SAGE: A Fast Computational Tool for Linear Epitope Grafting onto a Foreign Protein Scaffold

Computational design is becoming a driving force of structural vaccinology, whereby protein antigens are engineered to generate new biomolecules with optimized immunological properties. In particular, the design of new proteins that contain multiple, different epitopes can potentially provide novel highly efficient vaccine candidates. In this context, epitope grafting, which entails the transplantation of an antibody recognition motif from one protein onto a different protein scaffold (possibly containing other immunoreactive sequences) holds great promise for the realization of superantigens. Herein, we present SAGE (strategy for alignment and grafting of epitopes), an automated computational tool for the implantation of immunogenic epitopes onto a given scaffold. It is based on the comparison between the expected secondary structures of the candidates to be grafted with all the secondary structures in the target scaffold. Evaluating the differences both in sequence and in structure between the epitope and the scaffold returns a ranking of most probable molecules containing the new antigenic sequence. We validate this approach identifying the grafting positions obtained in previous works by experimental and computational methods, proving an efficient, flexible, and fast tool to perform the initial scanning for epitope grafting. This approach is fully general and may be applied to any target antigen and candidate epitopes with known 3D structures.