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Biochemistry, Genetics and Molecular Biology · Research topic

Open research questions in vaccines and immunoinformatics approaches

70 unresolved questions extracted from the limitations and future-work sections of 193 vaccines and immunoinformatics approaches papers in our library. Each links back to the study that raised it.

What the literature leaves open

  • The absence of observed complexes does not imply lack of binding. Related antigens in training and test data can lead to overestimation of generalization. Antigen-side epitope grounding remains a challenge.

    Strict OOD Antigen-to-Antibody Retrieval with CDR-Aware Slot Late Interaction · 2026 · DOI

  • The development of a vaccine against human cytomegalovirus is challenging due to the virus's complex biology and the need for a robust and durable immune response. The selection of suitable epitopes and adjuvants is a critical challenge in vaccine design. The evaluation of vaccine candidates requires a comprehensive assessment of immunogenicity, safety, and translational applicability.

    Integrative immunoinformatics and structural modeling for the rational design of a multi-epitope vaccine candidate against human cytomegalovirus · 2026 · DOI

  • The evidence base underlying the evolutionary-genomics-to-translational pipeline of hirudin comprises heterogeneous study designs. The lack of an antidote able to rapidly reverse the effect of recombinant-hirudin drugs.

    Genomic diversity of the hirudin multigene family and its implications for antithrombotic and antitumor biotherapeutic design · 2026 · DOI

  • Phase 3 of the evolutionary-engineering pipeline for the development of hirudin. The functional activation of hidden or latent hirudin-like factors.

    Genomic diversity of the hirudin multigene family and its implications for antithrombotic and antitumor biotherapeutic design · 2026 · DOI

  • Further research is needed to develop more accurate and generalizable TCR-pMHC binding prediction models. The application of the design of classifier architecture with auxiliary training objectives to other protein-protein interaction prediction tasks should be explored.

    Structure-based TCR-pMHC binding prediction and generalization to unseen peptides · 2026 · DOI

  • The generalization performance of GNN-based classifiers for TCR-pMHC binding prediction is poor for samples with unseen peptides. The potential factors that critically impact the generalization performance of classifiers trained with computationally predicted structures are not well understood.

    Structure-based TCR-pMHC binding prediction and generalization to unseen peptides · 2026 · DOI

  • However, it remains unclear how multiple TM bonds cooperate each other within the immune synapses to regulate T-cell activation.

    Multiscale SynapticDynamics of T-Cell AntigenRecognition · 2026 · DOI

  • Abstract Large language models can learn new tasks through in-context learning (ICL), yet this ability remains underexplored for biological sequence classification.

    A systematic evaluation of in-context learning in large language models for antibody characterization · 2026 · DOI

  • Together, these findings demonstrate that strong MHC binding and pMHC stability are insufficient to ensure CD8+ T-cell immunogenicity.

    Structural Basis for the Immunological Paradox of a High-Affinity Yet Non-Immunogenic MHC-I Epitope from Cryptosporidium parvum · 2026 · DOI

  • However, the structural basis for the limited immunogenicity of its T-cell epitopes remains poorly understood.

    Structural Basis for the Immunological Paradox of a High-Affinity Yet Non-Immunogenic MHC-I Epitope from Cryptosporidium parvum · 2026 · DOI

  • Over the past four decades, the field of immunogenetics has been defined by a dual revolution: the relentless advance of HLA typing technology from low-resolution serology to high-resolution NGS, and the concurrent rise of powerful computational tools. The progress in genotyping has improved graft survival in transplantation by enabling precise allele-level matching (Dehn et al. 2019). In parallel, AI and ML have amplified these gains, powering applications across the research pipeline: from imputing HLA genotypes in large biobanks and predicting peptide-HLA binding for vaccine design, to forecasting transplant outcomes and optimizing donor selection (Fig. 2). This synergy is further enriched by Fig. 2 Applications of Artificial Intelligence in HLA Research. The figure illustrates the central role of AI in advancing key areas of HLA research and its clinical applications to provide precision medicine. These domains include transplantation, vaccine design, neoantigen discovery, immunotherapy, and personalized medicine Immunogenetics (2026) 78:6 1 3 6 Page 18 of 21 a deeper biological understanding of phenomena like ASE and LOH, which reveal how HLA expression is dynamically regulated and how cancers can evade T-cell surveillance. Finally, as these technologies mature, we must ensure equitable access to prevent a future where the benefits of AI-driven immunogenetics are available only to a select few. A significant challenge in developing these models is that registry data is often heterogeneous, with different centers using varied protocols and having incomplete data. This can cause a model trained on one registry’s data to perform poorly on another’s. Federated learning, a method where models are trained across multiple decentralized datasets without sharing patient data, is a promising solution to improve generalizability while maintaining privacy. Another major hurdle is the “black box” nature of complex models, which can limit clinical adoption. The development of Explainable AI (XAI) is critical for building trust, allowing models to provide not just a prediction but also the key factors driving that prediction. Looking ahead, digital twins, dynamic, patient-specific models that simulate outcomes under alternative donors and regimens, are a compelling goal. Critically, all such tools require prospective, multicenter clinical validation before broad deployment. Safe, equitable use of HLA-aware AI demands guardrails across data, modeling, and practice (Fig. 1). First, data governance must ensure appropriate consent, de-identification, and secure handling of genetic information, using privacypreserving methods such as federated training when data cannot leave local institutions.

    The digital keystone: how artificial intelligence is reshaping HLA research and clinical practice · 2026 · DOI

  • Pre-emptive development of broad-spectrum vaccines and antibodies targeting conserved viral elements. Integration of One Health data spanning human, animal, and environmental surveillance. Development of global data-sharing platforms for real-time model updating.

    Artificial intelligence directed computational protein design: lessons from COVID-19 for pandemic-ready vaccines and antibody therapeutics · 2026 · DOI

  • The need for closer coupling between in silico design, laboratory experimentation, and clinical evaluation. The gap between computational promise and translational readiness.

    Artificial intelligence directed computational protein design: lessons from COVID-19 for pandemic-ready vaccines and antibody therapeutics · 2026 · DOI

  • The development of more advanced AI tools for vaccine design and antiviral discovery. The investigation of the use of AI in other areas of public health. The exploration of the potential applications of AI-enabled genomic surveillance tools.

    Artificial intelligence for coordinating vaccine design, antiviral discovery, and real-world monitoring in the era of emerging and endemic viral threats · 2026 · DOI

  • The lack of robust, deployment-ready pipelines for antiviral development. The need for improved global public health preparedness. The limited use of AI tools in vaccine technology and antiviral drug discovery.

    Artificial intelligence for coordinating vaccine design, antiviral discovery, and real-world monitoring in the era of emerging and endemic viral threats · 2026 · DOI

  • The lack of effective prevention and treatment strategies for bacterial vaginosis. The limitations of current treatments, including antibiotic resistance.

    In-silico design of a multi-epitope vaccine targeting conserved transmembrane proteins of Gardnerella vaginalis using immuno-informatics approach · 2026 · DOI

  • The study only evaluated the immunogenicity of the vaccine constructs in a mouse model. The sample size was limited to 30 mice. The study did not assess the protective efficacy of the vaccine.

    Preparation of a multiepitope vaccine candidate for camel bocavirus and evaluation of its immunogenicity in a mouse model · 2026 · DOI

  • Maintaining the appropriate balance between inflammation and tolerance. The dysfunction of this balance can drive pathologies such as type 1 diabetes and complications in transplantation. The COVID-19 pandemic further underscores how viral evasion and dysregulated cytokine production can result in severe outcomes.

    A comprehensive mechanistic multicellular model of the human immune system spanning 11 diseases · 2026 · DOI

  • To further validate the model's predictions against independent in vitro, ex vivo, and clinical observations. To use the model to simulate complex systems in a virtual setting. To explore the application of the model in personalized medicine.

    A comprehensive mechanistic multicellular model of the human immune system spanning 11 diseases · 2026 · DOI

  • In vivo evaluation of the efficacy of the peptide. Evaluation of the peptide in combination with other treatments. Design of novel peptides with improved pharmacokinetic properties and toxicity.

    From Bacteria to Breakthroughs: Design and Evaluation of Gallocin-Based Peptide for Colorectal Cancer Therapeutic · 2026 · DOI

  • The lack of targeted therapies for colorectal cancer with high selectivity, low toxicity, and cost-effectiveness. The need for novel peptides with high binding affinity for EGFR.

    From Bacteria to Breakthroughs: Design and Evaluation of Gallocin-Based Peptide for Colorectal Cancer Therapeutic · 2026 · DOI

  • Further functional assessment of Zot as a candidate virulence-associated protein is suggested. Experimental validation of its contribution to virulence in A. baumannii is needed.

    High resolution immunoinformatic profiling of Zonula occludens toxin reveals a conserved multiepitope vaccine candidate in Acinetobacter baumannii · 2026 · DOI

  • The study identifies a gap in the understanding of the Zonula occludens toxin's role in A. baumannii. The gap in the development of immune-based strategies against A. baumannii is also noted.

    High resolution immunoinformatic profiling of Zonula occludens toxin reveals a conserved multiepitope vaccine candidate in Acinetobacter baumannii · 2026 · DOI

  • The traditional vaccine development cycle is not adequate for addressing the urgent needs of rapid pandemics. There is a need for effective decision-making approaches in vaccine development.

    Evaluating Vaccine Development Strategies Using the TOPSIS Method · 2026 · DOI

  • Several limitations of this study should be acknowledged. First, while we have leveraged publicly available datasets (GTEx, TCGA, HPA, and DrugBank) for validation, these data were not generated in-house. To strengthen our findings, future studies should incor- porate proprietary experimental data, including additional prostate cancer cell lines and patient-derived samples, to confirm the G4- IKBKB regulatory axis. Second, the immune-related functions of IKBKB were primarily inferred from correlation analyses of publicly available transcriptomic data. Establishing in vivo models—such as G4-binding effector-dCas9 knock-in mice or patient-derived xeno- graft (PDX) models with IKBKB knockdown—will be essential to validate the immune regulatory roles of G4-mediated IKBKB expression and to assess the efficacy of the proposed therapeutic strategies in a physiological context. Third, while our AI-assisted peptide design provides promising candidates, experimental vali- dation of peptide binding affinity, specificity, and anti-tumor activity in vivo remains to be performed. Addressing these limita- tions in future work will be critical to translating our findings into clinically actionable therapeutic strategies.

    Identification of G4-regulated immune-related drug targets for prostate cancer based on G4 screen and machine learning · 2026 · DOI

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70 open questions have been extracted from the limitations and future-work passages of 193 vaccines and immunoinformatics approaches papers in our library. Each one below links back to the study that raised it, so you can read the original claim in context.

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