vaccines and immunoinformatics approaches

Neural Network Models for Sequence-Based TCR and HLA Association Prediction

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.

NeoHeadHunter: an algorithm for the detection, ranking and probabilistic classification of neoepitope candidates

NeoHeadHunter detects, ranks, and probabilistically classifies neoepitope candidates, outperforming existing methods in detection, ranking, and probability estimation.

De novo generation of T-cell receptors with desired epitope-binding property by leveraging a pre-trained large language model

The paper introduces ERTransformer, a large language model that generates T-cell receptors with desired epitope-binding properties, showing high functional similarity to natural TCRs.

De novo generation of antibody CDRH3 with a pre-trained generative large language model

This paper introduces PALM, a pre-trained generative large language model for de novo antibody CDRH3 design, and A2binder for predicting antigen-antibody binding, showing effectiveness against SARS-CoV-2 variants.

A novel approach to identify cross-identity peptides between Epstein-Barr virus and central nervous system proteins in Guillain-Barre syndrome and multiple sclerosis

The paper identifies cross-identity peptides between Epstein-Barr virus and central nervous system proteins using computational methods, suggesting molecular mimicry and potential autoantigen cross-reactivity.

Determining epitope specificity of T-cell receptors with transformers

This paper uses transformer models to predict T-cell receptor epitope specificity, finding that modified ProtXLNet achieves the highest accuracy among tested models.

HLA3DB: comprehensive annotation of peptide/HLA complexes enables blind structure prediction of T cell epitopes

This paper introduces HLA3DB, a curated database of peptide/HLA complexes, and presents RepPred, a method for predicting T cell epitope structures with improved accuracy.

Validation of an antigenic site targeted by monoclonal antibodies against Puumala virus

The paper identifies a critical epitope on PUUV Gc targeted by neutralizing antibodies and shows that mutations in this region confer neutralization escape.

The pitfalls of negative data bias for the T-cell epitope specificity challenge

The paper shows that biased negative data in TCR-epitope binding prediction leads to poor performance in realistic scenarios.

Reply to: The pitfalls of negative data bias for the T-cell epitope specificity challenge

The paper clarifies that improper negative data sampling strategies can bias TCR-antigen prediction models like PanPep and advocates for consistent comparison methods.

SABRE: Self-Attention Based model for predicting T-cell Receptor Epitope Specificity

SABRE predicts TCR-antigen recognition using a self-attention model trained on curated databases and experimental data, achieving an AUROC of 0.726.

POP-UP TCR: Prediction of Previously Unseen Paired TCR-pMHC

The paper introduces POP-UP TCR, a machine learning model predicting TCR-peptide binding for unseen sequences, and finds lower binding scores for SARS-CoV-2 epitopes in severe COVID-19 patients.

Multiepitope-based vaccine design against DiiA in Streptococcus pneumoniae, An immunoinformatics approach.

This paper designs a multi-epitope vaccine against Streptococcus pneumoniae using immunoinformatics tools to predict and validate epitopes and their interactions with TLR4.

An Integrated Comparative Genomics, Subtractive Proteomics and Immunoinformatics Framework for the Rational Design of a Pan-Salmonella Multi-Epitope Vaccine

This study designs a pan-Salmonella multi-epitope vaccine using subtractive proteomics and immunoinformatics, identifying CsgA as a conserved vaccine candidate with strong immunogenic properties.

Improved computational epitope profiling using structural models identifies a broader diversity of antibodies that bind the same epitope

SPACE2 improves epitope clustering by using structural models, showing higher coverage and diversity in antibodies binding the same epitope compared to sequence-based methods.

RapTCR: Rapid exploration and visualization of T-cell receptor repertoires

RapTCR provides a tool for rapid visualization and analysis of T-cell receptor repertoires, capturing both global and local sequence similarities.

Learning the differences: a transfer-learning approach to predict antigen immunogenicity and T-cell receptor specificity

This paper introduces diffRBM, a transfer-learning model using Restricted Boltzmann Machines to predict antigen immunogenicity and T-cell receptor specificity, achieving performance comparable to existing methods.

Deep unsupervised learning methods for the identification and characterization of TCR specificity to Sars-Cov-2

This study uses deep unsupervised learning methods to identify and characterize TCR specificity to Sars-Cov-2, achieving promising results in understanding TCR patterns.

Improved Predictions Of MHC-Peptide Binding Using Protein Language Models

The paper fine-tunes protein language models on peptide-MHC sequences to predict MHC-peptide binding, outperforming the existing NetMHCpan4.1 method.

Large-scale template-based structural modeling of T-cell receptors with known antigen specificity reveals complementarity features.

The paper uses template-based modeling to generate TCR:antigen:MHC structures and identifies amino acid contact features that influence antigen recognition.

Design of multi-epitope vaccine candidate against Brucella type IV secretion system (T4SS)

The paper designs a multi-epitope vaccine against Brucella T4SS by identifying and combining T and B cell epitopes from VirB8 and VirB10 using bioinformatics and molecular simulations.

Neo-epitope identification by weakly-supervised peptide-TCR binding prediction

This study introduces TCRBagger, a weakly-supervised framework for peptide-TCR binding prediction to improve neo-epitope identification in tumor immunotherapy.

Investigating the human and non-obese diabetic mouse MHC class II immunopeptidome using protein language modelling

The paper trains a transformer model to predict MHCII peptide presentation in humans and mice, showing improved performance with multi-organism data and enabling in silico prediction in a type 1 diabetes model.

Deep Neural Networks Predict MHC-I Epitope Presentation and Transfer Learn Neoepitope Immunogenicity

The paper introduces BigMHC, a deep learning model that predicts MHC-I epitopes and immunogenic neoepitopes with high accuracy, outperforming existing methods.

Proteasomal cleavage prediction: state-of-the-art and future directions

This paper reviews proteasomal cleavage prediction methods, benchmarks deep learning architectures, and finds that dataset quality limits performance more than model choice.

DeepTAP: an RNN-based method of TAP-binding peptide prediction in the selection of tumor neoantigens

DeepTAP predicts TAP-binding peptides using an RNN, outperforming existing methods on benchmark datasets.

Interpretable deep learning to uncover the molecular binding patterns determining TCR-epitope interactions

This study uses interpretable deep learning to analyze TCR-epitope interactions and identify molecular binding patterns that influence TCR affinity.

Accelerating Cancer Vaccine Development for Human T-Lymphotropic Virus (HTLV) Using a High-Throughput Molecular Dynamics Approach

This study designs a multi-epitope vaccine against HTLV using molecular dynamics and docking analyses, showing strong TLR binding and stability.

Integration of HLA-DR linkagedisequilibrium to MHC class II predictions

The paper introduces HLAAssoc-1.0 to infer HLA-DRB3/4/5 alleles from HLA-DRB1 data, improving allele and motif deconvolutions in immunopetidomics and training of binding prediction models.

Benchmark of tools for in silico prediction of MHC class I and class II genotypes from NGS data

The paper benchmarks 13 HLA genotyping tools on three datasets to identify the most accurate methods and provides consensus predictions for TCGA samples.