Design of a next-generation multi-target multi-epitope tuberculosis vaccine using an epitope order optimization...

Multi-epitope-based peptide (MEBP) vaccines have emerged as promising alternatives to conventional vaccines for combating Mycobacterium tuberculosis (M.tb) infection. Current immunoinformatics-based MEBP vaccine design strategies typically construct a single vaccine candidate by assembling the selected epitopes in an arbitrary linear order, interspersed with peptide linkers and adjuvants. This conventional approach implicitly assumes that epitope order has little or no influence on vaccine performance and consequently characterizes only one construct from the vast combinatorial space of possible epitope arrangements. As a result, the majority of structurally distinct MEBP variants remain unexplored, precluding a systematic evaluation of how epitope positional rearrangement influences structural conformation, physicochemical and energetic properties, and predicted immunogenicity. This limitation substantially constrains the rational identification and optimization of more effective MEBP vaccine candidates against M.tb.

The present study aims to systematically evaluate the influence of epitope order (positional rearrangement) on the structural stability, energetic profile, and predicted immunological performance of multi-target tuberculosis multi-epitope-based vaccine candidates (MEBPVC) using a rationally developed and comprehensive comparative computational workflow.

Five target proteins were selected from an initial set of fifteen antigenic proteins of the M.tb H37Rv proteome based on their high antigenicity and minimal sequence similarity to the human proteome. From each target protein, the two top-ranked Dual-Trigger Epitopes (DTEs) were identified, yielding a total of ten DTEs. These DTEs were assembled, together with an adjuvant and appropriate peptide linkers, generating a representative MEBPVC. Subsequently, a comprehensive library comprising all 10! (3628,800) unique positional rearrangements from the above ten DTEs' set was computationally generated. The resulting MEBPVC variants were systematically screened and evaluated based on their predicted structural, physicochemical, energetic, and immunological properties to identify the most promising vaccine candidates. The top-ranked candidates were further characterized through molecular docking with TLR4, followed by 500 ns molecular dynamics simulations, MM/GBSA binding free energy calculations, immune response simulations, and in silico cloning to assess receptor-binding affinity, complex stability, immunogenic potential, and expression feasibility.

Comprehensive evaluation of all 10! (3628,800) MEBPVC variants demonstrated that epitope order influences the structural, energetic, receptor-binding, and immunological properties of MEBPVCs. Systematic screening identified four top-ranked candidates, with M.Tb_MEBPVC_2595872 emerging as the lead construct. It achieved the highest influence score (3.15) and formed a structurally stable TLR4 complex characterized by low RMSD (0.58 nm), low RMSF (0.13 nm), persistent hydrogen-bond interactions (20-27), and favorable MM/GBSA binding free energy (-78.22 kcal/mol). Immune simulations predicted robust humoral and cellular immune responses, while in silico cloning confirmed compatibility with the pET expression system.

The findings support our hypothesis that epitope order is a critical determinant of the structural, receptor-binding, and immunological properties of MEBP vaccine constructs and should therefore be considered an essential optimization parameter in computational vaccine design. Systematic generation and comparative evaluation of positional variants exposed the limitations of the conventional single-construct design strategy and enabled the identification of four top-ranked vaccine candidates, with M.Tb_MEBPVC_2595872 emerging as the most promising candidate against M.tb. Experimental validation is warranted to confirm the predicted immunogenicity and protective efficacy of these candidates.