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Advances in predicting T cell epitope recognition for cancer immunotherapy.

T cell recognition of malignant cells is central to cancer immunotherapy. This process is elicited by interactions between T cell receptors (TCRs) and antigenic peptides displayed on major histocompatibility complex molecules. Sequencing technologies enable characterization of genomic, transcriptomic and epigenetic alterations that can give rise to epitopes in cancer cells, alongside TCR repertoire profiling in T cells. An important challenge is to determine which peptides are recognized by T cells and which TCRs mediate this recognition. This Perspective highlights how technological and compu

α-Synuclein-Targeted Immunotherapies in Parkinson's Disease: In Silico, In Vitro and Clinical Perspectives.

α-synuclein (α-syn) aggregation in dopaminergic neurons is a central event in Parkinson's disease (PD) pathogenesis. Immunotherapeutic strategies targeting α-syn, including passive and active approaches, aim to inhibit aggregation, propagation, and toxicity of pathological species while promoting their clearance via immune mechanisms. This review summarizes α-syn directed immunotherapies evaluated in in silico, in vitro, and in vivo models, as well as early phase clinical trials, focusing on how epitope selection and antibody formats influence efficacy, safety, and target engagement. Data on m

Machine Learning for TCR Repertoire Epitope Annotation and Pattern Discovery.

T cells are central to adaptive immunity, recognizing antigenic peptides, called epitopes, via the T cell receptor (TCR). The immense diversity and cross-reactivity of the TCR repertoire makes direct interpretation of antigen specificity from repertoire sequencing challenging. High-throughput sequencing enables large-scale profiling of TCRs but does not directly reveal their target epitopes, requiring computational approaches to bridge this gap. This review outlines two complementary strategies, bottom-up and top-down approaches, to annotate TCR specificity. Bottom-up methods predict TCR-epito

Adaptive bandwidth of immunity: a systems-level framework for understanding immune regulation across time.

Immune-mediated diseases display substantial variability in clinical expression, treatment durability, relapse timing, and long-term trajectories that are not always fully explained by genetic predisposition, molecular pathways, or pharmacological exposure alone. Increasing observations from systems immunology suggest that immune behavior emerges through interactions across molecular, environmental, and temporal dimensions. This article introduces Adaptive Bandwidth of Immunity (ABI) as a conceptual systems-level framework describing the functional range within which immune regulatory networks

Integrative agent-based modeling of cutaneous lupus: single-cell benchmarking and in silico evaluation of B-cell therapies.

Agent-based models (ABMs) offer a powerful framework for representing emergent immune dynamics, yet their integration with single-cell datasets and therapeutics simulation remains limited. Here, we presented an integrative, single-cell informed systems immunology modeling framework built on universal immune system simulator (UISS) to mechanistically represent autoimmune skin inflammation specifically systemic and cutaneous lupus erythematosus. The framework incorporates (i) data-driven initialization from lesion single-cell RNA sequence cell fractions, (ii) modular immune processes coupling ty

In Silico Analysis of Functional Overlap and Complementarity of HLA-DQ5 and DQ9 in Gliadin Presentation: Expanding the Immunopeptidomic Landscape of Celiac Disease.

Celiac disease (CD) is classically associated with the HLA-DQ2.5 and HLA-DQ8 heterodimers; however, a clinically relevant subset of patients fulfilling diagnostic criteria lacks these canonical risk HLA-DQ molecules, indicating the presence of alternative antigen-presentation pathways. Among potential contributors, HLA-DQ5 and HLA-DQ9 remain poorly characterised at the immunopeptidomic level. In this study, we applied an integrated in silico framework to systematically investigate the gliadin-derived peptide repertoire presented by canonical and non-canonical HLA-DQ molecules. Peptides derived

Advance in strategies to build efficient vaccines against tuberculosis.

Tuberculosis is a chronic consumptive infectious disease, which can cause great damage to human and animal health all over the world. The emergence of multi-drug resistant strains, the unstable protective effect of Bacillus Calmette-Guérin (BCG) vaccine on adults, and the mixed infection with HIV all warn people to exploit new approaches for conquering tuberculosis. At present, there has been significant progress in developing tuberculosis vaccines, such as improved BCG vaccine, subunit vaccine, DNA vaccine, live attenuated vaccine and inactivated vaccine. Among these candidate vaccines, there

Proteome conserved multi-epitope-based vaccine construct against Nipah virus.

Nipah virus (NiV) is a highly pathogenic virus with no available FDA-approved medication. Herein, immunoinformatic-based approaches may serve as a crucial method for rapid development of vaccine targeting the emerging strains of the NiV. The study utilizes immunoinformatic-based computational methodology to design multi-epitope subunit vaccine by deciphering highly conserved antigenic epitopes across NiV strains. The NiV whole proteome (UP000002330) was subjected to shortlist immune epitopes based on high antigenic scores, non-allergenic status, broad HLA population coverage, and 100% conserva

A comprehensive review on artificial intelligence driven approaches for vaccine development: Current advances, challenges, and future prospects.

Artificial intelligence (AI) has emerged as an exemplified tool in the field of modern biomedical technology. The unprecedent COVID-19 pandemic and other infectious disease crises have highlighted the critical need for rapid and accurate vaccine development processes. The traditional method of vaccine development methods are often time-consuming, costly, and inefficient. On contrary to this, the AI streamlines vaccine development from antigen prediction to clinical trial optimization by integrating computational biology, machine learning, structural bioinformatics, and immunoinformatic. AI has

A one-health approach to design an mRNA-based vaccine candidate against the lumpy skin disease virus as an alternative to live-attenuated vaccines.

OBJECTIVE: Recently, lumpy skin disease (LSD) has been spread over the Asian, European, and Middle Eastern regions making it a significant hazard to the chain of cattle production, milk production, and human milk consumption, requiring prompt attention. Lumpy skin disease virus has high morbidity and low fatality rates, but its infections have led to terrible economic and agricultural consequences. Although live-attenuated vaccines have been commercialized, farmers in different regions have not taken them well because of the allergic responses against the vaccines. The study aims to develop an

A stochastic agent-based model for simulating tumor-immune dynamics and evaluating therapeutic strategies.

Tumor-immune interactions are central to understanding cancer progression and treatment outcomes. In this study, we present a stochastic agent-based model that integrates cellular heterogeneity, spatial cell-cell interactions, and the evolution of drug resistance to simulate tumor growth and the immune response in a two-dimensional microenvironment. This model captures the dynamic behaviors of four major cell types, specifically tumor cells, cytotoxic T lymphocytes, helper T cells, and regulatory T cells, and incorporates key biological processes, including proliferation, apoptosis, migration,

Prediction of drug hypersensitivity by comprehensive modeling of HLA-peptidomes.

Human leukocyte antigen (HLA)-B*57:01 associated with abacavir-induced hypersensitivity syndrome (ABC-HSS) is one of the most extensively studied immune-mediated drug hypersensitivity reactions (DHRs). The high odds ratio and strong predictive values of HLA-B*57:01 for ABC-HSS have prompted the Food and Drug Administration and European Medicines Agency to require genetic testing before abacavir treatment. Abacavir binds to HLA-B*57:01 and alters the repertoire of presented peptides, resulting in the activation of autoimmunity. Previous studies employing computational approaches to investigate

Mapping the Altered Landscape of TCR Repertoire in Autoimmune Diseases.

Autoimmune diseases arise from the breakdown of immune tolerance through complex interactions between genetic predisposition, environmental exposures, and adaptive immune responses. High-throughput T-cell receptor (TCR) repertoire sequencing has transformed our ability to characterize these responses, providing unprecedented insights into clonal dynamics, antigen-driven selection, and immune history. In this review, we summarize the major alterations of TCR repertoires reported across autoimmune diseases, including changes in diversity, clonal expansion, repertoire architecture, and tissue dis

Leptospira-host interactions: advancing next-generation vaccines and diagnostics.

SUMMARYLeptospirosis, a widespread zoonotic disease caused by pathogenic Leptospira species, remains a major public health challenge, partic … , and highlight how emerging technologies, including pan-genomics, proteomics, reverse vaccinology, immunoinformatics, and omics-based antigen discovery, are facilitating the development of next-generation vaccines and diagnostics. Finally, we outline major translational challenges and future perspectives for improving clinical management, surveillance, and prevent …

Всеобъемлющий обзор существующих методов диагностики для обнаружения вируса обезьяноксина.

Mopkeypox (Mpox) — инфекционная болезнь, вызванная вирусом Monkeypox (MPXV), первоначально ограничившийся Центральной и Западной Африкой, но распространяющийся в настоящее время во всем мире. Клинические проявления часто нетипичны в нынешней вспышке Mpox в дополнение к критическим трудностям в машинописи MPXV и трудности с надёжным разграничением между клещами. Поэтому диагностика Mpox, основанная только на клинических признаках и симптомах, может быть сложной задачей. Текущее лечение не является специфичным для MPXV и в первую очередь связано с оказанием поддержки и противовирусными препарата

Совпадение в аннотации оригинала
Monkeypox (Mpox) is an infectious disease caused by the Monkeypox virus (MPXV), initially confined to Central and Western Africa, but now sp … e global diagnostic capabilities, including the potential roles of computational drug discovery and immunoinformatics in designing multi-epitope vaccines targeting MPXV and its variants. The most effective measure to control MPXV spread remains vaccination, timely diagnosis, isolation of infected individuals, maintaining personal hygiene, and avoiding contact with …

Design, evaluation, and immune simulation of potentially universal multi-epitope mpox vaccine candidate: focus on DNA vaccine.

Monkeypox (mpox) is a zoonotic infectious disease caused by the mpox virus. Mpox symptoms are similar to smallpox with less severity and low … ndidate against mpox based on conserved epitopes was designed and its efficacy was evaluated via an immunoinformatics approach. The vaccine candidate demonstrated potent humoral and cellular immune responses in silico, indicating the potential efficacy in vivo and the need for further research.

Improving B-cell Linear Epitope Prediction via Multiple Feature Fusion and an Integrated Machine Learning Algorithm.

INTRODUCTION: The identification of linear B-Cell epitopes (BCEs) is significantly important for the discovery of drugs, such as antibody production, peptide-based vaccines, and other therapeutics. MATERIALS AND METHODS: Unlike traditional laboratory-based methods, computational techniques can save cost and time in predicting large-scale BCEs. For this purpose, numerous in-silico methods have been designed to enhance the overall efficacy of BCE prediction. However, research gaps exist for further improvement in the context of using novel feature representations and learning models for BCE pre

pHLA-Bi-Mamba: A pan-specific deep learning model for peptide-HLA-Ⅰ binding affinity and eluted ligand prediction.

Human leukocyte antigen class I (HLA-I) molecules are central to immunosurveillance, and accurate prediction of peptide-HLA-I binding is cri … balanced datasets. The source code and curated datasets are available at GitHub: https://github.com/ImmunoInformatics-dev/pHLA-Bi-Mamba.

DUET: a graph-based workflow for TCR-epitope prioritization and tumor-reactive T-cell identification.

Accurate prioritization of T-cell receptor (TCR)-epitope interactions and identification of tumor-reactive T cells are important but difficult steps in immunotherapy-oriented bioinformatics workflows. Existing methods typically address these tasks separately and either model TCR-epitope pairs as independent observations or rely primarily on transcriptomic signatures. In this study, we present DUET (Dual Unified Evaluation of TCR-Epitopes and Tumor-reactive T cells), a graph-based computational workflow that unifies both applications within a single heterogeneous graph framework. The protocol r

Multiepitope mRNA Vaccine mRNA-mEp21-FL-IDT Provides Efficient Protection against M. tuberculosis.

Tuberculosis is a leading cause of death from a bacterial infection agent. The development of new tuberculosis vaccines can reduce the number of new cases and tuberculosis-related deaths. One of the most promising areas in vaccination is development of mRNA vaccines, which have already proven their high effectiveness against COVID-19 and other viral infections. Using modern immunoinformatic methods, we developed four new antituberculosis multiepitope mRNA vaccines differing in the encoded adjuvants and codon composition and tested their immunogenicity and protectivity in mice. Most of the deve