vaccines and immunoinformatics approaches

This study predicts an epitope-based peptide vaccine against Fructose-bisphosphate aldolase of Madurella mycetomatis using immunoinformatics tools and identifies FFKEHGVPL as a promising candidate.

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

NetMHCpan-4.0 improves peptide-MHC class I interaction predictions by integrating binding affinity and eluted ligand data, enhancing identification of naturally processed ligands and T cell epitopes.

The paper identifies HLA-I binding motifs from peptidomics data to improve neo-antigen predictions and reveals allosteric regulation of HLA specificity.

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 uses a convolutional neural network to predict HLA class I-peptide binding and identifies 15 genes with potential for self-binding.

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.