Evidence map›Paper›PMID 40418389›Full record

ArticleJournal of computer-aided molecular design2025

Multi-Omics Analysis of the virulence factors and designing of next-generation multi-epitopes Vaccines against Rickettsia prowazekii: a computer-aided vaccine designing approach.

Fahad M Alshabrmi

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Article in Journal of computer-aided molecular design, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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5 · Who and what money

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1 author.

Fahad M AlshabrmiDepartment of Medical Laboratories, College of Applied Medical Sciences, Qassim University, Buraydah, 51452, Saudi Arabia. Fshbrmy@qu.edu.sa.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Rickettsia is a genus of bacteria that are obligate intracellular parasites and are responsible for the febrile diseases known collectively as Rickettsioses. The emergence of antibiotic resistance is an escalating concern and thus developing a vaccine against Rickettsia is of paramount importance due to the significant public health threat posed by these bacteria. Thus, we employed structural vaccinology guided by machine learning algorithms to explore the virulence landscape of Rickettsia prowazekii to design a multi-epitopes-based vaccine (MEVC) that is immunogenic and safe. From a pool of virulence factors, we shortlisted five targets including sca0, sca1, sca4, sca5 and tlyA that were classified as non-allergenic as well as antigenic. The immune epitopes mapping results shortlisted five CTL epitopes, five HTL (IFN+) epitopes and five B cell epitopes as the best choice to design a vaccine construct of 475 amino acids. Various parameters were used to validate the designed MEVC which involved prediction of physiochemical properties, modeling and validation of the 3D structure, interaction with the immune receptors such as TLR2 (Toll-like receptor) and TLR4. Moreover, all-atoms simulation and binding free energy (BFE) results revealed a stable and favorable dynamic properties determined by these complexes. Jcat revealed that the improved sequence exhibits a GC content of 48.14% and a CAI (Codon Adaptation Index) value of 1.0. We used a multi-dose criterion at different time intervals i.e., 1st, 84th and 170th day to understand the immune potential of our constructed vaccine. The results provide a comprehensive overview of immune factors that ensure effective antigen memory cells generation after each injection, as predicted by the in silico pipeline. However, limitations in current algorithms particularly their inability to fully account for HLA polymorphism and the lack of experimental and clinical validation remain major shortcomings of the study. These issues should be addressed in future research to support the development of a robust immune response against Rickettsia infections.

Indexed as

Bacterial VaccinesComputer-Aided DesignEpitopesRickettsia InfectionsRickettsial VaccinesRickettsia prowazekiiVirulence FactorsEpitopes, B-LymphocyteEpitopes, T-LymphocyteHumansMachine LearningMolecular Docking SimulationMultiomicsBacterial VaccinesEpitopesEpitopes, B-LymphocyteEpitopes, T-LymphocyteRickettsial VaccinesVirulence FactorsImmune receptorsImmune simulationRickettsia prowazekiiVaccineVirulence factors

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.