Design and in silico evaluation of an mRNA vaccine against HTLV-1 using AI-driven reverse vaccinology approaches
Nadia Seifi, Navid Nezafat, Mohammad Soroosh Hajizade, Manica Negahdaripour
Abstract
Human T-lymphotropic virus type 1 (HTLV-1) is the first discovered human oncogenic retrovirus that can cause adult T-cell leukemia/lymphoma, HTLV-1-associated myelopathy/tropical spastic paraparesis, and several other diseases. Due to the poor prognosis of these diseases and the limited therapeutic modalities, the need for an HTLV-1 vaccine is crucial. The current study has used an artificial intelligence-driven reverse vaccinology approach to design an mRNA vaccine against HTLV-1.
Introduction
Human T-lymphotropic virus-1 (HTLV-1) is an important human oncogenic agent, first isolated from relatively mature T-cells of some American patients with human lymphoma and leukaemia [1]. Clinically, HTLV-1 is the most important virus of its family and was the first proven case of a cancer-causing pathogen [2]. The virus is a single-stranded and enveloped RNA retrovirus known as a human type-C RNA tumour virus [1,2].
Methods
Retrieval of viral protein sequences
The amino acid sequences of HTLV-1 target proteins (Taxonomy ID: 11908 NCBI), including 1) TAX, 2) HBZ, 3) gp21, 4) Gag (p15, p19, p24), 5) gp62, 6) p27, 7) pol, and 8) gag-pro-pol, were retrieved in FASTA format from the UniProt database (UniProtKB) (https://www.uniprot.org/uniprotkb) [18].
Results
Retrieval of viral protein sequences
The eight protein sequences of the HTLV-1 virus were retrieved from the UniProt database. Table 1 contains the accession number and length of each protein, and their sequences are presented in S1 Table.
Discussion
HTLV-1 can cause several diseases with poor prognosis, while efficacious therapeutic choices are limited [65]. Vaccination is an effective and safe strategy to protect against infectious diseases for years, decades, or even a lifetime [17]. In this study, an mRNA vaccine against HTLV-1 was designed in silico using “reverse vaccinology” approaches [66] by multiple computational and ML- and AI-based algorithms.
Conclusion
An mRNA vaccine candidate containing a TLR4 agonist adjuvant and PADRE epitope was designed against HTLV-1 using AI- and ML-driven reverse vaccinology approaches. Different performed analyses predicted its potential immunogenicity, physicochemical, and immunogenic characteristics after translation into protein in the body, as well as solid interactions between the vaccine and TLR4/MD2. However, further in vitro and in vivo experimental research is needed to confirm the efficacy of our mRNA vaccine candidate against HTLV-1.
Citation: Seifi N, Nezafat N, Hajizade MS, Negahdaripour M (2026) Design and in silico evaluation of an mRNA vaccine against HTLV-1 using AI-driven reverse vaccinology approaches. PLoS One 21(5): e0340201. https://doi.org/10.1371/journal.pone.0340201
Editor: Sheikh Arslan Sehgal, Cholistan University of Veterinary and Animal Sciences, PAKISTAN
Received: January 6, 2025; Accepted: December 14, 2025; Published: May 6, 2026
Copyright: © 2026 Seifi et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: All the data used in the study are included and explained in the manuscript.
Funding: This work was supported by the Vice-Chancellor for Research, Shiraz University of Medical Sciences, Iran (Grant number: 28569).
Competing interests: The authors have declared that no competing interests exist.
