SYFPEITHI: database for MHC ligands and peptide motifs

How can the MHC mediate social odor via the microbiota community? A deep dive into mechanisms

Genes of the major histocompatibility complex (MHC) have long been linked to odor signaling and recently researchers’ attention has focused on MHC structuring of microbial communities and how this may in turn impact odor. However, understanding of the mechanisms through which the MHC could affect the microbiota to produce a chemical signal that is both reliable and strong enough to ensure unambiguous transmission of behaviorally important information remains poor. This is largely because empirical studies are rare, predictions are unclear, and the underlying immunological mechanisms governing MHC–microbiota interactions are often neglected. Here, we review the immunological processes involving MHC class II (MHC-II) that could affect the commensal community. Focusing on immunological and medical research, we provide background knowledge for nonimmunologists by describing key players within the vertebrate immune system relating to MHC-II molecules (which present extracellular-derived peptides, and thus interact with extracellular commensal microbes). We then systematically review the literature investigating MHC–odor–microbiota interactions in animals and identify areas for future research. These insights will help to design studies that are able to explore the role of MHC-II and the microbiota in the behavior of wild populations in their natural environment and consequently propel this research area forward.

Designing a SARS-CoV-2 T-Cell-Inducing Vaccine for High-Risk Patient Groups

We describe the results of two vaccinations of a self-experimenting healthy volunteer with SARS-CoV-2-derived peptides performed in March and April 2020, respectively. The first set of peptides contained eight peptides predicted to bind to the individual's HLA molecules. The second set consisted of ten peptides predicted to bind promiscuously to several HLA-DR allotypes. The vaccine formulation contained the new TLR 1/2 agonist XS15 and was administered as an emulsion in Montanide as a single subcutaneous injection. Peripheral blood mononuclear cells isolated from blood drawn before and after vaccinations were assessed using Interferon-γ ELISpot assays and intracellular cytokine staining. We detected vaccine-induced CD4 T cell responses against six out of 11 peptides predicted to bind to HLA-DR after 19 days, following vaccination, for one peptide already at day 12. We used these results to support the design of a T-cell-inducing vaccine for application in high-risk patients, with weakened lymphocyte performance. Meanwhile, an according vaccine, incorporating T cell epitopes predominant in convalescents, is undergoing clinical trial testing.

Interruption of MDM2 signaling augments MDM2-targeted T cell-based antitumor immunotherapy through antigen-presenting machinery

Identification of immunogenic tumor antigens, their corresponding T cell epitopes and the selection of effective adjuvants are prerequisites for developing effective cancer immunotherapies such as therapeutic vaccines. Murine double minute 2 (MDM2) is an E3 ubiquitin-protein ligase that negatively regulates tumor suppressor p53. Because MDM2 overexpression serves as a poor prognosis factor in various types of tumors, it would be beneficial to develop MDM2-targeted cancer vaccines. In this report, we identified an MDM2-derived peptide epitope (MDM2_32-46) that elicited antigen-specific and tumor-reactive CD4⁺ T cell responses. These CD4⁺ T cells directly killed tumor cells via granzyme B. MDM2 is expressed in head and neck cancer patients with poor prognosis, and the T cells that recognize this MDM2 peptide were present in these patients. Notably, Nutlin-3 (MDM2-p53 blocker), inhibited tumor cell proliferation, was shown to augment antitumor T cell responses by increasing MDM2 expression, HLA-class I and HLA-DR through class II transactivator (CIITA). These results suggest that the use of this MDM2 peptide as a therapeutic vaccine combined with MDM2 inhibitors could represent an effective immunologic strategy to treat cancer.

Genetics of HLA Peptide Presentation and Impact on Outcomes in HLA-Matched Allogeneic Hematopoietic Cell Transplantation

Minor histocompatibility antigens (mHAs), recipient-derived peptide epitopes presented on the cell surface, are known to mediate graft-versus-host disease (GVHD); however, there are no current methods to associate mHA features with GVHD risk. This deficiency is due in part to the lack of technological means to accurately predict, let alone confirm, the tremendous number of potential mHAs in each individual transplant. Previous studies have shown that different HLA molecules present varying fractions of candidate peptide epitopes; however, the genetic "distance" between HLA-matched donors and recipients is relatively constrained. From these 2 observations, it is possible that the HLA type for a donor-recipient pair (DRP) would provide a surrogate measurement of the number of predicted mHAs, which could be related to GVHD risk. Because different HLA molecules present variable numbers of peptide antigens, a predicted cumulative peptide-binding efficiency can be calculated for individual DRP based on the pair's HLA type. The purpose of this study was to test whether cumulative peptide-binding efficiency is associated with the risk of acute GVHD (aGVHD) or relapse. In this retrospective Center for International Blood and Marrow Transplant Research study, a total of 3242 HLA-matched DRPs were analyzed for predicted cumulative peptide-binding efficiency using their HLA types and were divided into tertiles based on their scores. Univariable and multivariable analyses was performed to test for associations between cumulative peptide-binding efficiency for DRPs, divided into the HLA-matched related donor (MRD) and HLA-matched unrelated donor (MUD) cohorts, and the primary outcomes of aGVHD and relapse. Secondary outcomes investigated included overall survival, disease-free survival, and transplantation-related mortality. Using a computationally generated peptidome as a test dataset, the tested series of HLA class I displayed peptide-binding frequencies ranging from 0.1% to 3.8% of the full peptidome, and HLA class II molecules had peptide-binding frequencies of 12% to 77% across the HLA-DRB1 allotypes. By increasing binding efficiency tertile, the cumulative incidence of aGVHD at 6 months for MUD patients was 41%, 41%, and 45% for HLA class I (P = .336) and 44%, 41%, and 42% for HLA class II (P = .452). The cumulative incidences of relapse at 3 years for MUD transplant recipients were 36%, 38%, and 38% for HLA class I (P = .533) and 37%, 37%, and 38% for HLA class II (P = .896). The findings were similar for MRD transplant recipients. Multivariable analysis did not identify any impact of peptide-binding efficiency on aGVHD or relapse in MUD or MRD transplant recipients. Whereas GVHD is mediated by minor antigen mismatches in the context of HLA-matched allo-HCT, peptide-binding efficiency, which was used as a surrogate measurement for predicted number of binding antigens, did not provide additional clinical information for GVHD risk assessment. The negative result may be due to the limitations of this surrogate marker, or it is possible that GVHD is driven by a subset of immunogenic mHAs. Further research should be directed at direct mHA epitope and immunogenicity prediction.

Cytotoxic T-lymphocyte elicited therapeutic vaccine candidate targeting cancer against MAGE-A11 carcinogenic protein

Immunotherapy is a breakthrough approach for cancer treatment and prevention. By exploiting the fact that cancer cells have overexpression of tumor antigens responsible for its growth and progression, which can be identified and removed by boosting the immune system. In silico techniques have provided efficient ways for developing preventive measures to ward off cancer. Herein, we have designed a potent cytotoxic T-lymphocyte epitope to elicit a desirable immune response against carcinogenic melanoma-associated antigen-A11. Potent epitope was predicted using reliable algorithms and characterized by advanced computational avenue CABS molecular dynamics simulation, for full flexible binding with HLA-A*0201 and androgen receptor to large-scale rearrangements of the complex system. Results showed the potent immunogenic construct (KIIDLVHLL), from top epitopes using five algorithms. Molecular docking analyses showed the strong binding of epitope with HLA-A*0201 and androgen receptor with docking score of -780.6 and -641.06 kcal/mol, respectively. Molecular dynamics simulation analysis revealed strong binding of lead epitope with androgen receptor by involvement of 127 elements through atomic-model study. Full flexibility study showed stable binding of epitope with an average root mean square deviation (RMSD) 2.21 Å and maximum RMSD value of 6.48 Å in optimal cluster density area. The epitope also showed remarkable results with radius of gyration 23.0777 Å, world population coverage of 39.08% by immune epitope database, and transporter associated with antigen processing (TAP) affinity IC50 value of 2039.65 nm. Moreover, in silico cloning approach confirmed the expression and translation capacity of the construct within a suitable expression vector. The present study paves way for a potential immunogenic construct for prevention of cancer.

A Mouse Model of PPRV Infection for Elucidating Protective and Pathological Roles of Immune Cells

The study was aimed at developing an accessible laboratory animal model to elucidate protective and pathological roles of immune mediators during Peste des petits ruminants virus (PPRV) infection. It is because of the critical roles of type I IFNs in anti-viral defense, we assessed the susceptibility of IFN receptor knock out (IFNR KO) mice to PPRV infection. IFNR KO mice were exceedingly susceptible to the infection but WT animals efficiently controlled PPRV. Accordingly, the PPRV infected IFNR KO mice gradually reduced their body weights and succumbed to the infection within 10 days irrespective of the dose and route of infection. The lower infecting doses predominantly induced immunopathological lesions. The viral antigens as well as the replicating PPRV were abundantly present in most of the critical organs such as brain, lungs, heart and kidneys of IFNR KO mice infected with high dose of the virus. Neutrophils and macrophages transported the replicating virus to central nervous system (CNS) and contributed to pathology while the elevated NK and T cell responses directly correlated with the resolution of PPRV infection in WT animals. Using an array of fluorescently labeled H-2K^b tetramers, we discovered four immunogenic epitopes of PPRV. The PPRV-peptides interacted well with H-2K^b in acellular and cellular assay as well as expanded the virus-specific CD8⁺ T cells in immunized or infected mice. Adoptively transferred CD8⁺ T cells helped control PPRV in infected mice. Our study therefore established and employed a mouse model for investigating the pathogenesis of PPRV. The model could be useful for elucidating the contribution of immune cells in disease progression as well as to test anti-viral agents.

A minigene DNA vaccine encoding peptide epitopes derived from Galectin-1 has protective antitumoral effects in a model of neuroblastoma

We recently identified Galectin-1 (Gal-1), a β-galactoside-binding lectin, as a novel immune regulator in neuroblastoma (NB). Here, we characterized the tolerogenic function of Gal-1 within the CD8⁺ T cell compartment and further evaluated its relevance as an antigen for effective DNA vaccination against NB in a mouse model. NB cells with Gal-1 knockdown (NXS-2L) exhibited significantly reduced tumor growth compared to NXS-2 NB cells. Administration of anti-CD8 antibodies prevented this antitumor effect, with primary tumor growth comparable to that from Gal-1 (G1)-sufficient NB cells. Peptide epitope screening with online databases and in silico docking experiments predicted the sequences "FDQADLTI" (#1), "GDFKIKCV" (#2), and "AHGDANTI" (#3) to have superior H2-KK binding affinities and "KFPNRLNM" (#4), "DGDFKIKCV" (#5), and "LGKDSNNL" (#6) to have superior H2-DD binding affinities. Minigenes encoding G1-KK (#1-#2-#3), G1-DD (#4-#5-#6) and the triplet with the highest affinity, G1-H (#1-#2-#4), were generated and cloned into a ubiquitin-containing plasmid (pU). Mice receiving pU-G1-KK or pU-G-1H presented a reduction in the s.c. tumor volume and weight of up to 80% compared to control mice; this reduction was associated with increased cytotoxicity of isolated splenocytes from vaccinated animals. Vaccination with pUG1-DD showed a lower capability to suppress primary tumor progression. In conclusion, Gal-1 expression by NB negatively regulates CD8⁺ T cells. Vaccination with DNA plasmids encoding Gal-1 epitopes overcomes immune escape, enhances CD8⁺ T cell-dependent immunity and displays effective antitumor activity against NB.

HLA Ligand Atlas: a benign reference of HLA-presented peptides to improve T-cell-based cancer immunotherapy

BACKGROUND

The human leucocyte antigen (HLA) complex controls adaptive immunity by presenting defined fractions of the intracellular and extracellular protein content to immune cells. Understanding the benign HLA ligand repertoire is a prerequisite to define safe T-cell-based immunotherapies against cancer. Due to the poor availability of benign tissues, if available, normal tissue adjacent to the tumor has been used as a benign surrogate when defining tumor-associated antigens. However, this comparison has proven to be insufficient and even resulted in lethal outcomes. In order to match the tumor immunopeptidome with an equivalent counterpart, we created the HLA Ligand Atlas, the first extensive collection of paired HLA-I and HLA-II immunopeptidomes from 227 benign human tissue samples. This dataset facilitates a balanced comparison between tumor and benign tissues on HLA ligand level.

METHODS

Human tissue samples were obtained from 16 subjects at autopsy, five thymus samples and two ovary samples originating from living donors. HLA ligands were isolated via immunoaffinity purification and analyzed in over 1200 liquid chromatography mass spectrometry runs. Experimentally and computationally reproducible protocols were employed for data acquisition and processing.

RESULTS

The initial release covers 51 HLA-I and 86 HLA-II allotypes presenting 90,428 HLA-I- and 142,625 HLA-II ligands. The HLA allotypes are representative for the world population. We observe that immunopeptidomes differ considerably between tissues and individuals on source protein and HLA-ligand level. Moreover, we discover 1407 HLA-I ligands from non-canonical genomic regions. Such peptides were previously described in tumors, peripheral blood mononuclear cells (PBMCs), healthy lung tissues and cell lines. In a case study in glioblastoma, we show that potential on-target off-tumor adverse events in immunotherapy can be avoided by comparing tumor immunopeptidomes to the provided multi-tissue reference.

CONCLUSION

Given that T-cell-based immunotherapies, such as CAR-T cells, affinity-enhanced T cell transfer, cancer vaccines and immune checkpoint inhibition, have significant side effects, the HLA Ligand Atlas is the first step toward defining tumor-associated targets with an improved safety profile. The resource provides insights into basic and applied immune-associated questions in the context of cancer immunotherapy, infection, transplantation, allergy and autoimmunity. It is publicly available and can be browsed in an easy-to-use web interface at https://hla-ligand-atlas.org .

Advanced strategies for development of vaccines against human bacterial pathogens

Infectious diseases are one of the main grounds of death and disabilities in human beings globally. Lack of effective treatment and immunization for many deadly infectious diseases and emerging drug resistance in pathogens underlines the need to either develop new vaccines or sufficiently improve the effectiveness of currently available drugs and vaccines. In this review, we discuss the application of advanced tools like bioinformatics, genomics, proteomics and associated techniques for a rational vaccine design.

Screening for Mycobacterium tuberculosis Infection Using Beijing/K Strain-Specific Peptides in a School Outbreak Cohort

Background

The Beijing strain of Mycobacterium tuberculosis (M. tb) has been most frequently isolated from TB patients in South Korea, and the hyper-virulent Beijing/K genotype is associated with TB outbreaks. To examine the diagnostic potential of Beijing/K-specific peptides, we performed IFN-γ release assays (IGRA) using a MTBK antigen tube containing Beijing/K MTBK_24800, ESAT-6, and CFP-10 peptides in a cohort studied during a school TB outbreak.

Methods

A total of 758 contacts were investigated for M. tb infection, and 43 contacts with latent TB infection (LTBI) and 25 active TB patients were enrolled based on serial screening with QuantiFERON-TB Gold In-Tube tests followed by clinical examinations. Blood collected in MTBK antigen tubes was utilized for IGRA and multiplex cytokine bead arrays. Immune responses were retested in 24 patients after TB treatment, and disease progression was investigated in subjects with LTBI.

Results

Total proportions of active disease and LTBI during the outbreak were 3.7% (28/758) and 9.2% (70/758), respectively. All clinical isolates had a Beijing/K M. tb genotype. IFN-γ responses to the MTBK antigen identified M. tb infection and distinguished between active disease and LTBI. After anti-TB treatment, IFN-γ responses to the MTBK antigen were significantly reduced, and strong TNF-α responses at diagnosis were dramatically decreased.

Conclusions

MTBK antigen-specific IFN-γ has diagnostic potential for differentiating M. tb infection from healthy controls, and between active TB and LTBI as well. In addition, TNF-α is a promising marker for monitoring therapeutic responses. These data provide informative readouts for TB diagnostics and vaccine studies in regions where the Beijing/K strain is endemic.

Vaccinomics approach for scheming potential epitope-based peptide vaccine by targeting l-protein of Marburg virus

Marburg virus is one of the world’s most threatening diseases, causing extreme hemorrhagic fever, with a death rate of up to 90%. The Food and Drug Administration (FDA) currently not authorized any treatments or vaccinations for the hindrance and post-exposure of the Marburg virus. In the present study, the vaccinomics methodology was adopted to design a potential novel peptide vaccine against the Marburg virus, targeting RNA-directed RNA polymerase (l). A total of 48 l-proteins from diverse variants of the Marburg virus were collected from the NCBI GenBank server and used to classify the best antigenic protein leading to predict equally T and B-cell epitopes. Initially, the top 26 epitopes were evaluated for the attraction with major histocompatibility complex (MHC) class I and II alleles. Finally, four prospective central epitopes NLSDLTFLI, FRYEFTRHF, YRLRNSTAL, and YRVRNVQTL were carefully chosen. Among these, FRYEFTRHF and YRVRNVQTL peptides showed 100% conservancy. Though YRLRNSTAL showed 95.74% conservancy, it demonstrated the highest combined score as T cell epitope (2.5461) and population coverage of 94.42% among the whole world population. The epitope was found non-allergenic, and docking interactions with human leukocyte antigens (HLAs) also verified. Finally, in vivo analysis of the recommended peptides might contribute to the advancement of an efficient and exclusively prevalent vaccine that would be an active route to impede the virus spreading.

Predicting Immunogenicity Risk in Biopharmaceuticals

The assessment of immunogenicity of biopharmaceuticals is a crucial step in the process of their development. Immunogenicity is related to the activation of adaptive immunity. The complexity of the immune system manifests through numerous different mechanisms, which allows the use of different approaches for predicting the immunogenicity of biopharmaceuticals. The direct experimental approaches are sometimes expensive and time consuming, or their results need to be confirmed. In this case, computational methods for immunogenicity prediction appear as an appropriate complement in the process of drug design. In this review, we analyze the use of various In silico methods and approaches for immunogenicity prediction of biomolecules: sequence alignment algorithms, predicting subcellular localization, searching for major histocompatibility complex (MHC) binding motifs, predicting T and B cell epitopes based on machine learning algorithms, molecular docking, and molecular dynamics simulations. Computational tools for antigenicity and allergenicity prediction also are considered.

Identification and characterization of heteroclitic peptides in TCR-binding positions with improved HLA-binding efficacy

The antigenicity as well as the immunogenicity of tumor associated antigens (TAAs) may need to be potentiated in order to break the immunological tolerance. To this aim, heteroclitic peptides were designed introducing specific substitutions in the residue at position 4 (p4) binding to TCR. The effect of such modifications also on the affinity to the major histocompatibility class I (MHC-I) molecule was assessed. The Trp2 antigen, specific for the mouse melanoma B16F10 cells, as well as the HPV-E7 antigen, specific for the TC1 tumor cell lines, were used as models. Affinity of such heteroclitic peptides to HLA was predicted by bioinformatics tools and the most promising ones were validated by structural conformational and HLA binding analyses. Overall, we demonstrated that TAAs modified at the TCR-binding p4 residue are predicted to have higher affinity to MHC-I molecules. Experimental evaluation confirms the stronger binding, suggesting that this strategy may be very effective for designing new vaccines with improved antigenic efficacy.

Whole genome deep sequencing analysis of viral quasispecies diversity and evolution in HBeAg seroconverters

Background & Aims

We aimed to investigate how viral quasispecies of the HBV whole genome evolves and diversifies in response to HBeAg seroconversion and viral control utilising next-generation sequencing (NGS).

Methods

Fifty HBeAg-positive chronic hepatitis B patients, including 18 treatment-naïve and 32 interferon (IFN)-treated individuals, were recruited. Serial HBV whole genomes in serum were analysed by NGS to determine sequence characteristics and viral quasispecies.

Results

HBV quasispecies diversity, measured by nucleotide diversity, was negatively correlated with viral load and hepatitis activity. Spontaneous HBeAg seroconverters exhibited significantly greater viral quasispecies diversity than treatment-naïve non-seroconverters from >1 year before seroconversion (0.0112 vs. 0.0060, p <0.01) to >1 year after seroconversion (0.0103 vs. 0.0068, p <0.01). IFN-induced HBeAg seroconverters tended to have higher viral genetic diversity than non-seroconverters along with treatment. Particularly, the IFN responders, defined as IFN-induced HBeAg seroconversion with low viraemia, exhibited significantly greater genetic diversity of whole HBV genome at 6 months post-IFN treatment than IFN non-responders (0.0148 vs. 0.0106, p = 0.048). Moreover, spontaneous HBeAg seroconverters and IFN responders exhibited significantly higher evolutionary rates and more intra-host single-nucleotide variants. Interestingly, in spontaneous HBeAg seroconverters and IFN responders, there were distinct evolutionary patterns in the HBV genome.

Conclusions

Higher HBV quasispecies diversity is associated with spontaneous HBeAg seroconversion and IFN-induced HBeAg seroconversion with low viraemia, conferring a favourable clinical outcome.

Lay summary

HBeAg seroconversion is a landmark in the natural history of chronic HBV infection. Using next-generation sequencing, we found that the nucleotide diversity of HBV was negatively correlated with viral load and hepatitis activity. Patients undergoing HBeAg seroconversion had more diverse HBV genomes and a faster viral evolution rate. Our findings suggest HBeAg seroconversion is driven by host selection pressure, likely immune selection pressure.

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Deep sequencing of whole HBV genome uncovers the quasispecies changes in chronic hepatitis B patients.

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The nucleotide diversity of HBV negatively correlates with viraemia during HBeAg loss/seroconversion.

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Viral quasispecies diversity is greater in spontaneous HBeAg seroconverters before and after seroconversion than in treatment-naïve non-seroconverters.

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Responders to IFN have greater viral quasispecies diversity than non-responders at 24 weeks after treatment.

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The genome positions of non-synonymous intra-host single nucleotide variants (iSNVs) of HBV tend to be located at possible T cell epitopes.

Identification of HLA-A*02:01-restricted candidate epitopes derived from the non-structural polyprotein 1a of SARS-CoV-2 that may be natural targets of CD8+ T cell recognition in vivo

COVID-19 vaccines are being rapidly developed and human trials are underway. Almost all of these vaccines have been designed to induce antibodies targeting spike protein of SARS-CoV-2 in expectation of neutralizing activities. However, non-neutralizing antibodies are at risk of causing antibody-dependent enhancement. Further, the longevity of SARS-CoV-2-specific antibodies is very short. Therefore, in addition to antibody-induced vaccines, novel vaccines on the basis of SARS-CoV-2-specific cytotoxic T lymphocytes (CTLs) should be considered in the vaccine development. Here, we attempted to identify HLA-A*02:01-restricted CTL epitopes derived from the non-structural polyprotein 1a of SARS-CoV-2. Eighty-two peptides were firstly predicted as epitope candidates on bioinformatics. Fifty-four in 82 peptides showed high or medium binding affinities to HLA-A*02:01. HLA-A*02:01 transgenic mice were then immunized with each of the 54 peptides encapsulated into liposomes. The intracellular cytokine staining assay revealed that 18 out of 54 peptides were CTL epitopes because of the induction of IFN-γ-producing CD8⁺ T cells. In the 18 peptides, 10 peptides were chosen for the following analyses because of their high responses. To identify dominant CTL epitopes, mice were immunized with liposomes containing the mixture of the 10 peptides. Some peptides were shown to be statistically predominant over the other peptides. Surprisingly, all mice immunized with the liposomal 10 peptide mixture did not show the same reaction pattern to the 10 peptides. There were three response patterns, suggesting the existence of an immunodominance hierarchy following peptide vaccination, which may provide us more variations in the epitope selection for designing CTL-based COVID-19 vaccines.IMPORTANCE For the development of vaccines based on SARS-CoV-2-specific cytotoxic T lymphocytes (CTLs), we attempted to identify HLA-A*02:01-restricted CTL epitopes derived from the non-structural polyprotein 1a of SARS-CoV-2. Out of 82 peptides predicted on bioinformatics, 54 peptides showed good binding affinities to HLA-A*02:01. Using HLA-A*02:01 transgenic mice, 18 in 54 peptides were found to be CTL epitopes in the intracellular cytokine staining assay. Out of 18 peptides, 10 peptides were chosen for the following analyses because of their high responses. To identify dominant epitopes, mice were immunized with liposomes containing the mixture of the 10 peptides. Some peptides were shown to be statistically predominant. Surprisingly, all immunized mice did not show the same reaction pattern to the 10 peptides. There were three reaction patterns, suggesting the existence of an immunodominance hierarchy following peptide vaccination, which may provide us more variations in the epitope selection for designing CTL-based COVID-19 vaccines.

The Peptide Vaccine of the Future

The approach of peptide-based anticancer vaccination has proven the ability to induce cancer-specific immune responses in multiple studies for various cancer entities. However, clinical responses remain so far limited to single patients and broad clinical applicability was not achieved. Therefore, further efforts are required to improve peptide vaccination in order to integrate this low-side-effect therapy into the clinical routine of cancer therapy. To design clinically effective peptide vaccines in the future, different issues have to be addressed and optimized comprising antigen target selection as well as choice of optimal adjuvants and vaccination schedules. Furthermore, the combination of peptide-based vaccines with other immuno- and molecular targeted therapies as well as the development of predictive biomarkers could further improve efficacy. In this review, current approaches in the development of peptide-based vaccines and critical implications for optimal vaccine design are discussed.

An efficient T-cell epitope discovery strategy using in silico prediction and the iTopia assay platform

Functional T-cell epitope discovery is a key process for the development of novel immunotherapies, particularly for cancer immunology. In silico epitope prediction is a common strategy to try to achieve this objective. However, this approach suffers from a significant rate of false-negative results and epitope ranking lists that often are not validated by practical experience. A high-throughput platform for the identification and prioritization of potential T-cell epitopes is the iTopia(TM) Epitope Discovery System(TM), which allows measuring binding and stability of selected peptides to MHC Class I molecules. So far, the value of iTopia combined with in silico epitope prediction has not been investigated systematically. In this study, we have developed a novel in silico selection strategy based on three criteria: (1) predicted binding to one out of five common MHC Class I alleles; (2) uniqueness to the antigen of interest; and (3) increased likelihood of natural processing. We predicted in silico and characterized by iTopia 225 candidate T-cell epitopes and fixed-anchor analogs from three human tumor-associated antigens: CEA, HER2 and TERT. HLA-A2-restricted fragments were further screened for their ability to induce cell-mediated responses in HLA-A2 transgenic mice. The iTopia binding assay was only marginally informative while the stability assay proved to be a valuable experimental screening method complementary to in silico prediction. Thirteen novel T-cell epitopes and analogs were characterized and additional potential epitopes identified, providing the basis for novel anticancer immunotherapies. In conclusion, we show that combination of in silico prediction and an iTopia-based assay may be an accurate and efficient method for MHC Class I epitope discovery among tumor-associated antigens.

T Cell Peptides Derived from Invasive Stages of Schistosoma mansoni as Potential Schistosomiasis Vaccine

Schistosomiasis is a parasitic disease that affects 143 million people in endemic countries. This work analyzed overexpressed sequences from the cercaria phase to the early schistosomulum phase using bioinformatics tools to predict host interaction and selected proteins for predicting T cell epitopes. The final peptides were chemically synthesized, and their toxicity was evaluated in vitro. Peptides were formulated in the Adjuvant Adaptation (ADAD) vaccination system and injected into BALB/c mice that were challenged with S. mansoni cercariae to assess protection and immunogenicity. A total of 39 highly expressed S.mansoni proteins were identified as being of potential interest. Three T cell peptides predicted to bind MHC mouse and human class II were synthesized and formulated for vaccination. SmGSP and SmIKE reduced the number of eggs trapped in the liver by more than 50% in challenged BALB/c mice. The liver of mice vaccinated with either SmGSP or SmTNP had a significantly reduced affected liver surface. Transcriptome-based T cell peptides elicit partial protection and could be candidates for a multiantigen vaccine.

Anthem: a user customised tool for fast and accurate prediction of binding between peptides and HLA class I molecules

Neopeptide-based immunotherapy has been recognised as a promising approach for the treatment of cancers. For neopeptides to be recognised by CD8+ T cells and induce an immune response, their binding to human leukocyte antigen class I (HLA-I) molecules is a necessary first step. Most epitope prediction tools thus rely on the prediction of such binding. With the use of mass spectrometry, the scale of naturally presented HLA ligands that could be used to develop such predictors has been expanded. However, there are rarely efforts that focus on the integration of these experimental data with computational algorithms to efficiently develop up-to-date predictors. Here, we present Anthem for accurate HLA-I binding prediction. In particular, we have developed a user-friendly framework to support the development of customisable HLA-I binding prediction models to meet challenges associated with the rapidly increasing availability of large amounts of immunopeptidomic data. Our extensive evaluation, using both independent and experimental datasets shows that Anthem achieves an overall similar or higher area under curve value compared with other contemporary tools. It is anticipated that Anthem will provide a unique opportunity for the non-expert user to analyse and interpret their own in-house or publicly deposited datasets.

Immune-based mutation classification enables neoantigen prioritization and immune feature discovery in cancer immunotherapy

Genetic mutations lead to the production of mutated proteins from which peptides are presented to T cells as cancer neoantigens. Evidence suggests that T cells that target neoantigens are the main mediators of effective cancer immunotherapies. Although algorithms have been used to predict neoantigens, only a minority are immunogenic. The factors that influence neoantigen immunogenicity are not completely understood. Here, we classified human neoantigen/neopeptide data into three categories based on their TCR-pMHC binding events. We observed a conservative mutant orientation of the anchor residue from immunogenic neoantigens which we termed the “NP” rule. By integrating this rule with an existing prediction algorithm, we found improved performance in neoantigen prioritization. To better understand this rule, we solved several neoantigen/MHC structures. These structures showed that neoantigens that follow this rule not only increase peptide-MHC binding affinity but also create new TCR-binding features. These molecular insights highlight the value of immune-based classification in neoantigen studies and may enable the design of more effective cancer immunotherapies.

Identification of CTL Epitopes on Efflux Pumps of the ATP-Binding Cassette and the Major Facilitator Superfamily of Mycobacterium tuberculosis

Tuberculosis is the world's most deadly infectious disease, with 10 million people falling ill and 1.5 million people dying from the disease every year. With the increasing number of drug-resistant Mycobacterium tuberculosis (MTB) strains and prevalence of coinfection of MTB with human immunodeficiency virus, many challenges remain in the prevention and treatment of tuberculosis. Therefore, the development of safe and effective tuberculosis vaccines is an urgent issue. In this study, we identified cytotoxic T lymphocyte epitopes on drug resistance-associated membrane protein efflux pumps of MTB, the ATP-binding cassette and the major facilitator superfamilies. First, three online software were used to predict HLA-A2-restricted epitopes. Then, the candidate epitopes were confirmed with the T2A2 cell binding affinity and peptide/MHC (pMHC) complex stability assays and in vitro immune activity experiments. Two drug-resistant T lymphocyte epitopes, designated Rv1218c-p24 and Rv2477c-p182, were selected, and their immunogenic activities studied in vivo in genetically engineered mice. The immune activities of these two epitopes were improved with the help of complete Freund's adjuvant (CFA). The epitopes identified here provide a foundation for the diagnosis and treatment of patients infected with drug resistant and the future development of a multiepitope vaccine.

Experimental Antiglomerular Basement Membrane GN Induced by a Peptide from Actinomyces

BACKGROUND

Antiglomerular basement membrane (anti-GBM) disease is associated with HLA-DRB1*1501 (the major predisposing genetic factor in the disease), with α3_127-148 as a nephritogenic T and B cell epitope. Although the cause of disease remains unclear, the association of infections with anti-GBM disease has been long suspected.

METHODS

To investigate whether microbes might activate autoreactive T and B lymphocytes via molecular mimicry in anti-GBM disease, we used bioinformatic tools, including BLAST, SYFPEITHI, and ABCpred, for peptide searching and epitope prediction. We used sera from patients with anti-GBM disease to assess peptides recognized by antibodies, and immunized WKY rats and a humanized mouse model (HLA-DR15 transgenic mice) with each of the peptide candidates to assess pathogenicity.

RESULTS

On the basis of the critical motif, the bioinformatic approach identified 36 microbial peptides that mimic human α3_127-148. Circulating antibodies in sera from patients with anti-GBM recognized nine of them. One peptide, B7, derived from Actinomyces species, induced proteinuria, linear IgG deposition on the GBM, and crescent formation when injected into WKY rats. The antibodies to B7 also targeted human and rat α3_127-148. B7 induced T cell activation from human α3_127-148-immunized rats. T cell responses to B7 were detected in rats immunized by Actinomyces lysate proteins or recombinant proteins. We confirmed B7's pathogenicity in HLA-DR15 transgenic mice that developed kidney injury similar to that observed in α3_135-145-immunized mice.

CONCLUSIONS

Sera from patients with anti-GBM disease recognized microbial peptides identified through a bioinformatic approach, and a peptide from Actinomyces induced experimental anti-GBM GN by T and B cell crossreactivity. These studies demonstrate that anti-GBM disease may be initiated by immunization with a microbial peptide.

Expression of placenta-specific 1 and its potential for eliciting anti-tumor helper T-cell responses in head and neck squamous cell carcinoma

Placenta-specific 1 (PLAC1) is expressed primarily in placental trophoblasts but not in normal tissues and is a targetable candidate for cancer immunotherapy because it is a cancer testis antigen known to be up-regulated in various tumors. Although peptide epitopes capable of stimulating CD8 T cells have been previously described, there have been no reports of PLAC1 CD4 helper T lymphocyte (HTL) epitopes and the expression of this antigen in head and neck squamous cell carcinoma (HNSCC). Here, we show that PLAC1 is highly expressed in 74.5% of oropharyngeal and 51.9% of oral cavity tumors from HNSCC patients and in several HNSCC established cell lines. We also identified an HTL peptide epitope (PLAC1_31-50) capable of eliciting effective antigen-specific and tumor-reactive T cell responses. Notably, this peptide behaves as a promiscuous epitope capable of stimulating T cells in the context of more than one human leukocyte antigen (HLA)-DR allele and induces PLAC1-specific CD4 T cells that kill PLAC1-positive HNSCC cell lines in an HLA-DR-restricted manner. Furthermore, T-cells reactive to PLAC1_31-50 peptide were detected in the peripheral blood of HNSCC patients. These findings suggest that PLAC1 represents a potential target antigen for HTL based immunotherapy in HNSCC.

Deimmunization of protein therapeutics - Recent advances in experimental and computational epitope prediction and deletion

Biotherapeutics, and antimicrobial proteins in particular, are of increasing interest for human medicine. An important challenge in the development of such therapeutics is their potential immunogenicity, which can induce production of anti-drug-antibodies, resulting in altered pharmacokinetics, reduced efficacy, and potentially severe anaphylactic or hypersensitivity reactions. For this reason, the development and application of effective deimmunization methods for protein drugs is of utmost importance. Deimmunization may be achieved by unspecific shielding approaches, which include PEGylation, fusion to polypeptides (e.g., XTEN or PAS), reductive methylation, glycosylation, and polysialylation. Alternatively, the identification of epitopes for T cells or B cells and their subsequent deletion through site-directed mutagenesis represent promising deimmunization strategies and can be accomplished through either experimental or computational approaches. This review highlights the most recent advances and current challenges in the deimmunization of protein therapeutics, with a special focus on computational epitope prediction and deletion tools.

MHCII3D-Robust Structure Based Prediction of MHC II Binding Peptides

Knowledge of MHC II binding peptides is highly desired in immunological research, particularly in the context of cancer, autoimmune diseases, or allergies. The most successful prediction methods are based on machine learning methods trained on sequences of experimentally characterized binding peptides. Here, we describe a complementary approach called MHCII3D, which is based on structural scaffolds of MHC II-peptide complexes and statistical scoring functions (SSFs). The MHC II alleles reported in the Immuno Polymorphism Database are processed in a dedicated 3D-modeling pipeline providing a set of scaffold complexes for each distinct allotype sequence. Antigen protein sequences are threaded through the scaffolds and evaluated by optimized SSFs. We compared the predictive power of MHCII3D with different sequence-based machine learning methods. The Pearson correlation to experimentally determine IC₅₀ values for MHC II Automated Server Benchmarks data sets from IEDB (Immune Epitope Database) is 0.42, which is in the competitor methods range. We show that MHCII3D is quite robust in leaving one molecule out tests and is therefore not prone to overfitting. Finally, we provide evidence that MHCII3D can complement the current sequence-based methods and help to identify problematic entries in IEDB. Scaffolds and MHCII3D executables can be freely downloaded from our web pages.

SITC cancer immunotherapy resource document: a compass in the land of biomarker discovery

Since the publication of the Society for Immunotherapy of Cancer's (SITC) original cancer immunotherapy biomarkers resource document, there have been remarkable breakthroughs in cancer immunotherapy, in particular the development and approval of immune checkpoint inhibitors, engineered cellular therapies, and tumor vaccines to unleash antitumor immune activity. The most notable feature of these breakthroughs is the achievement of durable clinical responses in some patients, enabling long-term survival. These durable responses have been noted in tumor types that were not previously considered immunotherapy-sensitive, suggesting that all patients with cancer may have the potential to benefit from immunotherapy. However, a persistent challenge in the field is the fact that only a minority of patients respond to immunotherapy, especially those therapies that rely on endogenous immune activation such as checkpoint inhibitors and vaccination due to the complex and heterogeneous immune escape mechanisms which can develop in each patient. Therefore, the development of robust biomarkers for each immunotherapy strategy, enabling rational patient selection and the design of precise combination therapies, is key for the continued success and improvement of immunotherapy. In this document, we summarize and update established biomarkers, guidelines, and regulatory considerations for clinical immune biomarker development, discuss well-known and novel technologies for biomarker discovery and validation, and provide tools and resources that can be used by the biomarker research community to facilitate the continued development of immuno-oncology and aid in the goal of durable responses in all patients.

Analysis of latent T-cell epitopes in Epstein-Barr virus isolated from extranodal nasal-type natural killer/T-cell lymphoma in Taiwanese population

Extranodal nasal-type natural killer (NK)/T-cell lymphoma (NKTCL) is an aggressive lymphoma that is prevalent among East Asian and South American populations. Although Epstein-Barr virus (EBV) is commonly detected in NKTCL, there are limited studies that have analyzed the EBV genomic variations in NKTCL. In this study, 8 EBV latent genes were analyzed using targeted gene sequencing in 23 formalin-fixed paraffin-embedded tissues derived from 18 patients with NKTCL. Five cases with paired samples were comparatively analyzed. The consistency of EBV sequencing data between tissue samples was high (96.3%-98.7%), whereas that of variant calling among the tissue samples and plasma samples (74.3%-79.2%) was low. The highest densities of non-synonymous variants were detected in the EBNA3B gene. Among the 74 known T-cell epitopes, 363 non-synonymous variants were identified in 32 (43.2%) epitopes. Additionally, the AVFDRKSDAK (A1S/P and V2F/M/L) and YHLIVDTDSL (I4L and L10R/V/G/H) epitopes were associated with 5 patterns of amino acid changes in EBNA3B and EBNA-2, respectively. The frequency of variation in the human leukocyte antigen (HLA)-restricted epitopes with corresponding HLA types common among Taiwanese population was significantly low (P = 0.011), whereas that in anchor residues was significantly high (P = 0.012). In conclusion, this study demonstrated the genomic diversity of EBV in NKTCL and its correlation with the HLA-restricted epitope variations in Taiwanese population. The findings of this study provide useful insights for the development of novel therapeutic strategies for NKTCL.

dbPepNeo: a manually curated database for human tumor neoantigen peptides

Neoantigens can function as actual antigens to facilitate tumor rejection, which play a crucial role in cancer immunology and immunotherapy. Emerging evidence revealed that neoantigens can be used to develop personalized, cancer-specific vaccines. To date, large numbers of immunogenomic peptides have been computationally predicted to be potential neoantigens. However, experimental validation remains the gold standard for potential clinical application. Experimentally validated neoantigens are rare and mostly appear scattered among scientific papers and various databases. Here, we constructed dbPepNeo, a specific database for human leukocyte antigen class I (HLA-I) binding neoantigen peptides based on mass spectrometry (MS) validation or immunoassay in human tumors. According to the verification methods of these neoantigens, the collection of peptides was classified as 295 high confidence, 247 medium confidence and 407 794 low confidence neoantigens, respectively. This can serve as a valuable resource to aid further screening for effective neoantigens, optimize a neoantigen prediction pipeline and study T-cell receptor (TCR) recognition. Three applications of dbPepNeo are shown. In summary, this work resulted in a platform to promote the screening and confirmation of potential neoantigens in cancer immunotherapy.

Database URL: www.biostatistics.online/dbPepNeo/.

An In Silico Immunogenicity Analysis for PbHRH: An Antiangiogenic Peptibody by Fusing HRH Peptide and Human IgG1 Fc Fragment

Background:

Peptibodies, the hybrid of peptides and antibodies, represent a novel strategy in therapeutic use. Previously, we computationally designed an antiangiogenic peptibody PbHRH, which fused the HRH peptide with angiogenesis-suppressing effect and human IgG1 Fc fragment using Romiplostim as template. Molecular modeling and simulation results indicated that it would be a potential drug for the treatment of those angiogenesis related pathological disorders. However, its immunogenicity is not known.

Methods:

Several bioinformatics tools are used to predict the potential epitopes for the evaluation of the immunogenicity of PbHRH. Romiplostim is set as the control. IEDB-recommended method is used in MHC-I and MHC-II binding prediction, and the IEDB web server (http://tools.iedb.org/immunogenicity/) is used to determine the MHC-I immunogenicity of each peptide.

Results:

In this work, some peptides are predicted to have the potential ability to bind to MHC-I and MHC-II molecules both in PbHRH and Romiplostim as the potential epitopes. Most of these selected peptides are exactly the same. Allele frequency analysis shows a low population distribution. Combined with the analysis of MHC-I immunogenicity prediction, both HRH and PbHRH show low immunogenicity.

Conclusion:

Some potential epitopes which could bind to both MHC-I and MHC-II molecules are predicted using bioinformatics tools. The comparative analysis with Romiplostim and the results of MHC-I immunogenicity prediction indicate the low immunogenicity of both HRH and PbHRH. Thus, we form a strategy to evaluate the immunogenicity of peptibodies for the future improvement.

Mild Acid Elution and MHC Immunoaffinity Chromatography Reveal Similar Albeit Not Identical Profiles of the HLA Class I Immunopeptidome

To understand and treat immunology-related diseases, a comprehensive, unbiased characterization of major histocompatibility complex (MHC) peptide ligands is of key importance. Preceding the analysis by mass spectrometry, MHC class I peptide ligands are typically isolated by MHC immunoaffinity chromatography (MHC-IAC) and less often by mild acid elution (MAE). MAE may provide a cheap alternative to MHC-IAC for suspension cells but has been hampered by the high number of contaminating, MHC-unrelated peptides. Here, we optimized MAE, yielding MHC peptide ligand purities of more than 80%. When compared with MHC-IAC, obtained peptides were similar in numbers, identities, and to a large extent intensities, while the percentage of cysteinylated peptides was 5 times higher in MAE. The latter benefitted the discovery of MHC-allotype-specific, distinct cysteinylation frequencies at individual positions of MHC peptide ligands. MAE revealed many MHC ligands with unmodified, N-terminal cysteine residues which get lost in MHC-IAC workflows. The results support the idea that MAE might be particularly valuable for the high-confidence analysis of post-translational modifications by avoiding the exposure of the investigated peptides to enzymes and reactive molecules in the cell lysate. Our improved and carefully documented MAE workflow represents a high-quality, cost-effective alternative to MHC-IAC for suspension cells.

The neglected challenge: Vaccination against rickettsiae

Over the last decades, rickettsioses are emerging worldwide. These diseases are caused by intracellular bacteria. Although rickettsioses can be treated with antibiotics, a vaccine against rickettsiae is highly desired for several reasons. Rickettsioses are highly prevalent, especially in poor countries, and there are indications of the development of antibiotic resistance. In addition, some rickettsiae can persist and cause recurrent disease. The development of a vaccine requires the understanding of the immune mechanisms that are involved in protection as well as in immunopathology. Knowledge about these immune responses is accumulating, and efforts have been undertaken to identify antigenic components of rickettsiae that may be useful as a vaccine. This review provides an overview on current knowledge of adaptive immunity against rickettsiae, which is essential for defense, rickettsial antigens that have been identified so far, and on vaccination strategies that have been used in animal models of rickettsial infections.

Exploiting B-cell Receptor Stereotypy to Design Tailored Immunotherapy in Chronic Lymphocytic Leukemia

PURPOSE

Approximately 30% of patients with chronic lymphocytic leukemia (CLL) can be grouped into subsets with stereotyped B-cell receptor immunoglobulin (BcR IG) displaying remarkable similarity in the heavy complementarity-determining region 3 (VH CDR3). Here, we investigated whether the consensus VH CDR3 sequences from CLL stereotyped subsets can be exploited for immunotherapy approaches.

EXPERIMENTAL DESIGN

Immunogenic epitopes from the consensus VH CDR3 sequence of the clinically aggressive subsets #1 and #2 and from Eμ-TCL1 mice, which spontaneously develop CLL with BcR IG stereotypy, were identified and used to generate specific HLA class I- and II-restricted T cells in vitro. T-cell reactivity was assayed in vitro as IFNγ production. Bone marrow-derived dendritic cells loaded with the peptides were used as vaccination strategy to restrain leukemia development in the Eμ-TCL1 mouse model.

RESULTS

These stereotyped epitopes were naturally processed and presented by CLL cells to the VH CDR3-specific T cells. Furthermore, we validated the efficacy of VH CDR3 peptide-based immunotherapy in the Eμ-TCL1 transplantable mouse model. Immunization of mice against defined VH CDR3 peptide epitopes, prior to the challenge with the corresponding leukemia cells, resulted in the control of CLL development in a significant fraction of mice, and increased overall survival.

CONCLUSIONS

Our data highlight the immunogenicity of stereotyped VH CDR3 sequences and support the feasibility and efficacy of their use for novel cancer vaccine in CLL. Such approach has the advantage to generate "off-the-shelf" therapeutic vaccines for relevant groups of patients belonging to stereotyped subsets.See related commentary by Seiffert, p. 659.

The role of immunoinformatics in the development of T-cell peptide-based vaccines against Mycobacterium tuberculosis

INTRODUCTION

Tuberculosis (TB) is a major health problem worldwide. The BCG, the only authorized vaccine to fight TB, shows a variable protection in the adult population highlighting the need of a new vaccine. Immunoinformatics offers a variety of tools that can predict immunogenic T-cell peptides of Mycobacterium tuberculosis (Mtb) that can be used to create a new vaccine. Immunoinformatics has made possible the identification of immunogenic T-cell peptides of Mtb that have been tested in vitro showing a potential for using these molecules as part of a new TB vaccine.

AREAS COVERED

This review summarizes the most common immunoinformatics tools to identify immunogenic T-cell peptides and presents a compilation about research studies that have identified T-cell peptides of Mtb by using immunoinformatics. Also, it is provided a summary of the TB vaccines undergoing clinical trials.

EXPERT OPINION

In the next few years, the field of peptide-based vaccines will keep growing along with the development of more efficient and sophisticated immunoinformatic tools to identify immunogenic peptides with a greater accuracy.

POTN: A Human Leukocyte Antigen-A2 Immunogenic Peptides Screening Model and Its Applications in Tumor Antigens Prediction

Whole genome/exome sequencing data for tumors are now abundant, and many tumor antigens, especially mutant antigens (neoantigens), have been identified for cancer immunotherapy. However, only a small fraction of the peptides from these antigens induce cytotoxic T cell responses. Therefore, efficient methods to identify these antigenic peptides are crucial. The current models of major histocompatibility complex (MHC) binding and antigenic prediction are still inaccurate. In this study, 360 9-mer peptides with verified immunological activity were selected to construct a prediction of tumor neoantigen (POTN) model, an immunogenic prediction model specifically for the human leukocyte antigen-A2 allele. Based on the physicochemical properties of amino acids, such as the residue propensity, hydrophobicity, and organic solvent/water, we found that the predictive capability of POTN is superior to that of the prediction programs SYPEITHI, IEDB, and NetMHCpan 4.0. We used POTN to screen peptides for the cancer-testis antigen located on the X chromosome, and we identified several peptides that may trigger immunogenicity. We synthesized and measured the binding affinity and immunogenicity of these peptides and found that the accuracy of POTN is higher than that of NetMHCpan 4.0. Identifying the properties related to the T cell response or immunogenicity paves the way to understanding the MHC/peptide/T cell receptor complex. In conclusion, POTN is an efficient prediction model for screening high-affinity immunogenic peptides from tumor antigens, and thus provides useful information for developing cancer immunotherapy.

In silico analysis and in vivo assessment of a novel epitope-based vaccine candidate against uropathogenic Escherichia coli

Uropathogenic Escherichia coli (UPEC) are common pathogens in urinary tract infections (UTIs), which show resistance to antibiotics. Therefore, there is a need for a vaccine to reduce susceptibility to the infection. In the present study, bioinformatics approaches were employed to predict the best B and T-cell epitopes of UPEC virulence proteins to develop a multiepitope vaccine candidate against UPEC. Then, the efficacy of the candidate was studied with and without Freund adjuvant. Using bioinformatics methods, 3 epitope-rich domains of IutA and FimH antigens were selected to construct the fusion. Molecular docking and Molecular dynamics (MD) simulation were employed to investigate in silico interaction between designed vaccine and Toll-like receptor 4 (TLR4). Our results showed that the levels of IgG and IgA antibodies were improved in the serum and mucosal samples of the vaccinated mice, and the IgG responses were maintained for at least 6 months. The fusion protein was also able to enhance the level of cytokines IFN.γ (Th1), IL.4 (Th2), and IL.17. In challenge experiments, all vaccine combinations showed high potency in the protection of the urinary tract even after 6 months post first injection. The present study indicates that the designed candidate is able to evoke strong protective responses which warrant further studies.

Combining Three-Dimensional Modeling with Artificial Intelligence to Increase Specificity and Precision in Peptide-MHC Binding Predictions

The reliable prediction of the affinity of candidate peptides for the MHC is important for predicting their potential antigenicity and thus influences medical applications, such as decisions on their inclusion in T cell-based vaccines. In this study, we present a rapid, predictive computational approach that combines a popular, sequence-based artificial neural network method, NetMHCpan 4.0, with three-dimensional structural modeling. We find that the ensembles of bound peptide conformations generated by the programs MODELLER and Rosetta FlexPepDock are less variable in geometry for strong binders than for low-affinity peptides. In tests on 1271 peptide sequences for which the experimental dissociation constants of binding to the well-characterized murine MHC allele H-2Db are known, by applying thresholds for geometric fluctuations the structure-based approach in a standalone manner drastically improves the statistical specificity, reducing the number of false positives. Furthermore, filtering candidates generated with NetMHCpan 4.0 with the structure-based predictor led to an increase in the positive predictive value (PPV) of the peptides correctly predicted to bind very strongly (i.e., K d < 100 nM) from 40 to 52% (p = 0.027). The combined method also significantly improved the PPV when tested on five human alleles, including some with limited data for training. Overall, an average increase of 10% in the PPV was found over the standalone sequence-based method. The combined method should be useful in the rapid design of effective T cell-based vaccines.

SARS-CoV-2-derived peptides define heterologous and COVID-19-induced T cell recognition

T cell immunity is central for the control of viral infections. To characterize T cell immunity, but also for the development of vaccines, identification of exact viral T cell epitopes is fundamental. Here we identify and characterize multiple dominant and subdominant SARS-CoV-2 HLA class I and HLA-DR peptides as potential T cell epitopes in COVID-19 convalescent and unexposed individuals. SARS-CoV-2-specific peptides enabled detection of post-infectious T cell immunity, even in seronegative convalescent individuals. Cross-reactive SARS-CoV-2 peptides revealed pre-existing T cell responses in 81% of unexposed individuals and validated similarity with common cold coronaviruses, providing a functional basis for heterologous immunity in SARS-CoV-2 infection. Diversity of SARS-CoV-2 T cell responses was associated with mild symptoms of COVID-19, providing evidence that immunity requires recognition of multiple epitopes. Together, the proposed SARS-CoV-2 T cell epitopes enable identification of heterologous and post-infectious T cell immunity and facilitate development of diagnostic, preventive and therapeutic measures for COVID-19.

MMP-7 derived peptides with MHC class-I binding motifs from canine mammary tumor tissue elicit strong antigen-specific T-cell responses in BALB/c mice

Matrix Metalloproteinases (MMPs)-induced altered proteolysis of extracellular matrix proteins and basement membrane holds the key for tumor progression and metastasis. Matrix metalloproteinases-7 (Matrilysin), the smallest member of the MMP family also performs quite alike; thus serves as a potential candidate for anti-tumor immunotherapy. Conversely, being an endogenous tumor-associated antigen (TAA), targeting MMP-7 for immunization is challenging. But MMP-7-based xenovaccine can surmount the obstacle of poor immunogenicity and immunological tolerance, often encountered in TAA-based conventional vaccine for anti-tumor immunotherapy. This paves the way for investigating the potential of MMP-7-derived major histocompatibility complex (MHC)-binding peptides to elicit precise epitope-specific T-cell responses towards their possible inclusion in anti-tumor vaccine formulations. Perhaps it also ushers the path of achieving multiple epitope-based broad and universal cellular immunity. In current experiment, an immunoinformatics approach has been employed to identify the putative canine matrix matelloproteinases-7 (cMMP-7)-derived peptides with MHC class-I-binding motifs which can elicit potent antigen-specific immune responses in BALB/c mice. Immunization with the cMMP-7 DNA vaccine induced a strong CD8⁺ cytotoxic T lymphocytes (CTLs) and Th1- type response, with high level of gamma interferon (IFN-γ) production in BALB/c mice. The two identified putative MHC-I-binding nonameric peptides (Peptide_32-40 and Peptide_175-183) from cMMP-7 induced significant lymphocyte proliferation along with the production of IFN-γ from CD8⁺ T-cells in mice immunized with cMMP-7 DNA vaccine. The current observation has depicted the immunogenic potential of the two cMMP-7-derived nonapeptides for their possible exploitation in xenovaccine-mediated anti-tumor immunotherapy in mouse model.

Point mutation screening of tumor neoantigens and peptide-induced specific cytotoxic T lymphocytes using The Cancer Genome Atlas database

The aim of the present study was to use The Cancer Genome Atlas (TCGA) database to identify tumor neoantigens, combined with a bioinformatics analysis to design and analyze antigen epitope peptides. Epitopes were screened using immunogenicity tests to identify the ideal epitope peptides to target tumor neoantigens, which can specifically activate the immune response of T cells. The high-frequency mutation loci (top 10) of colorectal, lung and liver cancer genes were screened using TCGA database. The antigenic epitope peptides with high affinity for major histocompatibility complex molecules were selected and synthesized using computer prediction algorithms, and were subsequently detected using flow cytometry. The cytotoxicity of specific cytotoxic T lymphocytes (CTLs) on peptide-loaded T2 cells was initially verified using interferon IFN-γ detection and a calcein-acetoxymethyl (Cal-AM) release assay. Tumor cell lines expressing point mutations in KRAS, TP53 and CTNNB1 genes were constructed respectively, and the cytotoxicity of peptide-induced specific CTLs on wild-type and mutant tumor cells was verified using a Cal-AM release assay and carboxyfluorescein succinimidyl ester-propidium iodide staining. The high-frequency gene mutation loci of KRAS proto-oncogene (KRAS) G12V, tumor protein p53 (TP53) R158L and catenin β1 (CTNNB1) K335I were identified in TCGA database. A total of 3 groups of wild-type and mutant peptides were screened using a peptide prediction algorithm. The CTNNB1 group had a strong affinity for the human leukocyte antigen-A2 molecule, as determined using flow cytometry. The IFN-γ secretion of specific CTLs in the CTNNB1 group was the highest, followed by the TP53 and the KRAS groups. The killing rate of mutant peptide-induced specific CTLs on peptide-loaded T2 cells in the CTNNB1 group was higher compared with that observed in the other groups. The killing rate of specific CTLs induced by mutant peptides present on tumor cells was higher compared with that induced by wild-type peptides. However, when compared with the TP53 and KRAS groups, specific CTLs induced by mutant peptides in the CTNNB1 group had more potent cytotoxicity towards mutant and wild-type tumor cells. In conclusion, point mutant tumor neoantigens screened in the three groups improved the cytotoxicity of specific T cells, and the mutant peptides in the CTNNB1 group were more prominent, indicating that they may activate the cellular immune response more readily.

Repertoire-scale determination of class II MHC peptide binding via yeast display improves antigen prediction

CD4⁺ helper T cells contribute important functions to the immune response during pathogen infection and tumor formation by recognizing antigenic peptides presented by class II major histocompatibility complexes (MHC-II). While many computational algorithms for predicting peptide binding to MHC-II proteins have been reported, their performance varies greatly. Here we present a yeast-display-based platform that allows the identification of over an order of magnitude more unique MHC-II binders than comparable approaches. These peptides contain previously identified motifs, but also reveal new motifs that are validated by in vitro binding assays. Training of prediction algorithms with yeast-display library data improves the prediction of peptide-binding affinity and the identification of pathogen-associated and tumor-associated peptides. In summary, our yeast-display-based platform yields high-quality MHC-II-binding peptide datasets that can be used to improve the accuracy of MHC-II binding prediction algorithms, and potentially enhance our understanding of CD4⁺ T cell recognition.

Identifying peptides that can bind major histocompatibility complex II (MHC-II) is important for our understanding of T cell immunity and specificity. Here the authors present a yeast-display library screening approach that identifies more potential binders than various reported algorithms to help expand our understanding for antigen presentation.

TCR Redirected T Cells for Cancer Treatment: Achievements, Hurdles, and Goals

Adoptive T cell therapy (ACT) is a rapidly evolving therapeutic approach designed to harness T cell specificity and function to fight diseases. Based on the evidence that T lymphocytes can mediate a potent anti-tumor response, initially ACT solely relied on the isolation, in vitro expansion, and infusion of tumor-infiltrating or circulating tumor-specific T cells. Although effective in a subset of cases, in the first ACT clinical trials several patients experienced disease progression, in some cases after temporary disease control. This evidence prompted researchers to improve ACT products by taking advantage of the continuously evolving gene engineering field and by improving manufacturing protocols, to enable the generation of effective and long-term persisting tumor-specific T cell products. Despite recent advances, several challenges, including prioritization of antigen targets, identification, and optimization of tumor-specific T cell receptors, in the development of tools enabling T cells to counteract the immunosuppressive tumor microenvironment, still need to be faced. This review aims at summarizing the major achievements, hurdles and possible solutions designed to improve the ACT efficacy and safety profile in the context of liquid and solid tumors.

Exploring membrane proteins of Leishmania major to design a new multi-epitope vaccine using immunoinformatics approach

Leishmaniasis is one of the major global endemic diseases. Among all the different forms of the disease, cutaneous Leishmaniasis has the highest prevalence worldwide. Treatment with current drugs has not had a significant effect on the improvement of the disease. An attempt to replace an appropriate vaccine that can stimulate host cellular immunity and induce the response of Major histocompatibility complex I (MHCI) and Major histocompatibility complex II (MHCII) against Leishmania is essential. Vaccine production remains a challenge despite the use of different antigens for vaccination against Leishmania major. Hence, we were used the immunoinformatics approach to design a new multi-epitope vaccine against L. major using immunogenic outer membrane proteins. Helper T-lymphocyte (HTL) and Cytotoxic T lymphocyte (CTL) epitopes were predicted and for final confirmation of the selected epitopes, docking analysis, and molecular dynamics simulation was performed. Then, GDGDG linker and profilin adjuvant were added to enhance the immunity of vaccines. The designed vaccine was evaluated in terms of molecular weight, PI, immunogenicity, and allergenicity. Moreover, the secondary and three-dimensional structure of the final construct was identified. In silico cloning approach was carried out to improve expression of the vaccine construct. Finally, molecular docking, followed by molecular dynamic was performed to determine the interaction between multi-epitope vaccine and TLR11. We hope that the designed vaccine can be a good candidate for the development of cutaneous leishmaniasis vaccine. but its effectiveness should be assessed in vivo.

Immunoinformatics and molecular dynamics studies to predict T-cell-specific epitopes of four Klebsiella pneumoniae fimbriae antigens

Klebsiella pneumoniae (K. pneumoniae) is a causative agent of severe infections in humans. There is no publically available vaccine for K. pneumoniae infections yet. Here, using comprehensive immunoinformatics methods, T-cell-specific epitopes of four type 1 fimbriae antigens of K. pneumoniae were predicted and evaluated as potential vaccine candidates. Both CD8+ (class I) and CD4+ (class II) T-cell-specific epitopes were predicted and the epitopes similar to human proteome were excluded. Subsequently, the windows of class-II epitopes containing class-I epitopes were determined. The immunogenicity, IFN-γ production and population coverage were also estimated. Using the 3D structure of HLA and epitopes, molecular docking was carried out. Two best epitopes were selected for molecular dynamics studies. Our prediction and analyses resulted in the several dominant epitopes for each antigen. The docking results showed that all selected epitopes can bind to their restricted HLA molecules with high affinity. The molecular dynamics results indicated the stability of system with minimum possible deviation, suggesting the selected epitopes can be promising candidates for stably binding to HLA molecules. Altogether, our results suggest that the selected T-cell-specific epitopes of K. pneumoniae fimbriae antigens, particularly the two epitopes confirmed by molecular dynamics, can be applied for vaccine development. However, the in vitro and in vivo studies are required to authenticate the results of the present study.Communicated by Ramaswamy H. Sarma.

An overview of in silico vaccine design against different pathogens and cancer

INTRODUCTION

Due to overcome the hardness of the vaccine design, computational vaccinology is emerging widely. Prediction of T cell and B cell epitopes, antigen processing analysis, antigenicity analysis, population coverage, conservancy analysis, allergenicity assessment, toxicity prediction, and protein-peptide docking are important steps in the process of designing and developing potent vaccines against various viruses and cancers. In order to perform all of the analyses, several bioinformatics tools and online web servers have been developed. Scientists must take the decision to apply more suitable and precise servers for each part based on their accuracy.

AREAS COVERED

In this review, a wide-range list of different bioinformatics tools and online web servers has been provided. Moreover, some studies were proposed to show the importance of various bioinformatics tools for predicting and developing efficient vaccines against different pathogens including viruses, bacteria, parasites, and fungi as well as cancer.

EXPERT OPINION

Immunoinformatics is the best way to find potential vaccine candidates against different pathogens. Thus, the selection of the most accurate tools is necessary to predict and develop potent preventive and therapeutic vaccines. To further evaluation of the computational and in silico vaccine design, in vitro/in vivo analyses are required to develop vaccine candidates.

Progress in Neoantigen Targeted Cancer Immunotherapies

Immunotherapies that harness the immune system to kill cancer cells have showed significant therapeutic efficacy in many human malignancies. A growing number of studies have highlighted the relevance of neoantigens in recognizing cancer cells by intrinsic T cells. Cancer neoantigens are a direct consequence of somatic mutations presenting on the surface of individual cancer cells. Neoantigens are fully cancer-specific and exempt from central tolerance. In addition, neoantigens are important targets for checkpoint blockade therapy. Recently, technological innovations have made neoantigen discovery possible in a variety of malignancies, thus providing an impetus to develop novel immunotherapies that selectively enhance T cell reactivity for the destruction of cancer cells while leaving normal tissues unharmed. In this review, we aim to introduce the methods of the identification of neoantigens, the mutational patterns of human cancers, related clinical trials, neoantigen burden and sensitivity to immune checkpoint blockade. Moreover, we focus on relevant challenges of targeting neoantigens for cancer treatment.

Vaccine candidate designed against carcinoembryonic antigen-related cell adhesion molecules using immunoinformatics tools

Carcinoembryonic antigen-related cell adhesion (CEACAM) molecules belong to a family of membrane glycoproteins that mediate intercellular interactions influencing cellular growth, immune cell activation, apoptosis, and tumor suppression. Several family members (CEACAM1, CEACAM5, and CEACAM6) are highly expressed in cancers, and they share a conserved N-terminal domain that serves as an attractive target for cancer immunotherapy. A multi-epitope vaccine candidate against this conserved domain has been developed using immunoinformatics tools. Specifically, several epitopes predicted to interact with MHC class I and II molecules were linked together with appropriate linkers. The tertiary structure of the vaccine is generated by homology and ab initio modeling. Molecular docking of epitopes to MHC structures have revealed that the lowest energy conformations are the epitopes bound to the antigen-binding groove of the MHC molecules. Subsequent molecular dynamics simulation has confirmed the stability of the binding conformations in solution. The predicted vaccine has relatively high antigenicity and low allergenicity, suggesting that it is an ideal candidate for further refinement and development.Communicated by Ramaswamy H. Sarma.