vaccines and immunoinformatics approaches

COMBO-RATE: An experimentally validated bioinformatic tool to identify promiscuous HLA restrictions

COMBO-RATE identifies promiscuous HLA restrictions by analyzing immune response data, outperforming existing tools and revealing broader HLA allele coverage for T cell epitopes.

General prediction of T cell receptor antigen specificity from sequence using AlphaFold 3

This paper uses AlphaFold 3 to predict T cell receptor antigen specificity from sequence, achieving high accuracy on unseen epitopes.

OpenAntigens: a structure-aware database for antigen construct design across the human cell-surface and secreted proteome

A static web database proposes 55 305 construct suggestions for 5328 human secreted and cell-surface proteins, using AlphaFold pLDDT and PAE, with ortholog, paralog and BLAST cross-reactivity context.

BenchMHC: a reproducible framework for peptide-MHC presentation prediction

BenchMHC reproduces and evaluates pMHC presentation models using public data, showing improvements in prediction accuracy and speed.

Function-preserving CD8⁺ T Cell Immunogenicity minimization through protein design

The paper develops a computational framework to minimize CD8⁺ T cell immunogenicity in designed proteins while preserving function, using a de novo luciferase as a model.

UnibindMD: An Integrated AI and Molecular Dynamics Platform for De Novo Peptide Binder Design

UnibindMD integrates AI and molecular dynamics to design validated peptide binders by combining multiple stages and validating predictions through dynamics simulations.

CAI-Vax: Computational Design of Personalized mRNA Cancer Vaccine Constructs After Antigen Selection

CAI-Vax designs personalized mRNA cancer vaccines by optimizing antigen sequences based on HLA compatibility, reducing predicted junctional binding while varying performance across prediction models.

Multiple bottlenecks constrain presentation of Mycobacterium tuberculosis antigens on MHC class I

The paper identifies bottlenecks in MHC-I antigen presentation by Mtb, showing that only a small fraction of predicted binders are actually presented, and develops computational models to improve prediction.

Active Learning framework for Antibody Binding Affinity Prediction

This paper benchmarks a Gaussian process active learning framework for antibody-antigen binding affinity prediction, showing significant improvements over random selection.

Detection of endogenous MR1B protein in human 293F cells by immunoaffinity enrichment and two-dimensional western blotting

The paper detects endogenous MR1B protein in 293F cells using immunoaffinity enrichment and two-dimensional western blotting, confirming its presence without overexpression.

Beyond Better Models: Barriers and a Path Forward in Generalisable TCR Antigen Specificity Prediction

This paper identifies barriers to generalisable TCR:pMHC prediction and proposes solutions through data analysis, evaluation setup, and metric alignment.

Sequence-only prediction of VHH nanobody epitopes and paratopes on HER2 with a frozen protein language model ( ESM2) and a structure-derived residue prior

The paper predicts HER2 epitopes and VHH paratopes from sequence alone using ESM2 and a residue prior, achieving high accuracy for binder and paratope prediction but limited epitope performance.

An immune world model with multi-agent orchestration for peptide vaccines

This paper develops an immune world model for peptide vaccines that links molecular predictions with immune dynamics through multi-agent orchestration and native-recognition constraints.

Accelerating Molecular Dynamics with Hydrogen Mass Repartitioning to Prioritize Immunogenic Neoantigens for Cancer Vaccines through Peptide HLA Binding Free Energy Calculations

This study uses hydrogen mass repartitioning accelerated molecular dynamics to calculate peptide HLA binding free energies, identifying immunogenic neoantigens for cancer vaccines.

In Silico Design and Comprehensive Evaluation of a Novel Multi-Epitope Vaccine Candidate Against Chandipura Virus

This paper designs and evaluates a multi-epitope vaccine candidate against Chandipura virus using computational methods, showing strong antigenicity, low allergenicity, and predicted immune response.

Immunoinformatics-Based Design of a Chimeric Vaccine Against Necator americanus Targeting the Na-APR-1, Na-GST-1, and Na-ASP-2 Vaccine Candidates

This paper designs a chimeric vaccine against Necator americanus using immunoinformatics, combining epitopes from three antigens with an adjuvant to elicit immune responses and ensure safety.

NeoOmni Atlas: an integrated database for pan-cancer neoantigen derived from proteogenomics analysis

NeoOmni Atlas integrates proteogenomic data to catalog tumor neoantigens across 26 cancer types, identifying novel ligands and epitopes for immunotherapy.

Protein Deimmunization with a Generative Diffusion Model

The paper introduces MESSI, a generative diffusion model for deimmunizing proteins by designing sequences with minimal immunogenicity while preserving structure and function.

BCell-Epitope-Prediction: A local, reproducible pipeline for multi-epitope vaccine design: integration and structural validation against a panel of PDB antigen-antibody complexes

This paper presents a pipeline for multi-epitope vaccine design that integrates multiple tools to predict B-cell epitopes and validates them against structural data.

Hubness and local scaling in paired T cell receptor distance spaces

The paper quantifies hubness in T cell receptor distance spaces and evaluates local scaling as a correction, showing improvements in retrieval accuracy but no universal solution.

A monoallelic immunopeptidome atlas of 10 prevalent canine DLA-88 alleles

The paper generates a monoallelic immunopeptidome atlas for 10 prevalent canine DLA-88 alleles, identifying 32,702 peptides and revealing allele-specific binding motifs and conserved antigen-processing signatures.

In silico subtractive genomics and reverse vaccinology approach reveal outer membrane proteins OMP47 and LptD as dual-purpose drug and vaccine targets in Pasteurella multocida Pm70

AI-Driven Design of Next-Generation Immunoinformatics Multi-Epitope Subunit Vaccine Targeting the Most Virulent Mpox Proteome: A Structural and Kinetic Validation Framework

This paper develops an AI-driven vaccine design framework for Mpox, yielding three multi-epitope constructs validated for structural stability, immune binding, and expression efficiency.

Exploring noise in TCR specificity data through supervised learning

This paper introduces a supervised learning denoising method to identify low-quality labels in TCR specificity data, improving model reliability by filtering noisy examples.

PREpiBind: Protein Representation-integrated Epitope-MHC Class II Binding Prediction

PREpiBind evaluates ten protein representations for pMHC-II binding prediction across multiple datasets, showing varying performance depending on the task and evaluation method.

Antibody-Antigen Affinity Prediction with Chain-Aware Protein Language Modeling

AbAffinity predicts antibody-antigen affinity using a chain-aware sequence model, achieving high correlation on benchmark datasets and maintaining performance without structural data.

A functionally validated TCR-pMHC database for TCR specificity model development

The paper develops a validated TCR-pMHC database (TCRvdb) by assessing VDJdb entries with a high-throughput platform, finding only 50% of claims are functionally confirmed.

A biophysical framework for accurately identifying antigen single-amino acid escape variants and corresponding variant-specific compensatory TCR sequences

This paper develops a biophysical computational framework to identify antigen escape variants and compensatory TCR sequences by integrating mutagenesis data and structural modeling.

Limitations of general protein language models and public TCRpMHC data in training specificity prediction models

This paper evaluates the effectiveness of general protein language models and negative data generation strategies in predicting TCR-peptide-MHC interactions, finding that they struggle to distinguish positive from negative TCRs.

Beyond natural amino acids: Extending immunogenicity risk assessment to non-canonical peptide drugs through chemical feature encoding

The paper develops a machine learning framework to predict MHC class II binding for canonical and modified peptides by incorporating chemical features, improving immunogenicity risk assessment for non-canonical peptide drugs.