vaccines and immunoinformatics approaches

A major histocompatibility complex (MHC) class II molecule that binds the same viral pathogen peptide with both nonamer and decamer core sequences for presentation to T cells

The paper identifies a chicken MHC class II molecule that binds the same viral peptide with both nonamer and decamer core sequences, revealing binding plasticity in immune response to MDV.

A miniaturized MR1 metabolite display system with native-like protein features

This paper develops a stabilized MR1 variant, SMART-MR1, that preserves native-like ligand presentation and TCR recognition while enabling biochemical and structural studies.

Deep learning enables direct HLA typing from immunopeptidomics data

This paper introduces Immunotype, a deep learning model that directly types HLA alleles from immunopeptidomics data with 87.2% accuracy.

Deep learning-enabled scaffolding of spatial arrays of PfCSP epitopes

This study uses deep learning to design multi-epitope scaffolds mimicking CSP's spatial structure, showing accurate positioning of two epitopes and partial success with the third.

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

This study identifies conserved T-cell targets across betacoronaviruses using machine learning and validates their immunogenicity through functional assays and mRNA vaccination in mice.

Resolution of recursive data corruption to transform T-cell epitope discovery

The paper identifies methodological errors in epitope discovery that inflate in silico benchmarks and introduces a model trained on clean data that outperforms existing methods.

IMMREP25: Unseen Peptides

IMMREP25 evaluates TCR:pMHC binding prediction for unseen peptides, showing improved performance with structural modeling but still below random guessing.

An Integrated Computational Antigen Discovery Pipeline with Hierarchical Filtering for Emerging Viral Variants

This paper introduces a computational pipeline for antigen discovery that uses filtering and machine learning to identify conserved neutralizing epitopes in viruses like SARS-CoV-2 and RVFV.

Antigen-processing rewiring expose cryptic self promoting organ-specific autoimmunity

The paper identifies two distinct mechanisms of autoimmunity: altered antigen processing exposing cryptic self-antigens and breakdown of tolerance toward normally presented self-antigens.

MHCBind: A Pan- and Allele-Specific Model for Predicting Class I MHC-Peptide Binding Affinity

MHCBind is a deep learning model that predicts MHC class I peptide binding affinity across alleles and improves upon existing methods with better accuracy and ranking performance.

Computational Prediction of Plasmodium falciparum Antigen-T-cell Receptor Interactions via Molecular Docking: Implications for Malaria Vaccine Design

This study uses molecular docking to predict Plasmodium falciparum antigen-T-cell receptor interactions, identifying three antigens as potential vaccine candidates.

HLA Alleles Imprint Distinct Biases in the Usage Preferences of TCR Vβ segments

This study identifies HLA allele-specific biases in TCR Vβ usage, showing germline-encoded preferences shape TCR-HLA compatibility and influence TCR-pHLA specificity.

Unpaired TCRα + TCRβ sequencing is sufficient for training machine learning TCR-epitope recognition predictors

This study shows that unpaired TCRα + TCRβ sequencing can train accurate TCR-epitope recognition predictors at lower cost than paired sequencing.

Chemically informed representations of amino acids enable learning beyond the canonical protein alphabet

The paper introduces chemically informed peptide representations to enable machine learning models to learn from molecular structure rather than canonical amino acid symbols, improving prediction of MHC class I binding.

PepCABO: Latent-space Bayesian optimization for peptide-MHC binding using contrastive alignment

PepCABO improves peptide-MHC binding optimization using contrastive alignment and Bayesian optimization in a latent space, outperforming baselines across multiple alleles.

Phosphorylation of a tumor-derived ASXL2 epitope remodels 1 peptide-HLA binding affinity and interaction dynamics

This study investigates how phosphorylation of an ASXL2 epitope affects peptide-HLA binding affinity and dynamics, revealing structural and conformational changes that could inform cancer immunotherapy.

t2pmhc: A Structure-Informed Graph Neural Network to predict TCR-pMHC Binding

The paper introduces t2pmhc, a structure-informed graph neural network for predicting TCR-pMHC binding, showing improved generalization to unseen peptides compared to existing methods.

RePaRank: An Efficient Architecture for Antibody-Antigen Interface Prediction by Proximity Ranking

RePaRank predicts antibody-antigen interfaces using a proximity ranking task, outperforming benchmarks in paratope prediction and being competitive in epitope prediction without external resources.

Immunoinformatics Approach to Engineer a Multi-Epitope Vaccine Against SdrG in Skin Commensal Staphylococcus epidermidis

This study designs a multi-epitope vaccine against S. epidermidis SdrG using immunoinformatics, showing strong TLR4 binding and stability through computational analyses.

Structural profiling of the pneumolysin epitope landscape uncovers a cross-species neutralising site across cholesterol-dependent cytolysins

The paper maps the structural epitope landscape of pneumolysin and identifies a conserved cross-species neutralising site among cholesterol-dependent cytolysins.

STRUMP-I: Structure-based machine learning approach to pMHC-I binding prediction using force field energy features

STRUMP-I improves pMHC-I binding prediction by using force-field energy features, outperforming sequence-based methods on underrepresented MHC alleles and showing synergy with them.

AI predicted TCR-pMHC structures differentiate immune interactions

This study uses AI to predict TCR-pMHC structures and identifies structural features that better predict immune interactions than sequence-based features.

Contrastive learning for antibody-antigen sequence-to-specificity prediction

This paper introduces CALM, a model that predicts antibody-antigen binding from sequences using contrastive learning, achieving 7% top-1 retrieval on a test set.

AI-Driven Generation of Cortisol-Binding Peptides for Non-Invasive Stress Detection

The paper uses AI to generate cortisol-binding peptides for non-invasive stress detection, identifying high-affinity candidates from a large sequence library.

Advancements in Vaccine Development : A comprehensive design of a Multi-Epitopic Immunodominant Peptide Vaccine Targeting Kyasanur Forest Disease via Reverse Vaccinology

This paper designs a multi-epitopic peptide vaccine against KFDV using in silico tools, identifying 16 epitopes that show non-toxicity, non-allergenicity, and high immunogenicity.

AI/ML-Assisted Computational Design and Immunoinformatics Evaluation of a Multi-Epitope Vaccine Targeting Podoplanin in Glioblastoma Multiforme

The paper designs a multi-epitope vaccine targeting Podoplanin in GBM using immunoinformatics and 3D modeling, showing it could be a potential vaccine candidate.

Generative AI Guided Design of High-Affinity T cell Receptors

This paper introduces TCRPPO2, an AI-driven framework for optimizing TCR affinity, showing improved activity in engineered TCRs against tumor antigens.

Paraplume: A fast and accurate paratope prediction method provides insights into repertoire-scale binding dynamics

Paraplume predicts paratopes from antibody sequences using protein language models, improving downstream tasks and revealing evolutionary dynamics in antibody repertoires.

Molecular Dynamics Analysis of Self and Microbial Peptides Bound to HLA-B27: A Multi-Parameter Framework

The paper develops a multi-parameter molecular dynamics framework to compare structural and dynamic similarities between microbial and self-peptides bound to HLA-B27, identifying KP1 as structurally similar to a human self-peptide.

DecoderTCR: Compositional Pretraining and Entropy-Guided Decoding for TCR-pMHC Interactions

DecoderTCR improves TCR-pMHC interaction prediction using pretraining and entropy-guided decoding, achieving high AUROC scores without epitope-specific training.