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Article

Pangenome-Guided In Silico Design and Structural Evaluation of a Multi-Epitope Vaccine Candidate Against Streptococcus suis

by
Nada Saleh Alhaggass
1,*,
Waad A. Aljohani
2,
Reem Alromaihi
3,
Sarah Nasser Alnuwaysir
3,
Razan Abdalrahman Almohimid
3,
Ahmad Almatroudi
3 and
Khaled S. Allemailem
3,*
1
Department of Biology, College of Science, Qassim University, Buraydah 51452, Saudi Arabia
2
Department of Basic Health Sciences, College of Applied Medical Sciences, Qassim University, Buraydah 51452, Saudi Arabia
3
Department of Medical Laboratories, College of Applied Medical Sciences, Qassim University, Buraydah 51452, Saudi Arabia
*
Authors to whom correspondence should be addressed.
Pharmaceuticals 2026, 19(9), 1448; https://doi.org/10.3390/ph19091448
Submission received: 12 August 2026 / Revised: 5 September 2026 / Accepted: 10 September 2026 / Published: 12 September 2026
(This article belongs to the Special Issue Applications of In Silico Technologies in Drug Design)

Abstract

Background/Objectives: Streptococcus suis is an important zoonotic pathogen responsible for severe infections in animals and humans, and the emergence of diverse strains has reduced the effectiveness of conventional antimicrobial therapies. Since there is no broadly protective vaccine, there is a need for new vaccination strategies that focus on conserved antigens from a variety of strains. This study aimed to design and evaluate a multi-epitope vaccine candidate against diverse S. suis strains using an integrated pangenome-guided reverse vaccinology approach. Methods: To design a multi-epitope vaccine (MEV) candidate against diverse S. suis, an integrated computational framework was employed, incorporating pangenome analysis, subtractive proteomics, reverse vaccinology, immunoinformatics, structural modeling, molecular docking, molecular dynamics simulation, immune simulation, and in silico cloning. The conserved core proteins were systematically screened for essential, non-homologous, antigenic, non-allergenic and non-toxic vaccine candidates for epitope prediction. Results: A total of 7421 gene families, including 1169 conserved core genes, were identified through pangenome analysis of 24 complete S. suis genomes. Three computationally prioritized candidate proteins were identified through sequential subtractive proteomics: sucrose phosphorylase, peptidoglycan hydrolase PcsB and an RND transporter-associated adaptor protein, annotated in the source database as an RND efflux transporter periplasmic adaptor subunit. We selected eight cytotoxic T-lymphocyte (CTL) epitopes, five helper T-lymphocyte (HTL) epitopes, and three linear B-cell epitopes with favorable predicted immunological properties to develop a 397-amino acid multi-epitope vaccine construct that contains the S. suis 50S ribosomal protein L7/L12 adjuvant with rationally designed peptide linkers. The vaccine construct exhibited favorable physicochemical properties, predicted structural stability, and high antigenicity scores. The predicted combined HLA population coverage of the selected CTL and HTL epitopes was 90.77% across the populations included in the analysis. Immune simulation predicted patterns consistent with humoral and cellular immune activation, including sustained IgG production, elevated IFN-γ and IL-2 secretion, efficient antigen clearance, and generation of immunological memory, whereas molecular docking and molecular dynamics simulations characterized the predicted interaction and conformational behavior of the MEV–TLR1/TLR2 complex. Codon optimization (CAI = 0.996) and in silico cloning into the pET-30a(+) expression vector supported the potential feasibility of recombinant expression in Escherichia coli. Conclusions: In this study, a rationally designed multi-epitope vaccine candidate against diverse S. suis strains was developed using an integrated pangenome-guided reverse vaccinology approach. Based on these computational analyses, the proposed vaccine candidate showed favorable predicted immunogenicity, predicted structural quality, predicted HLA population coverage, and expression feasibility, providing a foundation for future experimental validation and development of a vaccine against diverse S. suis.
Keywords: Streptococcus suis; subtractive proteomics; immunoinformatics; molecular docking; molecular dynamic simulation; antimicrobial resistance; reverse vaccinology; multi-epitope vaccine Streptococcus suis; subtractive proteomics; immunoinformatics; molecular docking; molecular dynamic simulation; antimicrobial resistance; reverse vaccinology; multi-epitope vaccine

Share and Cite

MDPI and ACS Style

Alhaggass, N.S.; Aljohani, W.A.; Alromaihi, R.; Alnuwaysir, S.N.; Almohimid, R.A.; Almatroudi, A.; Allemailem, K.S. Pangenome-Guided In Silico Design and Structural Evaluation of a Multi-Epitope Vaccine Candidate Against Streptococcus suis. Pharmaceuticals 2026, 19, 1448. https://doi.org/10.3390/ph19091448

AMA Style

Alhaggass NS, Aljohani WA, Alromaihi R, Alnuwaysir SN, Almohimid RA, Almatroudi A, Allemailem KS. Pangenome-Guided In Silico Design and Structural Evaluation of a Multi-Epitope Vaccine Candidate Against Streptococcus suis. Pharmaceuticals. 2026; 19(9):1448. https://doi.org/10.3390/ph19091448

Chicago/Turabian Style

Alhaggass, Nada Saleh, Waad A. Aljohani, Reem Alromaihi, Sarah Nasser Alnuwaysir, Razan Abdalrahman Almohimid, Ahmad Almatroudi, and Khaled S. Allemailem. 2026. "Pangenome-Guided In Silico Design and Structural Evaluation of a Multi-Epitope Vaccine Candidate Against Streptococcus suis" Pharmaceuticals 19, no. 9: 1448. https://doi.org/10.3390/ph19091448

APA Style

Alhaggass, N. S., Aljohani, W. A., Alromaihi, R., Alnuwaysir, S. N., Almohimid, R. A., Almatroudi, A., & Allemailem, K. S. (2026). Pangenome-Guided In Silico Design and Structural Evaluation of a Multi-Epitope Vaccine Candidate Against Streptococcus suis. Pharmaceuticals, 19(9), 1448. https://doi.org/10.3390/ph19091448

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