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. 2025 Jun 23;358:199602. doi: 10.1016/j.virusres.2025.199602

A systematic review of T cell epitopes defined from the proteome of human immunodeficiency virus

Yan Ding a,1, Ling Huang a,1, Yandan Wu b,⁎, Jialai Yan c,⁎
PMCID: PMC12269980  PMID: 40562176

Highlights

  • •

    Human immunodeficiency virus (HIV) remains a global health threat, and there is no available curative therapy currently.

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    Host T cells have been proved to play a vital role in antiviral immune protection and pathology among HIV patients.

  • •

    The 321 T-cell epitope repertoires of HIV encompass the HLA polymorphisms of the main populations and subtypes in a particular geographical area, which seriously impedes the development of vaccines and specific T-cell detection systems.

  • •

    This manuscript offers a comprehensive review of T-cell epitopes covering the entire HIV proteome and global patients.

  • •

    This knowledge provides valuable insights for future studies on the development of T-cell epitope vaccines and the accurate assessment of host HIV-specific cellular immunity.

Keywords: Human immunodeficiency virus, HLA restriction, T-cell epitope

Abstract

Background

Human immunodeficiency virus (HIV) persists as a formidable and far - reaching threat without a cure. T cells are crucial for antiviral immunity and pathology in HIV patients, with specific T cell epitopes potentially key to effective therapies and HIV cure methods.

Methods

Identifying sufficient T-cell epitopes within the HIV proteome holds great significance. It can not only substantially accelerate the development of T-cell epitope-based vaccines but also enable a highly precise evaluation of the host's HIV-specific cellular immunity. This research provides an overview of functionally verified T-cell epitopes derived from HIV antigens, the human leukocyte antigen (HLA) alleles, as well as the screening and identification strategies.

Results

Totally, 239 and 82 epitopes have been verified for CD8+ T-cell and CD4+ T-cell respectively by functional experiments. The majority are presented by various HLA supertypes, such as HLA-B35, B5301, A6802 or A0201, and DRB1 molecules. Furthermore, 74 % of the epitopes for CD8+T-cell belong to Gag, Pol, as well as Nef Protein while 68 % of the CD4+ T-cell epitopes originate from Gag protein. Antigenic peptides of HIV-1 subtypes A/B/C/D/CRF01_AE account for 11.43 %, 58.26 %, 21.69 %, 4.96 %, and 3.65 %, respectively.

Conclusions

The 321 T-cell epitope repertoires of HIV encompass the HLA polymorphisms of the main populations and subtypes in a particular geographical area. These epitope catalogs provide strong support for researching therapeutic vaccines, specific T-cell detection, and the interaction mechanism between HIV and the immune system. However, the limitations of the identified T-cell epitope library, the polymorphism of HLA molecules, and the high mutation rate of HIV require more research to cover the entire HIV proteome and the comprehensive landscape of T-cell epitopes in global patients.

1. Introduction

AIDS (Acquired immunodeficiency syndrome) is resulting from infection with the human immunodeficiency virus (HIV) (Lee and Crotty, 2021). In accordance with the Joint United Nations Program on HIV/AIDS (UNAIDS) in August 2023, there are currently 39 million people with HIV/AIDS worldwide, with approximately 29.8 million individuals undergoing antiretroviral therapy by the end of 2022. Due to the large number of HIV variants and the existence of reservoirs, there is still no effective vaccine. The specific immune response against viral infections is mediated by both humoral and cellular immunity. While specific antibodies can neutralize extracellular free HIV virus, the main mechanism of immune response against intracellular HIV involves specific cellular immune suppression and elimination of infected cells (Kalams et al., 1999). Studies have proved the significant association of HIV-specific T cell responses with the decelerated progression of the disease in HIV-1 exposed seronegative individuals (Bernard et al., 1999; Kaul et al., 2000; Kaul et al., 2001),elite controllers (Pancré et al., 2007), and long-term non-progressors, specifically, CD8+ T cells, as key effector cells, are responsible for the elimination of cells infected by the virus and the production and release of cytokines. However, in the majority of individuals infected with HIV, chronic viremia develops gradually without treatment. The persistent stimulation of HIV-specific CD8+ T cells by continuously present HIV antigens causes the loss of their functional capabilities, resulting in exhaustion and further impairing the control of HIV infection (Fenwick et al., 2019). The major characteristics of CD8+ cell exhaustion include impaired proliferative capacity, diminished secretion of effector molecules(CD107, IL-2, and IFN-γ), upregulation of a series of co-inhibitory receptors (PD-1, TIM3, etc.), and the inability to form protective memory responses (Wherry, 2011; Hashimoto et al., 2018). Therefore, further in-depth research on HIV-specific T cells is not only a potential marker for monitoring disease progression and predicting antiviral treatment outcomes but also holds potential implications for novel HIV remission strategies. Identifying a large number of T cell epitopes from HIV antigens is essential for preventing and reversing the exhaustion of HIV-specific CD8+ T cells. It is of significance in enhancing their effector functions, developing therapeutic vaccines centered on HIV-specific T cells, and attaining a functional cure for AIDS.

In this review, epitopes for CD8+ and CD4+ T cells that define the function of the HIV proteome were systematically assembled. The data was sourced from English journal articles indexed in databases including PubMed, Scopus, Embase, SinoMed, and Google Scholar. The online search that was carried out most recently was on November 13, 2024. "T cell epitope" and "HIV or human immunodeficiency virus " were employed as search terms. 1638 studies were found from multiple databases and through manual exploration during the preliminary search. These articles were all imported into Endnote X9 software. Subsequently, 102 duplicate entries were eliminated. From 1990 to 2024, a total of 789 studies were collected. Subsequently, 695 articles were selected via the screening of abstracts and full-texts. The exclusion criteria are as follows: (1) lack of relevance to the screening or identification of T cell epitopes; (2) sole used for computational or structural bioinformatics analysis, without being used for cellular functional experiments, tetramer staining, binding assays or immunogenicity; (3) incomplete details about the sequences of epitopes; (4) presented by 4-digit class I allele(s). Finally, this review utilized and cited 296 articles.

2. Polymorphism of HLA alleles and association with HIV infection

HLA molecules consist of class I and class II molecules. Endogenous viral epitopes are presented to specific CD8+ T cells by Class I HLA molecules, whereas exogenous viral peptides are presented to specific CD4+ T cells by Class II HLA molecules (Elangovan et al., 2021). Naïve CD8+ T cells initiate a process of activation, proliferation, and then differentiate into cytotoxic T lymphocytes (CTLs). This transformation enables them to induce the lysis of virus-infected cells. Naïve CD4+ T cells undergo differentiation into Th1 or Th2 cells. The former facilitates the activation of virus-specific CD8+ T cells, whereas the latter promotes virus-specific B cells in antibody production (Nitschke et al., 2016). However, HLA molecules exhibit high levels of polymorphism among global populations, with different individuals carrying different HLA genotypes and presenting different antigenic peptide epitopes. This polymorphism determines the variation in immune responses to HIV among different individuals. According to the International ImMunoGeneTics information system/Human Leukocyte Antigen (IMGT/HLA) database (https://www.ebi.ac.uk/ipd/imgt/hla/), by November 2023, a total of 37,516 HLA class I and II gene loci alleles have been identified worldwide, including 8012 HLA-A, 9573 HLA-B, 7513 HLA-C, 4530 HLA-DRB, 2491 HLA-DQB1, and 2393 HLA-DPB1 alleles. Furthermore, the distribution of HLA alleles varies significantly among various racial populations and geographical regions. This diversity implies that different populations may exhibit distinct immune responses to specific pathogens (Elahi and Horton, 2012; Wang et al., 2016). Therefore, the diversity of HLA genotypes is an important aspect when studying the genetic factors influencing HIV/AIDS disease progression.

Researches have revealed associations between the polymorphisms of HLA and the onset of specific diseases (Boeijen et al., 2017; Littera et al., 2020). Despite the fact that the association between HLA alleles and HIV infection is not yet fully comprehended, past and ongoing research continues to elucidate this relationship. The HLA supertype A2 is widely recognized for its relationship with the prevention of HIV infection (Rallón et al., 2017; Li et al., 2017). The HLA-B locus exhibits the most diverse allelic genes, and HLA-B might offer greater protection against HIV- 1 compared to HLA-A (Rajapaksa et al., 2012). Specifically, B57, B27, and B7 are supertypes that are closely associated with the disease progression (Brener et al., 2015; Singh et al., 2008; Tang et al., 2010; Altfeld et al., 2003). Evidently, HIV-specific CD8+ cell response is primarily linked to HLA-B57 and is related to the slow progression of AIDS (Stephens, 2005; Kaslow et al., 1996; Dorak et al., 2003; Gao et al., 2001; Tang et al., 2002; Lobos et al., 2022). The HLA-B7 supertype is associated with high viral load and relatively poor CTL response in Caucasians and African Americans primarily infected with HIV-1 subtype B, leading to rapid disease progression. However, this association is not present in Africans infected with HIV-1 subtype C (Pereyra et al., 2010; Peterson et al., 2013). HLA-B27 has been found to be related to low viral load and delayed onset of AIDS in Caucasians infected with HIV-1 subtype B, but this association does not hold true for Africans (Gao et al., 2001; Trachtenberg et al., 2003; Flores-Villanueva et al., 2003). Despite numerous researches being conducted to explore the correlation between HIV infection and HLA allele genes, as described in the above review, contradictory results are often present, and these variations are partly caused by the HLA polymorphism of hosts from different races and regions (Matern et al., 2020; Zidi et al., 2016). Thus, further research in various regions is urgently required to reduce the result heterogeneity. Since the advancements in HLA typing techniques have strengthened the capacity to recognize the associations between HLA and the outcome of HIV disease, numerous new alleles are identified, such as HLA-B Bw4, B35, B39, B18, B08 and A29 (Kuse et al., 2021; Ramírez de Arellano et al., 2019; Zhang et al., 2018). Recently, HLA-C*03:02 and HLA-C*14 subtypes have the potential to impact viral control in cases of HIV-1 infection (Kyobe et al., 2021; Chikata et al., 2022). Reported alleles to include HLA-DQB1*03:02, HLA-DQB1*06:04 and HLA-DQB1*02:01 have been related with rapid disease progression and high risk of HIV infection, while HLA-DP1 *01:01, HLA-DP1*03:01, HLA-DRB1*01:01 and HLA-DRB1*01:02 are related with slower disease progression and a protective effect against HIV infection (Just et al., 1995; Just et al., 1996; Achord et al., 1997). Research on the correlation between HLA-C and HLA class II molecules is relatively limited. For future studies, it is necessary to include a more extensive range of HLA molecules so as to enhance the consistency and dependability of the results.

3. HIV protein

HIV is a type of retrovirus with two main subtypes, HIV-1 and HIV-2. HIV-1 can be distinguished into HIV-1 subtypes M (major), O (outlier), N (non-M, non-O), and P, with distinct global distribution patterns (Hemelaar et al., 2006; Osmanov et al., 2002). Its structure is approximately spherical, and the virus particle consists of an envelope, a capsid, and a core. HIV genome contains three structural genes, namely Gag, Pol, and Env; two regulatory genes, Tat and Rev; and four accessory genes, Nef, Vpr, Vif, and either Vpu (specific to HIV-1) or Vpx (specific to HIV-2). Proteins translated from the HIV genome play specific roles in the lifecycle of HIV. Gag gene, a highly conserved region of the HIV virus and with relatively low sequence variability among different HIV strains, has been demonstrated as an ideal target for candidate vaccines (Williamson and Rybicki, 2016). Pol gene, codes for the precursor enzymes of reverse transcriptase, integrase, and protease. Env gene, is the main target of the host humoral response, including neutralizing antibodies and Fc-dependent cell-mediated mechanisms (Wang et al., 2020). Tat protein is capable of activating T cells through mediating both classical and non-classical activation pathways within T cells (Sacks et al., 2018). Rev is expressed during the early stages of virus replication (Rausch and Le Grice, 2015). Nef exhibits high levels of expression during the course of infection. It manipulates the trafficking of multiple cell-surface proteins, aiming to disrupt the viral replication cycle (Buffalo et al., 2019). Vpr is believed to serve as a carrier for delivering antiviral molecules to viral particles and is also a potential therapeutic target for blocking HIV disease (Höhne et al., 2016). A deep understanding of the structure of HIV and the mechanism affecting immune cells is crucial for the advancement of both HIV vaccines and novel treatment modalities.

4. Strategies for screening T-cell epitopes

4.1. Selection of epitopes by bioinformatics

The development of bioinformatics has provided us with many potential applications for epitope prediction. Currently, over 30 different bioinformatics tools and databases are used for the prediction of epitopes (Sánchez-Burgos et al., 2021; Vita et al., 2025; Robinson et al., 2024). This computational prediction is rapid and convenient, requiring no expensive or highly complex instruments, and has been widely used for the identification of epitope in mycobacterium tuberculosis (Yun et al., 2024), vaccinia virus (Samantaray et al., 2025; Mamun et al., 2024), and cancer (Xu et al., 2024; T et al., 2024). Different methods or algorithms are employed by each bioinformatics tool to predict epitopes, leading to different levels of accuracy. The utilization of epitopes predicted by computer algorithms to bind to specific HLA alleles can reduce the number of subjects with diverse HLA subtypes. However, the theoretical inaccuracy of these predictions might cause some real-world epitopes to be overlooked. However, it has been reported that using multiple tools for epitope prediction can improve prediction accuracy (Bonsack et al., 2019). Furthermore, given the understanding of protective HLA allele genes in HIV controllers, bioinformatics methods can be used to select epitopes specific to protective HLA allele genes. In a recent investigation, researchers conducted an assessment of the NetMHCpan4.0 computational neural network's performance. They did this by attempting to re-identify 93 T-cell epitopes. Remarkably, it accurately predicted 88 out of the 93 experimentally mapped epitopes in 6 Ugandan individuals with HLA class I alleles who were infected with HIV-1 (Bugembe et al., 2020). Therefore, leveraging bioinformatics methods to narrow down the search space represents a highly rational and strategic approach. Due to the pre-designed program of computer, there may be some selection bias in predicting peptides. In order to reduce this bias, several methods will be selected to predict at the same time in general operation to select the best result.

There is another epitope prediction based on T cell receptor (TCR) recognition, which is receiving increasing attention due to its wide application in immunotherapy, especially in personalized cancer treatment (Garcia-Garijo et al., 2019; Gartner et al., 2021). Ogishi proposed a computational framework to simulate the thermodynamic interactions between pMHC complexes and public TCR clones to assess immunogenicity (Ogishi and Yotsuyanagi, 2019). Pham et al. used only CDR3β and peptide sequences to generate TCR-peptide predictions (Pham et al., 2023). Lin introduced a paired energy model RACER for high-throughput evaluation of TCR-peptide affinity at the immunoglobulin library level, accurately estimating the recognition rate of tumor-associated neoantigens and foreign peptides (Lin et al., 2021). Currently, structure-based methods present significant computational challenges. They attempt to predict epitopes recognized by T cell receptors rather than focus solely on the potential for binding to HLA molecules. Due to the continuous development of databases, machine learning algorithms and progress in the field is expected to be rapid.

4.2. Overlapping peptides

Overlapping peptides (OLPs) that span the entire proteome or specific target antigens (peptide scanning) are widely used in the screening of viral epitope peptides, especially CD4+ T-cell epitopes (Hoogeveen et al., 2022). While this method provides a comprehensive survey of T-cell epitopes within the target protein, it does not reveal the parameters that dictate epitope selection. Compared to computer-based prediction methods, peptide scanning is a costly and labor-intensive approach. Recently, researchers employed 17 - mer OLPs covering the Nef, Gag, and Pol regions of HIV - 1 subtype A/E consensus sequences to identify six T cell epitopes (Nguyen et al., 2022).

4.3. Validation of the immunogenicity of candidate epitope

Over the past two decades, significant progress has been made in the ways for validating the immunogenicity of candidate epitopes. At present, commonly used methods are as follows: cytotoxicity assays, intracellular cytokine staining (ICS), enzyme-linked immunospot assays (ELISpot)/fluorescence immunospot assays (FluorSpot), and polypeptide-MHC multimer staining. Each of these methods has its own unique set of advantages and disadvantages when considering factors such as practicality, cost, sensitivity, and functional evaluation.

Cytotoxicity assays directly or indirectly measure the release of lytic substances from targeT cells, such as chromium (McCoy et al., 1973) and lactate dehydrogenase (Decker and Lohmann-Matthes, 1988). For example, the chromium release assay has been used to detect virus-specific lysis of HIV-1-infected CD4+ T cells (Ranasinghe et al., 2016). There have also been reports using live cell imaging methods. For instance, quantitative live cell imaging of targeT cells expressing fluorescent dyes has demonstrated virus-specific lysis of SARS-CoV-2-infected cells by T cells (Wagner et al., 2022). Currently, T cell assays are based on cytokines produced by activated T cells after peptide stimulation such as ICS or ELISpot/FluoroSpot. In these assays, candidate peptide epitopes are used to stimulate patients’ PBMCs either in viro or ex vivo. ICS along with flow cytometry are utilized to ascertain whether CD4+ or CD8+ T cells are activated while ELISpot or FluoroSpot technologies are unable to make such a determination. ELISpot or FluoroSpot technologies are more sensitive than ICS, which detect single-activated cells within a population of 1 million PBMCs. Due to their accuracy, sensitivity, reproducibility, and robustness, these technologies have found extensive application and hold promising potential in the clinic (Portilho et al., 2022). Tetramer staining has long been regarded as the gold standard for highly sensitive and precise quantification of antigen-specific T cells and is therefore commonly used for epitope identification. Nevertheless, tetramers or multimers is costly, complex, and time-consuming. Novitsky et al. identified the highly immunogenic peptides derived from HIV-1 Gag, Tat, Rev, and Nef by tetramers and recommended incorporating these HIV-1 peptides as fundamental structural elements in the design of a multi-epitope HIV-1 vaccine (Novitsky et al., 2001).

Besides functional cell assays, peptide-HLA molecule binding assays are frequently utilized for epitope identification. Recently, Ferretti and others employed high-throughput whole-genome screening technology (T-Scan) to identify target cells presenting candidate epitopes. They found 29 epitopes for 6 of the most common HLA types in SARS-CoV-2 (Zhang et al., 2021). Chikata et al. employed immunocapture and liquid chromatography-mass spectrometry (LC-MS) to identify HIV-1 epitope peptides presented by HLA-C1202 (Chikata et al., 2019) and HLA-C*14 (Chikata et al., 2022) after pretreating the target protein, enabling large-scale characterization of the presented epitopes. Nevertheless, these alternatives are expensive and exhibit reduced detectability for peptides that are present at low frequencies. A variety of methods for identifying epitopes have been employed, each possessing distinct attributes and offering unique possibilities. Further technological developments will increasingly simplify high-throughput epitope characterization.

5. Defined T cell epitopes in HIV proteins over the last 33 years

Table 1, Table 2 respectively display the CD8+ and CD4+ cell epitopes and their HLA restrictions that have been defined from the HIV protein. Additionally, these epitopes’ references and the techniques or methods employed to confirm their peptide immunogenicity were all listed (Table S1/S2). Notably, CD8+ cell epitopes compiled in this review have a length of 8 to 15 aa (amino acids), while the CD4+ cell epitopes range from 10 to 20 aa. This is because these lengths correspond to the typical dimensions of the peptide-binding grooves of HLA molecules. In validation experiments, peptides that are excessively long or short often lead to false positives (Paul et al., 2020), so they have been excluded from this review.

Table 1.

List of CD8+ T cell epitopes validated from HIV proteins.

Epitope Protein HXB2 start HXB2 end HLA Epitope Protein HXB2 start HXB2 end HLA
GELDRWEKI Gag 11 19 B*40:02, B*49:01 THLEGKIIL Pol 781 789 B*15:10, B*15:17, B*39:01
KIRLRPGGK Gag 18 26 A*03:01 HVASGYIEA Pol 793 801 B*54:01
IRLRPGGKK Gag 19 27 A*68:02, B*27:02, B*27:05 IEAEVIPAET Pol 799 808 B*40:02, B*40:06
RLRPGGKKK Gag 20 28 A*03:01 HTDNGSNF Pol 829 836 C*05:01
RLRPGGKKKY Gag 20 29 A*03:01, A*30:01, B*15:01, B*15:40, B*42:01 STTVKAACWW Pol 838 847 B*57:01, B*58:01
RPGGKKHYM Gag 22 30 B*07:02, B*42:01, B*42:02 IQQEFGIPY Pol 850 858 B*15:01, B*15:03
RPGGKKKYML Gag 22 31 B*07:02, B*42:01 VRDQAEHL Pol 880 887 C*18:01
RPGGKKKYKL Gag 22 31 B*51:01 KTAVQMAVF Pol 888 896 B*15:24, B*35:02, B*35:03, B*53:01, B*57:01, B*57:03, B*58:01
GGKKKYKLK Gag 24 32 B*08:01 FKRKGGIGGY Pol 900 909 B*15:03, B*27:05
KYKLKHIVW/ Gag 28 36 A*24:02, A*68:02, A*23:01 KRKGGIGGY Pol 901 909 B*15:03, B*27:02, B*27:05
QYKLKHIVW/ GERIVDII Pol 912 919 B*40:02
KYRLKHIVW/ LQKQITKI Pol 928 935 B*52:01
HYMLKHIVW KIQNFRVYY Pol 934 942 A*03:01, A*11:01, A*29:02, A*30:01, A*30:02, A*32:01, A*80:01
HLVWASRELa Gag 33 41 C*06:02, C*08:04 YRDSRDPLW Pol 942 950 B*38:01
LVWASRELERFa Gag 34 44 B*57:03 VVPRRKAKII Pol 974 983 B*08:01
WASRELERFa Gag 36 44 B*35:01, B*42:01, B*35:03, B*53:01 VPRRKAKII Pol 975 983 B*42:01
ELRSLYNTVa Gag 74 82 B*08:01 RKAKIIRDY Pol 978 986 B*15:01, B*15:03
RSLYNTVATLYa Gag 76 86 A*02:01, A*30:02 RKAKIIRDYGK Pol 978 988 B*08:01
SLYNTVATLa Gag 77 85 A*02:01, A*02:02, A*02:05, A*02:14 KIIRDYGK Pol 981 988 B*08:01
LYNTVATLa Gag 78 85 C*14:03 IIKDYGKQM Pol 982 990 B*42:01
LYNTVATLYa Gag 78 86 A*29:02, B*44:03 RIRTWKSLVK Vif 17 26 A*03:01
TLYCVHQKa Gag 84 91 A*11:01 IRTWKSLVK Vif 18 26 A*30:01
IEIKDTKEAL Gag 92 101 B*35:02, B*35:03, B*40:01, B*42:01, B*53:01 IRTWKSLVKH Vif 18 27 B*27:05
NSSKVSQNY Gag 124 132 A*01:01, B*35:01 HMYISKKAKa Vif 28 36 A*03:01, A*68:02, B*58:02
GQMVHQAI Gag 140 147 B*13:02 ISKKAKGWF Vif 31 39 B*35:01, B*35:02, B*35:03, B*35:08, B*53:01, B*57:01, B*58:01
HQAISPRTL Gag 144 152 B*42:01 HPRVSSEVHIa Vif 48 57 B*07:02, B*42:01
QAISPRTLNAW Gag 145 155 A*25:01, B*35:01, B*35:08, B*58:01 WHLGHGVSI/ WHLGQGVSI Vif 79 87 B*15:10, B*15:10, B*35:02, B*35:03, B*38:01, B*53:01
ISPRTLNAW/ LSPRTLNAW Gag 147 155 B*35:02, B*35:03, B*53:01, B*57:01, B*57:02, B*57:03, B*58:01, C*06:02 LGHGVSIEW Vif 81 89 B*57:03
SPRTLNAWVa Gag 148 156 B*07:02 A*68:02 B*58:02 LADQLIHLHY Vif 102 111 B*18:01
VKVIEEKAF Gag 156 164 B*15:03 KTKPPLPSVKK Vif 158 168 A*03:01
EEKAFSPEV Gag 160 168 B*35:01, B*35:08, B*42:01, B*45:01 EAVRHFPRI Vpr 29 37 A*68:02, B*35:01, B*35:02, B*35:03, B*35:08, B*51:01, B*53:01
KAFSPEVI Gag 162 169 B*57:01, B*57:03 AVRHFPRIW Vpr 30 38 B*57:01, B*58:01
KAFSPEVIPMF Gag 162 172 B*48:01, B*57:01, B*57:02, B*57:03, B*58:01 VRHFPRIWL Vpr 31 39 A*68:02, B*27:02, B*27:05, B*35:02, B*35:03, B*53:01
EVIPMFSAL Gag 167 175 A*26:01, A*26:02, A*26:03 FPRIWLHGL Vpr 34 42 B*07:02, B*15:10, B*42:01, B*81:01
VIPMFSALa Gag 168 175 B*15:16, C*01:02 ETYGDTWTGV Vpr 48 57 A*68:02
SEGATPQDL Gag 176 184 B*35:02, B*35:03, B*40:01, B*53:01 DTWAGVEAIIR Vpr 52 62 A*68:01, A*68:02, B*35:02, B*35:03, B*53:01
TPQDLNTML/ TPQDLNMMLa Gag 180 188 B*07:02, B*35:02, B*35:03, B*39:10, B*42:01, B*42:02. B*53:01, B*67:01, B*81:01, C*08:02 AIIRILQQLa Vpr 59 67 A*02:01
GHQAAMQML Gag 193 201 B*15:10, B*35:02, B*35:03, B*42:01 CCFHCQVC Tat 30 37 C*12:03
KETINEEAAa Gag 202 210 B*35:02, B*35:03, B*40:02 FQTKGLGISY Tat 38 47 B*15:01, B*15:03, B*15:40
ETINEEAAEW/
DTINEEAAEWa 		Gag 203 212 A*25:01, A*68:01, B*35:01, B*35:08, B*53:01 ITKGLGISYGR Tat 39 49 A*68:01, B*15:01, B*15:03, B*35:02, B*35:03, B*53:01
EELLKTVRLa Rev 10 18 B*44:02, B*44:03
AEWDRVHPV Gag 210 218 B*35:02, B*35:03, B*40:02, B*53:01 KAVRLIKFLY/ QAVRIIKILY Rev 14 23 B*15:03, B*35:01, B*35:02, B*35:03, B*35:08, B*53:01, B*57:01, B*58:01
HPVHAGPIA Gag 216 224 A*68:02, B*07:02, B*35:01, B*35:02, B*35:03, B*39:10, B*53:01 ERILSTYLGRa Rev 57 66 A*03:01, B*58:01
GQMREPRGSDI Gag 226 236 B*15:40 RPAEPVPLQL Rev 66 75 B*07:02, B*42:02
TSTLQEQIGW Gag 240 249 A*68:02, B*57:01, B*57:03, B*58:01, SAEPVPLQL Rev 67 75 C*05:01
NPPIPVGDIY Gag 253 262 B*35:01, B*35:05 YRLGVGALI Vpu 5 13 C*18:01
PPIPVGDIY Gag 254 262 B*35:01, B*35:02, B*53:01 EYRKILRQR Vpu 29 37 B*35:02, B*35:03, B*53:01
EIYKRWIIa Gag 260 267 B*08:01, B*35:02, B*35:03, B*42:01, B*53:01 RVKEKYQHL Env 2 10 B*08:01
RRWIQLGLQK/ KRWIILGLNKa Gag 263 272 B*27:05 AENLWVTVY Env 31 39 B*15:17, B*15:40, B*18:01, B*44:03
GLNKIVRMYa Gag 269 277 B*15:01, B*35:02, B*35:03, B*53:01 AENLWVTVYY Env 31 40 A*68:02
VRMYSPVSI Gag 274 282 B*07:02, B*15:02, C*18:01 TVYYGVPVWK Env 37 46 A*03:01, B*15:40, B*42:02
RMYSPTSI Gag 275 282 B*52:01 VPVWKEATTT Env 42 51 B*35:03, B*53:01, B*55:01
YSPVSILDI Gag 277 285 C*01:02 VPVWKEATTTL Env 42 52 B*35:01
FRDYVDRFF Gag 293 301 A*01:01, B*42:02, C*18:01 KAYETEVHNVW Env 59 69 B*58:01
FRDYVDRFYK Gag 293 302 B*18:01 YETEVHNVW Env 61 69 B*18:01
RDYVDRFFKTL/ RDYVDRFYKTL Gag 294 304 A*24:02, A*68:02, B*44:02, DPNPQEVVL Env 78 86 B*15:02, B*35:01, B*35:02
YVDRFYKTL Gag 296 304 A*02:07, C*03:04, A*01:01, A*26:01, B*07:02, B*15:03, B*15:10, C*03:03, C*03:04 MHEDIISLWa Env 104 112 B*38:01
DRFYKTLRA Gag 298 306 B*14:02, B*14:01 SFEPIPIHY Env 209 217 A*29:02, B*58:01
AEQASQDVKNW Gag 306 316 B*15:03, B*15:24, B*35:01, B*35:08, B*44:02, B*58:01 CAPAGFAIL Env 218 226 C*01:02
AEQASQEVKNWM Gag 306 317 B*35:01, B*35:08, B*58:01 RPNNNTRKSI Env 298 307 B*07:02, B*42:01, B*42:02
QASQEVKNW/ QATQDVKNW/ QATQDVKNW Gag 308 316 B*35:01, B*35:08, B*53:01, B*57:01, B*58:01 HIGPGRAFY Env 310 318 A*30:02, B*35:02, B*35:03
RGPGRAFVTI Env 311 320 B*42:02
EIIGDIRQAY Env 321 330 A*25:01
VKNWMTETL Gag 313 321 B*48:01 SFNCGGEFF Env 375 383 B*15:16, C*04:01
DCKTILKAL Gag 329 337 B*08:01, B*15:03, B*35:02, B*35:03, B*53:01 LPCRIKQII Env 416 424 B*51:01
ACQGVGGPGHK Gag 349 359 A*11:01, A*11:03, B*35:02, B*35:03, B*53:01 RIKQIINMW Env 419 427 A*32:01
GPGHKARVL Gag 355 363 B*07:02, B*42:01, RAIEAQQHL/ RAIEAQQHM Env 557 565 B*15:01, B*15:03, B*51:01, C*03:04, C*12:02
AEAMSQVTNS Gag 364 373 B*40:02, B*42:01, B*45:01 QTRVLAIERYL Env 577 587 B*58:01, B*58:02
APRKKGCWK Gag 407 415 B*07:02 ERYLKDQQL Env 584 592 B*08:01, B*14:01, B*14:02
TERQANFL Gag 427 434 B*40:02 RYLKDQQLL Env 585 593 A*23:01, A*24:02, C*07:02
RQANFLGKI Gag 429 437 B*13:02, B*15:24, B*48:01 YLKDQQLL Env 586 593 A*24:02, B*08:01
FLGKIWPSYK Gag 433 442 A*02:01, A*02:05 TAVPWNASW Env 606 614 B*35:01, B*35:08, B*58:01
KELYPLTSL/ KELYPLASL Gag 481 489 B*35:02, B*35:03, B*40:01, B*53:01, B*40:02 IVNRNRQGY Env 704 712 A*30:02
NSPTRREL Pol 24 31 C*01:02 IVTRIVELL Env 777 785 A*02:05, B*15:17
ITLWQRPLV Pol 59 67 A*68:02, A*74:01, B*35:02, B*35:03, B*53:01 GRRGWEALKY Env 786 795 B*15:40, B*27:05, B*35:02, B*35:03, B*53:01
DTVLEEWNL/DTVLEEMNL Pol 86 94 A*68:02, B*35:02, B*35:03, B*53:01 RRGWEVLKY Env 787 795 A*01:01
EEMNLPGRW Pol 90 98 A*68:02, B*35:02, B*35:03, B*44:02, B*44:03 KYCWNLLQY Env 794 802 A*30:02
RQYDQILIEIa Pol 113 122 B*13:02 QELKNSAVSL Env 805 814 B*08:01, B*35:02, B*35:03, B*40:01, B*53:01
GKKAIGTVL Pol 124 132 B*15:03 SLLNATDIAV Env 813 822 A*02:01
KAIGTVLV Pol 126 133 B*35:01, B*35:08 LLNATDIAV Env 814 822 A*02:01, A*02:05
LVGPTPVNI Pol 132 140 A*02:01 IPRRIRQGL Env 843 851 B*07:02, B*42:01
TPVNIIGRNML Pol 136 146 B*81:01 RIRQGLERA Env 846 854 A*02:05
FPISPIETVa Pol 155 163 B*54:01 RQGLERALL Env 848 856 B*81:01
IETVPVKL Pol 160 167 B*15:02, B*35:02, B*35:03, B*40:01, B*53:01 WPTVRERM Nef 13 20 B*08:01, B*15:16
GPKVKQWPL Pol 173 181 B*08:01, B*42:02 RMRRAEPAA Nef 19 27 B*15:01, B*58:01
ALVEICTEM Pol 188 196 A*02:01 LEKHGAITS Nef 37 45 B*40:01
TVLDVGDAY Pol 262 270 B*15:02, B*35:01, B*35:02, B*35:03, B*53:01, B*57:01 FPVTPQVPL Nef 68 76 A*68:02, B*07:02
SVPLDEGFRK Pol 272 281 A*11:01 FPVTPQVPLR Nef 68 77 A*68:02, B*07:02
VPLDEDFRKY Pol 273 282 B*15:02, B*35:01, B*35:02, B*35:03, B*42:01, B*53:01 TPQVPLRPM/ RPQVPLRPM Nef 71 79 A*68:02, B*07:02, B*07:02, B*35:01, B*35:02, B*35:03, B*42:01, B*42:02, B*53:01, B*67:01, B*81:01
YTAFTIPSI Pol 282 290 A*02:01, A*02:05, B*35:02, B*35:03, B*53:01 RPQVPLRPMTY Nef 71 81 B*35:01, B*67:01, C*07:02
TAFTIPSI Pol 283 290 A*02:01, A*02:17, B*35:02, B*35:03, B*51:01, B*52:01, B*53:01 QVPLRPMTYK Nef 73 82 A*02:01, A*03:01, A*11:01, B*35:02, B*35:03, B*53:01
IRYQYNVL Pol 297 304 B*14:01 VPLRPMTY Nef 74 81 B*35:01, B*35:02, B*35:03, B*53:01
IRYQYNVLP Pol 297 305 B*73:01 PLRPMTYK Nef 75 82 A*11:01, B*15:01
LPQGWKGSPA Pol 304 313 B*54:01 LRPMTYKAA Nef 76 84 B*27:03
SPAIFQSSM Pol 311 319 A*68:02, B*07:02, B*15:17, B*35:01, B*42:01, B*42:02, B*81:01 RPMTYKAAL Nef 77 85 B*07:02, B*67:01, B*42:01, B*42:02
SPAIFQSSMTK Pol 311 321 A*11:01, A*68:01 B*07:02 KAAFDLSFF Nef 82 90 B*57:01, B*57:02, B*57:03, B*58:01, C*12:02
AIFQSSMTK Pol 313 321 A*03:01, A*11:01, A*68:01 KAAVDLSHFLa Nef 82 91 C*08:04
KQNPDIVIY Pol 328 336 A*30:02, B*15:01, B*15:03, B*35:02, B*35:03, B*53:01, C*12:02 GAFDLSFFL/ AAVDLSHFL/ AAFDLSFFL/ AALDLSHFLa Nef 83 91 A*02:05, A*02:06, B*15:17, B*14:02, B*42:02, B*35:02, B*35:03, B*53:01, B*57:03, C*06:02
NPEIVIYQY/ HPDIVIYQY Pol 330 338 B*35:01, B*35:02, B*35:03, B*35:08, B*42:01, B*53:01, AVDLSHFLKa Nef 84 92 A*03:01, A*11:01, B*15:03, B*15:17
EIVIYQYMD Pol 332 340 B*18:01 FLKEKGGL Nef 90 97 B*08:01, B*35:01, B*35:08
VIYQYMDDL Pol 334 342 A*02:01, B*35:02, B*35:03, B*53:01 KEKGGLEGL Nef 92 100 B*35:02, B*35:03, B*40:01, B*40:02, B*44:03, B*53:01
VIYQYMDDLYV Pol 334 344 A*02:01 KEKGGLEGLIY Nef 92 102 B*44:03
IEELRQHLL Pol 357 365 B*40:01 RRQDILDLWIa Nef 105 114 B*27:05
IVLPEKDSW Pol 399 407 B*35:01, B*35:02, B*35:03, B*35:08, B*53:01, B*57:01, B*57:02, B*57:03, B*58:01 RRQDILDLWVY/KRQEILDLWVY Nef 105 115 B*18:01, B*58:01, C*07:01, C*07:02, B*35:02, B*35:03
LVGKLNWASQIY Pol 415 426 B*15:01, B*15:03 RQDILDLWI Nef 106 114 B*13:02, B*15:24, B*13:02
KLNWASQIY Pol 418 426 A*30:02, B*15:01, B*15:02, B*35:02, B*35:03, B*53:01 QEILDLWVY Nef 107 115 B*18:01, B*44:02, B*44:03
QIYPGIKVR Pol 424 432 A*03:01, A*74:01 HTQGYFPDW Nef 116 124 B*15:03, B*57:01, B*57:03, B*58:01
YPGIKVRQL Pol 426 434 B*42:01, B*42:02, B*58:01 TQGYFPDWQNY Nef 117 127 B*15:01
IPLTEEAEL Pol 448 456 B*07:02, B*15:01, B*35:01, B*51:01, B*53:01 GYFPDWQNY Nef 119 127 B*58:01
ILKEPVHGV Pol 464 472 A*02:01, A*02:02, A*02:05 YFPDWQNYT Nef 120 128 B*35:01, B*37:01, B*57:01, B*58:01
ILKEPVHGVY Pol 464 473 B*15:01, B*15:10, C*03:03, C*12:02 FPDWQNYTP Nef 121 129 B*54:01
IYQEPFKNLK Pol 496 505 A*11:01 NYTPGPGIRY Nef 126 135 A*24:02
RMRGAHTNDV Pol 511 520 A*30:02, B*35:02, B*35:03, B*53:01 YTPGPGIRY Nef 127 135 B*35:02, B*35:03, B*53:01
IAMESIVIW Pol 530 538 B*35:01, B*35:08, B*57:02, B*57:03, B*58:01 TPGPGVRYPL Nef 128 137 B*07:02, B*35:01, B*35:02, B*35:03, B*42:01, B*42:02, B*53:01, B*81:01
PIQKETWETW Pol 547 556 A*32:01, B*15:17, B*35:01, B*35:08 TRYPLTFGW Nef 133 141 A*68:02, B*35:01, B*35:02, B*35:03, B*35:08, B*42:01, B*53:01
GAETFYVDGA Pol 591 600 A*68:02 RYPLTFGW Nef 134 141 A*23:01, A*24:02, B*35:02, B*35:03, B*53:01
ETFYVDGA Pol 593 600 A*68:02 RYPLTFGWCY Nef 134 143 A*24:02, C*07:02, A*23:01
ETFYVDGAANR Pol 593 603 A*68:01, A*68:02, B*15:16 YPLTFGWCY/ YPLTFGWCF Nef 135 143 B*07:02, B*18:01, B*35:01, B*35:02, B*35:03, B*35:08, B*53:01, B*58:01, B*67:01
ETKLGKAGY Pol 604 612 A*26:01, B*15:01 PLTFGWCYKL Nef 136 145 A*02:01, B*35:02, B*35:03, B*53:01, B*58:01
VTDSQYALGI Pol 651 660 B*14:02, B*15:03, B*35:02, B*35:03, B*53:01 LTFGWCFKL Nef 137 145 A*02:01, A*68:02, B*15:17, B*15:40
QIIEQLIKK Pol 675 683 A*11:01 VLEWRFDSRL Nef 180 189 A*02:01
LFLDGIDKA Pol 715 723 B*15:01 WRFDSRLAF Nef 183 191 B*15:03, B*27:05
LPPIVAKEI Pol 743 751 B*07:05, B*42:01, B*51:01
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Note:

a

stand for those peptides are located in surface exposed loops of the experimental structures or Alpha Fold models of virus proteome.

Table 2.

List of CD4+ cell epitopes validated from HIV proteins.

Epitope Protein HXB2 start HXB2 end HLA Epitope Protein HXB2 start HXB2 end HLA
SGGELDRWEKIRLRPGGK Gag 9 26 DRB1*07:01, DRB1*11:01, DRB1*13:01, DRB1*13:02, DRB3*01:01 RFYKTLRAEQAS Gag 299 310 DRB1*01:01, DRB1*04:01, DRB1*04:05, DRB1*07:01, DRB1*11:01, DRB1*15:01, DRB5*01:01
LRPGGKKKYKLKHIV Gag 21 35 DRB1*13:02 RFYKTLRAEQASQEVK Gag 299 314 DRB1*01:01
GKKKYKLKHIVWASREL Gag 25 41 DRB1*01:01, DRB1*07:01, DRB1*08:01, DRB1*11:01, DRB1*13:02, DRB1*13:03, DRB3*01:01, DRB3*03:01, DRB4*01:01, DRB5*01:01 FYKTLRAEQASQ Gag 300 311 DRB1*01:01, DRB1*04:01, DRB1*11:01, DRB5*01:01
KHIVWASRELERFAV Gag 32 46 DRB1*03:01, DRB1*13:01, DRB1*13:02, DRB1*13:03, DRB3*01:01, DRB5*01:01 FYKTLRAEQASQE Gag 300 312 DRB1*01:01, DRB1*04:01, DRB1*04:05, DRB1*11:01, DRB1*15:01, DRB5*01:01
ASRELERFAVNPGLL Gag 37 51 DRB1*01:01, DRB1*04:01, DRB1*04:05, DRB1*07:01, DRB1*13:01, DRB1*13:02, DRB1*15:01, DRB4*01:01 YKTLRAEQASQEVKN Gag 301 315 DRB1*01:01
LERFAVNPGLL Gag 41 51 DRB1*13:02 RAEQASQEVKNWMTE Gag 305 319 DRB1*01:01
LERFAVNPGLLETSE Gag 41 55 DRB1*01:01 EVKNWMTETLLVQNA Gag 312 326 DRB1*14:01
ERFAVNPGLLETSEGCR Gag 42 58 DRB1*01:01, DRB1*04:05, DRB1*11:01, DRB1*13:02, DRB1*13:03, DRB1*15:01, DRB3*03:01 TNSATIMMQRGNFRNQRK Gag 371 388 DRB1*15:02
TGSEELRSLYNTVATLY Gag 70 86 DRB1*04:05, DRB1*07:01 CFNCGKEGHIAKNCRAPR Gag 392 409 DRB1*09:01, DRB1*13:01
EELRSLYNTVATLYCa Gag 73 87 DRB1*01:01 HIAKNCRAPRKKGCWK Gag 400 415 DRB1*07:01, DRB1*13:01
EELRSLYNTVATLYCVHa Gag 73 89 DRB1*01:01, DRB1*04:01, DRB1*04:05, DRB1*07:01, DRB1*11:01, DRB1*13:02, DRB1*15:01 RAPRKKGCWKCGKEGHQM Gag 406 423 DRB1*09:01
SLYNTVATLYCVHQRa Gag 77 91 DRB1*01:01 RQANFLGKIWPSHKGR Gag 429 444 DRB1*01:01, DRB1*04:01, DRB1*04:05, DRB1*11:01, DRB1*13:01, DRB1*13:02, DRB1*15:01, DRB5*01:01
SLYNTVATLYCVHQRIEV Gag 77 94 DRB1*01:01, DRB1*04:01, DRB1*04:05, DRB1*07:01, DRB1*13:02, DRB1*14:01, DRB5*01:01 GKIWPSHKGRPGNFLQSR Gag 435 452 DRB1*07:01, DRB1*13:01, DRB1*15:01
PIVQNIQGQ Gag 133 150 DRB1*01:01 FLQSRPEPTAPPEESFRF Gag 448 465 DRB1*07:01
IVQNLQGQMVHQAISPR Gag 134 150 DRB1*09:01 DKELYPLASLRSLFG Gag 480 494 DRB1*01:01, DRB1*04:01, DRB1*04:05, DRB1*07:01, DRB1*11:01, DRB1*15:01, DRB5*01:01
PRTLNAWVKVVEEKAF Gag 149 164 DRB1*13:01, DRB1*13:04 QRPLVTIKIGGQLKE Pol 63 77 DRB1*01:01, DRB1*11:01, DRB1*15:01, DRB5*01:01
WVKVVEEKAFSPEVIPMF Gag 155 172 DRB1*11:01 TPVNIIGRNLLTQIG Pol 136 150 DRB1*01:01, DRB1*11:01, DRB1*13:02, DRB1*15:01
AFSPEVIPMFSALSEGA Gag 163 179 DRB1*04:01, DRB1*07:01, DRB1*15:01, DRB4*01:01 VIWGKTPKFKLPIQKETWa Pol 536 553 DRB1*13:01, DRB3*01:01
SPEVIPMFSALSE Gag 165 177 DRB1*01:01, DRB1*03:01, DRB1*04:01, DRB1*04:05, DRB1*11:01, DRB1*15:01 GKIILVAVHVASGYI Pol 785 799 DRB1*01:01, DRB1*04:01, DRB1*04:05, DRB1*07:01, DRB1*11:01, DRB1*13:02, DRB1*15:01, DRB5*01:01
ETINEEAAEWDRVHPVHA Gag 203 220 DRB1*01:01 SLQYLALVALVAPKK Vif 144 158 DRB1*01:01, DRB1*04:01, DRB1*04:05, DRB1*07:01, DRB1*11:01, DRB1*15:01, DRB5*01:01
AAEWDRLHPVHAGPIA Gag 209 224 DRB1*07:01 EAIIRILQQLLFIHF Vpr 58 72 DRB1*01:01, DRB1*04:05, DRB1*15:01
LHPVHAGPIAPGQMREPR Gag 215 232 DRB1*11:01 QQLLFIHFRIGCRHSRIGa Vpr 65 82 DRB1*01:01, DRB1*04:05, DRB1*07:01, DRB1*11:01, DRB1*13:02, DRB1*15:01, DRB5*01:01
GSDIAGTTSTLQEQI Gag 233 247 DRB1*01:01 TKGLGISYGRKKRRQRRR Tat 40 57 DRB1*13:02, DRB1*13:03, DRB3*01:01, DRB3*03:01
SDIAGTTSTLQEQIGWM Gag 234 250 DRB1*04:04, DRB1*15:02 ELLKTVRLIKFLYQSNP Rev 11 27 DRB1*01:01, DRB1*03:01, DRB1*04:01, DRB1*04:05, DRB1*07:01, DRB1*11:01, DRB1*13:02, DRB1*15:01
STLQEQIGWMTNNPP Gag 241 255 DRB1*01:01 PSPEGTRQARRNRRRRW Rev 29 45 DRB1*13:01, DRB3*01:01
STLQEQIGWMTNNPPIPV Gag 241 258 DRB1*01:01, DRB1*13:03, DRB3*01:01, DRB3*03:01 TMLLGMLMICSAA Env 19 31 DRB1*01:01, DRB1*04:05, DRB1*11:01
WMTNNPPIPVGEIYK Gag 249 263 DRB1*01:01 AAEQLWTVYYGVPPVW Env 30 45 DRB1*11:01, DRB1*15:01
WMTNNPPIPVGEIYKRWI Gag 249 266 DRB3*01:01, DRB3*03:03 NVTENFNMWKNNMVEQMH Env 88 105 DRB1*11:01, DRB1*15:01, DRB1*15:02
TNNPPIPBGEIYKRW Gag 251 265 DRB1*13:01 NFNMWKNNMVEQM Env 92 104 DRB1*15:02
NPPIPVGEIYKRWII Gag 253 267 DRB1*01:01 DKMQKEYALLYKLDI Env 167 181 DRB1*03:01, DRB1*12:01, DRB1*15:01, DRB4*01:01, DRB5*01:01
PVGEIYKRWIILGLNa Gag 257 271 DRB1*01:01 DKKQKVHALFYKLDIV Env 167 182 DRB1*12:02
IYKRWIILGLNKIVRa Gag 261 275 DRB1*01:01 NTSYRLISCNTSVI Env 188 201 DRB1*01:01, DRB1*04:01, DRB1*04:05, DRB1*07:01, DRB1*11:01, DRB1*13:02, DRB1*15:01, DRB5*01:01
WIILGLNKIVRMa Gag 265 276 DRB1*01:01, DRB1*04:01, DRB1*04:05, DRB1*11:01, DRB1*13:02, DRB1*15:01, DRB5*01:01 KVSFEPIPIHYCAPAGFA Env 207 224 DRB1*01:01, DRB1*11:01, DRB3*01:01
WIILGLNKIVRMYSP Gag 265 279 DQB1*06:02, DQB1*06:04, DRB1*13:02, DRB1*15:01, DRB3*03:01, DRB5*01:01, SELYLYKVVKIEPLGVAP Env 481 498 DRB1*01:01, DRB1*04:01, DRB1*04:05, DRB1*07:01, DRB1*11:01, DRB1*13:02, DRB1*15:01, DRB5*01:01
VIPMFSAL Gag 168 175 DQB1*06:01, DRB1*13:03, DRB1*15:02, DRB5*01:02 RDLLLIVTRIVELLGR Env 772 787 DRB1*01:01, DRB1*07:01, DRB1*11:01
WIILGLNKIVRMYSPVSI Gag 265 282 DRB1*01:01, DRB1*03:02, DRB1*04:01, DRB1*04:05, DRB1*07:01, DRB1*11:01, DRB1*13:01, DRB1*13:02, DRB1*15:01, DRB1*15:02, DRB5*01:01 RSVVGWPAVRERMRRA Nef 8 23 DRB1*13:02
GLNKIVRMYSPTSIL Gag 269 283 DRB1*01:01 YKAAVDLSHFLKEKGGL Nef 81 97 DRB1*07:01, DRB1*08:04
GPKEPFRDYVDRFYKTLR Gag 288 305 DRB1*13:01 LWVYHTQGYFPDWQNY Nef 112 127 DRB1*07:01, DRB1*13:01, DRB1*14:01, DRB3*01:01, DRB4*01:01
DYVDRFYKTLRAE Gag 295 307 DRB1*01:01 PEKEVLVWKFDSRLAFHH Nef 176 193 DRB1*01:01, DRB1*04:01, DRB1*07:01, DRB1*11:01, DRB1*13:02, DRB1*15:01, DRB5*01:01
YVDRFYKTLRAEQASQEV Gag 296 313 DRB1*01:01, DRB1*04:01, DRB1*04:05, DRB1*07:01, DRB1*08:01, DRB1*10:01, DRB1*11:01, DRB1*13:01 VLEWRFDSRLAFHHV Nef 180 194 DRB1*01:01, DRB1*07:01, DRB1*11:01, DRB1*15:01, DRB5*01:01
VDRFYKTLRAEQASQ Gag 297 311 DRB1*13:02, DRB1*13:03, DRB1*15:01, DRB1*15:02, DRB4*01:01, DRB5*01:01
DRB1*01:01		 KFDSRLAFHHMARELH Nef 184 199 DRB1*01:01, DRB1*03:02, DRB1*07:01, DRB1*11:01, DRB1*13:02, DRB1*13:03, DRB1*15:01, DRB3*03:01, DRB5*01:01
DRFYKTLRAEQASQ Gag 298 311 DRB1*04:01
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Note:

a

stand for those peptides are located in surface exposed loops of the experimental structures or Alpha Fold models of virus proteome.

In brief, 270 studies documented the validated epitopes presented by class I molecules, while 27 studies reported those presented by class II molecules. In total, these studies encompassed 321 unique epitopes, comprising 239 and 82 T cell epitopes for CD8+ T cell and CD4+ T cell (Table 1, Table 2). Among them, the majority of epitopes for CD8+ T cell are presented by HLA-B35, B5301, B5801, or B4201 (Fig. 1A). These are prevalent alleles in Caucasian populations and are less common in Asia and Africa (Barker et al., 2023). The remaining epitopes are restricted primarily by 25 HLA-A, 50 HLA-B, and 14 HLA-C alleles. In terms of epitopes for CD4+T cell, the majority apply to 8 DRB1, DRB5*01:01, and DRB3*01:01 (Fig. 1B). The combined frequency of the HLA-A supertypes was highest in Europe at 49.34 %, followed by North America at 39.03 %, Asia at 28.81 %, and Africa at 26.91 % (Fig. 1A). In contrast, the HLA-B supertypes exhibited an aggregate frequency of 49.67 % in Europe, 43.51 % in North America, 40.56 % in Africa, and 28.84 % in Asia. The DRB1 supertypes shown in Fig. 1B exhibited minimal variation in their cumulative gene frequency across Europe (82.52 %), North America (69.11 %), Africa (63.49 %), and Asia (59.77 %). (http://www.allelefrequencies.net/).

Fig. 1.

Fig. 1

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HLA restriction and protein distribution of validated CD8+ T cell epitopes and CD4+ T cell epitopes in HIV proteome. (A, B) displayed the number of CD8+ T cell epitopes and CD4+ T cell epitopes restricted by each HLA supertype, respectively. (C, D) showed the fraction of CD8+ T cell epitopes and CD4+ T cell epitopes in each HIV protein, respectively.

Furthermore, 88.7 % of the epitopes for CD8+ T cell belong to the Pol, Gag, Nef, and Env protein (Fig. 1C), while 87.81 % of epitopes for CD4+ T cell are from the Gag, Nef and Env protein (Fig. 1D). The biased distribution of epitopes is partially due to the lengths of proteins (Env 857aa, Gag 503aa, Pol 1008aa, and so on) and the sequence conservation. Notably, our review only includes epitopes with the four digits of explicitly HLA type. HLA typing that is specific to the last four digits can further refine the polymorphic information of HLA molecules, allowing for a more accurate understanding of the binding affinity between an individual’s HLA molecules and antigen peptides. In contrast, HLA typing that only specifies the first two digits may cover multiple molecules with different binding properties.

To inform epitope prediction, we attempted to analyze sequence or structural characteristics of 321 viral peptides. The presence of glycine may indicate high structural flexibility in this region, which is conductive to recognition by the immune system. 33 of 239 CD8+ T-cell epitope peptides and 15 of 82 CD4+ T-cell epitope peptides contain one, two or three glycine residues. 37 of 321 located in surface exposed loops of the experimental structures or Alpha Fold models of virus proteome. During antigen presentation, different HLA molecules bind to different antigenic peptides. Most 9 peptides with high or moderate binding affinity show anchor residue at the p2 and COOH terminal or p9. High-affinity peptides restricted by HLA-A02 mostly with x-L/V/M-x-x-x-x-x-x-L/I/V motif, A2402 with x -Y/F/M-x-x-x-x-x-x-F/L/I, HLA-A1101 with x-S/A/V-x-x-x-x-x-x-K/R, and HLA-A3303 with x-L/S/A-x-x-x-x-x-x-K/R in our previous research (Ding et al., 2025). In this paper, the pattern motifs of the top three epitopes restricted by HLA-B3503 or HLA-B3502 mostly with x-E/T/P-x-x-x-x-x-x-L/Y/I motif and HLA-B5301 with x-T/E/P-x-x-x-x-x-x-L/Y/W. In addition, every epitope’s sites of interest were analyzed according reported, which included drug resistance, spatial structure and so on in Table S1, TableS2.

At present, according to the statistics from the HIV Molecular Immunology Database (https://www.HIV.lanl.gov/content/immunology/), 2048 different entries of CTL epitopes and 702 CD4+ epitopes are listed along with associated HLA information. The reported CD8+ and CD4+ T cell epitopes screened by the in silico prediction strategy are about 88.33 % and 88.32 % of epitopes, respectively. Of these only 139 HIV-1 epitope peptides presented by HLA-C alleles have been screened. Epitopes from the Rev, Vif, Vpr, Vpu, and Tat proteins are also very rare. Antigenic peptides of HIV-1 subtypes A/B/C/D/CRF01_AE account for 11.43 %, 58.26 %, 21.69 %, 4.96 %, and 3.65 %, respectively. Evidently, the 321 validated T cell epitopes of HIV are insufficient to fully encompass the primary populations and HIV subtypes in a designated geographic zone. More efforts are urgently needed to concentrate on regional differences, especially regarding the HIV subtypes and populations in Asia, in order to design therapeutic vaccines and the detection system of HIV-specific T-cells.

6. Prospect and conclusion

As interest in T cell-based HIV cure strategies resurges, careful consideration of HIV-specific epitopes is crucial. Our research gathers T cell epitopes defined by the HIV proteome and demonstrates their HLA restriction with four digits and methods for validating their immunogenicity. These epitope catalogs provide strong support for researching therapeutic vaccines, specific T-cell detection, and the interaction mechanism between HIV and the immune system. Nevertheless, 321 T cell epitopes that have been identified thus far do not completely encompass the predominant populations and HIV subtypes found in certain geographical areas. To address this, we propose a three-tiered strategy: (1) establish global collaborative networks to prioritize sampling in Asia, Africa, and South America, where HIV epidemics were characterized by unique clade distributions; (2) implement longitudinal cohort studies combining high-throughput epitope-HLA sequencing with clinical data on viral load and treatment response; (3) employ single-cell RNA sequencing to map rare epitope-specific T-cell clones and track their dynamic evolution during infection. These efforts will enable the design of pan-subtype vaccines and precision immune monitoring tools, ultimately advancing T cell-based HIV cure strategies.

Consent for publication

Not applicable.

Ethics approval and consent to participate

Not applicable.

Funding

This work was supported in part by the Talent Support Program of the Second Hospital of Nanjing (RCZD24002); the Nanjing Medical Science and Technique Development Foundation (YKK24180); Laboratory Department, Key Medical Specialty of Nanjing City; Anhui Provincial Key Natural Science Research Project for Universities (KJ2020A0853); School Enterprise Cooperation grant of Quality Engineering Project of Department of Education of Anhui Province (2020sjjd067); and Human Anatomy Teaching Team grant of Anhui Quality Engineering Project of Department of Education of Anhui Province (2021jxtd141); The sponsors had no role in study design, data collection and analysis, preparation of the manuscript, or decision to submit the article for publication.

CRediT authorship contribution statement

Yan Ding: Methodology, Investigation. Ling Huang: Investigation, Data curation. Yandan Wu: Writing – original draft, Methodology. Jialai Yan: Writing – review & editing, Funding acquisition.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Footnotes

Supplementary material associated with this article can be found, in the online version, at doi:10.1016/j.virusres.2025.199602.

Contributor Information

Yandan Wu, Email: 230239113@seu.edu.cn.

Jialai Yan, Email: yanjialai2016@126.com.

Appendix. Supplementary materials

mmc1.docx (1.6MB, docx)

Data availability

Data will be made available on request.

References

  1. Achord A.P., Lewis R.E., Brackin M.N., Cruse J.M. HLA-DQB1 markers associated with human immunodeficiency virus type I disease progression. Pathobiology. 1997;65(4):210–215. doi: 10.1159/000164125. [DOI] [PubMed] [Google Scholar]
  2. Altfeld M., Addo M.M., Rosenberg E.S., Hecht F.M., Lee P.K., Vogel M., et al. Influence of HLA-B57 on clinical presentation and viral control during acute HIV-1 infection. AIDS. 2003;17(18):2581–2591. doi: 10.1097/00002030-200312050-00005. [DOI] [PubMed] [Google Scholar]
  3. Barker D.J., Maccari G., Georgiou X., Cooper M.A., Flicek P., Robinson J., et al. The IPD-IMGT/HLA Database. Nucleic Acids Res. 2023;51(D1):D1053–d1060. doi: 10.1093/nar/gkac1011. [DOI] [PMC free article] [PubMed] [Google Scholar]
  4. Bernard N.F., Yannakis C.M., Lee J.S., Tsoukas C.M. Human immunodeficiency virus (HIV)-specific cytotoxic T lymphocyte activity in HIV-exposed seronegative persons. J. Infect. Dis. 1999;179(3):538–547. doi: 10.1086/314621. [DOI] [PubMed] [Google Scholar]
  5. Boeijen L.L., Hoogeveen R.C., Boonstra A., Lauer G.M. Hepatitis B virus infection and the immune response: the big questions. Best Pract. Res. Clinic. Gastroenterol. 2017;31(3):265–272. doi: 10.1016/j.bpg.2017.05.003. [DOI] [PubMed] [Google Scholar]
  6. Bonsack M., Hoppe S., Winter J., Tichy D., Zeller C., Küpper M.D., et al. Performance evaluation of MHC class-I binding prediction tools based on an experimentally validated MHC-peptide binding data set. Cancer Immunol. Res. 2019;7(5):719–736. doi: 10.1158/2326-6066.CIR-18-0584. [DOI] [PubMed] [Google Scholar]
  7. Brener J., Gall A., Batorsky R., Riddell L., Buus S., Leitman E., et al. Disease progression despite protective HLA expression in an HIV-infected transmission pair. Retrovirology. 2015;12:55. doi: 10.1186/s12977-015-0179-z. [DOI] [PMC free article] [PubMed] [Google Scholar]
  8. Buffalo C.Z., Iwamoto Y., Hurley J.H., Ren X. How HIV nef proteins hijack membrane traffic to promote infection. J. Virol. 2019;93(24) doi: 10.1128/JVI.01322-19. [DOI] [PMC free article] [PubMed] [Google Scholar]
  9. Bugembe D.L., Ekii A.O., Ndembi N., Serwanga J., Kaleebu P., Pala P. Computational MHC-I epitope predictor identifies 95% of experimentally mapped HIV-1 clade A and D epitopes in a Ugandan cohort. BMC Infect. Dis. 2020;20(1):172. doi: 10.1186/s12879-020-4876-4. [DOI] [PMC free article] [PubMed] [Google Scholar]
  10. Chikata T., Paes W., Akahoshi T., Partridge T., Murakoshi H., Gatanaga H., et al. Identification of immunodominant HIV-1 epitopes presented by HLA-C*12:02, a protective allele, using an immunopeptidomics approach. J. Virol. 2019;93(17) doi: 10.1128/JVI.00634-19. [DOI] [PMC free article] [PubMed] [Google Scholar]
  11. Chikata T., Paes W., Kuse N., Partridge T., Gatanaga H., Zhang Y., et al. Impact of micropolymorphism outside the peptide binding groove in the clinically relevant allele HLA-C*14 on T cell responses in HIV-1 infection. J. Virol. 2022;96(10) doi: 10.1128/jvi.00432-22. [DOI] [PMC free article] [PubMed] [Google Scholar]
  12. Decker T., Lohmann-Matthes M.L. A quick and simple method for the quantitation of lactate dehydrogenase release in measurements of cellular cytotoxicity and tumor necrosis factor (TNF) activity. J. Immunol. Methods. 1988;115(1):61–69. doi: 10.1016/0022-1759(88)90310-9. [DOI] [PubMed] [Google Scholar]
  13. Ding Y., Yan J., Huang L., Yu J., Wu Y., Shen C., et al. Large-scale screening of HIV-1 T-cell epitopes restricted by 12 prevalent HLA-A allotypes in Northeast Asia and universal detection of HIV-1-specific CD8+ T cells. Front. Microbiol. 2025;16 doi: 10.3389/fmicb.2025.1529721. [DOI] [PMC free article] [PubMed] [Google Scholar]
  14. Dorak M.T., Tang J., Tang S., Penman-Aguilar A., Coutinho R.A., Goedert J.J., et al. Influence of human leukocyte antigen-B22 alleles on the course of human immunodeficiency virus type 1 infection in 3 cohorts of white men. J. Infect. Dis. 2003;188(6):856–863. doi: 10.1086/378071. [DOI] [PubMed] [Google Scholar]
  15. Elahi S., Horton H. Association of HLA-alleles with the immune regulation of chronic viral infections. Int. J. Biochem. Cell Biol. 2012;44(8):1361–1365. doi: 10.1016/j.biocel.2012.05.003. [DOI] [PMC free article] [PubMed] [Google Scholar]
  16. Elangovan R., Jenks M., Yun J., Dickson-Tetteh L., Kirtley S., Hemelaar J. Global and regional estimates for subtype-specific therapeutic and prophylactic HIV-1 vaccines: a modeling study. Front. Microbiol. 2021;12 doi: 10.3389/fmicb.2021.690647. [DOI] [PMC free article] [PubMed] [Google Scholar]
  17. Fenwick C., Joo V., Jacquier P., Noto A., Banga R., Perreau M., et al. T-cell exhaustion in HIV infection. Immunol. Rev. 2019;292(1):149–163. doi: 10.1111/imr.12823. [DOI] [PMC free article] [PubMed] [Google Scholar]
  18. Flores-Villanueva P.O., Hendel H., Caillat-Zucman S., Rappaport J., Burgos-Tiburcio A., Bertin-Maghit S., et al. Associations of MHC ancestral haplotypes with resistance/susceptibility to AIDS disease development. J. Immunol. 2003;170(4):1925–1929. doi: 10.4049/jimmunol.170.4.1925. [DOI] [PubMed] [Google Scholar]
  19. Gao X., Nelson G.W., Karacki P., Martin M.P., Phair J., Kaslow R., et al. Effect of a single amino acid change in MHC class I molecules on the rate of progression to AIDS. N. Engl. J. Med. 2001;344(22):1668–1675. doi: 10.1056/NEJM200105313442203. [DOI] [PubMed] [Google Scholar]
  20. Garcia-Garijo A., Fajardo C.A., Gros A. Determinants for neoantigen identification. Front. Immunol. 2019;10:1392. doi: 10.3389/fimmu.2019.01392. [DOI] [PMC free article] [PubMed] [Google Scholar]
  21. Gartner J.J., Parkhurst M.R., Gros A., Tran E., Jafferji M.S., Copeland A., et al. A machine learning model for ranking candidate HLA class I neoantigens based on known neoepitopes from multiple human tumor types. Nat. Cancer. 2021;2(5):563–574. doi: 10.1038/s43018-021-00197-6. [DOI] [PMC free article] [PubMed] [Google Scholar]
  22. Höhne K., Businger R., van Nuffel A., Bolduan S., Koppensteiner H., Baeyens A., et al. Virion encapsidated HIV-1 Vpr induces NFAT to prime non-activated T cells for productive infection. Open. Biol. 2016;6(7) doi: 10.1098/rsob.160046. [DOI] [PMC free article] [PubMed] [Google Scholar]
  23. Hashimoto M., Kamphorst A.O., Im S.J., Kissick H.T., Pillai R.N., Ramalingam S.S., et al. CD8 T cell exhaustion in chronic infection and cancer: opportunities for interventions. Annu. Rev. Med. 2018;69:301–318. doi: 10.1146/annurev-med-012017-043208. [DOI] [PubMed] [Google Scholar]
  24. Hemelaar J., Gouws E., Ghys P.D., Osmanov S. Global and regional distribution of HIV-1 genetic subtypes and recombinants in 2004. AIDS. 2006;20(16):W13–W23. doi: 10.1097/01.aids.0000247564.73009.bc. [DOI] [PubMed] [Google Scholar]
  25. Hoogeveen R.C., Dijkstra S., Bartsch L.M., Drescher H.K., Aneja J., Robidoux M.P., et al. Hepatitis B virus-specific CD4 T cell responses differentiate functional cure from chronic surface antigen(+) infection. J. Hepatol. 2022;77(5):1276–1286. doi: 10.1016/j.jhep.2022.05.041. [DOI] [PubMed] [Google Scholar]
  26. Just J.J., Abrams E., Louie L.G., Urbano R., Wara D., Nicholas S.W., et al. Influence of host genotype on progression to acquired immunodeficiency syndrome among children infected with human immunodeficiency virus type 1. J. Pediatr. 1995;127(4):544–549. doi: 10.1016/s0022-3476(95)70110-9. [DOI] [PubMed] [Google Scholar]
  27. Just J.J., Casabona J., Bertrán J., Montané C., Fortuny C., Rodrigo C., et al. MHC class II alleles associated with clinical and immunological manifestations of HIV-1 infection among children in Catalonia, Spain. Tissue antigens. 1996;47(4):313–318. doi: 10.1111/j.1399-0039.1996.tb02559.x. [DOI] [PubMed] [Google Scholar]
  28. Kalams S.A., Buchbinder S.P., Rosenberg E.S., Billingsley J.M., Colbert D.S., Jones N.G., et al. Association between virus-specific cytotoxic T-lymphocyte and helper responses in human immunodeficiency virus type 1 infection. J. Virol. 1999;73(8):6715–6720. doi: 10.1128/jvi.73.8.6715-6720.1999. [DOI] [PMC free article] [PubMed] [Google Scholar]
  29. Kaslow R.A., Carrington M., Apple R., Park L., Muñoz A., Saah A.J., et al. Influence of combinations of human major histocompatibility complex genes on the course of HIV-1 infection. Nat. Med. 1996;2(4):405–411. doi: 10.1038/nm0496-405. [DOI] [PubMed] [Google Scholar]
  30. Kaul R., Plummer F.A., Kimani J., Dong T., Kiama P., Rostron T., et al. HIV-1-specific mucosal CD8+ lymphocyte responses in the cervix of HIV-1-resistant prostitutes in Nairobi. J. Immunol. 2000;164(3):1602–1611. doi: 10.4049/jimmunol.164.3.1602. [DOI] [PubMed] [Google Scholar]
  31. Kaul R., Dong T., Plummer F.A., Kimani J., Rostron T., Kiama P., et al. CD8(+) lymphocytes respond to different HIV epitopes in seronegative and infected subjects. J. Clin. Invest. 2001;107(10):1303–1310. doi: 10.1172/JCI12433. [DOI] [PMC free article] [PubMed] [Google Scholar]
  32. Kuse N., Murakoshi H., Akahoshi T., Chikata T., James K.L., Gatanaga H., et al. Collaboration of a detrimental HLA-B*35:01 allele with HLA-A*24:02 in coevolution of HIV-1 with T cells leading to poorer clinical outcomes. J. Virol. 2021;95(23) doi: 10.1128/JVI.01259-21. [DOI] [PMC free article] [PubMed] [Google Scholar]
  33. Kyobe S., Mwesigwa S., Kisitu G.P., Farirai J., Katagirya E., Mirembe A.N., et al. Exome sequencing reveals a putative role for HLA-C*03:02 in control of HIV-1 in African pediatric populations. Front. Genet. 2021;12 doi: 10.3389/fgene.2021.720213. [DOI] [PMC free article] [PubMed] [Google Scholar]
  34. Lee J.H., Crotty S. HIV vaccinology: 2021 update. Semin. Immunol. 2021;51 doi: 10.1016/j.smim.2021.101470. [DOI] [PMC free article] [PubMed] [Google Scholar]
  35. Li W.X., Xia J.A., Zhou X., Ma Y., Shen G., Qiu F.W. Association of HLA alleles (A, B, DRB1) and HIV-1 infection in the Han population of Hubei, China. J. Huazhong Univ. Sci. Technol. Med. Sci. 2017;37(1):131–139. doi: 10.1007/s11596-017-1706-z. [DOI] [PubMed] [Google Scholar]
  36. Lin X., George J.T., Schafer N.P., Chau K.N., Birnbaum M.E., Clementi C., et al. Rapid assessment of T-cell receptor specificity of the immune repertoire. Nat. Comput. Sci. 2021;1(5):362–373. doi: 10.1038/s43588-021-00076-1. [DOI] [PMC free article] [PubMed] [Google Scholar]
  37. Littera R., Campagna M., Deidda S., Angioni G., Cipri S., Melis M., et al. Human leukocyte antigen complex and other immunogenetic and clinical factors influence susceptibility or protection to SARS-CoV-2 infection and severity of the disease course. The Sardinian experience. Front. Immunol. 2020;11 doi: 10.3389/fimmu.2020.605688. [DOI] [PMC free article] [PubMed] [Google Scholar]
  38. Lobos C.A., Downing J., D'Orsogna L.J., Chatzileontiadou D.S.M., Gras S. Protective HLA-B57: t cell and natural killer cell recognition in HIV infection. Biochem. Soc. Trans. 2022;50(5):1329–1339. doi: 10.1042/BST20220244. [DOI] [PMC free article] [PubMed] [Google Scholar]
  39. Mamun T.I., Ali M.A., Hosen M.N., Rahman J., Islam M.A., Akib M.G., et al. Designing a multi-epitope vaccine candidate against human rhinovirus C utilizing immunoinformatics approach. Front. Immunol. 2024;15 doi: 10.3389/fimmu.2024.1364129. [DOI] [PMC free article] [PubMed] [Google Scholar]
  40. Matern B.M., Olieslagers T.I., Voorter C.E.M., Groeneweg M., Tilanus M.G.J. Insights into the polymorphism in HLA-DRA and its evolutionary relationship with HLA haplotypes. HLa. 2020;95(2):117–127. doi: 10.1111/tan.13730. [DOI] [PubMed] [Google Scholar]
  41. McCoy J.L., Herberman R.B., Rosenberg E.B., Donnelly F.C., Levine P.H., Alford C. 51 Chromium-release assay for cell-mediated cytotoxicity of human leukemia and lymphoid tissue-culture cells. Natl. Cancer Inst. Monogr. 1973;37:59–67. [PubMed] [Google Scholar]
  42. Nguyen H.T., Kuse N., Zhang Y., Murakoshi H., Maeda Y., Tamura Y., et al. Control of HIV-1 replication by CD8(+) T cells specific for two novel pol protective epitopes in HIV-1 subtype A/E infection. J. Virol. 2022;96(19) doi: 10.1128/jvi.00811-22. [DOI] [PMC free article] [PubMed] [Google Scholar]
  43. Nitschke K., Luxenburger H., Kiraithe M.M., Thimme R., Neumann-Haefelin C. CD8+ T-cell responses in Hepatitis B and C: the (HLA-) A, B, and C of Hepatitis B and C. Dig. Dis. 2016;34(4):396–409. doi: 10.1159/000444555. [DOI] [PubMed] [Google Scholar]
  44. Novitsky V., Rybak N., McLane M.F., Gilbert P., Chigwedere P., Klein I., et al. Identification of human immunodeficiency virus type 1 subtype C gag-, Tat-, Rev-, and Nef-specific elispot-based cytotoxic T-lymphocyte responses for AIDS vaccine design. J. Virol. 2001;75(19):9210–9228. doi: 10.1128/JVI.75.19.9210-9228.2001. [DOI] [PMC free article] [PubMed] [Google Scholar]
  45. Ogishi M., Yotsuyanagi H. Quantitative prediction of the landscape of T cell epitope immunogenicity in sequence space. Front. Immunol. 2019;10:827. doi: 10.3389/fimmu.2019.00827. [DOI] [PMC free article] [PubMed] [Google Scholar]
  46. Osmanov S., Pattou C., Walker N., Schwardländer B., Esparza J. Estimated global distribution and regional spread of HIV-1 genetic subtypes in the year 2000. J. Acquir. Immune Defic. Syndr. 2002;29(2):184–190. doi: 10.1097/00042560-200202010-00013. [DOI] [PubMed] [Google Scholar]
  47. Pancré V., Delhem N., Yazdanpanah Y., Delanoye A., Delacre M., Depil S., et al. Presence of HIV-1 Nef specific CD4 T cell response is associated with non-progression in HIV-1 infection. Vaccine. 2007;25(31):5927–5937. doi: 10.1016/j.vaccine.2007.05.038. [DOI] [PubMed] [Google Scholar]
  48. Paul S., Croft N.P., Purcell A.W., Tscharke D.C., Sette A., Nielsen M., et al. Benchmarking predictions of MHC class I restricted T cell epitopes in a comprehensively studied model system. PLoS. Comput. Biol. 2020;16(5) doi: 10.1371/journal.pcbi.1007757. [DOI] [PMC free article] [PubMed] [Google Scholar]
  49. Pereyra F., Jia X., McLaren P.J., Telenti A., de Bakker P.I., Walker B.D., et al. The major genetic determinants of HIV-1 control affect HLA class I peptide presentation. Science. 2010;330(6010):1551–1557. doi: 10.1126/science.1195271. [DOI] [PMC free article] [PubMed] [Google Scholar]
  50. Peterson T.A., Kimani J., Wachihi C., Bielawny T., Mendoza L., Thavaneswaran S., et al. HLA class I associations with rates of HIV-1 seroconversion and disease progression in the Pumwani Sex Worker Cohort. Tissue Antigens. 2013;81(2):93–107. doi: 10.1111/tan.12051. [DOI] [PubMed] [Google Scholar]
  51. Pham M.N., Nguyen T.N., Tran L.S., Nguyen Q.B., Nguyen T.H., Pham T.M.Q., et al. epiTCR: a highly sensitive predictor for TCR-peptide binding. Bioinformatics. 2023;39(5) doi: 10.1093/bioinformatics/btad284. [DOI] [PMC free article] [PubMed] [Google Scholar]
  52. Portilho A.I., Gimenes Lima G., De Gaspari E. Enzyme-linked immunosorbent assay: an adaptable methodology to study SARS-CoV-2 humoral and cellular immune responses. J. Clin. Med. 2022;11(6) doi: 10.3390/jcm11061503. [DOI] [PMC free article] [PubMed] [Google Scholar]
  53. Rajapaksa U.S., Li D., Peng Y.C., McMichael A.J., Dong T., Xu X.N. HLA-B may be more protective against HIV-1 than HLA-A because it resists negative regulatory factor (Nef) mediated down-regulation. Proc. Natl. Acad. Sci. U.S.A. 2012;109(33):13353–13358. doi: 10.1073/pnas.1204199109. [DOI] [PMC free article] [PubMed] [Google Scholar]
  54. Rallón N., Restrepo C., Vicario J.L., Del Romero J., Rodríguez C., García-Samaniego J., et al. Human leucocyte antigen (HLA)-DQB1×03:02 and HLA-A*02:01 have opposite patterns in their effects on susceptibility to HIV infection. HIV Med. 2017;18(8):587–594. doi: 10.1111/hiv.12494. [DOI] [PubMed] [Google Scholar]
  55. Ramírez de Arellano E., Díez-Fuertes F., Aguilar F., de la Torre Tarazona H.E., Sánchez-Lara S., Lao Y., et al. Novel association of five HLA alleles with HIV-1 progression in Spanish long-term non progressor patients. PLoS ONE. 2019;14(8) doi: 10.1371/journal.pone.0220459. [DOI] [PMC free article] [PubMed] [Google Scholar]
  56. Ranasinghe S., Lamothe P.A., Soghoian D.Z., Kazer S.W., Cole M.B., Shalek A.K., et al. Antiviral CD8(+) T cells restricted by Human leukocyte antigen class II exist during natural HIV infection and exhibit clonal expansion. Immunity. 2016;45(4):917–930. doi: 10.1016/j.immuni.2016.09.015. [DOI] [PMC free article] [PubMed] [Google Scholar]
  57. Rausch J.W., Le Grice S.F. HIV Rev Assembly on the Rev Response element (RRE): a structural perspective. Viruses. 2015;7(6):3053–3075. doi: 10.3390/v7062760. [DOI] [PMC free article] [PubMed] [Google Scholar]
  58. Robinson J., Barker D.J., Marsh S.G.E. 25 years of the IPD-IMGT/HLA Database. HLa. 2024;103(6) doi: 10.1111/tan.15549. [DOI] [PubMed] [Google Scholar]
  59. Sánchez-Burgos G.G., Montalvo-Marin N.M., Díaz-Rosado E.R., Pérez-Rueda E. In Silico identification of Chikungunya Virus B- and T-cell epitopes with high antigenic potential for vaccine development. Viruses. 2021;13(12) doi: 10.3390/v13122360. [DOI] [PMC free article] [PubMed] [Google Scholar]
  60. Sacks D., Baxter B., Campbell B.C.V., Carpenter J.S., Cognard C., Dippel D., et al. Multisociety consensus quality improvement revised consensus Statement for Endovascular therapy of acute ischemic stroke. Int. J. Stroke. 2018;13(6):612–632. doi: 10.1177/1747493018778713. [DOI] [PubMed] [Google Scholar]
  61. Samantaray M., Pushan S.S., Rajagopalan M., Abrol K., Basumatari J., Murthy T.P.K., et al. Designing a multi-epitope vaccine candidate against pandemic influenza a virus: an immunoinformatics and structural vaccinology approach. Mol. Divers. 2025 doi: 10.1007/s11030-025-11124-7. [DOI] [PubMed] [Google Scholar]
  62. Singh P., Kaur G., Sharma G., Mehra N.K. Immunogenetic basis of HIV-1 infection, transmission and disease progression. Vaccine. 2008;26(24):2966–2980. doi: 10.1016/j.vaccine.2008.01.012. [DOI] [PubMed] [Google Scholar]
  63. Stephens H.A. HIV-1 diversity versus HLA class I polymorphism. Trends. Immunol. 2005;26(1):41–47. doi: 10.1016/j.it.2004.11.001. [DOI] [PubMed] [Google Scholar]
  64. T D., G S.B., Hebbar S.R., Selvam P.K., Vasudevan K. VaxOptiML: leveraging machine learning for accurate prediction of MHC-I and II epitopes for optimized cancer immunotherapy. Immunogenetics. 2024;77(1):8. doi: 10.1007/s00251-024-01361-9. [DOI] [PubMed] [Google Scholar]
  65. Tang J., Tang S., Lobashevsky E., Myracle A.D., Fideli U., Aldrovandi G., et al. Favorable and unfavorable HLA class I alleles and haplotypes in Zambians predominantly infected with clade C human immunodeficiency virus type 1. J. Virol. 2002;76(16):8276–8284. doi: 10.1128/JVI.76.16.8276-8284.2002. [DOI] [PMC free article] [PubMed] [Google Scholar]
  66. Tang J., Malhotra R., Song W., Brill I., Hu L., Farmer P.K., et al. Human leukocyte antigens and HIV type 1 viral load in early and chronic infection: predominance of evolving relationships. PLoS ONE. 2010;5(3):e9629. doi: 10.1371/journal.pone.0009629. [DOI] [PMC free article] [PubMed] [Google Scholar]
  67. Trachtenberg E., Korber B., Sollars C., Kepler T.B., Hraber P.T., Hayes E., et al. Advantage of rare HLA supertype in HIV disease progression. Nat. Med. 2003;9(7):928–935. doi: 10.1038/nm893. [DOI] [PubMed] [Google Scholar]
  68. Vita R., Blazeska N., Marrama D., Duesing S., Bennett J., Greenbaum J., et al. The Immune Epitope Database (IEDB): 2024 update. Nucleic Acids Res. 2025;53(D1) doi: 10.1093/nar/gkae1092. D436-d443. [DOI] [PMC free article] [PubMed] [Google Scholar]
  69. Wagner K.I., Mateyka L.M., Jarosch S., Grass V., Weber S., Schober K., et al. Recruitment of highly cytotoxic CD8(+) T cell receptors in mild SARS-CoV-2 infection. Cell Rep. 2022;38(2) doi: 10.1016/j.celrep.2021.110214. [DOI] [PMC free article] [PubMed] [Google Scholar]
  70. Wang L., Zou Z.Q., Wang K. Clinical relevance of HLA gene variants in HBV infection. J. Immunol. Res. 2016;2016 doi: 10.1155/2016/9069375. [DOI] [PMC free article] [PubMed] [Google Scholar]
  71. Wang Q., Finzi A., Sodroski J. The conformational states of the HIV-1 envelope glycoproteins. Trends. Microbiol. 2020;28(8):655–667. doi: 10.1016/j.tim.2020.03.007. [DOI] [PMC free article] [PubMed] [Google Scholar]
  72. Wherry E.J. T cell exhaustion. Nat. Immunol. 2011;12(6):492–499. doi: 10.1038/ni.2035. [DOI] [PubMed] [Google Scholar]
  73. Williamson A.L., Rybicki E.P. Justification for the inclusion of Gag in HIV vaccine candidates. Expert. Rev. Vaccines. 2016;15(5):585–598. doi: 10.1586/14760584.2016.1129904. [DOI] [PubMed] [Google Scholar]
  74. Xu L., Yang Q., Dong W., Li X., Wang K., Dong S., et al. Meta learning for mutant HLA class I epitope immunogenicity prediction to accelerate cancer clinical immunotherapy. Br. Bioinform. 2024;26(1) doi: 10.1093/bib/bbae625. [DOI] [PMC free article] [PubMed] [Google Scholar]
  75. Yun J.S., Kim A.R., Kim S.M., Shin E., Ha S.J., Kim D., et al. In silico analysis for the development of multi-epitope vaccines against mycobacterium tuberculosis. Front. Immunol. 2024;15 doi: 10.3389/fimmu.2024.1474346. [DOI] [PMC free article] [PubMed] [Google Scholar]
  76. Zhang X., Lu X., Moog C., Yuan L., Liu Z., Li Z., et al. KIR3DL1-Negative CD8 T cells and KIR3DL1-Negative natural killer cells contribute to the advantageous control of early Human Immunodeficiency Virus type 1 infection in HLA-B Bw4 homozygous individuals. Front. Immunol. 2018;9:1855. doi: 10.3389/fimmu.2018.01855. [DOI] [PMC free article] [PubMed] [Google Scholar]
  77. Zhang H., Deng S., Ren L., Zheng P., Hu X., Jin T., et al. Profiling CD8(+) T cell epitopes of COVID-19 convalescents reveals reduced cellular immune responses to SARS-CoV-2 variants. Cell Rep. 2021;36(11) doi: 10.1016/j.celrep.2021.109708. [DOI] [PMC free article] [PubMed] [Google Scholar]
  78. Zidi I., Laaribi A.B., Bortolotti D., Belhadj M., Mehri A., Yahia H.B., et al. HLA-E polymorphism and soluble HLA-E plasma levels in chronic hepatitis B patients. HLa. 2016;87(3):153–159. doi: 10.1111/tan.12767. [DOI] [PubMed] [Google Scholar]

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Supplementary Materials

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Data Availability Statement

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