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.