Abstract B055: A Genome-to-Vaccine: An Integrated Deep learning-Driven Neoantigen Identification and Multi-Epitope Vaccine Construction for Personalized Melanoma Immunotherapy

Expression-Guided Immunoinformatics Design of a Recombinant Multi-Epitope Vaccine Candidate for Triple-Negative Breast Cancer Incorporating a MyD88-Derived Immunomodulatory Domain

Triple-negative breast cancer (TNBC) remains a major therapeutic challenge because of its aggressive behavior, molecular heterogeneity, and limited subtype-specific targets. This study used an integrated immunoinformatics workflow to design a recombinant tumor-associated antigen-derived multi-epitope vaccine based on MMP1, CXorf61/CT83, and COL11A1, prioritized according to their tumor-to-normal transcript-expression ratios. Candidate cytotoxic T-lymphocyte, helper T-lymphocyte, and linear B-cell epitopes were screened for predicted HLA binding, antigenicity, allergenicity, toxicity, and IFN-γ-induction potential. The selected epitopes were assembled with PADRE, a MyD88-derived exploratory immunomodulatory domain, class-specific linkers, and a C-terminal histidine tag to generate a 621-amino-acid construct. Combined HLA class I and II analysis predicted 99.36% worldwide population coverage, with 3.14 epitope–HLA hits per individual. The construct was predicted to be antigenic, non-allergenic, soluble, and physicochemically compatible with recombinant production. Structural modeling yielded a ProSA Z-score of −7.43 and 97.45% of residues in favored Ramachandran regions, while disulfide engineering identified ten candidate intramolecular bridges. Docking produced an HDOCK score of −298.65 for the modeled vaccine–TLR4 complex, and flexibility analysis showed an average RMSF of 1.93 Å. Immune simulation predicted Th1-associated cytokine production, T-cell expansion, antibody responses, and memory-cell formation. Codon optimization generated a CAI of 0.95 and a GC content of 53.2%, followed by virtual cloning into pET-28a(+). These findings support the computational feasibility of the proposed vaccine candidate, which requires experimental validation of expression, antigen processing, HLA presentation, immunogenicity, safety, and antitumor activity.

Coevolutionary analysis-driven design of a p53 neoantigen multi-epitope peptide vaccine for lung cancer immunotherapy

Artificial intelligence in peptide cancer vaccine design: from neoantigen discovery to immunogenicity prediction

The current role of AI is summarized across the peptide cancer vaccine development pipeline, from neoantigen discovery and epitope prioritization to prediction of peptide–HLA binding, antigen presentation, and T-cell receptor recognition, and the application of modern computational frameworks.

Immunoinformatics-Driven Rational Design of Altered Peptide Ligands and In Vitro Validation of a Multi-Epitope CTL Formulation Targeting HPV16 E6/E7

Background: Therapeutic vaccination against human papillomavirus type 16 (HPV16) is frequently limited by the weak HLA-A*02:01 presentation of native E6/E7 oncoprotein epitopes. This study aims to utilize an integrated computational-experimental pipeline to design and evaluate anchor-optimized altered peptide ligands (APLs) for improving peptide presentation and HPV16-specific T-cell responses. Methods: Anchor-residue substitutions were introduced into three wild-type E6/E7 epitopes. Candidates were prioritized in silico based on predicted presentation, affinity, immunogenicity, and toxicity, and subsequently evaluated via in vitro functional assays using HLA-A*02:01-positive donor cells and molecular dynamics (MD) simulations. Results: Anchor optimization successfully converted weak binders into strong binders; for instance, E6apl improved predicted affinity from 329.33 to 6.21 nM. Crucially, candidate E7apl1 exhibited normal CD8+ T-cell expansion but reduced IFN-γ secretion, revealing a distinct binding–immunogenicity dissociation. MD simulations suggested that altered peptide conformational dynamics may contribute to differences in functional activity. Subsequently, an optimized six-peptide formulation (three APLs and three wild-type epitopes) was assembled. The resulting multi-epitope HPV-specific cytotoxic T lymphocytes (meHPV-CTLs) mediated target-specific cytotoxicity against cervical cancer cells, achieving 74.1% ± 5.1% specific lysis at an effector-to-target ratio of 30:1, which was largely abrogated by HLA class I blockade. Conclusions: These proof-of-concept findings demonstrate that stable peptide–MHC binding is a necessary but insufficient condition for optimal T-cell activation. The experimentally characterized multi-epitope formulation provides a proof-of-concept strategy for further preclinical evaluation of HPV16-targeted peptide-based immunotherapies. Moreover, because these anchor-optimized APLs are defined at the sequence level, these sequence-defined APLs may potentially be explored in alternative vaccine delivery platforms, including mRNA-based approaches, in future studies.

Endogenous Retrovirus Neoantigen Enables Personalized Cancer Vaccine in glioblastoma, IDH-wildtype 2259068

ERVs are consistently expressed and immunogenic in GBM, representing a scalable, tumor-specific antigen source that may overcome limitations of low mutational burden and provide a proof-of-concept for personalized vaccines targeting ERV-derived neoantigens.

Systems-level design of a multi-epitope immunotherapeutic vaccine targeting EBV-associated oncogenesis.

Epstein-Barr virus (EBV) is an oncogenic herpesvirus associated with multiple lymphoid and epithelial malignancies. Despite extensive investigation of EBV vaccine strategies, effective therapeutic approaches capable of targeting established EBV-associated cancers remain limited. In this study, we developed an integrated immunoinformatics and structure-guided framework for the design and prioritization of therapeutic multi-epitope vaccine candidates targeting both structural glycoproteins (gp350, gB, gH/gL, and gp42) and latency-associated proteins (EBNA1, LMP1, LMP2, and BZLF1). Sixteen multi-epitope vaccine constructs were generated and evaluated through sequence validation, structural refinement, reverse vaccinology assessment, immune-response simulation, receptor interaction analysis, molecular dynamics simulations, and expression-readiness profiling. The prioritized epitope repertoire achieved projected global population coverage exceeding 98% for both MHC class I and II pathways. Structural refinement improved model quality across vaccine constructs, while immunological and safety assessments supported favorable predicted antigenicity, non-allergenic potential, non-toxicity, and developability properties. Immune simulations predicted coordinated innate, humoral, and cellular responses, with several constructs demonstrating strong predicted immunogenic profiles. Molecular docking and molecular dynamics analyses further supported predicted structural compatibility and interaction stability with immune-associated receptors under simulated conditions. Integrated multi-parameter evaluation identified Constructs 4, 7, 10, 8, and 12 as the most promising candidates, with Construct 4 exhibiting the most balanced profile across immunological, structural, safety, and expression-related properties. Collectively, this study provides a comprehensive computational framework for therapeutic EBV vaccine development and identifies prioritized vaccine candidates for experimental validation. The proposed strategy offers a scalable approach for accelerating the development of multi-epitope vaccines targeting persistent viral infections and virus-associated malignancies.