Team:Queens Canada/Binding-Modeling
Binding Modelling
Binding Modelling
To analyze and predict binding between OspA Genbank (M57248.1) and our ScFv, Cluspro protein-protein docking supercomputer (1–4) was used. Specifically, the antibody mode predicted the binding between the receptor (ScFv) and the ligand (OspA)(5). The Cluspro supercomputer is designed to test billions of docking confirmations between two proteins with lowest-energy structures undergoing root-mean square deviation and energy minimization (Fig.1)(4).
| Cluster | Members | Representative | Weighted Score |
|---|---|---|---|
| 0 | 118 | Center | -323.9 |
| Lowest Energy | -330.9 | ||
| 1 | 81 | Center | -306.9 |
| Lowest Energy | -320.1 | ||
| 2 | 40 | Center | -275.4 |
| Lowest Energy | -306.0 | ||
| 3 | 40 | Center | -273.2 |
| Lowest Energy | -306.0 | ||
| 4 | 37 | Center | -337.2 |
| Lowest Energy | -337.2 |
From the models generated, the lowest energy conformation binding model was selected and the CDR binding interactions within PyMol (Fig.1, Fig.2 and Fig.3) was analyzed to determine how effectively the ScFv binds to OspA. To effectively analyze binding, all residues within the CDR region that were shown to have polar interactions with OspA residues were highlighted as stick structures.
Fusion Proteins
Once we were confident that the ScFv would theoretically bind to OspA, Chimera in combination with PyMol was used to build and model the resulting fusion proteins (6). The first of these fusion proteins was the combination of the 3-24 ScFv bound via C-terminal through a (GGGS)3 linker region to a GFP (Fig.4). The second fusion protein created being very similar to the first however with the GFP replaced with an alkaline phosphatase (Fig. 5).
Fusion Protein Binding Modelling
With the fusion proteins created we wanted to ensure the additional fluorescent proteins would not disrupt binding affinity between the antibody and the ligand. Therefore, we ran two more binding simulations through Cluspro. The first of these simulations tested the binding affinity between the fusion protein ScFv + GFP and the ligand OspA (Fig.6).
| Cluster | Members | Representative | Weighted Score |
|---|---|---|---|
| 0 | 100 | Center | -321.2 |
| Lowest Energy | -334.4 | ||
| 1 | 82 | Center | -285.5 |
| Lowest Energy | -322.9 | ||
| 2 | 39 | Center | -275.4 |
| Lowest Energy | -305.4 | ||
| 3 | 38 | Center | -272.1 |
| Lowest Energy | -292.2 | ||
| 4 | 35 | Center | -277.5 |
| Lowest Energy | -302.8 |
Based on Figure 6 and Table 2, we were able to determine that the addition of the GFP to the ScFv had only a small impact on binding affinity between antibody and ligand. As shown in Fig 4B, the addition of the GFP resulted in fewer atoms interacting during binding (118 members to 100 members) however, it also resulted in a lower energy conformation (-330.9 to -334.4) suggesting a possibly stronger binding affinity. Now knowing that a simple GFP protein only slightly impacted binding, we tested the binding for the protein intended for use in the lateral flow assay; 3-24 ScFv + alkaline phosphatase (phoA).
| Cluster | Members | Representative | Weighted Score |
|---|---|---|---|
| 0 | 49 | Center | -301.6 |
| Lowest Energy | -336.2 | ||
| 1 | 47 | Center | -313.8 |
| Lowest Energy | -341.9 | ||
| 2 | 42 | Center | -355.1 |
| Lowest Energy | -364.6 | ||
| 3 | 37 | Center | -340.8 |
| Lowest Energy | -349.8 | ||
| 4 | 35 | Center | -360.1 |
| Lowest Energy | -361.0 |
Similar with the previous fusion protein, the addition of the phoA caused a decrease in binding atoms (118 to 49 when comparing both model zeros) however, as shown in Table 3, this decrease is much more significant in comparison to the previous model. Interestingly with this fusion protein the theoretical energy conformation was further lowered to -336.2 for model zero. Upon further analysis of Table 3 a relationship seems to emerge between the number of members acting in binding in relation to the lowest conformation energy. This relationship seems to suggest that by reducing the number of atoms interacting in binding the overall conformation energy of the model becomes more stable. Based on this trend, an assumption could be made that binding between the antibody and the ligand is causing conformation strain within the protein. Therefore, with fewer atoms interacting in binding the protein is better able to exist in its non-bound conformation leading to the lower overall energy we see in Table 3.
Regardless of the relationship between energy confirmation and number of atoms interacting in binding, these Cluspro models allowed us to model and predict that the addition of both the GFP and phoA should theoretically not prevent binding between the 3-24 ScFv antibody and OspA.
References
1. Desta, I. T., Porter, K. A., Xia, B., Kozakov, D., and Vajda, S. (2020) Performance and Its Limits in Rigid Body Protein-Protein Docking. Structure. 28, 1071-1081.e3
2. ajda, S., Yueh, C., Beglov, D., Bohnuud, T., Mottarella, S. E., Xia, B., Hall, D. R., and Kozakov, D. (2017) New additions to the ClusPro server motivated by CAPRI. Proteins Struct. Funct. Bioinforma. 85, 435–444
3. Kozakov, D., Hall, D. R., Xia, B., Porter, K. A., Padhorny, D., Yueh, C., Beglov, D., and Vajda, S. (2017) The ClusPro web server for protein-protein docking. Nat. Protoc. 12, 255–278
4. Kozakov, D., Beglov, D., Bohnuud, T., Mottarella, S. E., Xia, B., Hall, D. R., and Vajda, S. (2013) How good is automated protein docking? Proteins Struct. Funct. Bioinforma. 81, 2159–2166
5. Brenke, R., Hall, D. R., Chuang, G.-Y., Comeau, S. R., Bohnuud, T., Beglov, D., Schueler-Furman, O., Vajda, S., and Kozakov, D. (2012) Structural bioinformatics Application of asymmetric statistical potentials to antibody-protein docking. 28, 2608–2614
6. Pettersen, E. F., Goddard, T. D., Huang, C. C., Couch, G. S., Greenblatt, D. M., Meng, E. C., and Ferrin, T. E. (2004) UCSF Chimera - A visualization system for exploratory research and analysis. J. Comput. Chem. 25, 1605–1612