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Bendjama, K.

Publications and source records attributed to Bendjama, K..

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Machine Learning-Driven Antigen Selection Reveals Conserved T-Cell Targets for Broad Coronavirus Vaccination

BackgroundCoronavirus outbreaks remain a persistent threat to global health, and vaccines based primarily on spike-specific immune responses are susceptible to antigenic variation. T-cell immunity directed against conserved internal viral proteins may provide a complementary and more variant-tolerant strategy for next-generation coronavirus vaccines. MethodsWe combined machine learning-guided antigen prioritization with ex vivo functional immunological validation to identify conserved non-spike T-cell targets across betacoronaviruses. Candidate sequences were screened for immunogenicity using primary human peripheral blood mononuclear cells from healthy donors using intracellular cytokine staining and activation-induced marker assays. Top-ranked conserved regions were incorporated into multiepitope mRNA constructs, and their intracellular expression and HLA class I presentation were confirmed by immunopeptidomics. Immunogenicity was further evaluated ex vivo and in vivo using mRNA immunization of mice and T-cell FluoroSpot assays. FindingsAcross a panel of 97 peptides derived from 19 viral proteins, evolutionary conservation across distinct betacoronavirus taxa was strongly associated with functional T-cell immunogenicity in human donors. Highly conserved peptides elicited significantly stronger and more frequent CD4 and CD8 T-cell responses than taxon-restricted peptides. Multiepitope mRNA constructs encoding conserved regions were efficiently expressed and presented on HLA class I molecules and induced T-cell responses in human PBMCs. In mice, mRNA immunization with conserved multiepitope constructs generated robust interferon-{gamma}- and interleukin-2-producing T-cell responses that exceeded those induced by unconserved control constructs. InterpretationThese results link evolutionary conservation to functional cellular immunogenicity and demonstrate the feasibility of multiepitope mRNA delivery for inducing conserved coronavirus-directed T-cell responses. Although protective efficacy remains to be established, conservation-guided antigen selection represents a scalable strategy for developing T-cell-focused vaccines with broad lineage coverage, supporting pandemic preparedness beyond spike-centered immunity. FundingThe research was supported by CEPI, NEC, University of Oslo and Oslo university hospital. Research in contextO_ST_ABSEvidence before this studyC_ST_ABSPrior coronavirus vaccine development has focused predominantly on spike protein-directed neutralizing antibodies. While highly effective against matched strains, spike-centered immunity is vulnerable to antigenic drift and lineage-specific escape. Multiple observational and experimental studies have shown that T-cell responses, particularly against internal viral proteins, are more conserved and correlate with reduced disease severity and cross-variant recognition. Epitope prediction algorithms and immunoinformatics approaches have been widely used to nominate candidate T-cell targets; however, systematic functional validation of conserved non-spike antigens across betacoronaviruses in primary human immune systems, combined with antigen presentation data and in vivo vaccine testing, has remained limited. Searches of PubMed and bioRxiv up to December 2025 using terms including "coronavirus T-cell vaccine," "conserved coronavirus epitopes," "betacoronavirus cross-reactive T cells," and "mRNA T-cell vaccine" identified studies demonstrating cross-reactive T-cell immunity and computational epitope selection, but few integrated machine-learning-guided antigen prioritization with ex vivo human functional screening, immunopeptidomics, and in vivo mRNA immunization in a unified workflow. Added value of this studyThis study provides an integrated experimental and computational framework for identifying and validating conserved non-spike T-cell antigens across betacoronaviruses. We functionally screened a panel of candidate peptides derived from multiple viral proteins and demonstrated that evolutionary conservation across species is strongly associated with T-cell immunogenicity. We further demonstrate that multiepitope mRNA constructs encoding these top-ranked conserved regions can be intracellularly expressed, presented on HLA class I molecules to induce polyfunctional T-cell responses in primary human PBMCs. Finally, in vivo mRNA immunization in mice induces robust interferon-{gamma} and interleukin-2 T-cell responses exceeding those induced by unconserved control constructs. Together, these findings link evolutionary conservation to functional cellular immunogenicity and extend beyond in silico prediction by demonstrating antigen processing, presentation, and immunogenicity across human and murine systems. Implications of all the available evidenceCollectively, the available evidence indicates that T-cell immunity directed toward conserved internal coronavirus proteins represents a complementary and potentially more variant-tolerant axis of vaccine design than spike-only strategies. Our findings suggest that evolutionary conservation can serve as a practical selection principle for prioritizing T-cell antigens with broad lineage coverage and that multiepitope mRNA delivery is a feasible platform for inducing such responses. While direct protection and heterologous challenge studies will be required to establish clinical efficacy, the integration of computational prioritization with functional validation supports a scalable approach to pandemic preparedness that may be applicable to other rapidly evolving viral families.

immunology↗

Generative AI Guided Design of High-Affinity T cell Receptors

Developing T cell receptors (TCRs) with sufficiently high affinity for tumor antigens (TAs) remains a fundamental challenge in TCR-T immunotherapy. Experimental methods such as affinity maturation and high-throughput screening have enabled the identification of TCRs with enhanced activity. However, their efficiency is often constrained by limited throughput, insufficient coverage, and the generally lower affinities of naturally occurring TCRs toward TAs. To address these challenges, we present TCRPPO2, an integrated AI-driven, in silico affinity maturation framework for peptide-specific TCR optimization. Using reinforcement learning, TCRPPO2 learns mutation policies that iteratively enhance the TCR binding affinity to the target peptide, derived from predictive models trained on carefully curated interaction data. The model is further augmented by a generative AI critic model that discourages implausible designs to ensure the biophysical validity. The designs are further screened by robust post-screening methods that leverage diverse functional annotations and physical prior knowledge. We applied TCRPPO2 to the clinically relevant MART-1 antigen and experimentally validated the designed candidates in Jurkat cell-based functional assays. Among the five engineered TCRs, all of which demonstrated positive cellular responses, three showed significantly increased activities relative to their templates and one showed substantial enhancement. These functional gains were consistent with more favorable interaction energy from structural and physical modeling. Together, our results support a generalizable paradigm for TCR engineering, in which learned mutation policies can efficiently navigate the peptide-specific binding landscape of TCRs and propose biologically enhanced candidates without explicit structural supervision, offering a practical route for early-stage computational TCR optimization for challenging tumor antigens.

immunology↗

BERTMHC: Improves MHC-peptide class II interaction prediction with transformer and multiple instance learning

MotivationIncreasingly comprehensive characterisation of cancer associated genetic alteration has paved the way for the development of highly specific therapeutic vaccines. Predicting precisely binding and presentation of peptides by MHC alleles is an important step towards such therapies. Recent data suggest that presentation of both class I and II epitopes is critical for the induction of a sustained effective immune response. However, the prediction performance for MHC class II has been limited compared to class I. ResultsWe present a transformer neural network model which leverages on self-supervised pretraining from a large corpus of protein sequences. We also propose a multiple instance learning (MIL) framework to deconvolve mass spectrometry data where multiple potential MHC alleles may have presented each peptide. We show that pretraining boosted the performance for these tasks. Combining pretraining and the novel MIL approach, our model outperforms state-of-the-art models for both binding and mass spectrometry presentation predictions. AvailabilityOur model is available at https://github.com/s6juncheng/BERTMHC Contactjun.cheng@neclab.eu, brandon.malone@neclab.eu

bioinformatics↗