vaccines and immunoinformatics approaches

This study identifies and characterizes OMBB proteins in P. multocida, designs a multi-epitope vaccine, and predicts its immunogenicity and structural properties.

The paper identifies MHC-II epitopes in C. neoformans chitin deacetylases that stimulate CD4+ T cells and demonstrates their protective role in a hybrid protein vaccine.

The paper introduces an interpretable model for predicting peptide-HLA binding that matches state-of-the-art performance using only sequence data.

OmniTCR is a foundation model that predicts TCR recognition and generates TCR sequences, outperforming existing methods on multiple tasks.

TAPAS integrates AlphaFold3 confidence and geometric features with sequence embeddings to improve TCR-pMHC binding prediction, outperforming individual metrics across benchmarks.

This paper retrospectively evaluates the safety and immunogenicity of three hookworm vaccine candidates using immunoinformatics, identifying Na-APR-1 as the most promising with broad epitope coverage and favorable TLR4 interactions.

This paper designs a stable, high-affinity CR2 binder using deep learning to enhance B cell activation as a vaccine scaffold.

The paper shows label noise limits TCR-pMHC specificity prediction and improves performance using AlphaFold3 and denoising.

The paper benchmarks computational models predicting B. pertussis booster vaccine responses using multi-omics data from three cohorts, identifying key predictors of immunity and establishing a community resource for systems vaccinology.

The paper presents DynaTPH, a dataset of dynamic TCR-pHLA interactions with molecular dynamics simulations and biophysical properties to study immune recognition dynamics.

EpiTune is a B-cell epitope prediction model that fine-tunes a protein language model to predict linear and conformational epitopes from antigen sequences with high accuracy.

The paper identifies conserved influenza A epitope regions and evaluates ESM-2 sequence features for epitope prioritization, finding no significant improvement over alignment-based conservation.

CALFP-MHC improves peptide-MHC binding prediction by using chemically grounded fingerprints and contrastive learning, achieving high AUC and PPV across diverse alleles and class imbalance.

The paper develops a deep learning model to predict peptide-HLA II interactions for post-translationally modified peptides, showing accurate predictions on unseen modifications.

This study identifies potential vaccine antigens in Staphylococcus aureus using computational methods, prioritizing 304 candidates based on sequence, structure, localization, and immunology features.

The paper proposes a two-phase framework combining distribution-learning and optimization to generate bioactive peptides, achieving high confidence scores and reduced KL divergence in IL-2 and IL-13 induction tasks.

This study designs a multi-epitope vaccine against Streptococcus pyogenes using immunoinformatics to identify high antigenic regions in conserved proteins.

MHChron improves pMHC binding prediction by creating a diverse dataset and testing models under strict generalization conditions, achieving strong performance despite smaller training data.

This paper benchmarks protein language models against sequence alignment methods for immune receptor retrieval, finding that alignments outperform PLMs at CDR3 level but PLMs match alignments at full variable domain by leveraging germline information.

PepCL updates peptide-MHC predictors with new assay data while preserving prior MS knowledge, improving prediction across alleles in clinical contexts.

The paper introduces enFoldX, a structure-based method for predicting TCR:pMHC binding using AlphaFold3 ensembles, achieving high accuracy for cognate vs. non-cognate classification.

The paper designs a multi-epitope vaccine against M. tuberculosis using computational methods, showing strong structural quality and simulated immune responses.

This study evaluates AlphaFold 3's ability to predict T cell antigen specificity using structural models and finds that binding scores correlate with TCR positioning over pMHC, not chemical interactions.

This paper introduces a hybrid quantum-classical pipeline for designing MHC-binding peptides, showing that quantum-derived priors improve the yield of strong binders, especially for understudied HLA alleles.

The paper designs a multi-epitope vaccine against Influenza A H10N5 and H3N2 using immunoinformatics and validates it computationally, showing activation of immune responses.

This paper introduces TCR-FramePose, a geometric framework for analyzing TCR-pMHC recognition, showing it captures novel structural features beyond conventional descriptors.

PRISM improves TCR specificity prediction by addressing label scarcity and viral bias through synthetic data and uncertainty quantification, outperforming existing models on rare epitopes.

TRIOPS predicts TCR-MHC restriction from amino acid sequences, achieving high AUC scores and generalizing to unseen alleles and HLA genotypes.

The paper introduces Ab-CASLR, a method for antigen-specific antibody retrieval under strict out-of-distribution conditions, achieving better performance than existing methods.