Background: Multi-epitope vaccines have become the preferred strategy for protection against infectious diseases by integrating multiple MHC-restricted T-cell and B-cell epitopes that elicit both humoral and cellular immune responses against pathogens. Computational methods address various aspects independently, yet their orchestration is technically challenging, as most bioinformatics tools are accessible through heterogeneous interfaces and lack interoperability features. The present work proposes a novel framework for rationalized multi-epitope vaccine design that streamlines end-to-end analyses through an integrated web-based environment. Results: VaccineDesigner is a comprehensive web-based framework that streamlines the design of protective epitope-based vaccines by seamlessly integrating computational methods for B-cell, CTL, and HTL epitope prediction. VaccineDesigner incorporates single-epitope prediction and evaluation as well as additional analyses, such as multi-epitope vaccine generation, estimation of population coverage, molecular mimicry, and proteasome cleavage. The functionalities are transparently integrated into a modular architecture, providing a single access point for rationalized, multi-epitope vaccine generation in a time- and cost-effective manner. Conclusions: VaccineDesigner is a web-based tool that identifies and evaluates candidate B-cell, CTL, and HTL epitopes and constructs a library of multi-epitope vaccines that combine strong immunogenic responses, safety, and broad population coverage. The source code is available under the academic license and freely accessible.

Background: Multi-epitope vaccines have become the preferred strategy for protection against infectious diseases by integrating multiple MHC-restricted T-cell and B-cell epitopes that elicit both humoral and cellular immune responses against pathogens. Computational methods address various aspects independently, yet their orchestration is technically challenging as most bioinformatics tools are accessible through heterogeneous interfaces and lack interoperability features. The present work proposes a novel framework for rationalized multi-epitope vaccine design that streamlines end-to-end analyses through an integrated Web-based environment. Results: VaccineDesigner is a comprehensive Web-based framework that streamlines the design of protective epitope-based vaccines by seamlessly integrating computational methods for B-cell, CTL, and HTL epitope prediction. VaccineDesigner incorporates single epitope prediction and evaluation as well as additional analyses, such as multi-epitope vaccine generation, estimation of population coverage, molecular mimicry, and proteasome cleavage. The functionalities are transparently integrated into a modular architecture, providing a single access point for rationalized, multi-epitope vaccine generation, time and cost-effectively. Conclusions: VaccineDesigner is a Web-based interface for developing multi-epitope vaccines. Given a protein sequence, VaccineDesigner identifies and evaluates candidate B-cell, CTL, and HTL epitopes and constructs a library of multi-epitope vaccines that combine strong immunogenic responses, safety, and broad population coverage. The source code is available under the academic license at: https://github.com/BiolApps/VaccineDesigner . VaccineDesigner is freely accessible at: http://bioinformatics.med.auth.gr/VaccineDesigner .

Background: Established risk assessment tools, such as the SYNTAX score and SYNTAX score II, have improved risk stratification in patients with coronary artery disease (CAD), but rely exclusively on clinical and angiographic information. Ongoing research suggests that genetic information may enhance outcome prediction beyond current clinical models. Objectives: This study aims to evaluate whether the addition of CAD-associated genetic variants improves the prognostic performance of established angiographic risk models for predicting all-cause mortality. Methods: Data were derived from the Genetic SYNTAX Score study (NCT03150680), involving patients with suspected obstructive CAD undergoing coronary angiography. The incremental prognostic value of incorporating a panel of CAD-related SNPs into established angiographic models for all-cause mortality prediction was assessed across different follow-up horizons. Results: 529 participants were included in the analysis. SNP integration significantly improved the model fit of all baseline models for all-cause mortality prediction (P < 0.001). SNP-augmented models also showed significant gains in discrimination and risk prediction accuracy, including improvements in Harrell’s C-index, integrated area under the curve (iAUC), continuous net reclassification improvement (NRI), and integrated discrimination improvement (IDI). Time-dependent ROC analyses demonstrated improved prognostic performance at 3- and 5-year follow-up. Five SNPs were consistently associated with mortality across all models: rs11568820 and rs9515203 were associated with increased mortality risk, whereas rs12041331, rs2252641, and rs2569190 showed protective associations. Conclusions: Incorporating genetic information into established angiographic risk assessment tools can improve mortality risk stratification in patients with CAD. However, external validation in larger, multi-center, and multi-ancestry cohorts is warranted prior to clinical implementation.