Structure-Based Optimization of Pathogen Signal Sequences for Enhanced Antigen Expression in Humans for Vaccine Designs

Signal peptides (SPs) are short amino acid sequences found at the N-terminus of nascent polypeptides, which serve critical roles in trafficking, folding, and post-translational processing of mature proteins. Many sequence-based computational methods have been developed to predict and design optimal SPs for protein secretion in human cells. In this work, we introduce a structure-based workflow for identifying SPs and their regions, aiming to optimize SPs for protein secretion in human cells. Structural modeling of the SPs in complex with human signal recognition particle 54 kDa (SRP54), combined with hydrophobicity plots of the SPs and identification of the cleavage motif, can be used to detect SPs and SP regions. Afterwards, LigandMPNN is applied to redesign the SPs based on their structural complexes with SRP54. We employ our workflow to optimize the bacterial Yersinia pestis F1 SP, Lassa virus glycoprotein (GP) SP, and Venezuelan equine encephalitis virus (VEEV) E3 envelope protein for protein secretion in human cells to serve as vaccine candidates. By comparing the redesigned SPs with the original SPs, we propose that the binding affinity of SPs to SRP54 serve as the most important molecular determinant in the activity of the SPs. Notably, our experiments confirm that the structurally optimized SPs can express the Y. pestis F1 protein, Lassa GP, and VEEV GP in human cells. Overall, we demonstrate that structural modeling can serve as a valuable tool to predict the SPs and SP regions for vaccine antigens, predict whether the SPs can express mature proteins in humans, and optimize the SPs for the expression of mature proteins in humans.