Research Appraisals
Evidence-based critical appraisals of the latest medical research, systematically evaluated using Oxford CEBM methodology.
Showing 4 appraisals
Journal of chemical information and modeling
HLABrew for Human Leukocyte Antigen Class I-Presented Epitope Recognition and Mimotope Discovery
Accurate identification of human leukocyte antigen (HLA) class I-presented epitopes is essential for developing personalized vaccines and immunotherapies. Here, we present HLABrew, a unified deep learning framework for HLA class I epitope prediction and allele-specific mimotope design. By integrating the Transformer, variational autoencoder, and dual cross-attention, HLABrew achieves state-of-the-art performance with hierarchical and allele-aware representations. HLABrew accurately deconvolves multiallelic immunopeptidomics data to recover allele-specific binding motifs and expand epitope coverage. Leveraging its generative capacity, HLABrew further designs mimotopes guided by learned peptide-HLA binding preferences, with the potential to enhance antigen presentation and provide candidates for downstream immunogenicity evaluation.
13 May 2026
Read appraisal →Journal of proteome research
NetMHCIIphosPan: A Machine Learning Tool for Predicting HLA Class II Antigen Presentation of Phosphorylated Peptides
Phosphorylated peptides presented by human leukocyte antigen (HLA) class II molecules play pivotal roles in immune regulation, yet their characterization and prediction remain challenging due to data noise and limited HLA coverage. Here, we introduce NetMHCIIphosPan, a prediction method for HLA-II antigen presentation of phosphorylated peptides, developed using mass spectrometry (MS)-based immunopeptidomics data sets. Employing a refined peptide identification workflow, we reanalyzed earlier HLA-II phospholigand data sets and trained predictive models, achieving superior performance compared to models trained on the original data. Binding motif analysis revealed that HLA-specific preferences for phospholigands closely aligned with those of unmodified ligands. Incorporating unmodified ligands into training further enhanced predictive accuracy, particularly for HLA-DP and HLA-DQ molecules. NetMHCIIphosPan outperformed existing tools, such as NetMHCIIpan-4.3 and MixMHC2pred-1.3, for prediction of HLA antigen presentation of phosphorylated peptides, demonstrating robustness and utility. This work establishes NetMHCIIphosPan as a state-of-the-art tool for understanding the HLA-II phospholigandome, with potential applications in immunotherapy and vaccine design.
4 May 2026
Read appraisal →PLoS computational biology
MsgaBpred: A B-cell epitope predictor integrating AlphaFold3-predicted structures with multi-scale GCNs and pre-trained language model ESM-C
Accurate prediction of B-cell epitopes plays a key role in facilitating advancements in vaccines, therapeutics, and diagnostics. In contrast to labor-intensive experimental approaches, computational strategies provide a more economical and efficient means of identifying potential epitopes. Existing methods are often limited by their reliance on experimentally resolved protein structures or by the use of lower-accuracy predicted structures. Sequence-based approaches, while fast, largely fail to capture the 3D spatial context essential for conformational epitopes. With the breakthroughs achieved by AlphaFold3 in predicting protein structures, we present MsgaBpred, the model to apply AlphaFold3-derived structures to B-cell epitope identification. Given only a protein sequence, our model employs a multi-scale graph convolutional network and additive attention to capture complex structural dependencies without relying on experimentally determined structures. The multi-scale design allows for effective modeling of both local and global contexts by aggregating information across different neighborhood ranges. Additionally, we leverage ESM-C, a more expressive protein language model than ESM-2, to enhance feature representation for B-cell epitope prediction. Extensive evaluations across multiple benchmark datasets demonstrate that MsgaBpred achieves competitive and robust performance; notably, it yields a statistically significant improvement in AUC compared to existing state-of-the-art methods. Moreover, the modular and scalable architecture of MsgaBpred holds promise for broader applications, including the structural analysis of other biomolecular entities such as nucleic acids and carbohydrates.
3 May 2026
Read appraisal →Vaccine
Applied immunoinformatics in modern vaccine design: a comprehensive review of available computational tools
The rapid evolution of computational biology has transformed vaccine design from a largely empirical process into a rational, data-driven discipline. At the center of this transformation lies immunoinformatics, which integrates immunology, molecular biology, and data science to accelerate the identification, evaluation, and optimization of vaccine candidates. By enabling the systematic transition from genomic data to viable immunotherapeutic candidates, these approaches offer an unprecedented capacity to shorten development timelines, reduce costs, and broaden population coverage compared to traditional trial-and-error vaccine pipelines. This review provides a state-of-the-art overview of the currently available immunoinformatic tool that underpins modern vaccine design. From identification of conserved and functionally essential pathogen targets, advances in structural prediction, epitope mapping, safety assessment, and systems-level analyses of host-pathogen interactions, immunoinformatics allows researchers to design vaccines with enhanced success rate. Special emphasis is placed on the shift from purely predictive models toward generative frameworks, where artificial intelligence and deep learning now support not only the evaluation, but also the rational engineering of optimized vaccine candidates. For this review, we systematically examined more than 250 computational tools and algorithms spanning every stage of the vaccine discovery pipeline. From this extensive survey, we distilled and discussed the approaches that currently demonstrate the highest performance and practical usability, thereby providing researchers with a curated overview of some the most reliable resources available.
5 Apr 2026
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