Computational Identification and Characterization of a Promiscuous T-Cell Epitope on the Extracellular Protein 85B of Mycobacterium spp. for Peptide-Based Subunit Vaccine Design

Design of a novel multi-epitopes vaccine against Escherichia fergusonii: a pan-proteome based in- silico approach

Escherichia fergusonii a gram-negative rod-shaped bacterium in the Enterobacteriaceae family, infect humans, causing serious illnesses such as urinary tract infection, cystitis, biliary tract infection, pneumonia, meningitis, hemolytic uremic syndrome, and death. Initially treatable with penicillin, antibiotic misuse led to evolving resistance, including resistance to colistin, a last-resort drug. With no licensed vaccine, the study aimed to design a multi-epitope vaccine against E. fergusonii. The study started with the retrieval of the complete proteome of all known strains and proceeded to filter the surface exposed virulent proteins. Seventeen virulent proteins (4 extracellular, 4 outer membranes, 9 periplasmic) with desirable physicochemical properties were identified from the complete proteome of known strains. Further, these proteins were processed for B-cell and T-cell epitope mapping. Obtained epitopes were evaluated for antigenicity, allergenicity, solubility, MHC-binding, and toxicity and the filtered epitopes were fused by specific linkers and an adjuvant into a vaccine construct. Structure of the vaccine candidate was predicted and refined resulting in 78.1% amino acids in allowed regions and VERIFY3D score of 81%. Vaccine construct was docked with TLR-4, MHC-I, and MHC-II, showing binding energies of -1040.8 kcal/mol, -871.4 kcal/mol, and -1154.6 kcal/mol and maximum interactions. Further, molecular dynamic simulation of the docked complexes was carried out resulting in a significant stable nature of the docked complexes (high B-factor and deformability values, lower Eigen and high variance values) in terms of intermolecular binding conformation and interactions. The vaccine was also reported to stimulate a variety of immunological pathways after administration. In short, the designed vaccine revealed promising predictions about its immune protective potential against E. fergusonii infections however experimental validation is needed to validate the results.

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.

Application of Reverse Vaccinology and Immunoinformatic Strategies for the Identification of Vaccine Candidates Against Shigella flexneri

Reverse vaccinology (RV) was first introduced by Rappuoli for the development of an effective vaccine against serogroup B Neisseria meningitidis (MenB). With the advances in next generation sequencing technologies, the amount of genomic data has risen exponentially. Since then, the RV approach has widely been used to discover potential vaccine protein targets by screening whole genome sequences of pathogens using a combination of sophisticated computational algorithms and bioinformatic tools. In contrast to conventional vaccine development strategies, RV offers a novel method to facilitate rapid vaccine design and reduces reliance on the traditional, relatively tedious, and labor-intensive approach based on Pasteur"s principles of isolating, inactivating, and injecting the causative agent of an infectious disease. Advances in biocomputational techniques have remarkably increased the significance for the rapid identification of the proteins that are secreted or expressed on the surface of pathogens. Immunogenic proteins which are able to induce the immune response in the hosts can be predicted based on the immune epitopes present within the protein sequence. To date, RV has successfully been applied to develop vaccines against a variety of infectious pathogens. In this chapter, we apply a pipeline of bioinformatic programs for identification of Shigella flexneri potential vaccine candidates as an illustration immunoinformatic tools available for RV.

An integrated computational framework to design a multi-epitopes vaccine against Mycobacterium tuberculosis

Tuberculosis (TB) is a highly contagious disease that mostly affects the lungs and is caused by a bacterial pathogen, Mycobacterium tuberculosis. The associated mortality rate of TB is much higher compared to any other disease and the situation is more worrisome by the rapid emergence of drug resistant strains. Bacillus Calmette-Guerin (BCG) is the only licensed attenuated vaccine available for use in humans however, many countries have stopped its use as it fails to confer protective immunity. Therefore, urgent efforts are required to identify new and safe vaccine candidates that are not only provide high immune protection but also have broad spectrum applicability. Considering this, herein, I performed an extensive computational vaccine analysis to investigate 200 complete sequenced genomes of M. tuberculosis to identify core vaccine candidates that harbor safe, antigenic, non-toxic, and non-allergic epitopes. To overcome literature reported limitations of epitope-based vaccines, I carried out additional analysis by designing a multi-epitopes vaccine to achieve maximum protective immunity as well as to make experimental follow up studies easy by selecting a vaccine that can be easily analyzed because of its favorable physiochemical profile. Based on these analyses, I identified two potential vaccine proteins that fulfill all required vaccine properties. These two vaccine proteins are diacylglycerol acyltransferase and ESAT-6-like protein. Epitopes: DSGGYNANS from diacylglycerol acyltransferase and AGVQYSRAD, ADEEQQQAL, and VSRADEEQQ from ESAT-6-like protein were found to cover all necessary parameters and thus used in a multi-epitope vaccine construct. The designed vaccine is depicting a high binding affinity for different immune receptors and shows stable dynamics and rigorous van der Waals and electrostatic binding energies. The vaccine also simulates profound primary, secondary, tertiary immunoglobulin production as well as high interleukins and interferons count. In summary, the designed vaccine is ideal to be evaluated experimentally to decipher its real biological efficacy in controlling drug resistant infections of M. tuberculosis.

Vaxign2: the second generation of the first Web-based vaccine design program using reverse vaccinology and machine learning

Vaccination is one of the most significant inventions in medicine. Reverse vaccinology (RV) is a state-of-the-art technique to predict vaccine candidates from pathogen's genome(s). To promote vaccine development, we updated Vaxign2, the first web-based vaccine design program using reverse vaccinology with machine learning. Vaxign2 is a comprehensive web server for rational vaccine design, consisting of predictive and computational workflow components. The predictive part includes the original Vaxign filtering-based method and a new machine learning-based method, Vaxign-ML. The benchmarking results using a validation dataset showed that Vaxign-ML had superior prediction performance compared to other RV tools. Besides the prediction component, Vaxign2 implemented various post-prediction analyses to significantly enhance users' capability to refine the prediction results based on different vaccine design rationales and considerably reduce user time to analyze the Vaxign/Vaxign-ML prediction results. Users provide proteome sequences as input data, select candidates based on Vaxign outputs and Vaxign-ML scores, and perform post-prediction analysis. Vaxign2 also includes precomputed results from approximately 1 million proteins in 398 proteomes of 36 pathogens. As a demonstration, Vaxign2 was used to effectively analyse SARS-CoV-2, the coronavirus causing COVID-19. The comprehensive framework of Vaxign2 can support better and more rational vaccine design. Vaxign2 is publicly accessible at http://www.violinet.org/vaxign2.

Vaxign-ML: supervised machine learning reverse vaccinology model for improved prediction of bacterial protective antigens

MOTIVATION

Reverse vaccinology (RV) is a milestone in rational vaccine design, and machine learning (ML) has been applied to enhance the accuracy of RV prediction. However, ML-based RV still faces challenges in prediction accuracy and program accessibility.

RESULTS

This study presents Vaxign-ML, a supervised ML classification to predict bacterial protective antigens (BPAgs). To identify the best ML method with optimized conditions, five ML methods were tested with biological and physiochemical features extracted from well-defined training data. Nested 5-fold cross-validation and leave-one-pathogen-out validation were used to ensure unbiased performance assessment and the capability to predict vaccine candidates against a new emerging pathogen. The best performing model (eXtreme Gradient Boosting) was compared to three publicly available programs (Vaxign, VaxiJen, and Antigenic), one SVM-based method, and one epitope-based method using a high-quality benchmark dataset. Vaxign-ML showed superior performance in predicting BPAgs. Vaxign-ML is hosted in a publicly accessible web server and a standalone version is also available.

AVAILABILITY AND IMPLEMENTATION

Vaxign-ML website at http://www.violinet.org/vaxign/vaxign-ml, Docker standalone Vaxign-ML available at https://hub.docker.com/r/e4ong1031/vaxign-ml and source code is available at https://github.com/VIOLINet/Vaxign-ML-docker.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Identification of Immune Responses to Japanese Encephalitis Virus Specific T Cell Epitopes

Background: Due to the similarity between the dengue (DENV) and the Japanese encephalitis virus (JEV) there is potential for immune cross-reaction. We sought to identify T cell epitopes that are specific to JEV and do not cross react with DENV. Methodology: 20mer peptides were synthesized from regions which showed >90% conservation. Using IFNγ cultured ELISpot assays, we investigated JEV-specific T cell responses in DENV^- and JEV^- non-immune individuals (DENV^-JEV^- = 21), JEV seronegative and had not received the JE vaccine, but who were DENV seropositive (DENV⁺JEV^- = 22), JEV⁺(seropositive for JEV and had received the JE vaccine), but seronegative for DENV (DENV^-JEV⁺ = 23). We further assessed the responses to these peptides by undertaking ex vivo IFNγ assays and flow cytometry. Results: None of DENV^-JEV^- individuals responded to any of the 20 JEV-specific peptides. High frequency of responses was seen to 6/20 peptides by individuals who were JEV⁺ but DENV^-, where over 75% of the individuals responded to at least one peptide. P34 was the most immunogenic peptide, recognized by 20/23 (86.9%) individuals who were DENV^-JEV⁺, followed by peptide 3 and peptide 7 recognized by 19/23 (82.6%). Peptide 34 from the NS2a region, showed <25% homology with any flaviviruses, and <20% homology with any DENV serotype. Peptide 20 and 32, which were also from the non-structural protein regions, showed <25% homology with DENV. Ex vivo responses to these peptides were less frequent, with only 40% of individuals responding to peptide 34 and 16-28% to other peptides, probably as 5/6 peptides were recognized by CD4+ T cells. Discussion: We identified six highly conserved, T cell epitopes which are highly specific for JEV, in the Sri Lankan population. Since both JEV and DENV co-circulate in the same regions and since both JE and dengue vaccines are likely to be co-administered in the same geographical regions in future, these JEV-specific T cell epitopes would be useful to study JEV-specific T cell responses, in order to further understand how DENV and JEV-specific cellular immune responses influence each other.

Tuberculosis: Current Status, Diagnosis, Treatment and Development of Novel Vaccines

Tuberculosis (TB) is an infectious disease that mainly affects the lungs and spreads to other organs of the body through the haematogenous route. It is one of the ten major causes of mortality worldwide. India has the highest incidence of new- and multidrug-resistant (MDR) - TB cases in the world. Bacille Calmette-Guerin (BCG) is the vaccine commonly available against TB. BCG does offer some protection against serious forms of TB in childhood but its protective effect wanes with age. Many new innovative strategies are being trailed for the development of effective and potent vaccines like mucosal- and epitope-based vaccines, which may replace BCG or boost BCG responses. The use of nanotechnology for diagnosis and treatment of TB is also in the pipeline along with many other vaccines, which are under clinical trials. Further, in-silico models were developed for finding new drug targets and designing drugs against Mycobacterium tuberculosis (Mtb). These models offer the benefit of computational experiments which are easy, inexpensive and give quick results. This review will focus on the available treatments and new approaches to develop potent vaccines for the treatment of TB.

Immunoinformatics Based Study of T Cell Epitopes in Zea m 1 Pollen Allergen

Background and Objectives: Zea m 1 is a pollen allergen, which is present in maize, is accountable for a type I hypersensitivity reaction in all over the world. Several effective medications are available for the disorder with various side effects. Design and verification of a peptide-based vaccine is a state-of-art technology which is more cost effective than conventional drugs. Materials and Methods: Using immunoinformatic methods, the T cell epitopes from the whole structure of this allergenic protein can be predicted. Worldwide conserved region study among the other pollen allergens has been performed for T cell predicted epitopes by using a conservancy tool. This analysis will help to identify completely conserved HLA (human leukocyte antigen) binding epitopes. Lastly, molecular docking study and MHC-oligopeptide complex binding energy calculation data are applied to determine the interacting amino acids and the affinity of the epitopes to the class II MHCmolecule. Results: The study of criteria-based analysis predicts the presence of two epitopes YVADDGDIV and WRMDTAKAL on this pollen allergen. Conclusions: The T cell epitopes identified in this study provide insight into a peptide-based vaccine for a type I hypersensitivity reaction induced by Zea m 1 grass pollen allergenic protein.

Protein-Protein Docking in Drug Design and Discovery

Protein-protein interactions (PPIs) are responsible for a number of key physiological processes in the living cells and underlie the pathomechanism of many diseases. Nowadays, along with the concept of so-called "hot spots" in protein-protein interactions, which are well-defined interface regions responsible for most of the binding energy, these interfaces can be targeted with modulators. In order to apply structure-based design techniques to design PPIs modulators, a three-dimensional structure of protein complex has to be available. In this context in silico approaches, in particular protein-protein docking, are a valuable complement to experimental methods for elucidating 3D structure of protein complexes. Protein-protein docking is easy to use and does not require significant computer resources and time (in contrast to molecular dynamics) and it results in 3D structure of a protein complex (in contrast to sequence-based methods of predicting binding interfaces). However, protein-protein docking cannot address all the aspects of protein dynamics, in particular the global conformational changes during protein complex formation. In spite of this fact, protein-protein docking is widely used to model complexes of water-soluble proteins and less commonly to predict structures of transmembrane protein assemblies, including dimers and oligomers of G protein-coupled receptors (GPCRs). In this chapter we review the principles of protein-protein docking, available algorithms and software and discuss the recent examples, benefits, and drawbacks of protein-protein docking application to water-soluble proteins, membrane anchoring and transmembrane proteins, including GPCRs.

Characterization and Thermal Denaturation Kinetic Analysis of Recombinant l-Amino Acid Ester Hydrolase from Stenotrophomonas maltophilia

Stenotrophomonas maltophilia HS1 exhibits l-amino acid ester hydrolase (SmAEH) activity, which can synthesize dipeptides such as Ile-Trp, Val-Gly, and Trp-His from the corresponding amino acid methyl esters and amino acids. The gene encoding SmAEH was cloned and expressed in Escherichia coli and was purified and characterized. SmAEH shared 77% sequence identity with a known amino acid ester hydrolase (AEH) from Xanthomonas citri, which belongs to a class of β-lactam antibiotic acylases. The thermal stability of SmAEH was evaluated using various mathematical models to assess its industrial potential. First-order kinetics provided the best description for the inactivation of the enzyme over a temperature range of 35-50 °C. Decimal reduction time ranged from 212.76 to 3.44 min, with a z value of 8.06 °C, and the deactivation energy was 204.1 kJ mol^-1.

Dynamics of Immune Responses during Experimental Mycobacterium kansasii Infection of Cynomolgus Monkeys (Macaca fascicularis)

To profile the dynamic changes of immune responses for M. kansasii infection, 3 cynomolgus monkeys were experimentally infected with M. kansasii by intratracheal inhalation of 1 × 10⁶ CFU bacteria per monkey. Every 2 to 4 weeks, tuberculin skin testings (TSTs) were performed and blood samples were collected for immunoassay. Multiple cytokines in a single sample were measured by Luminex xMAP technologies. IgM and IgA were detected by double-antibody sandwich ELISA. IgG against PPD and 11 M. tuberculosis proteins were detected by using of indirect ELISA. At week 16, all animals were euthanized for necropsy and histological analysis. Positivities of TSTs emerged from week 2 to 6 postinfection. Leukocyte counts and T lymphocyte subsets experienced moderate increases. Among 44 kinds of cytokines, 36 kinds of them showed increases of different dynamic types and 8 kinds of them showed no specific changes. Total IgM and IgA showed a transient increase at an early infection stage. Positivities of M. tuberculosis specific IgM and IgA emerged as early as week 2 postinfection. All animals showed positive IgG against PPD and negative IgG responses to 38 kDa, MPT64L, TB16.3, 16 kDa, U1, and MTB81 antigens during the infection period. IgG against ESAT-6, CFP10, CFP10-ESAT-6, Ag85b, and 14 kDa antigens reached positive levels. The IgG avidities of PPD, ESAT-6, CFP10-ESAT-6, and Ag85b were all above 50 percent. In conclusion, the data indicate that M. kansasii infection in monkeys can induce positivities of TSTs, increases of multiple cytokines, and cross-reactive antibody responses to M. tuberculosis antigens.

Current progress of immunoinformatics approach harnessed for cellular- and antibody-dependent vaccine design

Immunoinformatics plays a pivotal role in vaccine design, immunodiagnostic development, and antibody production. In the past, antibody design and vaccine development depended exclusively on immunological experiments which are relatively expensive and time-consuming. However, recent advances in the field of immunological bioinformatics have provided feasible tools which can be used to lessen the time and cost required for vaccine and antibody development. This approach allows the selection of immunogenic regions from the pathogen genomes. The ideal regions could be developed as potential vaccine candidates to trigger protective immune responses in the hosts. At present, epitope-based vaccines are attractive concepts which have been successfully trailed to develop vaccines which target rapidly mutating pathogens. In this article, we provide an overview of the current progress of immunoinformatics and their applications in the vaccine design, immune system modeling and therapeutics.