Part:BBa_K3767000:Design

Design Considerations

Design of 3-24 ScFv Antibody

To create the ScFv, the fragments of an OspA IgG VH and VL chains were first sourced from Ghosh and Huber, which were fetched from Genbank (EF028212 and EF028213) (1). The 3-24 ScFv clonal variant was selected over the 8-10 variant as the former had a stronger binding to OspA in the reviewed paper. The CDR anchor regions of the ScFv were then determined through a BLAST analysis of sequence homologies of the VH and VL sequences. This analysis indicated the top 100 similar sequences, which were then aligned using Seaview to analyze sequence similarities (3). The CDR anchor regions were chosen based on the most conserved residues from the aligned sequences (see figure 1).

Figure 1. Seaview sequence alignment of homologous sequences from BLAST search of generated ScFv. Linker region between VH and VL shown as (GGGS)3 linkers. Our engineered ScFv noted as AAS111111.1

The folding of the ScFv was subsequently predicted by SAbPred's ABodyBuilder, a free software that predicts the structural folding of antibodies (2). Structures are based on orientation prediction, CDR modelling, and side chain prediction, and results are given a confidence score based on the root-mean square deviation threshold. The following sequences were submitted for folding prediction of the ScFv:

Heavy Chain (VH): EVQLVQSGAEVKKPGASVKVSCKASGYTFTDYYLHWVRQAPGQGLEWLGRINPSSGATYSPQRFQGRVTMTTDTSIS TAYMELSSLRSDDTAVYFCATLTTFNIWGFDYWGQGTLVSS

Light Chain (VL): DIQMTQSPSSLSASVGDRVTITCRASQSISTYLNWYQQKPGKAPKLLIFTASSLQSGVPSTFSGSGSGTDFTLTISSLQPEDFATYYCQQSYSATFTFGGGTKVEIKR

Figure X: Molecular Model of 3-24 ScFv. VH and VL CDRs highlighted in Blue and Magenta respectively.

The model that resulted from ABodyBuilder indicated VH and VL confidence scores of 0.89 and 0.99 respectively (Fig.2), indicating high model confidence.

Figure 2. ABodyBuilder folding prediction confidence values.

When comparing the predicted CDR regions from ABodyBuilder to that of Ghosh and Huber (1) all CDRs were identical except VH CDR3 (Fig.3). In the predicted model, VH CDR3 was extended by an additional four residues, however these additional residues were shown to have little to no impact on OspA binding. The PDB file of the model was then downloaded for later analysis of the generated ScFv’s affinity for binding to the OspA molecule of Borrelia Burgdorferi.

Figure 3. Complimentary determining regions (CDRs) as predicted by Ghosct and Huber and ABodyBuilder. Note that the independently determined CDRs are in complete agreement with each other, except for an additional four C-terminal amino acids in the ABodyBuilder VH CDR3.

Binding Modelling

The Cluspro protein-protein docking supercomputer antibody mode was first used to analyze and predict binding between OspA (sequence found in Genbank under M57248.1) and the 3-24 ScFv (4–8). 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.4)(7).

Figure 4. Table of the top six lowest docking energy conformations depicted by Cluspro supercomputer. Model selected for analysis was Cluster 0 as this model had the lowest energy conformation.

The lowest energy conformation binding model (Cluster 0) was then selected and analyzed. The CDR binding interactions were analyzed within PyMol (Fig.5) to determine how effectively the 3-24 ScFv binds to OspA. To effectively analyze binding, all residues within the CDR region that have polar interactions with OspA residues are highlighted as stick structures.

Figure 5A. Overview of OspA and Scfv docking. OspA and ScFv shown in cyan and green respectively.

Figure 5B. Enhanced view of interaction between VH and OspA. Polar interactions between VH and OspA shown as yellow dashes.

Figure 5C. Enhanced view of interactions between VL and OspA. Polar interactions between VL and OspA shown as yellow dashes.

Source

Clonal diversification in OspA-specific antibodies from peripheral circulation of a chronic Lyme arthritis patient (2007) by Ghosh et al. (https://pubmed.ncbi.nlm.nih.gov/17307198/)

PDB:

Genbank:

References

1. Ghosh, S., and Huber, B. T. (2007) Clonal diversification in OspA-specific antibodies from peripheral circulation of a chronic Lyme arthritis patient. J. Immunol. Methods. 321, 121–134

2. Dunbar, J., Krawczyk, K., Leem, J., Marks, C., Nowak, J., Regep, C., Georges, G., Kelm, S., Popovic, B., and Deane, C. M. (2016) SAbPred: a structure-based antibody prediction server. Nucleic Acids Res. 44, W474–W478

3. Gouy, M., Guindon, S., and Gascuel, O. (2010) Sea view version 4: A multiplatform graphical user interface for sequence alignment and phylogenetic tree building. Mol. Biol. Evol. 27, 221–224

4. 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

5. Vajda, 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

6. 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

7. 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

8. 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