vaccines and immunoinformatics approaches

DeepBCR predicts cancer type and binding affinity of BCRs using deep learning, validated with anti-PD1 drug affinities.

The paper identifies two unique tick AQP1 peptide motifs with high immunogenic potential for peptide-based anti-tick vaccine development using in silico analysis.

The paper proposes computational methods to identify low variability pathogen targets for medical countermeasure development using variation analysis.

HABIT is a webserver that uses machine learning to predict and analyze T cell epitopes for neoepitope discovery in immunotherapy.

This paper designs a multi-epitope peptide vaccine against Cryptococcus neoformans var. grubii heat shock 70KDa protein using computational vaccinology methods, identifying ten epitopes with high population coverage and immunogenic potential.

Vaxrank selects neoantigen vaccine peptides from tumor mutations, RNA data, and HLA type to design personalized cancer vaccines.

The paper designs a peptide vaccine against Madurella mycetomatis TCTP using immunoinformatics tools, identifying epitopes that bind to MHC I and II alleles with high population coverage.

The paper designs an epitope-based peptide vaccine against Cryptococcus neoformans using in silico simulations and identifies conserved B- and T-cell epitopes with high population coverage.

A sequence-only convolutional network trained on T cell receptor (TCR)-peptide pairs for HLA-A*02:01 can single out TCRs that bind a target peptide among many non-binding ones.

The paper proposes a protocol for designing country-specific peptide vaccines against Nipah and Hendra viruses by analyzing epitope differences in surface proteins across affected regions.

The paper presents a TensorFlow-based deep learning model for MHC class I peptide binding prediction, along with code, benchmark datasets, and a Snakemake workflow for parallel analysis.

AI-MHC improves Class I and Class II HLA-binding predictions using a deep learning framework that integrates allele information and achieves state-of-the-art performance.

DeepMHC uses deep convolutional neural networks to predict MHC-I peptide binding affinity, outperforming conventional methods without requiring additional features.

This study evaluates machine learning methods for predicting peptide-MHC Class I binding affinities, finding that MHCnuggets performs similarly to existing methods but is faster and handles variable-length peptides.

The paper predicts protein binding affinity using recurrent neural networks and word embeddings for drug discovery.

This study predicts B-cell and T-cell epitopes of PPRV nucleoprotein using in silico tools and analyzes their 3D structure to identify potential diagnostic targets for PPRV.