vaccines and immunoinformatics approaches

RACER-m predicts T cell specificity using structural features, requiring fewer training examples than sequence-based methods.

The paper fine-tunes AlphaFold using high-resolution MHC-peptide crystal structures to improve prediction accuracy for MHC-peptide complex structures, outperforming existing methods in RMSD and lDDT scores.

BacScan identifies highly conserved immunogenic bacterial proteins across serotypes, showing potential for vaccine development.

The paper develops a machine learning method to modify protein immune-visibility to CTLs, showing it can alter HIV Nef's visibility while preserving sequence diversity.

This paper introduces MixTCRpred, a deep learning model that predicts TCR-epitope interactions and identifies epitope-specific T cells, showing enrichment in COVID-19 patients.

The paper identifies a concise set of 137 virus-specific CD8 T cell epitopes recognized by human T cells, with 84% showing prevalent reactivity and associations with effector and memory phenotypes.

GraphMHC predicts neoantigen binding using graph neural networks on molecular structures, achieving high accuracy and showing survival differences in melanoma patients.

The paper identifies SARS-CoV-2 BA.1 epitopes recognized by mouse CD4+ T cells and generates corresponding tetramer reagents for studying immune responses.

X-ray structures of anti-MHC-I/MHC-I complexes define epitopes overlooked by computational prediction.

This paper uses machine learning to design cytotoxic T cell epitopes by modeling proteasomal degradation scores and testing their immunogenicity.

The paper introduces MHCXAI, an explainable AI framework to interpret deep learning predictors of MHC class I peptide presentation, validating explanations against ground truth and robustness.

NetTCR 2.2 improves TCR specificity predictions by combining pan- and peptide-specific training, loss-scaling, and sequence similarity integration, achieving state-of-the-art performance on the IMMREP 2022 benchmark.

The paper introduces two methods to map mimotopes to epitopes, including an ensemble approach and a novel algorithm called MimoTree that handles gaps in sequences.

The paper develops Dolphyn, a machine learning method to improve PhIP-Seq by prioritizing epitopes, increasing antibody-bound peptides from 10% to 31% and reducing synthesized peptides by 78%.

PoxiPred predicts potential antigens and T-cell epitopes for poxviruses using an AI-based tool trained on viral proteomes.

This study curates a dataset of influenza HA antibodies and develops an explainable language model to predict antibody specificity, identifying and validating HA stem antibodies.

This study compares clustering models for predicting T cell receptor (TCR) antigen specificity, finding variability in performance and scalability but high similarity in clusters for TCRs recognizing the same epitope.

The paper develops a hybrid model for predicting both linear and conformational B-cell epitopes using machine learning and BLAST, achieving an AUC of 0.83 on an independent dataset.

The paper introduces a database of TCR-peptide mutational assays and a Bayesian model to predict TCR cross-reactivity, showing improved performance over existing methods.

The paper examines bias in NetMHCpan training datasets and evaluates prediction accuracy on novel alleles, finding no significant impact of training data disparities on prediction quality.

HLA-EpiCheck predicts B-cell epitopes on HLA proteins using molecular dynamics simulations and outperforms existing methods, aiding in organ transplant matching.

This paper describes using MixMHC2pred to predict MHC-II binding specificity and ligands from amino acid sequences across species.

epiTCR-KDA improves TCR-peptide binding prediction by incorporating structural dihedral angles and knowledge distillation, achieving high AUC for both seen and unseen data.

This paper develops a Transformer-based model to predict TCR-peptide binding using amino acid sequences and associates attention weights with structural properties.

PANDORA v2.0 benchmarks peptide-MHC II modeling software and improves its performance and usability for generating 3D pMHC-II structures.

Brewpitopes refines B-cell epitope predictions by integrating protein features, improving neutralizing epitope identification in SARS-CoV-2.

HOGVAX selects peptides to maximize population coverage in vaccine design by exploiting sequence overlaps, achieving high coverage and robust immunity against SARS-CoV-2 variants.

ACE optimizes ELISpot assay design by using an epitope similarity model to reduce false positives and improve efficiency.

The paper introduces a neural network model, DePTH, to predict TCR-HLA associations from amino acid sequences and shows its association with cancer patient survival outcomes.