• MAbTope: define and secure your epitope

  • Prediction process

    MAbTope is a docking-based method and needs structures. The structure of the antibody can be modeled so we need only the sequence. The structure of the target has to be solved to ensure the best efficiency of the prediction. Our algorithm generates 108 complex conformations among which the interfaces predicted in the 30 best ranked solutions are kept for analysis. The number of times each amino acid is predicted to be in the epitope among the 30 best solutions is calculated and peptides are designed among these amino acids. For this reason, linear as well as conformational epitopes can be considered for validations. The peptides are then used to performed cutting-hedge techniques of protein-protein interactions.

    What we need:

    • Sequence of the antibody
    • 3D structure of the target

    What for:

    • Characterize the epitope
    • Optimize your lead selection
    • Secure your IP
    • Fulfill the due diligences requirements

    Deliverables:

    • PDB files of the 30 top-ranked antibody-antigen complexes structures
    • Peptides sequences
    • Raw and analyzed experimental data
    • Complete report, with validation methods

     

  • We are better than the other methods

  • Mapping the epitope of Golimumab on TNFα

    Prediction - Docking

    For the docking step, we need structures of the Ab and the target. TNFα forms a trimer that has been crystallized. On the other hand, we modeled the structure of the Golimumab. Here is shown the top-ranked pose among 108 generated by MAbTope. Four peptides were designed on the interface and used to proceed to experimental validation below.

    Experimental validation 1 - Peptides validation

    The purpose of the first round of validation was to determine which peptide(s) was (were) binding the Golimumab. Using first HTRF (Cisbio Bioassay’s system), we highlighted an energy transfer between P3 (which biotin was bound to an acceptor-fused streptavidin) and the Golimumab (detected by a cryptate-coupled anti-IgG). This observation was confirmed when spotting the peptides on a micro-array slide where P3 specifically bound the iFlur680-coupled Golimumab.

    Experimental validation 2 - Alanine scanning

    We proceeded to a second round of validation to identify the amino-acid residues within P3 which were actively implicated in the Golimumab interaction. We designed mutant variants of P3, replacing by Alanines the residues we suspected to be implicated. As shown in the graph, the mutants m1, m3 and m4 seemed to lose the interaction. On the contrary, m2 and m5 did not exhibited lower interaction with the Golimumab.

    Conclusion

    We identified here the sequence between aa169 and 183 of the TNFα as targeted but the Golimumab. Our mutation strategy further identified Y172, T174 and K175 as being the residues directly implicated in this interaction.