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Viruses
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Immunity in Persistent Viral Infection and AI Guided Antiviral Drug...
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Immunity in Persistent Viral Infection and AI Guided Antiviral Drug Design

A special issue of Viruses (ISSN 1999-4915). This special issue belongs to the section "Viral Immunology, Vaccines, and Antivirals".

Deadline for manuscript submissions: 30 September 2027 | Viewed by 167

Editor

Dr. Zhenlong Liu

E-Mail Website
Guest Editor
Département de Medicine, McGill University, Montréal, QC, Canada
Interests: persistent viral infection; viral immunology; virus–host interactions; innate immunity; host restriction factors; antiviral drug discovery; HIV–1; influenza virus; SARS–CoV–2; therapeutic development

Special Issue Information

Dear Colleagues,

Persistent viral infections remain a major challenge in global health because they involve complex and dynamic interactions between viruses and the host immune system. Chronic infection, immune evasion, viral latency, sustained inflammation, and incomplete viral clearance can all contribute to long-term disease progression and limit the effectiveness of current therapeutic strategies. At the same time, rapid advances in virology, immunology, and antiviral development are opening new opportunities to better understand and intervene in these processes.

This Special Issue aims to highlight recent progress in the study of immunity during persistent viral infection, with particular emphasis on innate and adaptive immune responses, virus–host interactions, immune modulation, viral persistence mechanisms, and translational strategies for antiviral intervention. In addition, we are particularly interested in the emerging role of artificial intelligence (AI) in guiding antiviral drug design. AI-driven approaches—including machine learning, deep learning, and structure-based modeling—offer powerful tools for predicting viral epitopes, optimizing lead compounds, accelerating drug repurposing, and designing novel antiviral agents that target both viral and host factors.

We welcome original research articles, reviews, and short communications covering molecular mechanisms, host restriction factors, immune signaling pathways, biomarkers of chronic infection, and novel antiviral or immune-based therapeutic approaches. Submissions that integrate AI-guided methodologies with immunological insights to combat persistent viral infections are especially encouraged.

Through this collection, we hope to provide an interdisciplinary platform for virologists, immunologists, and translational researchers to share new findings and perspectives that will advance both fundamental understanding and clinical innovation in the field.

We look forward to your valuable contributions.

Dr. Zhenlong Liu
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Viruses is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • persistent viral infection
  • viral immunology
  • virus–host interaction
  • innate immunity
  • adaptive immunity
  • immune evasion
  • viral persistence
  • host restriction factors
  • chronic infection
  • antiviral therapy
  • HIV–1
  • influenza virus
  • SARS–CoV–2

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Published Papers (1 paper)

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Research

27 pages, 4517 KB  
Article
HIV-V3Augur: A Novel Machine Learning Model for Predicting HIV-1 Tropism in Sub-Subtype A6 and CRF63_02A6, Predominant Variants in Russia and Countries of the Former Soviet Union
by Kirill Elfimov, Ludmila Gotfrid, Alina Nokhova, Mariya Gashnikova, Vasiliy Ekushov, Maksim Halikov, Irina Osipova, Dmitriy Baboshko, Andrey Murzin, Ivan Kondeikin, Arina Kiryakina, Aleksey Totmenin, Aleksandr Agaphonov and Natalya Gashnikova
Viruses 2026, 18(7), 703; https://doi.org/10.3390/v18070703 (registering DOI) - 25 Jun 2026
Abstract
Determining HIV-1 tropism provides the prognosis of HIV infection and is required before prescribing maraviroc, an entry inhibitor that blocks the interaction between the viral gp120 and the CCR5 coreceptor. However, existing prediction algorithms have been developed primarily for the globally most prevalent [...] Read more.
Determining HIV-1 tropism provides the prognosis of HIV infection and is required before prescribing maraviroc, an entry inhibitor that blocks the interaction between the viral gp120 and the CCR5 coreceptor. However, existing prediction algorithms have been developed primarily for the globally most prevalent subtypes (B, C, and CRF01_AE) and often show reduced performance for other HIV-1 genetic variants. Sub-subtype A6 and circulating recombinant form CRF63_02A6 dominate the HIV-1 epidemic in Russia and other Former Soviet Union (FSU) countries, yet the reliability of tropism prediction for these viruses remains virtually unexplored. We phenotypically determined the tropism of 25 clinical isolates (11 R5, 1 X4, and 7 dual-tropic R5/X4) using U87.CD4.CCR5 and U87.CD4.CXCR4 cell lines and performed a comparative analysis of eight existing genotypic tools (Geno2pheno, WebPSSM, T-CUP 2.0, the Delobel/Garrido rules, and others) or their modifications on a combined dataset that included Los Alamos National Laboratory (LANL) reference sequences (subtypes A, B, C, CRF01_AE, and CRF02_AG) and our laboratory-derived isolates. Most models achieved high accuracy for globally prevalent subtypes (≈95% for B, C, and CRF01_AE) but showed markedly reduced performance for sub-subtype A6 (best accuracy among existing models, 85%) and CRF63_02A6 (best accuracy, 72%), with a poor balance between sensitivity and specificity. To address this problem, we developed HIV-V3Augur, an ensemble stacking model based on the Random Forest and Support Vector Machine (SVM) machine learning algorithms, trained on Pseudo Amino Acid Composition (PseAAC) and Relative Synonymous Codon Usage (RSCU) features with 10-fold stratified cross-validation. HIV-V3Augur achieved an accuracy of 77%, sensitivity of 79%, and specificity of 79% on sub-subtype A6, and on CRF63_02A6 it reached an accuracy of 95%, sensitivity of 87%, and specificity of 100%. Cross-validation demonstrated that HIV-V3Augur represents a balanced genotypic tropism prediction tool for understudied HIV-1 variants circulating in the FSU region. HIV-V3Augur can be used locally through a graphical user interface. Full article
(This article belongs to the Special Issue Immunity in Persistent Viral Infection and AI Guided Antiviral Drug Design)
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