vaccines and immunoinformatics approaches

When Multimodal Fusion Fails: Contrastive Alignment as a Necessary Stabilizer for TCR--Peptide Binding Prediction

The paper shows that naive fusion of sequence and graph modalities in TCR-peptide binding prediction can degrade performance and introduces TRACE, a contrastive alignment method that stabilizes multimodal learning.

Design of a novel epitope-based tetravalent subunit vaccine against dengue virus: an immunoinformatics approach

This paper designs a tetravalent subunit vaccine against dengue virus using immunoinformatics, showing high antigenicity and population coverage with promising interactions and immune responses.

Quantum Convolutional HLA Immunogenic Peptide Prediction (Q-CHIPP): Next-Generation Neoantigen Prediction with Quantum Neural Networks

This paper introduces Q-CHIPP, a quantum neural network for predicting HLA immunogenic peptides, combining MHC binding and immunogenicity prediction using quantum hardware.

A germline shortcut in protein language model retrieval of adaptive immune receptors

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.

Pocket Restraints Guided by B-Cell Epitope Prediction improves Chai-1 Antibody-Antigen Structure Modeling

The paper improves antibody-antigen structure prediction by integrating B-cell epitope prediction tools into Chai-1 modeling, enhancing accuracy and diversity of predicted complexes.

Benchmarking Sequence-Based and AlphaFold-Based Methods for pMHC-II Binding Core Prediction: Distinct Strengths and Consensus Approaches

This paper benchmarks sequence-based and AlphaFold-based methods for predicting pMHC-II binding cores, finding that AlphaFold-based methods excel at identifying binders while NetMHCIIpan is better at identifying non-binders.

PepCL: A replay-based continual learning framework for updating peptide-MHC models

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

Ensembles of in silico structures enable T cell peptide-MHC binding prediction

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.

Computational design of a multi-epitope vaccine against M. tuberculosis

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

Characterising AlphaFold 3s ability to predict T cell antigen specificity

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.

Rational in silico discovery and serological validation of Trypanosoma cruzi-specific B-cell epitopes for high-precision Chagas disease diagnosis

This paper identifies T. cruzi-specific B-cell epitopes using computational methods and experimental validation, achieving high diagnostic accuracy for Chagas disease.

Hybrid quantum-classical de novo design of MHC-binding peptides

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.

Immunoinformatics-Guided Design and In Silico Evaluation of a Multi-Epitope Vaccine Against Influenza A H10N5 and H3N2 Strains Based on Hemagglutinin and Neuraminidase Proteins

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

TCR-FramePose: a local-frame representation for decomposing global docking and CDR3 loop geometry in TCR-pMHC recognition

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

PRISM : Peptide-specificity annotation of T-cell receptors with uncertainty quantification

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: A deep learning framework for prediction of T cell receptor-MHC binding specificity

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

Strict OOD Antigen-to-Antibody Retrieval with CDR-Aware Slot Late Interaction

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

Generation and MHC class II loading of an endogenous influenza epitope revealed by a T cell receptor-like antibody

An mRNA vaccine-based pipeline generated a TCR-like antibody to study noncanonical MHC-II antigen processing of an influenza epitope.

HALPred-B: Host-Aware Linear B-Cell Epitope Prediction: Challenges, Limitations, and Variability Across Species

The paper evaluates host-specific linear B-cell epitope prediction models across human, mouse, and non-human primate datasets, showing significant performance differences and highlighting the need for host-aware approaches.

DUET: a graph-based workflow for TCR-epitope prioritization and tumor-reactive T-cell identification

DUET improves TCR-epitope prioritization and tumor-reactive T-cell identification using a graph-based workflow, outperforming existing methods on benchmark datasets.

Generative design of antigen-specific T-cell receptor sequences with a conditional diffusion model

This paper introduces TCRDiff, a conditional diffusion model for generating antigen-specific TCR sequences, validated through in vitro assays.

Simplifying principles underlying the highly complex peptide motif of the promiscuous chicken class I molecule, BF2*21:01

The paper identifies simplifying principles governing peptide binding by the promiscuous chicken class I molecule BF2*21:01, revealing patterns that enable broad peptide repertoire while maintaining stable binding.

Peptide:MHC Binding Stability Prediction Using Protein Language Models

The paper predicts pMHC-I binding stability using a protein language model, showing improved performance over existing methods and demonstrating its utility in immunogenicity and antigen-presentation modeling.

Digging deeper into the immunopeptidome with TripleToolWF

TripleToolWF improves immunopeptide identification and quantification by combining three search engines, increasing results by 6-25% and enhancing confidence in bacterial immunopeptide binding.

EpiESM-GA: Resource-Efficient Protein Foundation Model Features for Equitable B-Cell Epitope Prediction

This paper introduces a resource-efficient pipeline for B-cell epitope prediction using a frozen ESM-2 model and genetic algorithm compression, achieving strong performance on an IEDB benchmark.

Biophysical modeling for accurate T cell specificity prediction of viral and tumor antigens

The paper develops a biophysical model to predict T cell receptor specificity for viral and tumor antigens using structural data and sequence-based clustering.

Measuring peptide-MHC generalization to unseen alleles across both HLA classes

The paper evaluates peptide-MHC generalization to unseen alleles and introduces a benchmark and model that improves prediction accuracy for rare HLA alleles.

Multi-Scale Machine Learning for Antibody-Antigen Binding Affinity Prediction Using Deep Mutational Scanning and Structural Features

This paper develops a multi-scale machine learning framework to predict antibody-antigen binding affinity using deep mutational scanning and structural features, achieving improved performance over single-modality approaches.

Structure-Based Immunoinformatics Design of a CTB-Adjuvanted Multi-Epitope Mucosal Vaccine Against Helicobacter pylori

This paper designs a multi-epitope mucosal vaccine against H. pylori using immunoinformatics, incorporating CTB as an adjuvant and predicting structural stability and immune responses.

Bioinf-Farma: supervised integration of epitope prediction and recombinant protein developability for automated vaccine candidate prioritization

BIOINF-farma integrates epitope prediction and developability assessment to prioritize vaccine candidates by combining antigenicity and expression feasibility scores.