Translational and structural vaccinomics approach to design a multi-epitope vaccine against NOL4 autologous antigen of small cell lung cancer

Small cell lung cancer (SCLC) is one of the most common cancers and it is the sixth common cause for cancer-related deaths. The high plasticity and metastasis have been a major challenge for humanity to treat the disease. Hence, a vaccine for SCLC has become an urgent need of the hour due to public health concern. Implementation of immunoinformatics technique is one of the best way to find a suitable vaccine candidate. Immunoinformatics tools can be used to overcome the limitations and difficulties of traditional vaccinological techniques. Multi-epitope cancer vaccines have become a next-generation technique in vaccinology which can be used to stimulate more potent immune response against a particular antigen by eliminating undesirable molecules. In this study, we used multiple computational and immunoinformatics approach to design a novel multi-epitope vaccine for small cell lung cancer. Nucleolar protein 4 (NOL4) is an autologous cancer-testis antigen overexpressed in SCLC cells. Seventy-five percent humoral immunity have been identified for this particular antigen. In this study, we mapped immunogenic cytotoxic T lymphocyte, helper T lymphocyte, and interferon-gamma epitopes present in NOL4 antigen and designed a multi-epitope-based vaccine using the predicted epitopes. The designed vaccine was antigenic, non-allergenic, and non-toxic with 100% applicability on human population. The chimeric vaccine construct showed stable and significant interaction with endosomal and plasmalemmal toll-like receptors in molecular docking and protein-peptide interaction analysis, thus assuring a strong potent immune response against the vaccine upon administration. Therefore, these preliminary results can be used to carry out further experimental investigations.

Recent trends in next generation immunoinformatics harnessed for universal coronavirus vaccine design

The ongoing pandemic of coronavirus disease 2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has globally devastated public health, the economies of many countries and quality of life universally. The recent emergence of immune-escaped variants and scenario of vaccinated individuals being infected has raised the global concerns about the effectiveness of the current available vaccines in transmission control and disease prevention. Given the high rate mutation of SARS-CoV-2, an efficacious vaccine targeting against multiple variants that contains virus-specific epitopes is desperately needed. An immunoinformatics approach is gaining traction in vaccine design and development due to the significant reduction in time and cost of immunogenicity studies and increasing reliability of the generated results. It can underpin the development of novel therapeutic methods and accelerate the design and production of peptide vaccines for infectious diseases. Structural proteins, particularly spike protein (S), along with other proteins have been studied intensively as promising coronavirus vaccine targets. Numbers of promising online immunological databases, tools and web servers have widely been employed for the design and development of next generation COVID-19 vaccines. This review highlights the role of immunoinformatics in identifying immunogenic peptides as potential vaccine targets, involving databases, and prediction and characterization of epitopes which can be harnessed for designing future coronavirus vaccines.

Anti-leishmanial therapy: Caught between drugs and immune targets

Leishmaniasis is an enigmatic disease that has very restricted options for chemotherapy and none for prophylaxis. As a result, deriving therapeutic principles for curing the disease has been a major objective in Leishmania research for a long time. Leishmania is a protozoan parasite that lives within macrophages by subverting or switching cell signaling to the pathways that ensure its intracellular survival. Therefore, three groups of molecules aimed at blocking or eliminating the parasite, at least, in principle, include blockers of macrophage receptor- Leishmania ligand interaction, macrophage-activating small molecules, peptides and cytokines, and signaling inhibitors or activators. Macrophages also act as an antigen-presenting cell, presenting antigen to the antigen-specific T cells to induce activation and differentiation of the effector T cell subsets that either execute or suppress anti-leishmanial functions. Three groups of therapeutic principles targeting this sphere of Leishmania-macrophage interaction include antibodies that block pro-leishmanial response of T cells, ligands that activate anti-leishmanial T cells and the antigens for therapeutic vaccines. Besides these, prophylactic vaccines have been in clinical trials but none has succeeded so far. Herein, we have attempted to encompass all these principles and compose a comprehensive review to analyze the feasibility and adoptability of different therapeutics for leishmaniasis.

Design of Linear B Cell Epitopes and Evaluation of Their Antigenicity, Allergenicity, and Toxicity: An Immunoinformatics Approach

Immunoinformatics is a modern branch of science formed as a result of the intersection between immunology and computer science. One of the important steps in the design of multi-epitope vaccines is the prediction of B cell epitopes. B cell epitopes are of two types, linear and discontinuous. Linear epitope residues lie next to each other in the primary structure of a protein. The amino acids that constitute discontinuous epitopes lie close to each other in the three-dimensional structure of the protein. Recognition of B cell epitopes by antibodies on an antigen constitutes an important event in the immune responses toward the antigenic challenge and also forms the basis for several immunological applications. Prediction of B cell epitopes in an antigen constitutes one of the important steps in the design of multi-epitope-based vaccines. This chapter explains the prediction of linear B cell epitopes in an antigen as well as their allergenicity, antigenicity, and toxicity by using online tools.

COVID-19 vaccine design using reverse and structural vaccinology, ontology-based literature mining and machine learning

Rational vaccine design, especially vaccine antigen identification and optimization, is critical to successful and efficient vaccine development against various infectious diseases including coronavirus disease 2019 (COVID-19). In general, computational vaccine design includes three major stages: (i) identification and annotation of experimentally verified gold standard protective antigens through literature mining, (ii) rational vaccine design using reverse vaccinology (RV) and structural vaccinology (SV) and (iii) post-licensure vaccine success and adverse event surveillance and its usage for vaccine design. Protegen is a database of experimentally verified protective antigens, which can be used as gold standard data for rational vaccine design. RV predicts protective antigen targets primarily from genome sequence analysis. SV refines antigens through structural engineering. Recently, RV and SV approaches, with the support of various machine learning methods, have been applied to COVID-19 vaccine design. The analysis of post-licensure vaccine adverse event report data also provides valuable results in terms of vaccine safety and how vaccines should be used or paused. Ontology standardizes and incorporates heterogeneous data and knowledge in a human- and computer-interpretable manner, further supporting machine learning and vaccine design. Future directions on rational vaccine design are discussed.

VirVACPRED: A Web Server for Prediction of Protective Viral Antigens

Viral antigens are key in the development of vaccines that prevent or eradicate infections caused by these pathogens. Bioinformatics tools are modern alternatives that facilitate the discovery of viral antigens, reducing the costs of experimental assays. We developed a bioinformatics tool called VirVACPRED, which is highly efficient in predicting viral antigens. In this study, we obtained a model based on the gradient boosting classifier, which showed high performance during the training, leave-one-out cross-validation (accuracy = 0.7402, sensitivity = 0.7319, precision = 0.7503, F1 = 0.7251, kappa = 0.4774, Matthews correlation coefficient = 0.4981) and testing (accuracy = 0.8889, sensitivity = 1.0, precision = 0.8276, F1 = 0.9057, kappa = 0.7734, Matthews correlation coefficient = 0.7941). VirVACPRED is a robust tool that can be of great help in the search and proposal of new viral antigens, which can be considered in the development of future vaccines against infections caused by viruses.

Tracking the pipeline: immunoinformatics and the COVID-19 vaccine design

With the onset of the COVID-19 pandemic, the amount of data on genomic and proteomic sequences of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) stored in various databases has exponentially grown. A large volume of these data has led to the production of equally immense sets of immunological data, which require rigorous computational approaches to sort through and make sense of. Immunoinformatics has emerged in the recent decades as a field capable of offering this approach by bridging experimental and theoretical immunology with state-of-the-art computational tools. Here, we discuss how immunoinformatics can assist in the development of high-performance vaccines and drug discovery needed to curb the spread of SARS-CoV-2. Immunoinformatics can provide a set of computational tools to extract meaningful connections from the large sets of COVID-19 patient data, which can be implemented in the design of effective vaccines. With this in mind, we represent a pipeline to identify the role of immunoinformatics in COVID-19 treatment and vaccine development. In this process, a number of free databases of protein sequences, structures and mutations are introduced, along with docking web servers for assessing the interaction between antibodies and the SARS-CoV-2 spike protein segments as most commonly considered antigens in vaccine design.

Cancer immune control dynamics: a clinical data driven model of systemic immunity in patients with metastatic melanoma

Background

Recent clinical advances in cancer immuno-therapeutics underscore the need for improved understanding of the complex relationship between cancer and the multiple, multi-functional, inter-dependent, cellular and humoral mediators/regulators of the human immune system. This interdisciplinary effort exploits engineering analysis methods utilized to investigate anomalous physical system behaviors to explore immune system behaviors. Cancer Immune Control Dynamics (CICD), a systems analysis approach, attempts to identify differences between systemic immune homeostasis of 27 healthy volunteers versus 14 patients with metastatic malignant melanoma based on daily serial measurements of conventional peripheral blood biomarkers (15 cell subsets, 35 cytokines). The modeling strategy applies engineering control theory to analyze an individual’s immune system based on the biomarkers’ dynamic non-linear oscillatory behaviors. The reverse engineering analysis uses a Singular Value Decomposition (SVD) algorithm to solve the inverse problem and identify a solution profile of the active biomarker relationships. Herein, 28,605 biologically possible biomarker interactions are modeled by a set of matrix equations creating a system interaction model. CICD quantifies the model with a participant’s biomarker data then computationally solves it to measure each relationship’s activity allowing a visualization of the individual’s current state of immunity.

Results

CICD results provide initial evidence that this model-based analysis is consistent with identified roles of biomarkers in systemic immunity of cancer patients versus that of healthy volunteers. The mathematical computations alone identified a plausible network of immune cells, including T cells, natural killer (NK) cells, monocytes, and dendritic cells (DC) with cytokines MCP-1 [CXCL2], IP-10 [CXCL10], and IL-8 that play a role in sustaining the state of immunity in advanced cancer.

Conclusions

With CICD modeling capabilities, the complexity of the immune system is mathematically quantified through thousands of possible interactions between multiple biomarkers. Therefore, the overall state of an individual’s immune system regardless of clinical status, is modeled as reflected in their blood samples. It is anticipated that CICD-based capabilities will provide tools to specifically address cancer and treatment modulated (immune checkpoint inhibitors) parameters of human immunity, revealing clinically relevant biological interactions.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-021-04025-7.

Exploring the cancer-testis antigen BORIS to design a novel multi-epitope vaccine against breast cancer based on immunoinformatics approaches

Recently, cancer immunotherapy has gained lots of attention to replace the current chemoradiation approaches and multi-epitope cancer vaccines are manifesting as the next generation of cancer immunotherapy. Therefore, in this study, we used multiple immunoinformatics approaches along with other computational approaches to design a novel multi-epitope vaccine against breast cancer. The most immunogenic regions of the BORIS cancer-testis antigen were selected according to the binding affinity to MHC-I and II molecules as well as containing multiple cytotoxic T lymphocyte (CTL) epitopes by multiple immunoinformatics servers. The selected regions were linked together by GPGPG linker. Also, a T helper epitope (PADRE) and the TLR-4/MD-2 agonist (L7/L12 ribosomal protein from mycobacterium) were incorporated by A(EAAAK)3A linker to form the final vaccine construct. Then, its physicochemical properties, cleavage sites, TAP transport efficiency, B cell epitopes, IFN-γ inducing epitopes and population coverage were predicted. The final vaccine construct was reverse translated, codon-optimized and inserted into pcDNA3.1 to form the DNA vaccine. The final vaccine construct was a stable, immunogenic and non-allergenic protein that contained numerous CTL epitopes, IFN-γ inducing epitopes and several linear and conformational B cell epitopes. Also, the final vaccine construct formed stable and significant interactions with TLR-4/MD-2 complex according to molecular docking and dynamics simulations. Moreover, its world population coverage for HLA-I and HLA-II were about 93% and 96%, respectively. Taking together, these preliminary results can be used as an appropriate platform for further experimental investigations.Communicated by Ramaswamy H. Sarma.

Whole proteome screening and identification of potential epitopes of SARS-CoV-2 for vaccine design-an immunoinformatic, molecular docking and molecular dynamics simulation accelerated robust strategy

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the most cryptic pandemic outbreak of the 21st century, has gripped more than 1.8 million people to death and infected almost eighty six million. As it is a new variant of SARS, there is no approved drug or vaccine available against this virus. This study aims to predict some promising cytotoxic T lymphocyte epitopes in the SARS-CoV-2 proteome utilizing immunoinformatic approaches. Firstly, we identified 21 epitopes from 7 different proteins of SARS-CoV-2 inducing immune response and checked for allergenicity and conservancy. Based on these factors, we selected the top three epitopes, namely KAYNVTQAF, ATSRTLSYY, and LTALRLCAY showing functional interactions with the maximum number of MHC alleles and no allergenicity. Secondly, the 3D model of selected epitopes and HLA-A*29:02 were built and Molecular Docking simulation was performed. Most interestingly, the best two epitopes predicted by docking are part of two different structural proteins of SARS-CoV-2, namely Membrane Glycoprotein (ATSRTLSYY) and Nucleocapsid Phosphoprotein (KAYNVTQAF), which are generally target of choice for vaccine designing. Upon Molecular Docking, interactions between selected epitopes and HLA-A*29:02 were further validated by 50 ns Molecular Dynamics (MD) simulation. Analysis of RMSD, Rg, SASA, number of hydrogen bonds, RMSF, MM-PBSA, PCA, and DCCM from MD suggested that ATSRTLSYY is the most stable and promising epitope than KAYNVTQAF epitope. Moreover, we also identified B-cell epitopes for each of the antigenic proteins of SARS CoV-2. Findings of our work will be a good resource for wet lab experiments and will lessen the timeline for vaccine construction.Communicated by Ramaswamy H. Sarma.

Proteomic Exploration of Listeria monocytogenes for the Purpose of Vaccine Designing Using a Reverse Vaccinology Approach

Listeriosis is a major foodborne infection provoked by a bacterium known as Listeria monocytogenes. It is one of the predominant causes of death in pregnant women, infants, and immunocompromised persons. Despite such fatal effects, until now there is no proper medication or drug available for such a serious foodborne infection. One of the most promising ways to deal with this challenge is vaccination. This present study aims at the prediction of B cell epitopes for subunit vaccine designing against Listeria monocytogenes using a reverse vaccinology approach. Among screened out 299 epitopes of strain F2365 of Listeria monocytogenes, based on the VaxiJen score, the top 20 epitopes were selected. 3D modeling of epitopes and alleles was generated by PEPstrMOD and Swiss Model respectively. Molecular docking reveals 4 epitopes viz., MKFLFPLKL, CEETFGIRL, FLKIDPPIL, and VRHHGGGHK based on binding energy. All 4 epitopes were investigated for non-toxicity, binding affinity, and population coverage. After vigorous investigation, epitope FLKIDPPIL was anticipated as the best vaccine contender. The stability of the FLKIDPPIL-HLA DRB1 _0101 complex was proved by performing the simulation. Here, predicted peptide through the Insilico approach may become a potential remedy against listeriosis, after the wet-lab approach and clinical trials.

An immunoinformatics study on the spike protein of SARS-CoV-2 revealing potential epitopes as vaccine candidates

Background

The pandemic situation of SARS-CoV-2 infection has sparked global concern due to the disease COVID-19 caused by it. Since the first cluster of confirmed cases in China in December 2019, the infection has been reported across the continents and inflicted upon a substantial number of populations.

Method

This study is focused on immunoinformatics analyses of the SARS-CoV-2 spike glycoprotein (S protein) which is key for the viral attachment to human host cells. Computational analyses were carried out for the prediction of B-cell and T-cell (MHC class I and II) epitopes of S protein and the analyses were extended further for the prediction of their immunogenic properties. The interaction and binding affinity of T-cell epitopes with HLA-B7 were also investigated by molecular docking.

Result

Three distinct epitopes for vaccine design were predicted from the sequence of S protein. The potential B-cell epitope was KNHTSPDVDLG possessing the highest antigenicity score of 1.4039 among other B-cell epitopes. T-cell epitope for human MHC class I was VVVLSFELL with an antigenicity score of 1.0909 and binding ability to 29 MHC-I alleles. The predicted T-cell epitope for human MHC class II molecule was VVIGIVNNT with a corresponding 1.3063 antigenicity score, less digesting enzymes, and 7 MHC-II alleles binding ability. All these three peptides were predicted to be highly antigenic, non-allergenic, and non-toxic. Analyses of the physiochemical properties of these predicted epitopes indicate their stable nature for plausible vaccine design. Furthermore, molecular docking investigation between the MHC class-I epitopes and human HLA-B7 reflects the stable interaction with high affinity among them.

Conclusion

The present study posits three potential epitopes of S protein of SARS-CoV-2 predicted by immunoinformatic methods based on their immunogenic properties and interactions with the host counterpart that can facilitate the development of vaccine against SARS-CoV-2. This study can act as the springboard for the future development of the COVID-19 vaccine.

Design of a Multiepitope-Based Peptide Vaccine against the E Protein of Human COVID-19: An Immunoinformatics Approach

Background

A new endemic disease has spread across Wuhan City, China, in December 2019. Within few weeks, the World Health Organization (WHO) announced a novel coronavirus designated as coronavirus disease 2019 (COVID-19). In late January 2020, WHO declared the outbreak of a “public-health emergency of international concern” due to the rapid and increasing spread of the disease worldwide. Currently, there is no vaccine or approved treatment for this emerging infection; thus, the objective of this study is to design a multiepitope peptide vaccine against COVID-19 using an immunoinformatics approach.

Method

Several techniques facilitating the combination of the immunoinformatics approach and comparative genomic approach were used in order to determine the potential peptides for designing the T-cell epitope-based peptide vaccine using the envelope protein of 2019-nCoV as a target.

Results

Extensive mutations, insertion, and deletion were discovered with comparative sequencing in the COVID-19 strain. Additionally, ten peptides binding to MHC class I and MHC class II were found to be promising candidates for vaccine design with adequate world population coverage of 88.5% and 99.99%, respectively.

Conclusion

The T-cell epitope-based peptide vaccine was designed for COVID-19 using the envelope protein as an immunogenic target. Nevertheless, the proposed vaccine rapidly needs to be validated clinically in order to ensure its safety and immunogenic profile to help stop this epidemic before it leads to devastating global outbreaks.

Epitope Prediction by Novel Immunoinformatics Approach: A State-of-the-art Review

Immunoinformatics is a science that helps to create significant immunological information using bioinformatics softwares and applications. One of the most important applications of immunoinformatics is the prediction of a variety of specific epitopes for B cell recognition and T cell through MHC class I and II molecules. This method reduces costs and time compared to laboratory tests. In this state-of-the-art review, we review about 50 papers to find the latest and most used immunoinformatic tools as well as their applications for predicting the viral, bacterial and tumoral structural and linear epitopes of B and T cells. In the clinic, the main application of prediction of epitopes is for designing peptide-based vaccines. Peptide-based vaccines are a considerably potential alternative to low-cost vaccines that may reduce the risks related to the production of common vaccines.

Computational Approaches and Challenges to Developing Universal Influenza Vaccines

The traditional design of effective vaccines for rapidly-evolving pathogens, such as influenza A virus, has failed to provide broad spectrum and long-lasting protection. With low cost whole genome sequencing technology and powerful computing capabilities, novel computational approaches have demonstrated the potential to facilitate the design of a universal influenza vaccine. However, few studies have integrated computational optimization in the design and discovery of new vaccines. Understanding the potential of computational vaccine design is necessary before these approaches can be implemented on a broad scale. This review summarizes some promising computational approaches under current development, including computationally optimized broadly reactive antigens with consensus sequences, phylogenetic model-based ancestral sequence reconstruction, and immunomics to compute conserved cross-reactive T-cell epitopes. Interactions between virus-host-environment determine the evolvability of the influenza population. We propose that with the development of novel technologies that allow the integration of data sources such as protein structural modeling, host antibody repertoire analysis and advanced phylodynamic modeling, computational approaches will be crucial for the development of a long-lasting universal influenza vaccine. Taken together, computational approaches are powerful and promising tools for the development of a universal influenza vaccine with durable and broad protection.

In silico analysis of transmembrane protein 31 (TMEM31) antigen to design novel multiepitope peptide and DNA cancer vaccines against melanoma

Multiepitope cancer vaccines are announcing themselves as the future of melanoma treatment. Herein, high immunogenic regions of transmembrane protein 31 (TMEM31) antigen were selected according to cytotoxic T lymphocytes' (CTL) epitopes and major histocompatibility complex (MHC) binding affinity through in silico analyses. The 32-62, 77-105, and 125-165 residues of the TMEM31 were selected as the immunodominant fragments. They were linked together by RVRR and HEYGAEALERAG motifs to improve epitopes separation and presentation. In addition, to activate helper T lymphocytes (HTL), Pan HLA DR-binding epitope (PADRE) peptide sequence and tetanus toxin fragment C (TTFrC) were incorporated into the final construct. Also, the Beta-defensin conserved domain was utilized in the final construct as a novel adjuvant for Toll-like receptor 4/myeloid differentiation factor (TLR4-MD) activation. The CTL epitopes, cleavage sites, post-translational modifications, TAP transport efficiency, and B cells epitopes were predicted for the peptide vaccine. The final construct contained multiple CTL and B cell epitopes. In addition, it showed 93.55% and 99.13% population coverage in the world for HLA I and HLA II, respectively. According to these preliminary results, the multiepitope cancer vaccine can be an appropriate choice for further experimental investigations.

Immunoinformatic and systems biology approaches to predict and validate peptide vaccines against Epstein-Barr virus (EBV)

Epstein-Barr virus (EBV), also known as human herpesvirus 4 (HHV-4), is a member of the Herpesviridae family and causes infectious mononucleosis, Burkitt's lymphoma, and nasopharyngeal carcinoma. Even in the United States of America, the situation is alarming, as EBV affects 95% of the young population between 35 and 40 years of age. In this study, both linear and conformational B-cell epitopes as well as cytotoxic T-lymphocyte (CTL) epitopes were predicted by using the ElliPro and NetCTL.1.2 webservers for EBV proteins (GH, GL, GB, GN, GM, GP42 and GP350). Molecular modelling tools were used to predict the 3D coordinates of peptides, and these peptides were then docked against the MHC molecules to obtain peptide-MHC complexes. Studies of their post-docking interactions helped to select potential candidates for the development of peptide vaccines. Our results predicted a total of 58 T-cell epitopes of EBV;  where the most potential were selected based on their TAP, MHC binding and C-terminal Cleavage score. The top most peptides were subjected to MD simulation and stability analysis. Validation of our predicted epitopes using a 0.45 µM concentration was carried out by using a systems biology approach. Our results suggest a panel of epitopes that could be used to immunize populations to protect against multiple diseases caused by EBV.

HHV-5 epitope: A potential vaccine candidate with high antigenicity and large coverage

Outbreak of Human Herpes virus-5 (HHV-5) infection in emerging countries has raised worldwide health concern owing to prevalence of congenital impairments and life threatening consequences in immunocompromised individuals. Thus, there lies an impending need to develop vaccine against HHV-5. HHV-5 enters into host cells with the help of necessary components glycoprotein B (gB) and H/L. In this study, the conformational linear B-cell and T-cell epitopes for gB of HHV-5 have been predicted using conformational approaches, for their possible collective use as vaccine candidates. We examined epitope's interactions with major histocompatibility complexes using molecular docking and also investigated their stable binding with specific toll like receptor-2 (TLR2), present on host cells during HHV-5 infection. Predicted MHC-I epitope 'LVAIAVVII' with high antigenicity and large coverage of HLA alleles was found to superimpose on MHC-II epitope (Rank 1) and was also identified to be the core sequence of putative B cell epitope 'ILVAIAVVIITYLI'. Resulting epitope was found to have consistent interaction with TLR2 during long term (100 ns) MD run. We also validated this nonamer epitope for its dissimilarity with human genome and high population coverage, suggesting it to be a potential vaccine candidate with higher coverage for both the MHC alleles of Indian population. Communicated by Ramaswamy H. Sarma.

Selection of Specific Peptides for Coccidioides spp. Obtained from Antigenic Fractions through SDS-PAGE and Western Blot Methods by the Recognition of Sera from Patients with Coccidioidomycosis

Antigenic fractions of 100, 50, 37, and 28 kDa obtained through the SDS-PAGE method that were more frequently recognized by anti-Coccidioides antibodies in the sera of coccidioidomycosis patients were selected using western blotting. Subsequently, these bands were sequenced, and the obtained proteins were analysed by BLAST to choose peptides specific for Coccidioides spp. from among the shared aligned sequences of related fungi. A peptide specific for C. immitis was selected from the "GPI anchored serine-threonine rich protein OS C. immitis", while from the "uncharacterized protein of C. immitis", we selected a peptide for C. immitis and C. posadasii. These proteins arose from the 100 kDa antigenic fraction. From the protein "fatty acid amide hydrolase 1 of C. posadasii" that was identified from the 50 kDa antigenic fraction, a peptide was selected that recognized C. immitis and C. posadasii. In addition, the analysis of all the peptides (353) of each of the assembled proteins showed that only 35 had 100% identity with proteins of C. immitis and C. posadasii, one had 100% identity with only C. immitis, and one had 100% identity with only C. posadasii. These peptides can be used as diagnostic reagents, vaccines, and antifungals.

Immunoinformatics and molecular docking studies reveal potential epitope-based peptide vaccine against DENV-NS3 protein

Dengue, still a "Neglected Tropical Disease" is somehow injustice and remains uncontrolled globally. World Health Organization (2012-2020) reported that the world's half population is living in dengue-affected regions. Therefore, effective drug candidates or promising vaccines are urgently needed to control the dengue. It is an acute febrile disease caused by mosquito borne dengue viruses (DENVs) which belong to the genus Flavivirus with four serotypes. In present work, immunoinformatics approach was utilized to predict the antigenic epitopes of dengue proteins for the development of DENV vaccine. B-cell and cytotoxic T-lymphocyte epitopes were predicted for NS3 dengue protein. Docking complexes of 17 antigenic B-cell epitopes of various lengths and 4 CTL epitopes with antigenic sites were investigated followed by binding interaction analyses of top predicted peptides with MHC-I HLA-A2 molecule. These predicted epitopes with antigenic amino acids might present a preliminary set of peptides for future vaccine development against DENV.

In Silico Methods in Antibody Design

Antibody therapies with high efficiency and low toxicity are becoming one of the major approaches in antibody therapeutics. Based on high-throughput sequencing and increasing experimental structures of antibodies/antibody-antigen complexes, computational approaches can predict antibody/antigen structures, engineering the function of antibodies and design antibody-antigen complexes with improved properties. This review summarizes recent progress in the field of in silico design of antibodies, including antibody structure modeling, antibody-antigen complex prediction, antibody stability evaluation, and allosteric effects in antibodies and functions. We listed the cases in which these methods have helped experimental studies to improve the affinities and physicochemical properties of antibodies. We emphasized how the molecular dynamics unveiled the allosteric effects during antibody-antigen recognition and antibody-effector recognition.

Peptide Vaccines for Leishmaniasis

Due to an increase in the incidence of leishmaniases worldwide, the development of new strategies such as prophylactic vaccines to prevent infection and decrease the disease have become a high priority. Classic vaccines against leishmaniases were based on live or attenuated parasites or their subunits. Nevertheless, the use of whole parasite or their subunits for vaccine production has numerous disadvantages. Therefore, the use of Leishmania peptides to design more specific vaccines against leishmaniases seems promising. Moreover, peptides have several benefits in comparison with other kinds of antigens, for instance, good stability, absence of potentially damaging materials, antigen low complexity, and low-cost to scale up. By contrast, peptides are poor immunogenic alone, and they need to be delivered correctly. In this context, several approaches described in this review are useful to solve these drawbacks. Approaches, such as, peptides in combination with potent adjuvants, cellular vaccinations, adenovirus, polyepitopes, or DNA vaccines have been used to develop peptide-based vaccines. Recent advancements in peptide vaccine design, chimeric, or polypeptide vaccines and nanovaccines based on particles attached or formulated with antigenic components or peptides have been increasingly employed to drive a specific immune response. In this review, we briefly summarize the old, current, and future stands on peptide-based vaccines, describing the disadvantages and benefits associated with them. We also propose possible approaches to overcome the related weaknesses of synthetic vaccines and suggest future guidelines for their development.

Computational identification, characterization and validation of potential antigenic peptide vaccines from hrHPVs E6 proteins using immunoinformatics and computational systems biology approaches

High-risk human papillomaviruses (hrHPVs) are the most prevalent viruses in human diseases including cervical cancers. Expression of E6 protein has already been reported in cervical cancer cases, excluding normal tissues. Continuous expression of E6 protein is making it ideal to develop therapeutic vaccines against hrHPVs infection and cervical cancer. Therefore, we carried out a meta-analysis of multiple hrHPVs to predict the most potential prophylactic peptide vaccines. In this study, immunoinformatics approach was employed to predict antigenic epitopes of hrHPVs E6 proteins restricted to 12 Human HLAs to aid the development of peptide vaccines against hrHPVs. Conformational B-cell and CTL epitopes were predicted for hrHPVs E6 proteins using ElliPro and NetCTL. The potential of the predicted peptides were tested and validated by using systems biology approach considering experimental concentration. We also investigated the binding interactions of the antigenic CTL epitopes by using docking. The stability of the resulting peptide-MHC I complexes was further studied by molecular dynamics simulations. The simulation results highlighted the regions from 46–62 and 65–76 that could be the first choice for the development of prophylactic peptide vaccines against hrHPVs. To overcome the worldwide distribution, the predicted epitopes restricted to different HLAs could cover most of the vaccination and would help to explore the possibility of these epitopes for adaptive immunotherapy against HPVs infections.

Breast cancer vaccination comes to age: impacts of bioinformatics

Introduction: Breast cancer, as one of the major causes of cancer death among women, is the central focus of this study. The recent advances in the development and application of computational tools and bioinformatics in the field of immunotherapy of malignancies such as breast cancer have emerged the new dominion of immunoinformatics, and therefore, next generation of immunomedicines .

Methods: Having reviewed the most recent works on the applications of computational tools, we provide comprehensive insights into the breast cancer incidence and its leading causes as well as immunotherapy approaches and the future trends. Furthermore, we discuss the impacts of bioinformatics on different stages of vaccine design for the breast cancer, which can be used to produce much more efficient vaccines through a rationalized time- and cost-effective in silico approaches prior to conducting costly experiments.

Results: The tools can be significantly used for designing the immune system-modulating drugs and vaccines based on in silico approaches prior to in vitro and in vivo experimental evaluations. Application of immunoinformatics in the cancer immunotherapy has shown its success in the pre-clinical models. This success returns back to the impacts of several powerful computational approaches developed during the last decade.

Conclusion: Despite the invention of a number of vaccines for the cancer immunotherapy, more computational and clinical trials are required to design much more efficient vaccines against various malignancies, including breast cancer.

Towards peptide vaccines against Zika virus: Immunoinformatics combined with molecular dynamics simulations to predict antigenic epitopes of Zika viral proteins

The recent outbreak of Zika virus (ZIKV) infection in Brazil has developed to a global health concern due to its likely association with birth defects (primary microcephaly) and neurological complications. Consequently, there is an urgent need to develop a vaccine to prevent or a medicine to treat the infection. In this study, immunoinformatics approach was employed to predict antigenic epitopes of Zika viral proteins to aid in development of a peptide vaccine against ZIKV. Both linear and conformational B-cell epitopes as well as cytotoxic T-lymphocyte (CTL) epitopes were predicted for ZIKV Envelope (E), NS3 and NS5 proteins. We further investigated the binding interactions of altogether 15 antigenic CTL epitopes with three class I major histocompatibility complex (MHC I) proteins after docking the peptides to the binding groove of the MHC I proteins. The stability of the resulting peptide-MHC I complexes was further studied by molecular dynamics simulations. The simulation results highlight the limits of rigid-body docking methods. Some of the antigenic epitopes predicted and analyzed in this work might present a preliminary set of peptides for future vaccine development against ZIKV.

Pangenome and immuno-proteomics analysis of Acinetobacter baumannii strains revealed the core peptide vaccine targets

BACKGROUND

Acinetobacter baumannii has emerged as a significant nosocomial pathogen during the last few years, exhibiting resistance to almost all major classes of antibiotics. Alternative treatment options such as vaccines tend to be most promising and cost effective approaches against this resistant pathogen. In the current study, we have explored the pan-genome of A. baumannii followed by immune-proteomics and reverse vaccinology approaches to identify potential core vaccine targets.

RESULTS

The pan-genome of all available A. baumannii strains (30 complete genomes) is estimated to contain 7,606 gene families and the core genome consists of 2,445 gene families (~32 % of the pan-genome). Phylogenetic tree, comparative genomic and proteomic analysis revealed both intra- and inter genomic similarities and evolutionary relationships. Among the conserved core genome, thirteen proteins, including P pilus assembly protein, pili assembly chaperone, AdeK, PonA, OmpA, general secretion pathway protein D, FhuE receptor, Type VI secretion system OmpA/MotB, TonB dependent siderophore receptor, general secretion pathway protein D, outer membrane protein, peptidoglycan associated lipoprotein and peptidyl-prolyl cis-trans isomerase are identified as highly antigenic. Epitope mapping of the target proteins revealed the presence of antigenic surface exposed 9-mer T-cell epitopes. Protein-protein interaction and functional annotation have shown their involvement in significant biological and molecular processes. The pipeline is validated by predicting already known immunogenic targets against Gram negative pathogen Helicobacter pylori as a positive control.

CONCLUSION

The study, based upon combinatorial approach of pan-genomics, core genomics, proteomics and reverse vaccinology led us to find out potential vaccine candidates against A. baumannii. The comprehensive analysis of all the completely sequenced genomes revealed thirteen putative antigens which could elicit substantial immune response. The integration of computational vaccinology strategies would facilitate in tackling the rapid dissemination of resistant A.baumannii strains. The scarcity of effective antibiotics and the global expansion of sequencing data making this approach desirable in the development of effective vaccines against A. baumannii and other bacterial pathogens.

Vaccine Design for H5N1 Based on B- and T-cell Epitope Predictions

From 2003 to 2013, Indonesia had the highest number of avian influenza A cases in humans, with 192 cases and 160 fatalities. Avian influenza is caused by influenza virus type A, such as subtype H5N1. This virus has two glycoproteins: hemagglutinin and neuraminidase, which will become the primary target to be neutralized by vaccine. Vaccine is the most effective immunologic intervention. In this study, we use the epitope-based vaccine design from hemagglutinin and neuraminidase of H5N1 Indonesian strain virus by using immunoinformatics approach in order to predict the binding of B-cell and T-cell epitopes (class I and class II human leukocyte antigen [HLA]). BCPREDS was used to predict the B-cell epitope. Propred, Propred I, netMHCpan, and netMHCIIpan were used to predict the T-cell epitope. Two B-cell epitopes of hemagglutinin candidates and one B-cell epitope of neuraminidase candidates were obtained to bind T-cell CD4(+) (class II HLA), and also five T-cell epitope hemagglutinin and four T-cell epitope neuraminidase were obtained to bind T-cell CD8(+) (class I HLA). The visualization of epitopes was done using MOE 2008.10. It shows that the binding affinity of epitope-HLA was based on minimum binding free energy (ΔG binding). Based on this result, visualization, and dynamic simulation, four hemagglutinin epitopes (MEKIVLLLA, CPYLGSPSF, KCQTPMGAI, and IGTSTLNQR) and two neuraminidase epitopes (NPNQKIITI and CYPDAGEIT) were computed as having the best binding affinity from HLA ligand. The results mentioned above are from in silico experiments and need to be validated using wet experiment.

Novel Structural Components Contribute to the High Thermal Stability of Acyl Carrier Protein from Enterococcus faecalis

Enterococcus faecalis is a Gram-positive, commensal bacterium that lives in the gastrointestinal tracts of humans and other mammals. It causes severe infections because of high antibiotic resistance. E. faecalis can endure extremes of temperature and pH. Acyl carrier protein (ACP) is a key element in the biosynthesis of fatty acids responsible for acyl group shuttling and delivery. In this study, to understand the origin of high thermal stabilities of E. faecalis ACP (Ef-ACP), its solution structure was investigated for the first time. CD experiments showed that the melting temperature of Ef-ACP is 78.8 °C, which is much higher than that of Escherichia coli ACP (67.2 °C). The overall structure of Ef-ACP shows the common ACP folding pattern consisting of four α-helices (helix I (residues 3-17), helix II (residues 39-53), helix III (residues 60-64), and helix IV (residues 68-78)) connected by three loops. Unique Ef-ACP structural features include a hydrophobic interaction between Phe(45) in helix II and Phe(18) in the α1α2 loop and a hydrogen bonding between Ser(15) in helix I and Ile(20) in the α1α2 loop, resulting in its high thermal stability. Phe(45)-mediated hydrophobic packing may block acyl chain binding subpocket II entry. Furthermore, Ser(58) in the α2α3 loop in Ef-ACP, which usually constitutes a proline in other ACPs, exhibited slow conformational exchanges, resulting in the movement of the helix III outside the structure to accommodate a longer acyl chain in the acyl binding cavity. These results might provide insights into the development of antibiotics against pathogenic drug-resistant E. faecalis strains.

Current trends in cancer vaccines--a bioinformatics perspective

Cancer vaccine development is in the process of becoming reality in future, due to successful phase II/III clinical trials. However, there are still problems due to the specificity of tumor antigens and weakness of tumor associated antigens in eliciting an effective immune response. Computational models to assess the vaccine efficacy have helped to improve and understand what is necessary for personalized treatment. Further research is needed to elucidate the mechanisms of activation of antigen specific cytotoxic T lymphocytes, decreased TREG number functionality and antigen cascade, so that overall improvement in vaccine efficacy and disease free survival can be attained. T cell epitomic based in sillico approaches might be very effective for the design and development of novel cancer vaccines.

Information technologies for vaccine research

Vaccinology is a combinatorial science which studies the diversity of pathogens and the human immune system, and formulations that can modulate immune responses and prevent or cure disease. Huge amounts of data are produced by genomics and proteomics projects and large-scale screening of pathogen-host and antigen-host interactions. Current developments in computational vaccinology mainly support the analysis of antigen processing and presentation and the characterization of targets of immune response. Future development will also include systemic models of vaccine responses. Immunomics, the large-scale screening of immune processes which includes powerful immunoinformatic tools, offers great promise for future translation of basic immunology research advances into successful vaccines.

Synthesis and antibacterial evaluation of novel Schiff's base derivatives of nitroimidazole nuclei as potent E. coli FabH inhibitors

Series of novel Schiff's base derivatives have been synthesized. Compound 10q showed the most potent inhibitory activity (IC₅₀ = 2.6883 μM).

T-cell epitope vaccine design by immunoinformatics

Vaccination is generally considered to be the most effective method of preventing infectious diseases. All vaccinations work by presenting a foreign antigen to the immune system in order to evoke an immune response. The active agent of a vaccine may be intact but inactivated ('attenuated') forms of the causative pathogens (bacteria or viruses), or purified components of the pathogen that have been found to be highly immunogenic. The increased understanding of antigen recognition at molecular level has resulted in the development of rationally designed peptide vaccines. The concept of peptide vaccines is based on identification and chemical synthesis of B-cell and T-cell epitopes which are immunodominant and can induce specific immune responses. The accelerating growth of bioinformatics techniques and applications along with the substantial amount of experimental data has given rise to a new field, called immunoinformatics. Immunoinformatics is a branch of bioinformatics dealing with in silico analysis and modelling of immunological data and problems. Different sequence- and structure-based immunoinformatics methods are reviewed in the paper.

Emerging vaccine informatics

Vaccine informatics is an emerging research area that focuses on development and applications of bioinformatics methods that can be used to facilitate every aspect of the preclinical, clinical, and postlicensure vaccine enterprises. Many immunoinformatics algorithms and resources have been developed to predict T- and B-cell immune epitopes for epitope vaccine development and protective immunity analysis. Vaccine protein candidates are predictable in silico from genome sequences using reverse vaccinology. Systematic transcriptomics and proteomics gene expression analyses facilitate rational vaccine design and identification of gene responses that are correlates of protection in vivo. Mathematical simulations have been used to model host-pathogen interactions and improve vaccine production and vaccination protocols. Computational methods have also been used for development of immunization registries or immunization information systems, assessment of vaccine safety and efficacy, and immunization modeling. Computational literature mining and databases effectively process, mine, and store large amounts of vaccine literature and data. Vaccine Ontology (VO) has been initiated to integrate various vaccine data and support automated reasoning.

Bioinformatics for study of autoimmunity

Recent years have witnessed an explosive growth in available biological data pertaining to autoimmunity research. This includes a tremendous quantity of sequence data (biological structures, genetic and physical maps, pathways, etc.) generated by genome and proteome projects plus extensive clinical and epidemiological data. Autoimmunity research stands to greatly benefit from this data so long as appropriate strategies are available to enable full access to and utilization of this data. The quantity and complexity of this biological data necessitates use of advanced bioinformatics strategies for its efficient retrieval, analysis and interpretation. Major progress has been made in development of specialized tools for storage, analysis and modeling of immunological data, and this has led to development of a whole new field know as immunoinformatics. With advances in novel high-throughput immunology technologies immunoinformatics is transforming understanding of how the immune system functions. This paper reviews advances in the field of immunoinformatics pertinent to autoimmunity research including databases, tools in genomics and proteomics, tools for study of B- and T-cell epitopes, integrative approaches, and web servers.

Screening of flavonoids as candidate antibiotics against Enterococcus faecalis

beta-Ketoacyl acyl carrier protein synthase (KAS) III, the most divergent member of the condensing enzyme family, is a key catalyst in bacterial fatty acid biosynthesis and, thus, an attractive target for novel antibiotics. Here, we perform docking studies between Enterococcus faecalis KAS III (efKAS III) and one flavanone and 11 hydroxyflavanones with hydroxy groups at various positions. The MIC values of these flavanones for E. faecalis and vancomycin-resistant E. faecalis (VREF) were measured, and binding affinities to efKAS III were determined. Naringenin (9), eriodictyol (10), and taxifolin (12), with high-scoring functions and good binding affinities, docked well with efKAS III, resulting in MIC values in the range 128-512 microg/mL. Our results indicate that hydrogen bonds between the 5- and 4'-hydroxy groups and the side-chain of Arg38 and the backbone carbonyl of Phe308 are the key interactions for efKAS III inhibition. These flavanones are good candidate KAS III inhibitors and may be utilized as effective antimicrobials.

Novel E. coli beta-ketoacyl-acyl carrier protein synthase III inhibitors as targeted antibiotics

Beta-ketoacyl-acyl carrier protein synthase (KAS) III is a condensing enzyme that initiates fatty acid biosynthesis in most bacteria. We determined three pharmacophore maps from receptor-oriented pharmacophore-based in silico screening of the X-ray structure of Escherichia coli KAS III (ecKAS III) and choose 16 compounds as candidate ecKAS III inhibitors. Binding inhibitors were characterized using saturation-transfer difference NMR spectroscopy (STD-NMR), and binding constants were determined with fluorescence quenching experiments. Based on the results, we propose that the antimicrobial compound, 4-cyclohexyliminomethyl-benzene-1,3-diol (YKAs3003), is a potent inhibitor of pathogenic KAS III, displaying minimal inhibitory concentration (MIC) values in the range 128-256 microg/mL against various bacteria.

PRED(TAP): a system for prediction of peptide binding to the human transporter associated with antigen processing

Background

The transporter associated with antigen processing (TAP) is a critical component of the major histocompatibility complex (MHC) class I antigen processing and presentation pathway. TAP transports antigenic peptides into the endoplasmic reticulum where it loads them into the binding groove of MHC class I molecules. Because peptides must first be transported by TAP in order to be presented on MHC class I, TAP binding preferences should impact significantly on T-cell epitope selection.

Description

PRED^TAP is a computational system that predicts peptide binding to human TAP. It uses artificial neural networks and hidden Markov models as predictive engines. Extensive testing was performed to valid the prediction models. The results showed that PRED^TAP was both sensitive and specific and had good predictive ability (area under the receiver operating characteristic curve Aroc>0.85).

Conclusion

PRED^TAP can be integrated with prediction systems for MHC class I binding peptides for improved performance of in silico prediction of T-cell epitopes. PRED^TAP is available for public use at [1].