vaccines and immunoinformatics approaches

HLA-DR4Pred2: an improved method for predicting HLA-DRB1*04:01 binders

This paper introduces HLA-DR4Pred2, an improved method for predicting HLA-DRB1*04:01 binders using a larger dataset and hybrid approaches, achieving higher AUC scores than existing methods.

Pep2Vec: An Interpretable Model for Peptide-MHC Presentation Prediction and Contaminant Identification in Ligandome Datasets

Deep Learning-Based Motif Discovery in MajorHistocompatibility Complex: A Primer

This work introduces a deep learning approach using CNNs to discover peptide binding motifs in MHC molecules, showing alignment with experimental motifs and potential applications in vaccine design.

Phylogeny-aware linear B-cell epitope predictor detects candidate targets for specific immune responses to Monkeypox virus

The paper develops a phylogeny-aware model to predict linear B-cell epitopes for Monkeypox virus, identifying nine specific peptides as potential diagnostic targets.

IMGT/RobustpMHC: Robust Training for class-I MHCPeptide Binding Prediction

The paper introduces RobustpMHC, a self-supervised learning method for peptide-MHC class I binding prediction, showing improvements over existing approaches across multiple datasets.

Computational Prediction of Multiple Antigen Epitopes

The paper introduces ISPIPab, a program that predicts multiple antigen epitopes by combining feature-based and docking-based methods, outperforming existing approaches and revealing novel targets.

Phylogeny-based selection of representative variants with Navargator: Proof of principle using humoral cross-reactivity data from two immunization studies

Navargator selects representative variants based on phylogenetic trees to improve cross-reactivity in vaccines, validated using immunization studies.

Utilizing Protein Bioinformatics to Delve Deeper Into Immunopeptidomic Datasets

This paper introduces AIMS, a tool for analyzing immunopeptidomic datasets to identify biophysical signatures and generate machine learning models for classification.

Evaluating Cross-linking-driven integrative modeling in peptide-HLAII complexes prediction with insights for refining predictive accuracy

This study evaluates AlphaLink2 and AlphaFold2 Multimer for predicting peptide-HLAII complexes using cross-linking data and benchmarking against experimental datasets.

Energy landscapes of peptide-MHC binding

This paper analyzes the sequence-dependent energy landscapes of MHC-peptide binding, showing class-specific nonlinearities and their implications for immune recognition and escape.

Machine-learning-based Structural Analysis of Interactions between Antibodies and Antigens

This paper develops a deep learning model to analyze antibody-antigen interactions, achieving high accuracy in distinguishing antibody-antigen complexes from other protein complexes and identifying antigens from epitope information.

Assessing the Predictive Ability of Computational Epitope Prediction Methods on Fel d 1 Allergen and Other Allergens

This study evaluates computational epitope prediction tools on Fel d 1 allergen and other allergens, finding limited effectiveness and highlighting the need for improved methods.

Predicting T Cell Receptor Functionality against Mutant Epitopes

P-TEAM predicts the effect of epitope mutations on T cell functionality using a Random Forest model trained on murine and human TCR-epitope interactions.

In Silico Vaccine Design: Targeting Highly Epitopic Regions of Clostridium perfringens Type D Epsilon Toxin and Clostridium novyi Type B Alpha Toxin for Optimal Immunogenicity

This study designs a recombinant fusion protein vaccine targeting epitopes of two toxins from Clostridium perfringens and Clostridium novyi to enhance immunogenicity against livestock infections.

PepCleav: a classification method for evaluating the cleavability of peptide fragments presented by MHC class I

PepCleav evaluates peptide cleavability by classifying amino acids at proteasome and aminopeptidase sites, improving neoantigen identification with 84-89% cleavable peptide accuracy.

Restoring data balance via generative models of T cell receptors for antigen-binding prediction

The paper uses generative models to balance T cell receptor data for improved antigen-binding prediction.

Immuno-informatics Study Identifies Conserved T Cell Epitopes in Non-structural Proteins of Bluetongue Virus Serotypes: Formulation of Computationally Optimized Next-Generation Broad-spectrum Multiepitope Vaccine

The paper identifies conserved T cell epitopes in Bluetongue Virus non-structural proteins and evaluates their potential as a broad-spectrum vaccine using computational methods.

Cmai: Predicting Antigen-Antibody Interactions from Massive Sequencing Data

Cmai predicts antigen-antibody interactions from high-throughput BCR sequencing data, revealing immune responses to tumor antigens and ICI treatment outcomes.

TCRfinder: Improved TCR virtual screening for novel antigenic peptides with tailored language models

TCRfinder improves TCR-peptide binding prediction using tailored language models, achieving higher accuracy than existing methods in identifying interacting TCRs and tumor neoantigens.

WT1 targeting multi-epitope vaccine design for glioblastoma multiforme using immuno-informatics approaches

This paper designs a multi-epitope vaccine targeting WT1 protein in glioblastoma multiforme using immuno-informatics methods, showing antigenic and non-toxic properties.

Designing a novel Scaffold-Based Multi-Epitope Vaccine to Combat Melioidosis Caused by Burkholderia pseudomallei: An In-silico and Immunoinformatics approach

This paper designs a multi-epitope vaccine against Burkholderia pseudomallei using in-silico and immunoinformatics methods, showing it is non-toxic, non-allergic, and immunogenic.

A structure-guided approach to predict MHC-I restriction of T cell receptors for public antigens

The paper predicts MHC-I restriction of T cell receptors for public antigens using structural analysis of TCR:pHLA complexes.

TULIP - a Transformer based Unsupervised Language model for Interacting Peptides and T-cell receptors that generalizes to unseen epitopes

TULIP is an unsupervised transformer model for predicting TCR epitope binding, outperforming existing methods on unseen epitopes by addressing data limitations and bias.

Immunoinformatics design of a novel multiepitope vaccine candidate against Non-typhoidal Salmonellosis caused by Salmonella Kentucky using outer membrane proteins A, C, and F

This paper designs a multi-epitope vaccine against Salmonella Kentucky using bioinformatics, identifying T-cell and B-cell epitopes and demonstrating in silico immunogenicity.

Design of a multi-epitope camel mastitis vaccine candidate using a reverse vaccinology approach

The paper designs a multi-epitope vaccine candidate for camel mastitis using reverse vaccinology, identifying antigenic and epitopic proteins from bacterial genomes.

Systematic analysis of CDR contacts and sequence constraints between T cell receptor αβ chains.

This study systematically analyzes CDR contacts and sequence constraints between TCR αβ chains using TCR structures and mutual information, showing sequence dependencies and co-evolution patterns.

Genesis: A Modular Protein Language Modelling Approach to Immunogenicity Prediction

Genesis is a modular protein language model that improves immunogenicity prediction for CD8+ epitopes by integrating pMHC and TCR context, outperforming existing models in precision.

Designing meaningful continuous representations of T cell receptor sequences with deep generative models

This paper introduces TCR-VALID, a variational autoencoder for generating and analyzing TCR sequences, showing improved clustering and representation quality.

Do Domain-Specific Protein Language Models Outperform General Models on Immunology-Related Tasks?

This study evaluates whether domain-specific protein language models outperform general models in immunology tasks, finding nuanced performance differences and highlighting key hyperparameters.

VitTCR: A deep learning method for peptide recognition prediction

VitTCR predicts TCR-peptide interactions using a vision transformer-based model, outperforming existing methods in classification accuracy.