PLoS ONE, 6(4). doi: 10.1371/journal.pone.0018934
Alam, Nawsad., Goldstein, Oriel., Xia, Bing., Porter, Kathryn A., Kozakov, Dima. & Schueler-Furman, Ora. (2017). High-resolution global peptide-protein docking using fragments-based PIPER-FlexPepDock. PLoS Computational Biology, 13(12), 1-20. doi: 10.1371/journal.pcbi.1005905
Barrientos, Arturo. & Concha, Fernando. (1990). Phenomenological model of classification in conventional hydrocylones. Comminution, 819(1), 287-305. doi: 10.1007/978-1-61779-465-0
Li, Haiou., Lu, Liyao., Chen, Rong., Quan, Lijun., Xia, Xiaoyan. & Lü, Qiang. (2014). PaFlexPepDock: Parallel Ab-initio docking of peptides onto their receptors with full flexibility based on Rosetta. PLoS ONE, 9(5). doi: 10.1371/journal.pone.0094769
Ciemny, Maciej., Kurcinski, Mateusz., Kamel, Karol., Kolinski, Andrzej., Alam, Nawsad., Schueler-Furman, Ora. & Kmiecik, Sebastian. (2018). Protein–peptide docking: opportunities and challenges. Drug Discovery Today, 23(8), 1530-1537. doi: 10.1016/j.drudis.2018.05.006

Overview

The weakness of our design is that the peptides from the gold nanoparticles and the ones from the glass fiber compete for the same binding sites on the E protein. Moreover, if the E proteins on the virus particles are fully covered by the gold nanoparticles, there are no sites available to interact the peptides from the glass fiber.

Repulsion between gold nanoparticles

To assess this potential problem, we used DLVO theory to calculate the repulsion between gold nanoparticles to estimate the number of gold nanoparticles that would bind to a virus particle. The structure of dengue virus is icosahedral, and there are nine E proteins on each surface. The distances between the potential binding sites can be obtained from the structure in the protein data bank (1K4R). DLVO theory can be described as follows,

W_total(D) = W_a(D) + W_r(D) = -AR/12D + 2πεε₀RΨ_δ²exp(-κD)

W_total(D): total energy
W_a(D): van der Waals interaction energy
W_r(D): electrostatic interaction energy
A: Hamaker constant = 2.5×10^-19 J
R: radius = 1.3x10^-8 m
C: center to center distance
D: C-R
ε: permittivity of vacuum = 8.854x10^-12 F/m
ε₀: dielectric of solution = 80.4 (at 20°C)
Ψ_δ²: Stern layer potential (zeta potential) = 0.001794 V
κ^-1: diffuse layer thickness = 7.95x10-10 m

We took several representative positions on adjoining faces of the icosahedron to calculate the interactions between the gold nanoparticles based on DLVO theory. We found the total energies are all positive (Table 1) and these total energies are also larger than the typical biological interactions (~0.5 kcal/mol or 3.49 x10²¹ J). The results suggest that there will always be free faces on the virus particles to interact with the peptides conjugated on the test line.

Table 1. Energies of the gold nanoparticles between representative positions.

C (nm)	54.58	42.71	31.71	43.24
Attractive energy (10-21J)	-3.3	-4.6	-7.2	-4.5
Repulsive energy (10-21J)	51.0	51.3	51.6	51.3
Total energy (10-21J)	47.7	46.7	44.4	46.8
C (nm)	30.32	31.06	33.19	30.32
Attractive energy (10-21J)	-7.8	-7.5	-6.7	-7.8
Repulsive energy (10-21J)	51.7	51.7	51.6	51.7
Total energy (10-21J)	43.9	44.2	44.9	43.9

References

Elena Pokidysheva, Ying Zhang, Anthony J. Battisti, Carol M. Bator-Kelly, Paul R. Chipman, Chuan Xiao, G. Glenn Gregorio, Wayne A. Hendrickson, Richard J. Kuhn, Michael G. Rossmann. Cryo-EM Reconstruction of Dengue Virus in Complex with the Carbohydrate Recognition Domain of DC-SIGN. Cell Press, 124(3). doi:10.1016
Jörg Polte. Fundamental growth principles of colloidal metal nanoparticles – a new perspective. CrystEngComm, 36. doi:10.1039
Phillip E. Mason, Adrien Lerbret, Marie-Louise Saboungi, George W. Neilson, Christopher E. Dempsey, John W. Brady. Glucose Interactions with a Model Peptide. NCBI, 79(7). doi:10.1002
Taehoon Kim, Kangtaek Lee, Myoung-seon Gong, Sang-Woo Joo. Control of Gold Nanoparticle Aggregates by Manipulation of Interparticle Interaction. ACS Publications. doi:10.1021