PAComplex: a web server to infer peptide antigen families and binding models from TCR-pMHC complexes

Insights into structural vaccinology harnessed for universal coronavirus vaccine development

Structural vaccinology is pivotal in expediting vaccine design through high-throughput screening of immunogenic antigens. Leveraging the structural and functional characteristics of antigens and immune cell receptors, this approach employs protein structural comparison to identify conserved patterns in key pathogenic components. Molecular modeling techniques, including homology modeling and molecular docking, analyze specific three-dimensional (3D) structures and protein interactions and offer valuable insights into the 3D interactions and binding affinity between vaccine candidates and target proteins. In this review, we delve into the utilization of various immunoinformatics and molecular modeling tools to streamline the development of broad-protective vaccines against coronavirus disease 2019 variants. Structural vaccinology significantly enhances our understanding of molecular interactions between hosts and pathogens. By accelerating the pace of developing effective and targeted vaccines, particularly against the rapidly mutating severe acute respiratory syndrome coronavirus 2 and other prevalent infectious diseases, this approach stands at the forefront of advancing immunization strategies. The combination of computational techniques and structural insights not only facilitates the identification of potential vaccine candidates but also contributes to the rational design of vaccines, fostering a more efficient and targeted approach to combatting infectious diseases.

Multi-epitopevaccines, from design to expression; an in silico approach

The development of vaccines against a wide range of infectious diseases and pathogens often relies on multi-epitope strategies that can effectively stimulate both humoral and cellular immunity. Immunoinformatics tools play a pivotal role in designing such vaccines, enhancing immune response potential, and minimizing the risk of failure. This review presents a comprehensive overview of practical tools for epitope prediction and the associated immune responses. These immunoinformatics tools facilitate the selection of epitopes based on parameters such as antigenicity, absence of toxic and allergenic sequences, secondary and tertiary structures, sequence conservation, and population coverage. The chosen epitopes can be tailored for B-cells or T-cells, both of which require further assessments covered in this study. We offer a range of suitable linkers that effectively separate cytotoxic T lymphocyte and helper T lymphocyte epitopes while preserving their functionality. Additionally, we identify various adjuvants for specific purposes. We delve into the evaluation of MHC-epitope interactions, MHC clusters, and the simulation of final constructs through molecular docking techniques. We provide diverse linkers and adjuvants optimized for epitope functions to bolster immune responses through epitope attachment. By leveraging these comprehensive tools, the development of multi-epitope vaccines holds the promise of robust immunity and a significant reduction in experimental costs.

Not all T cell epitopes are equally desired: a review of in silico tools for the prediction of cytokine-inducing potential of T-cell epitopes

Assessment of protective or harmful T cell response induced by any antigenic epitope is important in designing any immunotherapeutic molecule. The understanding of cytokine induction potential also helps us to monitor antigen-specific cellular immune responses and rational vaccine design. The classical immunoinformatics tools served well for prediction of B cell and T cell epitopes. However, in the last decade, the prediction algorithms for T cell epitope inducing specific cytokines have also been developed and appreciated in the scientific community. This review summarizes the current status of such tools, their applications, background algorithms, their use in experimental setup and functionalities available in the tools/web servers.

A structural vaccinology approach for in silico designing of a potential self-assembled nanovaccine against Leishmania infantum

Visceral leishmaniasis (VL) remains a major public health problem across 98 countries. To date, VL has no effective drug. Vaccines, as the most successful breakthroughs in medicine, can promise an effective strategy to fight various diseases. More recently, self-assembled peptide nanoparticles (SAPNs) have attracted considerable attention in the field of vaccine design due to their multivalency. In this study, a SAPN nanovaccine was designed using various immunoinformatics methods. High-ranked epitopes were chosen from a number of antigens, including Leishmania-specific hypothetical protein (LiHy), Leishmania-specific antigenic protein (LSAP), histone H1, and sterol 24-c-methyltransferase (SMT). To facilitate the oligomerization process, pentameric and trimeric coiled-coil domains were employed. RpfE, a resuscitation-promoting factor of Mycobacterium tuberculosis, was added to induce strong immune responses. Pentameric and trimeric coiled-coil domains as well as eight immunodominant epitopes from antigenic proteins of Leishmania infantum, the causative agent of VL, were joined together using appropriate linkers. High-quality 3D structure of monomeric and oligomeric structures followed by refinement and validation processes demonstrated that the designed nanovaccine could be considered to be a promising medication against the parasite; however, experimental validation is essential to confirm the effectiveness of the nanovaccine.

Cancer Neoantigens: Challenges and Future Directions for Prediction, Prioritization, and Validation

Prioritization of immunogenic neoantigens is key to enhancing cancer immunotherapy through the development of personalized vaccines, adoptive T cell therapy, and the prediction of response to immune checkpoint inhibition. Neoantigens are tumor-specific proteins that allow the immune system to recognize and destroy a tumor. Cancer immunotherapies, such as personalized cancer vaccines, adoptive T cell therapy, and immune checkpoint inhibition, rely on an understanding of the patient-specific neoantigen profile in order to guide personalized therapeutic strategies. Genomic approaches to predicting and prioritizing immunogenic neoantigens are rapidly expanding, raising new opportunities to advance these tools and enhance their clinical relevance. Predicting neoantigens requires acquisition of high-quality samples and sequencing data, followed by variant calling and variant annotation. Subsequently, prioritizing which of these neoantigens may elicit a tumor-specific immune response requires application and integration of tools to predict the expression, processing, binding, and recognition potentials of the neoantigen. Finally, improvement of the computational tools is held in constant tension with the availability of datasets with validated immunogenic neoantigens. The goal of this review article is to summarize the current knowledge and limitations in neoantigen prediction, prioritization, and validation and propose future directions that will improve personalized cancer treatment.

Design of a Novel Recombinant Multi-Epitope Vaccine against Triple-Negative Breast Cancer

Background

Triple-negative breast cancer (TNBC) is determined by the absence of ERBB2, estrogen and progesterone receptors’ expression. Cancer vaccines, as the novel immunotherapy strategies, have emerged as promising tools for treating the advanced stage of TNBC. The aim of this study was to evaluate Carcinoembryonic antigen (CEA), Metadherin (MTDH), and Mucin 1 (MUC-1) proteins as vaccine candidates against TNBC.

Methods

In this research, a novel vaccine was designed against TNBC by using different immunoinformatics and bioinformatics approaches. Effective immunodominant epitopes were chosen from three antigenic proteins, namely CEA, MTDH, and MUC-1. Recombinant TLR4 agonists were utilized as an adjuvant to stimulate immune responses. Following the selection of antigens and adjuvants, appropriate linkers were chosen to generate the final recombinant protein. To achieve an excellent 3D model, the best predicted 3D model was required to be refined and validated. To demonstrate whether the vaccine/TLR4 complex is stable or not, we performed docking analysis and dynamic molecular simulation.

Result

Immunoinformatics and bioinformatics evaluations of the designed construct demonstrated that this vaccine candidate could effectively be used as a therapeutic armament against TNBC.

Conclusion

Bioinformatics studies revealed that the designed vaccine has an acceptable quality. Investigating the effectiveness of this vaccine can be confirmed by supplementary in vitro and in vivo studies.

Peptide-Based Vaccines for Tuberculosis

Tuberculosis (TB) is an infectious disease caused by Mycobacterium tuberculosis. As a result of the coronavirus disease 2019 (COVID-19) pandemic, the global TB mortality rate in 2020 is rising, making TB prevention and control more challenging. Vaccination has been considered the best approach to reduce the TB burden. Unfortunately, BCG, the only TB vaccine currently approved for use, offers some protection against childhood TB but is less effective in adults. Therefore, it is urgent to develop new TB vaccines that are more effective than BCG. Accumulating data indicated that peptides or epitopes play essential roles in bridging innate and adaptive immunity and triggering adaptive immunity. Furthermore, innovations in bioinformatics, immunoinformatics, synthetic technologies, new materials, and transgenic animal models have put wings on the research of peptide-based vaccines for TB. Hence, this review seeks to give an overview of current tools that can be used to design a peptide-based vaccine, the research status of peptide-based vaccines for TB, protein-based bacterial vaccine delivery systems, and animal models for the peptide-based vaccines. These explorations will provide approaches and strategies for developing safer and more effective peptide-based vaccines and contribute to achieving the WHO's End TB Strategy.

Design of a multi-epitope protein vaccine against herpes simplex virus, human papillomavirus and Chlamydia trachomatis as the main causes of sexually transmitted diseases

Sexually transmitted diseases (STDs) have a profound effect on reproductivity and sexual health worldwide. According to world health organization (WHO) 375 million new case of STD, including chlamydia trachomatis (chlamydia), Neisseria gonorrhoeae, HSV, HPV has been reported in 2016. More than 30 diverse pathogenesis have identified to be transmitted through sexual intercourse. Of these, viral infections (hepatitis B, herpes simplex virus (HSV or herpes), HIV, and human papillomavirus (HPV) are incurable. However, symptoms caused by the incurable viral infections can be alleviated through treatment. Antimicrobial resistance (AMR) of sexually transmitted infections (STIs) to antibiotics has increased recent years, in this regard, vaccination is proposed as an important strategy for prevention or treatment of STDs. Vaccine against HPV 16 and 18 suggests a new approach for controlling STDs but until now, there is no prophylactic or therapeutic vaccine have been approved for HSV-2 and Chlamydia trachomatis (CT); in this reason, developing an efficient vaccine is inevitable. Recently, different combinatorial forms of subunit vaccines against two or three type of bacteria have been designed. In this study, to design a combinatorial vaccine against HSV, CT, and HPV, the E7 and L2 from HPV, glycoprotein D from HSV-2 and ompA from CT were selected as final antigens. Afterward, the immunodominant helper T lymphocytes (HTLs) and cytolytic T lymphocytes (CTLs) epitopes were chosen from aforesaid antigens. P30 (tetanus toxoid epitope) as universal T-helper were also added to the vaccine. Moreover, flagellin D_1/D₀ as TLR5 agonist and the RS09 as a TLR4 ligand were incorporated to N and C-terminals of peptide vaccine, respectively. Finally, all selected parts were fused together by appropriate linkers to enhance vaccine efficiency. The physicochemical, structural, and immunological properties of the designed vaccine protein were assessed. To achieve the best 3D model of the protein vaccine, modeling, refinement, and validation of modeled structures were also done. Docking evaluation demonstrated suitable interaction between the vaccine and TLR5. Moreover, molecular dynamics (MD) studies showed an appropriate and stable structure of protein and TLR5. Based on immunoinformatic analysis, our vaccine candidate could potentially incite humoral and cellular immunities, which are critical for protection against HPV, HSV-2, and chlamydia trachomatis. It should be noted that, experimental studies are needed to confirm the efficacy of the designed vaccine.

Infectious disease mRNA vaccines and a review on epitope prediction for vaccine design

Messenger RNA (mRNA) vaccines have recently emerged as a new type of vaccine technology, showing strong potential to combat the COVID-19 pandemic. In addition to SARS-CoV-2 which caused the pandemic, mRNA vaccines have been developed and tested to prevent infectious diseases caused by other viruses such as Zika virus, the dengue virus, the respiratory syncytial virus, influenza H7N9 and Flavivirus. Interestingly, mRNA vaccines may also be useful for preventing non-infectious diseases such as diabetes and cancer. This review summarises the current progresses of mRNA vaccines designed for a range of diseases including COVID-19. As epitope study is a primary component in the in silico design of mRNA vaccines, we also survey on advanced bioinformatics and machine learning algorithms which have been used for epitope prediction, and review on user-friendly software tools available for this purpose. Finally, we discuss some of the unanswered concerns about mRNA vaccines, such as unknown long-term side effects, and present with our perspectives on future developments in this exciting area.

Prediction of Epitope based Peptides for Vaccine Development from Complete Proteome of Novel Corona Virus (SARS-COV-2) Using Immunoinformatics

COVID-19 is an infectious disease caused by a newly discovered corona virus SARS-COV-2. It is the most dangerous epidemic existing currently all over the world. To date, there is no licensed vaccine and not any particular efficient therapeutic agent available to prevent or cure the disease. So development of an effective vaccine is the urgent need of the time. The proposed study aims to identify potential vaccine candidates by screening the complete proteome of SARS-COV-2 using the computational approach. From 14 protein entries in UniProtKB, 4 proteins were screened for epitope prediction based on consensus antigenicity predictions and various physico-chemical criteria like transmembrane domain, allergenicity, GRAVY value, toxicity, stability index. Comprehensive analysis of these 4 antigens revealed that spike protein (P0DTC2) and nucleoprotein (P0DTC9) show the greatest potential for experimental immunogenicity analysis. These 2 proteins have several potential CD4+ and CD8+ T-cell epitopes, as well as high probability of B-cell epitope regions as compared to well-characterized antigen the matrix protein 1 [Influenza A virus (H5N1)]. In addition, the epitope SIIAYTMSL predicted from spike protein (P0DTC2) and epitope SPRWYFYYL predicted from nucleoprotein (P0DTC9) exhibited more than 60% population coverage in the target populations Europe, North America, South Asia, Northeast Asia taken in this study. These epitopes have also been found to exhibit highly significant TCR–pMHC interactions having a joint Z value of 4.51 and 4.37 respectively. Therefore, this analysis suggests that the predicted epitopes might be suitable vaccine candidates and should be subjected to further in-vivo and in-vitro studies.

Peptides-based vaccine against SARS-nCoV-2 antigenic fragmented synthetic epitopes recognized by T cell and β-cell initiation of specific antibodies to fight the infection

The World Health Organization has declared the rapidly spreading coronavirus to be a global pandemic. The FDA is yet to approve a vaccine for human novel coronavirus. Here, we developed a peptide-based vaccine and used high-throughput screening by molecular dynamics simulation to identify T-cell- and β-cell-recognized epitopes for producing specific antibodies against SARS-nCoV-2. We construct ~ 12 P' antigenic epitope peptides to develop a more effective vaccine and identify specific antibodies. These epitope peptides selectively presented the best antigen presentation scores for both human pMHC class I and II alleles to develop a strong binding affinity. All antigens identified of SARS-nCoV-2 different proteins by each attached specific ~ 1-7 L linker adaptor were used to construct a broad single peripheral peptide vaccine. It is expected to be highly antigenic with a minimum allergic effect. As a result of these exciting outcomes, expressing a vaccine using the intimated peptide was highly promising and positive to be highly proposed as epitope-based peptide vaccine of specific antibody against SARS-nCoV-2 by initiating T cells and β-cells. An in vitro study for the proposed peptide-based vaccine is mostly recommended. Further clinical trials are required to check the efficacy of this vaccine.

T and B cell Epitope analysis of SARS-CoV-2 S protein based on immunoinformatics and experimental research

COVID-19 caused by SARS-CoV-2 is pandemic with a severe morbidity and mortality rate across the world. Despite the race for effective vaccine and drug against further expansion and fatality rate of this novel coronavirus, there is still lack of effective antiviral therapy. To this effect, we deemed it necessary to identify potential B and T cell epitopes from the envelope S protein. This can be used as potential targets to develop anti-SARS-CoV-2 vaccine preparations. In this study, we used immunoinformatics to identify conservative B and T cell epitopes for S proteins of SARS-CoV-2, which might play roles in the initiation of SARS-CoV-2 infection. We identified the B cell and T cell peptide epitopes of S protein and their antigenicity, as well as the interaction between the peptide epitopes and human leucocyte antigen (HLA). Among the B cell epitopes, 'EILDITPCSFGGVS' has the highest score of antigenicity and great immunogenicity. In T cell epitopes, MHC-I peptide 'KIADYNYKL' and MHC-II peptide 'LEILDITPC' were identified as high antigens. Besides, docking analysis showed that the predicted peptide 'KIADYNYKL' was closely bound to the HLA-A*0201. The results of molecular dynamics simulation through GROMACS software showed that 'HLA-A*0201~peptide' complex was very stable. And the peptide we selected could induce the T cell response similar to that of SARS-CoV-2 infection. Moreover, the predicted peptides were highly conserved in different isolates from different countries. The antigenic epitopes presumed in this study were effective new vaccine targets to prevent SARS-CoV-2 infection.

Immuno-informatics analysis and expression of a novel multi-domain antigen as a vaccine candidate against glioblastoma

Glioblastoma multiform is the most common of primary malignant brain tumors in adults. Currently, surgical resection of the tumor mass, followed by adjuvant radiotherapy and chemotherapy are standard treatments for glioblastoma multiform but so far are not effective treatments. Thus, the development of a vaccine, as a safe and efficient strategy for prophylactic or therapeutic purposes against glioblastoma multiform is very necessary. The present study aimed to design the multi-domain vaccine for glioblastoma multiform. An in silico approach was used to select the most potent domains of proteins to induce the host's B- and T-cell immune response against glioblastoma multiform. IL-13Rα-2 (amino acid positions 27-144), TNC (amino acid positions 1900-2100), and PTPRZ-1(amino acid positions 731-884) were found to have potent inducible immune responses. So, we considered them for fusing with a linker A(EAAAK)₃A to construct the multi-domain recombinant vaccine. The immuno-informatics analysis of the designed recombinant vaccine construct was performed to evaluate its efficacy. Although the designed recombinant vaccine construct did not show allergen property, its antigenicity was estimated at 0.78. The Physico-chemical properties of the recombinant vaccine construct were characterized and revealed the potency of the vaccine candidate. Then its secondary and tertiary structures, mRNA structure, molecular docking, and immune simulation were predicted using bioinformatics tools. Next, the designed recombinant vaccine construct was synthesized, and cloned into the pET28a vector and expressed in E. coli BL21. Besides, the circular dichroism spectroscopy was utilized for the investigation of the secondary structure changes of the recombinant vaccine construct. The results of the verification assessment of the recombinant vaccine construct expression indicated that in silico analysis was relatively accurate, and relatively change occurred on the protein secondary structure. In our future plan, the vaccine candidate that was confirmed by in silico tools should be validated by further in vitro and in vivo experimental studies.

Epitope based peptide vaccine against SARS-COV2: an immune-informatics approach

World is witnessing exponential growth of SARS-CoV2 and fatal outcomes of COVID 19 has proved its pandemic potential already by claiming more than 3 lakhs deaths globally. If not controlled, this ongoing pandemic can cause irreparable socio-economic and psychological impact worldwide. Therefore a safe and effective vaccine against COVID 19 is exigent. Recent advances in immunoinformatics approaches could potentially decline the attrition rate and accelerate the process of vaccine development in these unprecedented times. In the present study, a multivalent subunit vaccine targeting S2 subunit of the SARS-CoV2 S glycoprotein has been designed using open source, immunoinformatics tools. Designed construct comprises of epitopes capable of inducing T cell, B cell (Linear and discontinuous) and Interferon γ. physiologically, vaccine construct is predicted to be thermostable, antigenic, immunogenic, non allergen and non toxic in nature. According to population coverage analysis, designed multiepitope vaccine covers 99.26% population globally. 3D structure of vaccine construct was designed, validated and refined to obtain high quality structure. Refined structure was docked against Toll like receptors to confirm the interactions between them. Vaccine peptide sequence was reverse transcribed, codon optimized and cloned in pET vector. Our in-silico study suggests that proposed vaccine against fusion domain of virus has the potential to elicit an innate as well as humoral immune response in human and restrict the entry of virus inside the cell. Results of the study offer a framework for in-vivo analysis that may hasten the process of development of therapeutic tools against COVID 19.Communicated by Ramaswamy H. Sarma.

Benchmarking predictions of MHC class I restricted T cell epitopes in a comprehensively studied model system

T cell epitope candidates are commonly identified using computational prediction tools in order to enable applications such as vaccine design, cancer neoantigen identification, development of diagnostics and removal of unwanted immune responses against protein therapeutics. Most T cell epitope prediction tools are based on machine learning algorithms trained on MHC binding or naturally processed MHC ligand elution data. The ability of currently available tools to predict T cell epitopes has not been comprehensively evaluated. In this study, we used a recently published dataset that systematically defined T cell epitopes recognized in vaccinia virus (VACV) infected C57BL/6 mice (expressing H-2D^b and H-2K^b), considering both peptides predicted to bind MHC or experimentally eluted from infected cells, making this the most comprehensive dataset of T cell epitopes mapped in a complex pathogen. We evaluated the performance of all currently publicly available computational T cell epitope prediction tools to identify these major epitopes from all peptides encoded in the VACV proteome. We found that all methods were able to improve epitope identification above random, with the best performance achieved by neural network-based predictions trained on both MHC binding and MHC ligand elution data (NetMHCPan-4.0 and MHCFlurry). Impressively, these methods were able to capture more than half of the major epitopes in the top N = 277 predictions within the N = 767,788 predictions made for distinct peptides of relevant lengths that can theoretically be encoded in the VACV proteome. These performance metrics provide guidance for immunologists as to which prediction methods to use, and what success rates are possible for epitope predictions when considering a highly controlled system of administered immunizations to inbred mice. In addition, this benchmark was implemented in an open and easy to reproduce format, providing developers with a framework for future comparisons against new tools.

Computational prediction tools are used to screen peptides to identify potential T cell epitope candidates. These tools, developed using machine learning methods, save time and resources in many immunological studies including vaccine discovery and cancer neoantigen identification. In addition to the already existing methods several epitope prediction tools are being developed these days but they lack a comprehensive and uniform evaluation to see which method performs best. In this study we did a comprehensive evaluation of publicly accessible MHC I restricted T cell epitope prediction tools using a recently published dataset of Vaccinia virus epitopes identified in the context of H-2D^b and H-2K^b. We found that methods based on artificial neural network architecture and trained on both MHC binding and ligand elution data showed very high performance (NetMHCPan-4.0 and MHCFlurry). This benchmark analysis will help immunologists to choose the right prediction method for their desired work and will also serve as a framework for tool developers to evaluate new prediction methods.

T-cell receptor affinity in the age of cancer immunotherapy

The strength of the interaction between T-cell receptors (TCRs) and their ligands, peptide/major histocompatibility complex complexes (pMHCs), is one of the most frequently discussed and investigated features of T cells in immuno-oncology today. Although there are many molecules on the surface of T cells that interact with ligands on other cells, the TCR/pMHC is the only receptor-ligand pair that offers antigen specificity and dictates the functional response of the T cell. The strength of the TCR/pMHC interaction, along with the environment in which this interaction takes place, is key to how the T cell will respond. The TCR repertoire of T cells that interact with tumor-associated antigens is vast, although typically of low affinity. Here, we focus on the low-affinity interactions between TCRs from CD8+ T cells and different models used in immuno-oncology.

TCRpMHCmodels: Structural modelling of TCR-pMHC class I complexes

The interaction between the class I major histocompatibility complex (MHC), the peptide presented by the MHC and the T-cell receptor (TCR) is a key determinant of the cellular immune response. Here, we present TCRpMHCmodels, a method for accurate structural modelling of the TCR-peptide-MHC (TCR-pMHC) complex. This TCR-pMHC modelling pipeline takes as input the amino acid sequence and generates models of the TCR-pMHC complex, with a median Cα RMSD of 2.31 Å. TCRpMHCmodels significantly outperforms TCRFlexDock, a specialised method for docking pMHC and TCR structures. TCRpMHCmodels is simple to use and the modelling pipeline takes, on average, only two minutes. Thanks to its ease of use and high modelling accuracy, we expect TCRpMHCmodels to provide insights into the underlying mechanisms of TCR and pMHC interactions and aid in the development of advanced T-cell-based immunotherapies and rational design of vaccines. The TCRpMHCmodels tool is available at http://www.cbs.dtu.dk/services/TCRpMHCmodels/.

Toward in silico Identification of Tumor Neoantigens in Immunotherapy

Cancer immunotherapy includes cancer vaccination, adoptive T cell transfer (ACT) with chimeric antigen receptor (CAR) T cells, and administration of tumor-infiltrating lymphocytes and immune-checkpoint blockade such as anti-CTLA4/anti-PD1 inhibitors that can directly or indirectly target tumor neoantigens and elicit a T cell response. Accurate, rapid, and cost-effective identification of neoantigens, however, is critical for successful immunotherapy. Here, we review computational issues for neoantigen identification by summarizing the various sources of neoantigens and their identification from high-throughput sequencing data. Several opinions are presented to inspire further discussions toward improving neoantigen identification. Continuing efforts are required to improve the sensitivity and specificity of bona fide neoantigens, taking advantage of the development of high-throughput sequencing techniques for effective and personalized cancer immunotherapy.

Immunomics Datasets and Tools: To Identify Potential Epitope Segments for Designing Chimeric Vaccine Candidate to Cervix Papilloma

Immunomics tools and databases play an important role in the designing of prophylactic or therapeutic vaccines against pathogenic bacteria and viruses. Therefore, we aimed to illustrate the different immunological databases and web servers used to design a chimeric vaccine candidate against human cervix papilloma. Initially, cellular immunity inducing major histocompatibility complex class I and II epitopes from L2 protein of papilloma 58 strain were predicted using the IEDB, NetMHC, and Tepi tools. Then, the overlapped segments from the above analysis were used to calculate efficiency on interferon-gamma and humoral immunity production. In addition, the allergenicity, antigenicity, cross-reactivity with human proteomes, and epitope conservancy of elite segments were determined. The chimeric vaccine candidate (SGD58) was constructed with two different overlapped peptide segments (23–36) and (29–42), adjuvants (flagellin and RS09), two Th epitopes, and amino acid linkers. The results of homology modeling demonstrated that SGD58 have 88.6% of favored regions based on Ramachandran plot. Protein–protein docking with Swarm Dock reveals SGD58 with receptor complex have −54.74 kcal/mol of binding energy with more than 20 interacting residues. Docked complex are stable in 100ns of molecular dynamic simulation. Further, coding sequences of SGD58 also show elevated gene expression in E. coli. In conclusion, SGD58 may prompt vaccine against cervix papilloma. This study provides insight of vaccine design against different pathogenic microbes as well.

Exploring the Papillomaviral Proteome to Identify Potential Candidates for a Chimeric Vaccine against Cervix Papilloma Using Immunomics and Computational Structural Vaccinology

The human papillomavirus (HPV) 58 is considered to be the second most predominant genotype in cervical cancer incidents in China. HPV type-restriction, non-targeted delivery, and the highcost of existing vaccines necessitate continuing research on the HPV vaccine. We aimed to explore the papillomaviral proteome in order to identify potential candidates for a chimeric vaccine against cervix papilloma using computational immunology and structural vaccinology approaches. Two overlapped epitope segments (23⁻36) and (29⁻42) from the N-terminal region of the HPV58 minor capsid protein L2 are selected as capable of inducing both cellular and humoral immunity. In total, 318 amino acid lengths of the vaccine construct SGD58 contain adjuvants (Flagellin and RS09), two Th epitopes, and linkers. SGD58 is a stable protein that is soluble, antigenic, and non-allergenic. Homology modeling and the structural refinement of the best models of SGD58 and TLR5 found 96.8% and 93.9% favored regions in Rampage, respectively. The docking results demonstrated a HADDOCK score of -62.5 ± 7.6, the binding energy (-30 kcal/mol) and 44 interacting amino acid residues between SGD58-TLR5 complex. The docked complex are stable in 100 ns of simulation. The coding sequences of SGD58 also show elevated gene expression in Escherichia coli with 1.0 codon adaptation index and 59.92% glycine-cysteine content. We conclude that SGD58 may prompt the creation a vaccine against cervix papilloma.

Immunoinformatics-aided design of a potential multi-epitope peptide vaccine against Leishmania infantum

Visceral leishmaniasis (VL) or kala-azar, the most severe form of the disease, is endemic in more than eighty countries across the world. To date, there is no approved vaccine against VL in the market. Recent advances in reverse vaccinology could be promising approach in designing the efficient vaccine for VL treatment. In this study, an efficient multi-epitope vaccine against Leishmania infantum, the causative agent of VL, was designed using various computational vaccinology methods. Potential immunodominant epitopes were selected from four antigenic proteins, including histone H1, sterol 24-c-methyltransferase (SMT), Leishmania-specific hypothetical protein (LiHy), and Leishmania-specific antigenic protein (LSAP). To enhance vaccine immunogenicity, two resuscitation-promoting factor of Mycobacterium tuberculosis, RpfE and RpfB, were employed as adjuvants. All the aforesaid segments were joined using proper linkers. Homology modeling, followed by refinement and validation was performed to obtain a high-quality 3D structure of designed vaccine. Docking analyses and molecular dynamics (MD) studies indicated vaccine/TLR4 complex was in the stable form during simulation time. In sum, we expect our designed vaccine is able to induce humoral and cellular immune responses against L. infantum, and may be promising medication for VL, after in vitro and in vivo immunological assays.

Identification of the cognate peptide-MHC target of T cell receptors using molecular modeling and force field scoring

Interactions of T cell receptors (TCR) to peptides in complex with MHC (p:MHC) are key features that mediate cellular immune responses. While MHC binding is required for a peptide to be presented to T cells, not all MHC binders are immunogenic. The interaction of a TCR to the p:MHC complex holds a key, but currently poorly comprehended, component for our understanding of this variation in the immunogenicity of MHC binding peptides. Here, we demonstrate that identification of the cognate target of a TCR from a set of p:MHC complexes to a high degree is achievable using simple force-field energy terms. Building a benchmark of TCR:p:MHC complexes where epitopes and non-epitopes are modelled using state-of-the-art molecular modelling tools, scoring p:MHC to a given TCR using force-fields, optimized in a cross-validation setup to evaluate TCR inter atomic interactions involved with each p:MHC, we demonstrate that this approach can successfully be used to distinguish between epitopes and non-epitopes. A detailed analysis of the performance of this force-field-based approach demonstrate that its predictive performance depend on the ability to both accurately predict the binding of the peptide to the MHC and model the TCR:p:MHC complex structure. In summary, we conclude that it is possible to identify the TCR cognate target among different candidate peptides by using a force-field based model, and believe this works could lay the foundation for future work within prediction of TCR:p:MHC interactions.

Genome-Wide Prediction of Potential Vaccine Candidates for Campylobacter jejuni Using Reverse Vaccinology

Campylobacteriosis is a deadly disease which has developed resistance to most of the available chemotherapeutic agents. Although various studies provide evidence of acquired immunity following exposure to Campylobacter jejuni, no effective vaccine has been developed, still. Hence, there is an urgent need to identify potential vaccine candidates for Campylobacter species. In the proposed study, Campylobacter jejuni subsp. jejuni serotype O:2 (strain NCTC 11168) was taken and computational approach was employed to screen C. jejuni genome for promising vaccine candidates. From 1623 protein-coding sequences, 37 potential antigens were screened for epitope prediction based on surface association, consensus antigenicity predictions, solubility, transmembrane domain, and ortholog analysis. Comprehensive immunogenic analysis of these 37 antigens revealed that antigen Q0PA22 shows the greatest potential for experimental immunogenicity analysis. It has several potential CD4+ and CD8+ T-cell epitopes, as well as high probability of B-cell epitope regions as compared to well-characterized antigen Omp18 (Uniprot ID:Q0PC24). Among the highest scoring predicted epitopes, an optimal set of epitopes with respect to overall immunogenicity in target populations for campylobacteriosis viz. Europe, North America and Southwest Asia was determined. An epitope AMLTYMQWL from antigen no. 6(Q0PA22) binds to the most prevalent allele HLA-A*0201, and this epitope has most immunogenicity for all the target populations. In addition, this epitope exhibited highly significant TCR-pMHC interactions having a joint Z value of 4.87. Homology mapping studies of the predicted epitope show best homology to a well-studied antigenic peptide from influenza virus H5N1. Therefore, the predicted epitope might be a suitable vaccine candidate.

Computational screening and characterization of putative vaccine candidates of Plasmodium vivax

Plasmodium vivax is the most prevalent species of malaria affecting millions of people annually worldwide and demands effective interventions to develop a successful vaccine. In this milieu, we have dedicated noteworthy efforts to characterize the proteome of P. vivax to give a lead for the epitope-based vaccine development. Membrane proteins of P. vivax were collected from SWISS PROT database and 10 antigenic proteins were identified among them by in silico analysis using multiple servers. T-cell and B-cell epitopes were identified and their immunity was assessed. Their ability to trigger humoral and cell-mediated responses was determined. Three dimensional models were constructed for the antigenic proteins using Modeller, Phyre2, and Modloop tools and their quality was validated using PROCHECK and ProSA-web validation servers. Further, the binding affinity and molecular interactions of these antigenic proteins were characterized by performing protein-protein docking against transmission-blocking anti-malaria antibody Fab2A8 (PDB ID: 3S62) using Z-dock module of Discovery Studio 4.0. The presence of potential B & T-cell epitopes, major histocompatibility complex-binding sites, and their efficient interactions with Fab2A8 antibody suggests the use of predicted antigenic proteins for the construction of multi-epitope peptide vaccine against P. vivax.

Immunogenic peptide discovery in cancer genomes

As immunotherapies to treat malignancy continue to diversify along with the tumor types amenable to treatment, it will become very important to predict which treatment is most likely to benefit a given patient. Tumor neoantigens, novel peptides resulting from somatic tumor mutations and recognized by the immune system as foreign, are likely to contribute significantly to the efficacy of immunotherapy. Multiple in silico methods have been developed to predict whether peptides, including tumor neoantigens, will be presented by the major histocompatibility complex (MHC) Class I or Class II, and interact with the T cell receptor (TCR). The methods for neoantigen prediction will be reviewed here, along with the most important examples of their use in the field of oncology.

A novel multi-epitope peptide vaccine against cancer: an in silico approach

Cancer immunotherapy has an outstanding position in cancer prevention and treatment. In this kind of therapy, the immune system is activated to eliminate cancerous cells. Multi-epitope peptide cancer vaccines are manifesting as the next generation of cancer immunotherapy. In the present study, we have implemented various strategies to design an efficient multi-epitope vaccine. CD8+ cytolytic T lymphocytes (CTLs) epitopes, which have a pivotal role in cellular immune responses, helper epitopes and adjuvant, are three crucial components of peptide vaccine. CTL epitopes were determined from two high immunogenic protein Wilms tumor-1 (WT1) and human papillomavirus (HPV) E7 by various servers, which apply different algorithms. CTL epitopes were linked together by AAY and HEYGAEALERAG motifs to enhance epitope presentation. Pan HLA DR-binding epitope (PADRE) peptide sequence and helper epitopes, which have defined from Tetanus toxin fragment C (TTFrC) by various servers, were used to induce CD4+ helper T lymphocytes (HTLs) responses. Additionally, helper epitopes were conjugated together via GPGPG motifs that stimulate HTL immunity. Heparin-Binding Hemagglutinin (HBHA), a novel TLR4 agonist was employed as an adjuvant to polarize CD4+ T cells toward T-helper 1 to induce strong CTL responses. Moreover, the EAAAK linker was introduced to N and C terminals of HBHA for efficient separation. 3D model of protein was generated and predicted B cell epitopes were determined from the surface of built structure. Our protein contains several linear and conformational B cell epitopes, which suggests the antibody triggering property of this novel vaccine. Hence, our final protein can be used for prophylactic or therapeutic usages, because it can potentially stimulate both cellular and humoral immune responses.

Genome-wide structural modelling of TCR-pMHC interactions

Background

The adaptive immune response is antigen-specific and triggered by pathogen recognition through T cells. Although the interactions and mechanisms of TCR-peptide-MHC (TCR-pMHC) have been studied over three decades, the biological basis for these processes remains controversial. As an increasing number of high-throughput binding epitopes and available TCR-pMHC complex structures, a fast genome-wide structural modelling of TCR-pMHC interactions is an emergent task for understanding immune interactions and developing peptide vaccines.

Results

We first constructed the PPI matrices and iMatrix, using 621 non-redundant PPI interfaces and 398 non-redundant antigen-antibody interfaces, respectively, for modelling the MHC-peptide and TCR-peptide interfaces, respectively. The iMatrix consists of four knowledge-based scoring matrices to evaluate the hydrogen bonds and van der Waals forces between sidechains or backbones, respectively. The predicted energies of iMatrix are high correlated (Pearson's correlation coefficient is 0.6) to 70 experimental free energies on antigen-antibody interfaces. To further investigate iMatrix and PPI matrices, we inferred the 701,897 potential peptide antigens with significant statistic from 389 pathogen genomes and modelled the TCR-pMHC interactions using available TCR-pMHC complex structures. These identified peptide antigens keep hydrogen-bond energies and consensus interactions and our TCR-pMHC models can provide detailed interacting models and crucial binding regions.

Conclusions

Experimental results demonstrate that our method can achieve high precision for predicting binding affinity and potential peptide antigens. We believe that iMatrix and our template-based method can be useful for the binding mechanisms of TCR-pMHC complexes and peptide vaccine designs.

DelPhi Web Server: A comprehensive online suite for electrostatic calculations of biological macromolecules and their complexes

Here we report a web server, the DelPhi web server, which utilizes DelPhi program to calculate electrostatic energies and the corresponding electrostatic potential and ionic distributions, and dielectric map. The server provides extra services to fix structural defects, as missing atoms in the structural file and allows for generation of missing hydrogen atoms. The hydrogen placement and the corresponding DelPhi calculations can be done with user selected force field parameters being either Charmm22, Amber98 or OPLS. Upon completion of the calculations, the user is given option to download fixed and protonated structural file, together with the parameter and Delphi output files for further analysis. Utilizing Jmol viewer, the user can see the corresponding structural file, to manipulate it and to change the presentation. In addition, if the potential map is requested to be calculated, the potential can be mapped onto the molecule surface. The DelPhi web server is available from http://compbio.clemson.edu/delphi_webserver.