vaccines and immunoinformatics approaches

This paper develops methods to predict IL-5 inducing peptides using alignment-based and alignment-free approaches, achieving high performance with a hybrid model.

GraphBepi predicts B-cell epitopes using AlphaFold2 structures and a pretrained language model, outperforming existing methods in AUC and AUPR.

epitope1D predicts linear B-cell epitopes using explainable machine learning with graph-based descriptors and organism ontology, achieving high accuracy on cross-validation and blind tests.

The paper fine-tunes AlphaFold to predict peptide binding specificity for MHC and other domains, achieving performance comparable to sequence-based methods.

TEINet predicts TCR-epitope binding specificity using a deep learning framework with transfer learning, achieving an AUROC of 0.760 and outperforming baselines.

The paper benchmarks 23 TCR-epitope prediction models, finding that using paired-chain alpha-beta and CDR1/2/V/J data improves performance, and simple distance-based methods can be competitive.

Epitopedia identifies regions of molecular mimicry between pathogens and immune epitopes by analyzing structural similarity and sequence identity.

This paper introduces TCRpeg, a deep autoregressive generative model for TCR repertoires, achieving higher accuracy in probability estimation and improving multiple TCR-related tasks.

This paper develops computational tools to predict celiac disease-associated epitopes and motifs in proteins, achieving high accuracy using machine learning and motif-based approaches.

This paper designs a hybrid anti-variant COVID-19 vaccine using spike and nucleocapsid peptides with T-cell immunity, optimized for cellular biosynthesis and adaptable to other viruses.

Paragraph predicts antibody paratopes using graph neural networks with minimal features, outperforming existing tools without antigen information.

BepiPred-3.0 improves B-cell epitope prediction using protein language models, achieving high accuracy for both linear and conformational epitopes.

The paper identifies eukaryotic host-like SLiMs in microbial mimitopes and suggests some could be novel drug targets for autoimmune diseases.

This study uses reverse vaccinology to identify four potential vaccine targets against Ascaris in humans and pigs by analyzing proteomes and predicting antigenic properties.

NetBCE is a deep learning model that accurately predicts linear B-cell epitopes using sequence features, outperforming existing tools with an AUC improvement of 22.06%.

This study designs a multi-epitope peptide vaccine targeting the SARS-CoV-2 Omicron variant using computational methods to predict epitopes and evaluate vaccine properties.

SEMA predicts conformational B-cell epitopes using transfer learning on antigen sequences and structures, achieving ROC AUC of 0.76 on an independent test set.

Jupytope extracts structural properties of viral epitopes from 3D structures and demonstrates their utility in distinguishing viral subtypes and clades.

This paper introduces MHCrank, a deep learning model that improves MHC Class I antigen processing predictions using cleavage site-specific kernels and learned amino acid representations, outperforming existing methods.

Epitope-Evaluator is a web tool that allows users to interactively analyze and filter predicted T-cell epitopes from immunoinformatic tools.

This paper introduces ATMTCR, a contrastive learning model for predicting TCR-antigen binding specificity, showing improved performance over existing methods.

The paper predicts epitope candidates for SARS-CoV-2 by analyzing epitope sequences from related coronaviruses and identifying conserved and diverse antigen regions across 62k genomes.

This study improves epitope prediction by integrating AlphaFold with the AbAdapt pipeline, showing better prediction of antibody-specific epitope residues.

The paper proposes a deep learning model that combines local and global features to predict B-cell epitopes, showing improved performance over existing methods.

The paper identifies molecular mimicry between SARS-CoV-2 Spike and human proteins that may contribute to autoimmune responses in COVID-19.

The paper develops HLAncPred, an AI-based method to predict non-classical HLA (HLA-G and HLA-E) binders, achieving high AUC scores and providing a web server for this purpose.

DeepMHCII improves MHC II-peptide binding affinity prediction by incorporating binding core-aware convolution layers, outperforming existing methods across multiple evaluation settings.

The paper introduces a Bayesian regression model to detect HLA-associated mutations by integrating multiple sources of information, improving detection performance over existing methods.

This paper presents a method to predict epitope activity of non-peptidic compounds using machine learning and the IEDB database, achieving ROC-AUC values between 0.69-0.96.

This paper introduces IL-13Pred, a method to predict IL-13 inducing peptides in proteins, with high accuracy for managing COVID-19 severity.