vaccines and immunoinformatics approaches

STAPLER: Efficient learning of TCR-peptide specificity prediction from full-length TCR-peptide data

STAPLER is a transformer model that predicts TCR-pMHC specificity by learning from TCR and peptide sequences, outperforming existing methods in antigen recognition prediction.

CcBHLA: pan-specific peptide-HLA class I binding prediction via Convolutional and BiLSTM features

The paper introduces a deep learning model called CcBHLA for predicting HLA class I peptide binding, showing improved performance over existing methods.

Machine learning reveals limited contribution of trans-only encoded variants to the HLA-DQ immunopeptidome by accurate and comprehensive HLA-DQ antigen presentation prediction

The paper improves HLA-DQ antigen presentation prediction using machine learning and shows trans-only variants have limited impact on the immunopeptidome.

Performance comparison of TCR-pMHC prediction tools reveals a strong data dependency

The paper compares TCR-pMHC prediction models and finds they lack generalization and robustness due to data dependency.

Context-Aware Amino Acid Embedding Advances Analysis of TCR-Epitope Interactions

The paper introduces context-aware amino acid embeddings for TCR sequences, showing improved performance in binding affinity prediction and clustering compared to existing methods.

Computational prediction of MHC anchor locations guide neoantigen identification and prioritization

The paper predicts MHC anchor positions for 328 HLA alleles and shows that incorporating these improves neoantigen identification by correcting misclassified candidates in tumor samples.

Prediction and scanning of IL-5 inducing peptides using alignment-free and alignment-based method

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

Identifying B-cell epitopes using AlphaFold2 predicted structures and pretrained language model

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

TCR2vec: a deep representation learning framework of T-cell receptor sequence and function

TCR2vec is a deep learning framework that learns representations of full T-cell receptors, outperforming methods that focus only on CDR3 regions in predicting binding specificity and clustering.

An interpretable ML model to characterize patient-specific HLA-I antigen presentation

The paper introduces epiNB, an interpretable machine learning model for predicting patient-specific HLA-I antigen presentation using Mass Spectrometry data, achieving state-of-the-art accuracy with fewer parameters and no hyperparameter tuning.

epitope1D: Accurate Taxonomy-Aware B-Cell Linear Epitope Prediction

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.

Peptide binding specificity prediction using fine-tuned protein structure prediction networks

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

Integrated analysis and annotation for T-cell receptor sequences using TCRosetta

TCRosetta is a web server for TCR repertoire analysis that integrates sequence clustering, motif analysis, and disease annotation, simplifying TCR data analysis for researchers.

TEINet: a deep learning framework for prediction of TCR-epitope binding specificity

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

An algorithm that combines machine learning ensemble modeling and network analysis to predict self-tolerant tumor-associated antigens for anti-cancer immunotherapy

The paper presents a computational pipeline that combines machine learning and network analysis to predict self-tolerant tumor-associated antigens for immunotherapy in uveal melanoma.

Benchmarking solutions to the T-cell receptor epitope prediction problem: IMMREP22 workshop report

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: identifying molecular mimicry between pathogens and known immune epitopes

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

Deep autoregressive generative models capture the intrinsics embedded in T-cell receptor repertoires

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

Prediction of celiac disease associated epitopes and motifs in a protein

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.

De novo design of anti-variant COVID-19 vaccine

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.

A robust deep learning platform to predict CD8+ T-cell epitopes

This paper introduces TRAP, a deep learning platform for predicting CD8+ T-cell epitopes, which outperforms existing methods in cancer and pathogenic settings.

Epitopes- Based Vaccine Design Against Foot and Mouth Disease SAT2 Serotype from Sudanese Isolate by Using Immunoinformatic Approaches

This study designs a multi-epitope peptide vaccine against FMD SAT2 serotype from Sudan using immunoinformatic methods, identifying T-cell and B-cell epitopes with high binding affinity and conservation.

Structure modeling and specificity of peptide-MHC class I interactions using geometric deep learning

The paper introduces MHCfold, a deep learning model that predicts peptide-MHC class I structures and binding specificity, achieving high accuracy and enabling high-throughput analysis.

Exploring B and T-cell epitopes for constructing a Novel Multiepitope vaccine to combat emerging Monkeypox infection: A Reverse Vaccinology approach

The paper uses reverse vaccinology to identify and validate epitopes for a multiepitope vaccine against monkeypox, focusing on immunogenicity and molecular interactions.

Discordant results among MHC binding affinity prediction tools

The paper evaluates four MHC binding affinity prediction tools across different peptide sources and alleles, revealing inconsistencies in their predictions and training data quality.

Predicting Immune Escape with Pretrained Protein Language Model Embeddings

The paper predicts immune escape by using pretrained protein language model embeddings, showing they outperform zero-shot methods but are not superior to simple baselines.

T-cell receptor specific protein language model for prediction and interpretation of epitope binding (ProtLM.TCR)

The paper introduces ProtLM.TCR, a TCR-specific language model for predicting TCR-epitope binding and shows it outperforms BLOSUM scores and hand-crafted embeddings.

Peptide-MHC Structure Prediction With Mixed Residue and Atom Graph Neural Network

The paper introduces a graph neural network for peptide-MHC structure prediction, achieving accuracy comparable to AlphaFold 2 with significantly fewer parameters.

Paragraph - Antibody paratope prediction using Graph Neural Networks with minimal feature vectors

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

BepiPred-3.0: Improved B-cell epitope prediction using protein language models

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