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Team:ICS BKK/Results

Team:ICS BKK/Results - 2020.igem.org




  We utilized three different softwares: The Scripps Research Insitute’s Autodock Vina [8], BIOVIA Discovery Studio [2], and MODELLER [1]. Autodock Vina is the main tool we used to predict the binding affinity between the phage and the bacteria. Therefore, the first step was to validate the software as well as having the binding affinity for the control group.

  The Scripps Research Insitute’s Autodock Vina was validated as we docked Phage P2 with the receptor of its natural host, Lactococcus lactis. This also serves as our control group. However, the 3D model of the macromolecule on L. lactis in which Phage P2 binds onto wasn’t available. Therefore, utilizing previous research and 2D structures of the macromolecule from A dual-chain assembly pathway generates the high structural diversity of cell-wall polysaccharides in Lactococcus lactis [12], we were able to model the receptor by utilizing BIOVIA Discovery Studio to predict possible bond angles. Hence, we were able to computationally model a three-dimensional structure of the L. lactis receptor.

  Using Autodock Vina, we then docked this modelled macromolecule with the Phage P2’s PDB structure [11], which can be found in RCSB Protein Data Bank. The binding affinity given by Autodock Vina was -11.38 kcal/mol. From our experience, Autodock Vina’s binding affinity score varies +/- 1.0 kcal/mol.

  Thereafter, we used Autodock Vina to dock the PDB format of Phage P2 with the receptor of S. pyogenes, which is peptidoglycan [7], in which its three-dimensional structure can be found on PubChem [9]. We found the binding affinity to be -4.50 kcal/mol, indicating that Phage P2 and S. pyogenes are unlikely to bind with one another as expected.This value of -4.50 kcal/mol will serve as our baseline.

  In an attempt to decrease the binding affinity value between Phage P2 and S. pyogenes, we used MODELLER to model 450 different versions of Phage P2. MODELLER is used to predict a protein’s three-dimensional structures when the program is provided with an alignment of known sequences. The program then calculates many models that satisfies its restraints. MODELLER created 450 different probable models of the mutated Phage P2. Thereafter, we docked all 450 versions with the receptor of S. pyogenes via Autodock Vina. The average binding affinity was -4.90 kcal/mol, the maximum was -3.51 kcal/mol, and the minimum was -6.60 kcal/mol; the minimum occurred at Phage P2 Mutant #327, indicating that though it may not bind to S. pyogenes as well as it would to L. lactis, it has significantly improved its binding affinity from the natural form of Phage P2.

  Moreover, to create another control group, we decided to dock Phage A25, a bacteriophage that naturally binds to S. pyogenes, to S. pyogenes. The binding affinity calculated by Autodock Vina was -7.54 kcal/mol. This indicates that Phage P2 Mutant #327 may not bind to S. pyogenes as well as it would to L. lactis, nor does it bind to S. pyogenes as well as Phage A25. However, the value of -7.54 kcal/mol is close to -6.6 kcal/mol to suggest that it is probable that the Phage Mutant #327 will bind to S. pyogenes. Phage Mutant #327 also has significantly improved its binding affinity from the natural form of Phage P2, since the original Phage P2 with S. pyogenes resulted in -4.5kcal/mol as its binding affinity.

  Now remember, we wanted for this Phage P2 Mutant to be able to infect both S. pyogenes and L. lactis. Therefore, we tested this by docking the two together and found a binding affinity of -10.01 kcal/mol, which can be compared to the original Phage P2 binding with L. lactis’s binding affinity, which is -11.38 kcal/mol. This is as expected that Phage P2 Mutant will bind slightly worse than the original Phage P2. However, the close to similar value of binding affinity still means that Phage P2 Mutant #327 is still most likely to be able to still bind to L. lactis. Hence, Phage P2 Mutant #327’s unique properties of most likely being able to bind to both S. pyogenes and L. lactis means its sequence is ready to be compared with the phage from the wet lab, so we may study what makes a bacteriophage able to bind to S. pyogenes.



  The scatterplot above represents the growth curve of S. aureus. We placed a solution of S. aureus into a shaking incubator, and every 30 minutes we sampled it and used a spectrophotometer to check its OD600 value. The OD600 value represents the concentration of S. aureus in our liquid sample. The difference in OD600 value between two points (two time intervals) represents the growth of S. aureus. We calculated the greatest difference between two points, which amounted to 0.5985, from 0.4789 to 1.0774. This point of the greatest growth of S. aureus is called the mid log phase, where the bacteria is the most productive.



  Using ultraviolet–visible spectroscopy, the optical densities of each selected phage clone were determined and graphed. The clones were named after the wells they were assigned to. The optical density indicates the concentration of M13 phages that successfully bound to S. aureus.

  The results of ELISA show us that there was one phage clone, C3, that has a high OD value, meaning that this phage has a high binding affinity with S. aureus. However, we see that the error bars are high as well. This does not give us confirmation that this phage had truly bound to S. pyogenes, but it is a promising phage candidate. Currently, the plasmid is being sequenced. To better our results in the future, we can propagate the C3 phage and repeat ELISA multiple times.

  In conclusion, our team is able to select for a specific nanobody that binds to a bacteria. Besides engineering the nanobody to decrease S. pyogenes populations, phage therapy can also be used to target other infectious bacteria, because identifying and isolating the nanobody requires a short period of time.

  This method of phage therapy can also be used to combat drug resistance to antibiotics. Since antibodies are much more specific that antibiotics, phage therapy can be used to fight infectious bacteria before it develops antibiotic resistance.


  The next steps that we hope to continue for next year’s project, and we encourage others to also take on this challenge, is to use S. pyogenes for the wet lab instead of S. aureus, as we did due to the unforeseen circumstances of COVID-19. This will then allow us to compare the structures or properties of the bacteriophages that allows it to bind to S. pyogenes. After this analysis, we hope to manually engineer the computational model of Phage P2 to better its binding affinity even further.

  After testing this phage’s binding abilities computationally, we can then utilize lab techniques to engineer the phage in the wet lab, such as using the CRISPR-Cas system. After that, we would incorporate the bacteriophage into Lactococcus lactis, and finally incorporate this engineering probiotic into mouthwash.


[1] A. Šali and T. L. Blundell. Comparative protein modelling by satisfaction of spatial restraints. J. Mol. Biol. 234, 779-815, 1993.

[2] BIOVIA, D. S. (2015). Discovery studio modeling environment. San Diego, Dassault Systemes, Release, 4.

[3] Fukunaga, K., & Taki, M. (2012). Practical Tips for Construction of Custom Peptide Libraries and Affinity Selection by Using Commercially Available Phage Display Cloning Systems. Journal of Nucleic Acids, 2012(295719), 1-9. https://doi.org/10.1155/2012/295719

[4] Gagic, D., Ciric, M., Wen, W. X. et al. (2016). Exploring the Secretomes of Microbes and Microbial Communities Using Filamentous Phage Display. Frontiers in Microbiology. 7. https://doi.org/10.3389/fmicb.2016.00429

[5] Harada, L. K., Silva, E. C., Campos, W. F., et al. (2018). Biotechnological applications of bacteriophages: State of the art. Microbiological Research, 212-213(7), 38-58. https://doi.org/10.1016/j.micres.2018.04.007.

[6] Lee, C., Iorno, N., Sierro, F. et al. (2017). Selection of human antibody fragments by phage display. Nat Protoc 2, 3001–3008 https://doi.org/10.1038/nprot.2007.448

[7] McShan WM, Nguyen SV. The Bacteriophages of Streptococcus pyogenes. 2016 Feb 10 [Updated 2016 Mar 25]. In: Ferretti JJ, Stevens DL, Fischetti VA, editors. Streptococcus pyogenes : Basic Biology to Clinical Manifestations [Internet]. Oklahoma City (OK): University of Oklahoma Health Sciences Center; 2016-. Available from: https://www.ncbi.nlm.nih.gov/books/NBK333409/

[8] Morris, G. M., Goodsell, D. S., Halliday, R.S., Huey, R., Hart, W. E., Belew, R. K. and Olson, A. J. (1998), Automated Docking Using a Lamarckian Genetic Algorithm and Empirical Binding Free Energy Function J. Computational Chemistry, 19: 1639-1662.

[9] National Center for Biotechnology Information. "PubChem Compound Summary for CID 9816401, alpha-Muramic acid" PubChem, https://pubchem.ncbi.nlm.nih.gov/compound/alpha-Muramic-acid. Accessed 11 October, 2020.

[10] Nian, S., Wu, T., Ye, Y. et al. (2016). Development and identification of fully human scFv-Fcs against Staphylococcus aureus. BMC Immunol 17, 8. https://doi.org/10.1186/s12865-016-0146-z

[11] PDB ID: 1ZRU Receptor-binding protein of Lactococcus lactis phages: identification and characterization of the saccharide receptor-binding site. Tremblay, D.M., Tegoni, M., Spinelli, S., Campanacci, V., Blangy, S., Huyghe, C., Desmyter, A., Labrie, S., Moineau, S., Cambillau, C. (2006) J Bacteriol 188: 2400-2410

[12] Theodorou I, Courtin P, Palussiere S, Kulakauskas S, Bidnenko E, Pechoux C, Fenaille F, Penno C, Mahony J, van Sinderen D, Chapot-Chartier MP. 2019. A dual-chain assembly pathway generates the high structural diversity of cell-wall polysaccharides in Lactococcus lactis. J Biol Chem 294:17612–17625. doi:10.1074/jbc.RA119.009957.

More information about the methods implemented in MODELLER, their use, applications, and limitations can be found in the papers listed on our web site at https://salilab.org/publications/. Here is a subset of these publications:

  1. A. Šali and T. L. Blundell. Comparative protein modelling by satisfaction of spatial restraints. J. Mol. Biol. 234, 779-815, 1993.

  2..A. Šali and J. P. Overington. Derivation of rules for comparative protein modeling from a database of protein structure alignments. Protein Science 3, 1582-1596, 1994.

  3. R. Sánchez and A. Šali. Comparative protein structure modeling: Introduction and practical examples with MODELLER. In Protein Structure Prediction: Methods and Protocols, D.M. Webster, editor, 97-129. Humana Press, 2000.

  4. M. A. Martı-Renom, A. Stuart, A. Fiser, R. Sánchez, F. Melo and A. Šali. Comparative protein structure modeling of genes and genomes. Ann. Rev. Biophys. Biomolec. Struct. 29, 291-325, 2000.

  5. A. Fiser, R. K. G. Do and A. Šali. Modeling of loops in protein structures. Protein Science 9, 1753-1773, 2000.

  6. F. Melo, R. Sánchez, A. Šali. Statistical potentials for fold assessment. Protein Science 11, 430-448, 2002.

  7. M. A. Martı-Renom, B. Yerkovich, and A. Šali. Comparative protein structure prediction. John Wiley & Sons, Inc. Current Protocols in Protein Science 1, 2.9.1 - 2.9.22, 2002.

  8. U. Pieper, N. Eswar, A. C. Stuart, V. A. Ilyin and A. Šali. MODBASE, a database of annotated comparative protein structure models. Nucleic Acids Research 30, 255-259, 2002.

  9. A. Fiser and A. Šali. MODELLER: generation and refinement of homology-based protein structure models. In Methods in Enzymology, C.W. Carter and R.M. Sweet, eds. Academic Press, San Diego, 374, 463-493, 2003.

  10. N. Eswar, B. John, N. Mirkovic, A. Fiser, V. A. Ilyin, U. Pieper, A. C. Stuart, M. A. Martı-Renom, M. S. Madhusudhan, B. Yerkovich and A. Šali. Tools for comparative protein structure modeling and analysis. Nucleic Acids Research 31, 3375-3380, 2003.