vaccines and immunoinformatics approaches

This paper introduces a Bayesian model to identify vaccine-specific BCR sequences by analyzing B cell receptor repertoires across time and individuals, achieving 69% sensitivity.

The paper identifies a highly conserved B-cell epitope in ZIKV envelope protein using immunoinformatic methods and structural analysis.

The paper predicts T cell and B cell immunogenic epitopes of SARS-CoV-2 using genomic data and compares them with SARS-CoV and MERS-CoV to identify immune vulnerability patterns.

The paper identifies novel drug targets and vaccine candidates for Legionella pneumophila using subtractive proteomics and reverse vaccinology approaches.

This study designs a heterologous recombinant vaccine against monkeypox virus by analyzing surface glycoproteins and poxin-schlafen protein to identify immunogenic epitopes.

This study designs three potential subunit vaccines against HSV-1 using reverse vaccinology and bioinformatics, selecting three antigenic glycoproteins and epitopes, and identifies one optimal vaccine construct through molecular docking and simulation.

pVACtools provides a suite of tools for neoantigen prediction, prioritization, and vaccine design in cancer immunotherapy.

This study predicts potential peptide-based vaccines against COVID-19 using the Nucleocapsid and Spike proteins through immunoinformatics tools, identifying peptides with high MHC binding affinity and safety.

The paper introduces INeo-Epp, a method for predicting T-cell HLA class-I immunogenic epitopes and neoantigens using sequence-related amino acid features, achieving AUC scores of 0.81 and 0.78.

This study identifies potential epitopes for an epitope-based vaccine against Pseudomonas aeruginosa FBA using immunoinformatics tools.

This study identifies a potential Zika virus vaccine candidate through immunoinformatics analysis of the viral proteome, predicting an epitope cluster with broad population coverage.

This study designs a multi-epitope peptide vaccine against the E protein of SARS-CoV-2 using immunoinformatics, identifying ten peptides with high population coverage for potential vaccine development.

This study predicts epitope-based peptides for a Nipah virus glycoprotein G vaccine using immunoinformatics tools and docking, identifying FLIDRINWI as a promising candidate.

The paper designs a heterosubtypic vaccine candidate against Arenaviridae by identifying conserved epitopes from nucleoproteins of multiple viruses and evaluating their immunogenicity and compatibility for expression.

This paper identifies antigenic peptides from HIV proteins p10, p21, and p51 using in-silico methods for potential multi-peptide vaccine design.

The paper introduces an R package for predicting tumor-specific neoantigens from various mutations and integrating additional biological information.

This paper uses immunoinformatics to predict epitopes in TCTP for a peptide vaccine against Schistosoma japonicum, identifying YEHYIGESM as a promising candidate shared between MHC I and II alleles.

The paper benchmarks PEPOP version 2.0 methods for predicting peptides mimicking discontinuous epitopes, finding most outperform SUPERFICIAL and chance.

TCRex identifies epitope-specific T cell receptor sequences in repertoires using machine learning and statistical analysis, validated on vaccine and infectious disease datasets.

The paper identifies and ranks recurrent neo-epitopes in cancer using TCGA data and MHC class I binding prediction to prioritize candidates for immunotherapy.

This study designs a multi-epitope vaccine against Elizabethkingia anophelis using immunoinformatic methods and dynamic simulations, showing high immunogenicity and stability.

The paper introduces DeepAttentionPan, a deep learning model for MHC-I peptide binding prediction that uses attention mechanisms to improve interpretability and performance.

This paper designs a multi-epitope subunit vaccine against Hantavirus using immunoinformatics methods and validates its potential through computational simulations.

This study predicts epitopes for a peptide vaccine against Aichi virus using immunoinformatic methods, identifying conserved regions and epitopes with high MHC binding affinity and global coverage.

NNAlign_MA deconvolves poly-specific MHC peptidome data to accurately characterize binding motifs and improve T cell epitope predictions.

This study predicts four epitope peptides and one B-cell peptide for a tuberculosis vaccine using immunoinformatics methods.

This paper predicts epitope-based peptide vaccine candidates against Schistosoma Mansoni FBA protein using immunoinformatics methods, identifying epitopes with high binding affinity for B-cells and MHC classes I and II.

The paper introduces K-TOPE, a method to identify antibody-binding epitopes across whole proteomes, successfully recovering known epitopes and discovering common viral and bacterial epitopes.

This paper predicts multi-epitope peptides for a Candida albicans vaccine targeting pyruvate kinase using immunoinformatics tools.

This study designs a multi-epitope T vaccine targeting TPX2 in HCC using immunoinformatics tools, showing stability, immunogenicity, and potential for TLR4 activation.