vaccines and immunoinformatics approaches

A structure-informed deep learning framework for modeling TCR-peptide-HLA interactions

StriMap models TCR-peptide-HLA interactions using structural and sequence features, achieving state-of-the-art performance and identifying disease-associated peptides in ankylosing spondylitis.

Viral Burden: New insights into Estimating IgG Recognition Across Human Populations

The paper estimates IgG recognition of viral epitopes across populations using a PhIP-based approach, finding broad virus detection and population-specific epitope recognition patterns.

Cryo-EM and X-ray structures of an anti-MHC-I monoclonal antibody/H2-Kb complex reveal the basis of conformational dependence and allelic specificity

The paper reports cryo-EM and X-ray structures of an anti-MHC-I mAb/H2-Kb complex, revealing conformational epitope details and allelic specificity.

Targeting Outer Membrane β-barrel Proteins of Burkholderia mallei to design a Multi-epitope Vaccine Against Human Glanders

This study identifies 59 outer membrane β-barrel proteins from B. mallei and constructs a multi-epitope vaccine predicted to induce strong immune responses.

TCRdenoise - an unsupervised similarity-based approach for denoising of TCR-pMHC specificity data

The paper introduces TCRdenoise, a method to denoise TCR-pMHC interaction data by clustering and similarity metrics, improving the quality of training data for predictive models.

SemVac: A Semantic Vaccinology Paradigm Powered by LLMs for Antigen Discovery

SemVac uses LLMs to predict vaccine antigens by analyzing literature-derived protein descriptions, outperforming existing methods and identifying new candidates with experimental validation.

Characterization of Outer Membrane β-barrel Proteins of Pasteurella multocida for Multi-Epitope Vaccine Design against Human Pasteurellosis

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

A protective hybrid protein vaccine informed by mapping major histocompatibility class II epitopes in Cryptococcus neoformans chitin deacetylases that stimulate CD4+ T cell responses

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.

An interpretable peptide-HLA model emergently learns binding energetics and structure

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

OmniTCR: a foundation model unifying T cell receptor recognition prediction and conditional sequence generation

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

TAPAS: Learned integration of AlphaFold3 confidence and geometric features for TCR-pMHC binding prediction

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

Retrospective Computational and Immunoinformatics Validation of Clinically Evaluated Necator americanus Protein Vaccine Candidates: Na-APR-1, Na-GST-1, and Na-ASP-2

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.

De novo design of CR2 binder as vaccine scaffold

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

MHCXGraph: A Graph-Based approach to detecting T cell receptor cross-reactivity

MHCXGraph identifies conserved structural regions in pMHC complexes to detect T cell cross-reactivity using graph-based analysis.

Label Noise Limits TCR-pMHC Specificity Prediction: Improved Performance Through AlphaFold3-Based Structural Modeling and Data Denoising

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

An Open Benchmark for Systems Vaccinology: Insights from the CMI-PB Challenges

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.

GDA-Pred: Generative AI-Driven Data Augmentation for Improved Prediction of IL-6 and IL-13 Inducing Peptides

GDA-Pred improves prediction of IL-6 and IL-13 inducing peptides by using generative AI to augment small datasets, enhancing classifier performance through data expansion.

A multi-scale structural and biophysical atlas of TCR-peptide-HLA recognition dynamics

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

VIProDesign: Viral Protein Panel Design for Highly Variable Viruses to Evaluate Immune Responses and Identify Broadly Neutralizing Antibodies

VIProDesign is a computational tool that designs viral protein panels to balance diversity and size, enabling better evaluation of immune responses and identification of broadly neutralizing antibodies.

EpiTune: An Accurate Epitope Prediction Model with Mechanistic Insights

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.

A single-antigen, multi-epitope subunit vaccine candidate against lumpy skin disease virus designed by conservation-guided reverse vaccinology

Conserved influenza A epitope candidate regions and a benchmark of ESM-2 sequence features

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: Interpretable Pan-Allelic Prediction of Peptide-MHC Binding and Presentation Using Chemically Grounded Fingerprints and Contrastive Learning

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.

Peptide-HLA II interaction prediction for post-translationally modified peptides

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

Structure-Based TCR-pMHC Binding Prediction and Generalization to Unseen Peptides

This work evaluates factors affecting generalization of GNN-based TCR-pMHC binding predictors to unseen peptides and proposes architectural improvements.

Predicting specificity of TCR-pMHC interactions using machine learning and biophysical models

This study evaluates ML and biophysical models for predicting TCR-pMHC specificity, showing ML struggles with novel peptides while biophysical methods excel but underperform on known ones, leading to a hybrid model with improved performance.

What Makes a Good Vaccine Antigen Target? Defining Key Features and Predicting Candidates in the Staphylococcus aureus Proteome

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

A distribution-aware and functionally relevant novel framework for generation and discovery of bioactive peptides

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.

Computational Designing of a Multi-Epitope Vaccine Against Streptococcus pyogenes

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

MHChron: diversity-balanced dataset design for robust peptide-MHC binding prediction across MHC class I and II

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