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

Killing tumor-associated bacteria with a liposomal antibiotic generates neoantigens that induce anti-tumor immune responses

Increasing evidence implicates the tumor microbiota as a factor that can influence cancer progression. In patients with colorectal cancer (CRC), we found that pre-resection antibiotics targeting anaerobic bacteria substantially improved disease-free survival by 25.5%. For mouse studies, we designed an antibiotic silver-tinidazole complex encapsulated in liposomes (LipoAgTNZ) to eliminate tumor-associated bacteria in the primary tumor and liver metastases without causing gut microbiome dysbiosis. Mouse CRC models colonized by tumor-promoting bacteria (Fusobacterium nucleatum spp.) or probiotics (Escherichia coli Nissle spp.) responded to LipoAgTNZ therapy, which enabled more than 70% long-term survival in two F. nucleatum-infected CRC models. The antibiotic treatment generated microbial neoantigens that elicited anti-tumor CD8+ T cells. Heterologous and homologous bacterial epitopes contributed to the immunogenicity, priming T cells to recognize both infected and uninfected tumors. Our strategy targets tumor-associated bacteria to elicit anti-tumoral immunity, paving the way for microbiome-immunotherapy interventions.

The Impact of Artificial Intelligence on Microbial Diagnosis

Traditional microbial diagnostic methods face many obstacles such as sample handling, culture difficulties, misidentification, and delays in determining susceptibility. The advent of artificial intelligence (AI) has markedly transformed microbial diagnostics with rapid and precise analyses. Nonetheless, ethical considerations accompany AI adoption, necessitating measures to uphold patient privacy, mitigate biases, and ensure data integrity. This review examines conventional diagnostic hurdles, stressing the significance of standardized procedures in sample processing. It underscores AI's significant impact, particularly through machine learning (ML), in microbial diagnostics. Recent progressions in AI, particularly ML methodologies, are explored, showcasing their influence on microbial categorization, comprehension of microorganism interactions, and augmentation of microscopy capabilities. This review furnishes a comprehensive evaluation of AI's utility in microbial diagnostics, addressing both advantages and challenges. A few case studies including SARS-CoV-2, malaria, and mycobacteria serve to illustrate AI's potential for swift and precise diagnosis. Utilization of convolutional neural networks (CNNs) in digital pathology, automated bacterial classification, and colony counting further underscores AI's versatility. Additionally, AI improves antimicrobial susceptibility assessment and contributes to disease surveillance, outbreak forecasting, and real-time monitoring. Despite a few limitations, integration of AI in diagnostic microbiology presents robust solutions, user-friendly algorithms, and comprehensive training, promising paradigm-shifting advancements in healthcare.

Design and validation of a novel multi-epitopes vaccine against hantavirus

Hantavirus is a member of the order Bunyavirales and an emerging global pathogen. Hantavirus infections have affected millions of people globally based on available epidemiological data and research studies. Hemorrhagic fever with renal syndrome (HFRS) and hantavirus pulmonary syndrome (HPS) are the two main human diseases associated with hantavirus infections. Hence, efforts are required to develop a potent vaccine against the pathogen. The only vaccine that is in use for hantavirus is an inactivated virus vaccine, "Hantavax", but it failed to produce neutralizing antibodies. Vaccine development is of much importance in dealing with the surge of hantavirus globally. In this study, hantavirus five proteins (N protein, G1 and G2, L protein, and non-structural proteins) were used in NetCTL 1.2 program to predict T-cell epitopes. To predict major histocompatibility complex (MHC) binding alleles, an immune epitope database (IEDB) was used. All predicted epitopes were then investigated for different immunoinformatics analyses such as antigenicity and toxicity analyses. The good water-soluble, non-toxic, probable antigenic, and DRB*0101 binder was selected. A multi-epitopes-based vaccine designing was then done where linkers were used to connect the shortlisted epitopes. In addition, an adjuvant molecule was supplementary to the multi-epitopes peptide to improve the vaccine's immunogenic potential. The final vaccine construct's three-dimensional structure was modeled by ab initio method. The vaccine molecule was then evaluated for its binding potential with TLR-3 immune receptor, which is key for its recognition and processing by the host immune system. Docking studies were performed using HADDOCK software. The best-docked complex was selected and visualized for intermolecular binding and interactions using UCSF Chimera 1.16 software. The findings revealed that the designed vaccine might be a potential vaccine against hantavirus and can be used in experimental animal model testings.Communicated by Ramaswamy H. Sarma.

Ontological representation, modeling, and analysis of parasite vaccines

BACKGROUND

Pathogenic parasites are responsible for multiple diseases, such as malaria and Chagas disease, in humans and livestock. Traditionally, pathogenic parasites have been largely an evasive topic for vaccine design, with most successful vaccines only emerging recently. To aid vaccine design, the VIOLIN vaccine knowledgebase has collected vaccines from all sources to serve as a comprehensive vaccine knowledgebase. VIOLIN utilizes the Vaccine Ontology (VO) to standardize the modeling of vaccine data. VO did not model complex life cycles as seen in parasites. With the inclusion of successful parasite vaccines, an update in parasite vaccine modeling was needed.

RESULTS

VIOLIN was expanded to include 258 parasite vaccines against 23 protozoan species, and 607 new parasite vaccine-related terms were added to VO since 2022. The updated VO design for parasite vaccines accounts for parasite life stages and for transmission-blocking vaccines. A total of 356 terms from the Ontology of Parasite Lifecycle (OPL) were imported to VO to help represent the effect of different parasite life stages. A new VO class term, 'transmission-blocking vaccine,' was added to represent vaccines able to block infectious transmission, and one new VO object property, 'blocks transmission of pathogen via vaccine,' was added to link vaccine and pathogen in which the vaccine blocks the transmission of the pathogen. Additionally, our Gene Set Enrichment Analysis (GSEA) of 140 parasite antigens used in the parasitic vaccines identified enriched features. For example, significant patterns, such as signal, plasma membrane, and entry into host, were found in the antigens of the vaccines against two parasite species: Plasmodium falciparum and Toxoplasma gondii. The analysis found 18 out of the 140 parasite antigens involved with the malaria disease process. Moreover, a majority (15 out of 54) of P. falciparum parasite antigens are localized in the cell membrane. T. gondii antigens, in contrast, have a majority (19/24) of their proteins related to signaling pathways. The antigen-enriched patterns align with the life cycle stage patterns identified in our ontological parasite vaccine modeling.

CONCLUSIONS

The updated VO modeling and GSEA analysis capture the influence of the complex parasite life cycles and their associated antigens on vaccine development.

Surface-Exposed Protein Moieties of Burkholderia cenocepacia J2315 in Microaerophilic and Aerobic Conditions

Burkholderia cepacia complex infections remain life-threatening to cystic fibrosis patients, and due to the limited eradication efficiency of current treatments, novel antimicrobial therapies are urgently needed. Surface proteins are among the best targets to develop new therapeutic strategies since they are exposed to the host's immune system. A surface-shaving approach was performed using Burkholderia cenocepacia J2315 to quantitatively compare the relative abundance of surface-exposed proteins (SEPs) expressed by the bacterium when grown under aerobic and microaerophilic conditions. After trypsin incubation of live bacteria and identification of resulting peptides by liquid chromatography coupled with mass spectrometry, a total of 461 proteins with ≥2 unique peptides were identified. Bioinformatics analyses revealed a total of 53 proteins predicted as localized at the outer membrane (OM) or extracellularly (E). Additionally, 37 proteins were predicted as moonlight proteins with OM or E secondary localization. B-cell linear epitope bioinformatics analysis of the proteins predicted to be OM and E-localized revealed 71 SEP moieties with predicted immunogenic epitopes. The protegenicity higher scores of proteins BCAM2761, BCAS0104, BCAL0151, and BCAL0849 point out these proteins as the best antigens for vaccine development. Additionally, 10 of the OM proteins also presented a high probability of playing important roles in adhesion to host cells, making them potential targets for passive immunotherapeutic approaches. The immunoreactivity of three of the OM proteins identified was experimentally demonstrated using serum samples from cystic fibrosis patients, validating our strategy for identifying immunoreactive moieties from surface-exposed proteins of potential interest for future immunotherapies development.

An Immunoinformatics-Based Study of Mycobacterium tuberculosis Region of Difference-2 Uncharacterized Protein (Rv1987) as a Potential Subunit Vaccine Candidate for Preliminary Ex Vivo Analysis

Mycobacterium tuberculosis (Mtb) is the pathogen that causes tuberculosis and develops resistance to many of the existing drugs. The sole licensed TB vaccine, BCG, is unable to provide a comprehensive defense. So, it is crucial to maintain the immunological response to eliminate tuberculosis. Our previous in silico study reported five uncharacterized proteins as potential vaccine antigens. In this article, we considered the uncharacterized Mtb H37Rv regions of difference (RD-2) Rv1987 protein as a promising vaccine candidate. The vaccine quality of the protein was analyzed using reverse vaccinology and immunoinformatics-based quality-checking parameters followed by an ex vivo preliminary investigation. In silico analysis of Rv1987 protein predicted it as surface localized, secretory, single helix, antigenic, non-allergenic, and non-homologous to the host protein. Immunoinformatics analysis of Rv1987 by CD4 + and CD8 + T-cells via MHC-I and MHC-II binding affinity and presence of B-cell epitope predicted its immunogenicity. The docked complex analysis of the 3D model structure of the protein with immune cell receptor TLR-4 revealed the protein's capability for potential interaction. Furthermore, the target protein-encoded gene Rv1987 was cloned, over-expressed, purified, and analyzed by mass spectrometry (MS) to report the target peptides. The qRT-PCR gene expression analysis shows that it is capable of activating macrophages and significantly increasing the production of a number of key cytokines (TNF-α, IL-1β, and IL-10). Our in-silico analysis and ex vivo preliminary investigations revealed the immunogenic potential of the target protein. These findings suggest that the Rv1987 be undertaken as a potent subunit vaccine antigen and that further animal model immuno-modulation studies would boost the novel TB vaccine discovery and/or BCG vaccine supplement pipeline.

mtx-COBRA: Subcellular localization prediction for bacterial proteins

BACKGROUND

Bacteria can have beneficial effects on our health and environment; however, many are responsible for serious infectious diseases, warranting the need for vaccines against such pathogens. Bioinformatic and experimental technologies are crucial for the development of vaccines. The vaccine design pipeline requires identification of bacteria-specific antigens that can be recognized and can induce a response by the immune system upon infection. Immune system recognition is influenced by the location of a protein. Methods have been developed to determine the subcellular localization (SCL) of proteins in prokaryotes and eukaryotes. Bioinformatic tools such as PSORTb can be employed to determine SCL of proteins, which would be tedious to perform experimentally. Unfortunately, PSORTb often predicts many proteins as having an "Unknown" SCL, reducing the number of antigens to evaluate as potential vaccine targets.

METHOD

We present a new pipeline called subCellular lOcalization prediction for BacteRiAl Proteins (mtx-COBRA). mtx-COBRA uses Meta's protein language model, Evolutionary Scale Modeling, combined with an Extreme Gradient Boosting machine learning model to identify SCL of bacterial proteins based on amino acid sequence. This pipeline is trained on a curated dataset that combines data from UniProt and the publicly available ePSORTdb dataset.

RESULTS

Using benchmarking analyses, nested 5-fold cross-validation, and leave-one-pathogen-out methods, followed by testing on the held-out dataset, we show that our pipeline predicts the SCL of bacterial proteins more accurately than PSORTb.

CONCLUSIONS

mtx-COBRA provides an accessible pipeline that can more efficiently classify bacterial proteins with currently "Unknown" SCLs than existing bioinformatic and experimental methods.

Discovering conserved epitopes of Monkeypox: Novel immunoinformatic and machine learning approaches

The Monkeypox virus, an Orthopoxvirus with zoonotic origins, has been responsible for a growing number of human infections reminiscent of smallpox since May 2022, as reported by the World Health Organization. As of now, there are no established medical treatments for managing Monkeypox infections. In this study, we used machine learning to select conserved epitopes. Proteins were determined using Reverse Vaccinology and Gene Ontology subcellular localization, and their epitopes were predicted. NextClade was used to calculate the number of mutations in each amino acid position using 2433 Monkeypox sequences. The Unsupervised Nearest Neighbor machine learning algorithm and ideal matrix [0 0] were used to calculate the conservancy score of epitopes. Six proteins were determined for epitope prediction. Finally, 47 MHC-I epitopes, 5 MHC-II epitopes, and 10 Linear B cell epitopes were discovered. Our method can select epitopes for vaccine design to prevent viruses with accelerated evolution and high mutation rate.

Proteomic profiling of membrane vesicles from Mycobacterium avium subsp. paratuberculosis: Navigating towards an insilico design of a multi-epitope vaccine targeting membrane vesicle proteins

Bacteria typically produce membrane vesicles (MVs) at varying levels depending on the surrounding environments. Gram-negative bacterial outer membrane vesicles (OMVs) have been extensively studied for over 30 years, but MVs from Gram-positive bacteria only recently have been a focus of research. In the present study, we isolated MVs from Mycobacterium avium subsp. paratuberculosis (MAP) and analyzed their protein composition using LC-MS/MS. A total of 316 overlapping proteins from two independent preparations were identified in our study, and topology prediction showed these cargo proteins have different subcellular localization patterns. When MVs were administered to bovine-derived macrophages, significant up-regulation of pro-inflammatory cytokines was observed via qRT-PCR. Proteome functional annotation revealed that many of these proteins are involved in the cellular protein metabolic process, tRNA aminoacylation, and ATP synthesis. Secretory proteins with high antigenicity and adhesion capability were mapped for B-cell and T-cell epitopes. Antigenic, Immunogenic and IFN-γ inducing B-cell, MHC-I, and MHC-II epitopes were stitched together through linkers to form multi-epitope vaccine (MEV) construct against MAP. Strong binding energy was observed during the docking of the 3D structure of the MEV with the bovine TLR2, suggesting that the putative MEV may be a promising vaccine candidate against MAP. However, in vitro and in vivo analysis is required to prove the immunogenic concept of the MEV which we will follow in our future studies. SIGNIFICANCE: Johne's disease is a chronic infection caused by Mycobacterium avium subsp. paratuberculosis that has a potential link to Crohn's disease in humans. The disease is characterized by persistent diarrhea and enteritis, resulting in significant economic losses due to reduced milk yield and premature culling of infected animals. The dairy industry in the United States alone experiences losses of approximately USD 250 million due to Johne's disease. The current vaccine against Johne's disease is limited by several factors, including variable efficacy, limited duration of protection, interference with diagnostic tests, inability to prevent infection, and logistical and cost-related challenges. Nevertheless, a multiepitope vaccine design approach targeting M. avium subsp. paratuberculosis has the potential to overcome these challenges and offer improved protection against Johne's disease.

Development and use of machine learning algorithms in vaccine target selection

Computer-aided discovery of vaccine targets has become a cornerstone of rational vaccine design. In this article, I discuss how Machine Learning (ML) can inform and guide key computational steps in rational vaccine design concerned with the identification of B and T cell epitopes and correlates of protection. I provide examples of ML models, as well as types of data and predictions for which they are built. I argue that interpretable ML has the potential to improve the identification of immunogens also as a tool for scientific discovery, by helping elucidate the molecular processes underlying vaccine-induced immune responses. I outline the limitations and challenges in terms of data availability and method development that need to be addressed to bridge the gap between advances in ML predictions and their translational application to vaccine design.

Bioinformatics and immunoinformatics assisted multiepitope vaccine construct against Burkholderia anthina

Burkholderia anthina is a pathogenic bacterial species belonging to the Burkholderiaceae family and it is mainly considered the etiological agent of chronic obstructive pulmonary diseases associated with cystic fibrosis, due to being intrinsic antibiotic resistant making it difficult to treat pulmonary infections. Hence increased rate of antibiotic-resistant bacterial species vaccine development is the priority to tackle this problem. In research work, we designed a multi-epitope-based vaccine construct against B. anthina using reverse vaccinology immunoinformatics and biophysical approaches. Based on the subtractive proteomic screening of core proteins we identified 3 probable antigenic proteins and good vaccine targets namely, type VI secretion system tube protein hcp Burkholderia, fimbria/pilus periplasmic chaperone and fimbrial biogenesis outer membrane usher protein. The selected 3 proteins were used for B and B cells B-derived T-cell epitopes prediction. In epitopes prediction, different epitopes were predicted with various lengths and percentile scores and subjected to further immunoinformatics analysis. In immunoinformatics screening a total number of 06, IDDGNANAL, KTVKPDPRY, SEVESGSAP, YGGDLTVEV, SVSHDTNGR, and GSKADGYQR epitopes were considered good vaccine target candidates and shortlisted for vaccine construct designing. The vaccine construct was designed by joining selected epitopes with the help of a GPGPG linker and additionally linked with cholera toxin b subunit adjuvant to increase the efficacy of the vaccine construct the sequence of the said adjuvant were retrieved from protein data bank through its (PDB ID: 5ELD). The designed vaccine construct was evaluated for its physiochemical properties analysis in which we reported that the vaccine construct comprises 216 amino acids with a molecular weight of 22.37499 kilo Dalton, 15.55 instability index (II) is computed, and this classifies that the vaccine construct is properly stable. VaxiJen v2.0 web server predicted that the vaccine construct is probable antigenic in nature with 0.6320 predicted value. Furthermore AllerTOP v. 2.0 tool predicted that the designed vaccine construct is non allergic in nature. Molecular docking analysis was done for analysis of the binding affinity of the vaccine construct with TLR-2 (PDB ID: 6NIG), the docking results predicted 799.2 kcal/mol binding energy score that represents the vaccine construct has a good binding ability with TLR-2. Moreover, molecular dynamic simulation analysis results revealed that the vaccine construct and immune cell receptor has proper binding stability over various environmental condition, i.e. change in pressure range, temperature, and motion. After each analysis, we observed that the vaccine construct is safe stable, and probably antigenic and could generate an immune response against the target pathogen but in the future, experimental analysis is still needed to verify in silico base results.

An In Silico Multi-epitopes Vaccine Ensemble and Characterization Against Nosocomial Proteus penneri

Proteus penneri (P. penneri) is a bacillus-shaped, gram-negative, facultative anaerobe bacterium that is primarily an invasive pathogen and the etiological agent of several hospital-associated infections. P. penneri strains are naturally resistant to macrolides, amoxicillin, oxacillin, penicillin G, and cephalosporins; in addition, no vaccines are available against these strains. This warrants efforts to propose a theoretical based multi-epitope vaccine construct to prevent pathogen infections. In this research, reverse vaccinology bioinformatics and immunoinformatics approaches were adopted for vaccine target identification and construction of a multi-epitope vaccine. In the first phase, a core proteome dataset of the targeted pathogen was obtained using the NCBI database and subjected to bacterial pan-genome analysis using bacterial pan-genome analysis (BPGA) to predict core protein sequences which were then used to find good vaccine target candidates. This identified two proteins, Hcp family type VI secretion system effector and superoxide dismutase family protein, as promising vaccine targets. Afterward using the IEDB database, different B-cell and T-cell epitopes were predicted. A set of four epitopes "KGSVNVQDRE, NTGKLTGTR, IIHSDSWNER, and KDGKPVPALK" were chosen for the development of a multi-epitope vaccine construct. A 183 amino acid long vaccine design was built along with "EAAAK" and "GPGPG" linkers and a cholera toxin B-subunit adjuvant. The designed vaccine model comprised immunodominant, non-toxic, non-allergenic, and physicochemical stable epitopes. The model vaccine was docked with MHC-I, MHC-II, and TLR-4 immune cell receptors using the Cluspro2.0 web server. The binding energy score of the vaccine was - 654.7 kcal/mol for MHC-I, - 738.4 kcal/mol for MHC-II, and - 695.0 kcal/mol for TLR-4. A molecular dynamic simulation was done using AMBER v20 package for dynamic behavior in nanoseconds. Additionally, MM-PBSA binding free energy analysis was done to test intermolecular binding interactions between docked molecules. The MM-GBSA net binding energy score was - 148.00 kcal/mol, - 118.00 kcal/mol, and - 127.00 kcal/mol for vaccine with TLR-4, MHC-I, and MHC-II, respectively. Overall, these in silico-based predictions indicated that the vaccine is highly promising in terms of developing protective immunity against P. penneri. However, additional experimental validation is required to unveil the real immune response to the designed vaccine.

Probiogenomics of Leuconostoc Mesenteroides Strains F-21 and F-22 Isolated from Human Breast Milk Reveal Beneficial Properties

Bacteria of the Leuconostoc genus are Gram-positive bacteria that are commonly found in raw milk and persist in fermented dairy products and plant food. Studies have already explored the probiotic potential of L. mesenteroides, but not from a probiogenomic perspective, which aims to explore the molecular features responsible for their phenotypes. In the present work, probiogenomic approaches were applied in strains F-21 and F-22 of L. mesenteroides isolated from human milk to assess their biosafety at the molecular level and to correlate molecular features with their potential probiotic characteristics. The complete genome of strain F-22 is 1.99 Mb and presents one plasmid, while the draft genome of strain F-21 is 1.89 Mb and presents four plasmids. A high percentage of average nucleotide identity among other genomes of L. mesenteroides (≥ 96%) corroborated the previous taxonomic classification of these isolates. Genomic regions that influence the probiotic properties were identified and annotated. Both strains exhibited wide genome plasticity, cell adhesion ability, proteolytic activity, proinflammatory and immunomodulation capacity through interaction with TLR-NF-κB and TLR-MAPK pathway components, and no antimicrobial resistance, denoting their potential to be candidate probiotics. Further, the strains showed bacteriocin production potential and the presence of acid, thermal, osmotic, and bile salt resistance genes, indicating their ability to survive under gastrointestinal stress. Taken together, our results suggest that L. mesenteroides F-21 and F-22 are promising candidates for probiotics in the food and pharmaceutical industries.

Computational Methods in Immunology and Vaccinology: Design and Development of Antibodies and Immunogens

The design of new biomolecules able to harness immune mechanisms for the treatment of diseases is a prime challenge for computational and simulative approaches. For instance, in recent years, antibodies have emerged as an important class of therapeutics against a spectrum of pathologies. In cancer, immune-inspired approaches are witnessing a surge thanks to a better understanding of tumor-associated antigens and the mechanisms of their engagement or evasion from the human immune system. Here, we provide a summary of the main state-of-the-art computational approaches that are used to design antibodies and antigens, and in parallel, we review key methodologies for epitope identification for both B- and T-cell mediated responses. A special focus is devoted to the description of structure- and physics-based models, privileged over purely sequence-based approaches. We discuss the implications of novel methods in engineering biomolecules with tailored immunological properties for possible therapeutic uses. Finally, we highlight the extraordinary challenges and opportunities presented by the possible integration of structure- and physics-based methods with emerging Artificial Intelligence technologies for the prediction and design of novel antigens, epitopes, and antibodies.

Screening of immunogenic proteins and evaluation of vaccine candidates against Mycoplasma synoviae

Mycoplasma synoviae (M. synoviae) is a serious avian pathogen that causes significant economic losses to chicken and turkey producers worldwide. The currently available live attenuated and inactivated vaccines provide limited protection. The objective of this study was to identify potential subunit vaccine candidates using immunoproteomics and reverse vaccinology analyses and to evaluate their preliminary protection. Twenty-four candidate antigens were identified, and five of them, namely RS01790 (a putative sugar ABC transporter lipoprotein), BMP (a substrate-binding protein of the BMP family ABC transporter), GrpE (a nucleotide exchange factor), RS00900 (a putative nuclease), and RS00275 (an uncharacterized protein), were selected to evaluate their immunogenicity and preliminary protection. The results showed that all five antigens had good immunogenicity, and they were localized on the M. synoviae cell membrane. The antigens induced specific humoral and cellular immune responses, and the vaccinated chickens exhibited significantly greater body weight gain and lower air sac lesion scores and tracheal mucosal thicknesses. Additionally, the vaccinated chickens had lower M. synoviae loads in throat swabs than non-vaccinated chickens. The protective effect of the RS01790, BMP, GrpE, and RS00900 vaccines was better than that of the RS00275 vaccine. In conclusion, our study demonstrates the potential of subunit vaccines as a new approach to developing M. synoviae vaccines, providing new ideas for controlling the spread of M. synoviae worldwide.

The race to understand immunopathology in COVID-19: Perspectives on the impact of quantitative approaches to understand within-host interactions

The COVID-19 pandemic has revealed the need for the increased integration of modelling and data analysis to public health, experimental, and clinical studies. Throughout the first two years of the pandemic, there has been a concerted effort to improve our understanding of the within-host immune response to the SARS-CoV-2 virus to provide better predictions of COVID-19 severity, treatment and vaccine development questions, and insights into viral evolution and the impacts of variants on immunopathology. Here we provide perspectives on what has been accomplished using quantitative methods, including predictive modelling, population genetics, machine learning, and dimensionality reduction techniques, in the first 26 months of the COVID-19 pandemic approaches, and where we go from here to improve our responses to this and future pandemics.

The Deceptive COVID-19: Lessons from Common Molecular Diagnostics and a Novel Plan for the Prevention of the Next Pandemic

The COVID-19 pandemic took place during the years 2020-2022 and the virus, named SARS-CoV-2, seems likely to have resulted in an endemic disease. Nevertheless, widespread COVID-19 has given rise to several major molecular diagnostics' facts and concerns that have emerged during the overall management of this disease and the subsequent pandemic. These concerns and lessons are undeniably critical for the prevention and control of future infectious agents. Furthermore, most populaces were introduced to several new public health maintenance strategies, and again, some critical events arose. The purpose of this perspective is to thoroughly analyze all these issues and the concerns, such as the molecular diagnostics' terminologies, their role, as well as the quantity and quality issues with a molecular diagnostics' test result. Furthermore, it is speculated that society will be more vulnerable in the future and prone to emerging infectious diseases; thus, a novel preventive medicine's plan for the prevention and control of future (re)emerging infectious diseases is presented, so as to aid the early prevention of future epidemics and pandemics.

Reverse engineering approach: a step towards a new era of vaccinology with special reference to Salmonella

INTRODUCTION

Salmonella is responsible for causing enteric fever, septicemia, and gastroenteritis in humans. Due to high disease burden and emergence of multi- and extensively drug-resistant Salmonella strains, it is becoming difficult to treat the infection with existing battery of antibiotics as we are not able to discover newer antibiotics at the same pace at which the pathogens are acquiring resistance. Though vaccines against Salmonella are available commercially, they have limited efficacy. Advancements in genome sequencing technologies and immunoinformatics approaches have solved the problem significantly by giving rise to a new era of vaccine designing, i.e. 'Reverse engineering.' Reverse engineering/vaccinology has expedited the vaccine identification process. Using this approach, multiple potential proteins/epitopes can be identified and constructed as a single entity to tackle enteric fever.

AREAS COVERED

This review provides details of reverse engineering approach and discusses various protein and epitope-based vaccine candidates identified using this approach against typhoidal Salmonella.

EXPERT OPINION

Reverse engineering approach holds great promise for developing strategies to tackle the pathogen(s) by overcoming the limitations posed by existing vaccines. Progressive advancements in the arena of reverse vaccinology, structural biology, and systems biology combined with an improved understanding of host-pathogen interactions are essential components to design new-generation vaccines.

Multi-Omics of Corynebacterium Pseudotuberculosis 12CS0282 and an In Silico Reverse Vaccinology Approach Reveal Novel Vaccine and Drug Targets

Corynebacterium pseudotuberculosis is an important animal pathogen, which is also able to infect humans. An optimal treatment of infections with this pathogen is not available today and consequently, more research is necessary to understand the infection process. Here, we present a combined -omics and bioinformatics approach to characterize C. pseudotuberculosis 12CS0282. The genome sequence of strain 12CS0282 was determined, analyzed in comparison with the available 130 C. pseudotuberculosis sequences and used as a basis for proteome analyses. In a reverse vaccinology approach, putative vaccine and drug targets for 12CS0208 were identified. Mass spectrometry analyses revealed the presence of multiple virulence factors even without host contact. In macrophage interaction studies, C. pseudotuberculosis 12CS0282 was highly resistant against human phagocytes and even multiplied within human THP-1 cells. Taken together, the data indicate a high pathogenic potential of the strain.

Immunopeptidomics-based design of mRNA vaccine formulations against Listeria monocytogenes

Listeria monocytogenes is a foodborne intracellular bacterial pathogen leading to human listeriosis. Despite a high mortality rate and increasing antibiotic resistance no clinically approved vaccine against Listeria is available. Attenuated Listeria strains offer protection and are tested as antitumor vaccine vectors, but would benefit from a better knowledge on immunodominant vector antigens. To identify novel antigens, we screen for Listeria peptides presented on the surface of infected human cell lines by mass spectrometry-based immunopeptidomics. In between more than 15,000 human self-peptides, we detect 68 Listeria immunopeptides from 42 different bacterial proteins, including several known antigens. Peptides presented on different cell lines are often derived from the same bacterial surface proteins, classifying these antigens as potential vaccine candidates. Encoding these highly presented antigens in lipid nanoparticle mRNA vaccine formulations results in specific CD8⁺ T-cell responses and induces protection in vaccination challenge experiments in mice. Our results can serve as a starting point for the development of a clinical mRNA vaccine against Listeria and aid to improve attenuated Listeria vaccines and vectors, demonstrating the power of immunopeptidomics for next-generation bacterial vaccine development.

Viral informatics: bioinformatics-based solution for managing viral infections

Several new viral infections have emerged in the human population and establishing as global pandemics. With advancements in translation research, the scientific community has developed potential therapeutics to eradicate or control certain viral infections, such as smallpox and polio, responsible for billions of disabilities and deaths in the past. Unfortunately, some viral infections, such as dengue virus (DENV) and human immunodeficiency virus-1 (HIV-1), are still prevailing due to a lack of specific therapeutics, while new pathogenic viral strains or variants are emerging because of high genetic recombination or cross-species transmission. Consequently, to combat the emerging viral infections, bioinformatics-based potential strategies have been developed for viral characterization and developing new effective therapeutics for their eradication or management. This review attempts to provide a single platform for the available wide range of bioinformatics-based approaches, including bioinformatics methods for the identification and management of emerging or evolved viral strains, genome analysis concerning the pathogenicity and epidemiological analysis, computational methods for designing the viral therapeutics, and consolidated information in the form of databases against the known pathogenic viruses. This enriched review of the generally applicable viral informatics approaches aims to provide an overview of available resources capable of carrying out the desired task and may be utilized to expand additional strategies to improve the quality of translation viral informatics research.

Modern vaccine development via reverse vaccinology to combat antimicrobial resistance

With the continuous evolution of bacteria, the global antimicrobial resistance health threat is causing millions of deaths yearly. While depending on antibiotics as a primary treatment has its merits, there are no effective alternatives thus far in the pharmaceutical market against some drug-resistant bacteria. In recent years, vaccinology has become a key topic in scientific research. Combining with the growth of technology, vaccine research is seeing a new light where the process is made faster and more efficient. Although less discussed, bacterial vaccine is a feasible strategy to combat antimicrobial resistance. Some vaccines have shown promising results with good efficacy against numerous multidrug-resistant strains of bacteria. In this review, we aim to discuss the findings from studies utilizing reverse vaccinology for vaccine development against some multidrug-resistant bacteria, as well as provide a summary of multi-year bacterial vaccine studies in clinical trials. The advantages of reverse vaccinology in the generation of new bacterial vaccines are also highlighted. Meanwhile, the limitations and future prospects of bacterial vaccine concludes this review.

In silico SARS-CoV-2 vaccine development for Omicron strain using reverse vaccinology

BACKGROUND

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic began in 2019 but it remains as a serious threat today. To reduce and prevent spread of the virus, multiple vaccines have been developed. Despite the efforts in developing vaccines, Omicron strain of the virus has recently been designated as a variant of concern (VOC) by the World Health Organization (WHO).

OBJECTIVE

To develop a vaccine candidate against Omicron strain (B.1.1.529, BA.1) of the SARS-CoV-19.

METHODS

We applied reverse vaccinology methods for BA.1 and BA.2 as the vaccine target and a control, respectively. First, we predicted MHC I, MHC II and B cell epitopes based on their viral genome sequences. Second, after estimation of antigenicity, allergenicity and toxicity, a vaccine construct was assembled and tested for physicochemical properties and solubility. Third, AlphaFold2, RaptorX and RoseTTAfold servers were used to predict secondary structures and 3D structures of the vaccine construct. Fourth, molecular docking analysis was performed to test binding of our construct with angiotensin converting enzyme 2 (ACE2). Lastly, we compared mutation profiles on the epitopes between BA.1, BA.2, and wild type to estimate the efficacy of the vaccine.

RESULTS

We collected a total of 10 MHC I, 9 MHC II and 5 B cell epitopes for the final vaccine construct for Omicron strain. All epitopes were predicted to be antigenic, non-allergenic and non-toxic. The construct was estimated to have proper stability and solubility. The best modelled tertiary structures were selected for molecular docking analysis with ACE2 receptor.

CONCLUSIONS

These results suggest the potential efficacy of our newly developed vaccine construct as a novel vaccine candidate against Omicron strain of the coronavirus.

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.

Artificial Intelligence-Based Data-Driven Strategy to Accelerate Research, Development, and Clinical Trials of COVID Vaccine

The global COVID-19 (coronavirus disease 2019) pandemic, which was caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has resulted in a significant loss of human life around the world. The SARS-CoV-2 has caused significant problems to medical systems and healthcare facilities due to its unexpected global expansion. Despite all of the efforts, developing effective treatments, diagnostic techniques, and vaccinations for this unique virus is a top priority and takes a long time. However, the foremost step in vaccine development is to identify possible antigens for a vaccine. The traditional method was time taking, but after the breakthrough technology of reverse vaccinology (RV) was introduced in 2000, it drastically lowers the time needed to detect antigens ranging from 5-15 years to 1-2 years. The different RV tools work based on machine learning (ML) and artificial intelligence (AI). Models based on AI and ML have shown promising solutions in accelerating the discovery and optimization of new antivirals or effective vaccine candidates. In the present scenario, AI has been extensively used for drug and vaccine research against SARS-COV-2 therapy discovery. This is more useful for the identification of potential existing drugs with inhibitory human coronavirus by using different datasets. The AI tools and computational approaches have led to speedy research and the development of a vaccine to fight against the coronavirus. Therefore, this paper suggests the role of artificial intelligence in the field of clinical trials of vaccines and clinical practices using different tools.

Reverse Vaccinology and Immunoinformatic Assisted Designing of a Multi-Epitopes Based Vaccine Against Nosocomial Burkholderia cepacia

Burkholderia cepacia is a Gram-negative nosocomial pathogen and is considered as a troublesome bacterium due to its resistance to many common antibiotics. There is no licensed vaccine available to prevent the pathogen infections, thus making the condition more alarming and warrant the search for novel therapeutic and prophylactic approaches. In order to identify protective antigens from pathogen proteome, substantial efforts are put forth to prioritized potential vaccine targets and antigens that can be easily evaluated experimentally. In this vaccine design investigation, it was found that B. cepacia completely sequenced proteomes available in NCBI genome database has a total of 28,966 core proteins. Out of total, 25,282 proteins were found redundant while 3,684 were non-redundant. Subcellular localization revealed that 18 proteins were extracellular, 31 were part of the outer membrane, 75 proteins were localized in the periplasm, and 23 were virulent proteins. Five proteins namely flagellar hook protein (FlgE), fimbria biogenesis outer membrane usher protein, Type IV pilus secretin (PilQ), cytochrome c4, flagellar hook basal body complex protein (FliE) were tested for positive for antigenic, non-toxic, and soluble epitopes during predication of B-cell derived T-cell epitopes. A vaccine peptide of 14 epitopes (joined together via GPGPG linkers) and cholera toxin B subunit (CTBS) adjuvant (joined to epitopes peptide via EAAAK linker) was constructed. Binding interaction of the modeled vaccine with MHC-I, MHC-II, and Toll-like receptor 4 (TLR-4) immune receptors was studied using molecular docking studies and further analyzed in molecular dynamics simulations that affirms strong intermolecular binding and stable dynamics. The maximum root mean square deviation (RMSD) score of complexes in the simulation time touches to 2 Å. Additionally, complexes binding free energies were determined that concluded robust interaction energies dominated by van der Waals. The total energy of each complex is < -190 kcal/mol. In summary, the designed vaccine showed promising protective immunity against B. cepacia and needs to be examined in experiments.

Consensus Enolase of Trypanosoma Cruzi: Evaluation of Their Immunogenic Properties Using a Bioinformatics Approach

There is currently no vaccine against American trypanosomiasis, caused by the parasite Trypanosoma cruzi. This is due to the genomic variation observed in the six DTUs of T. cruzi. This work aims to propose a consensus sequence of the enolase protein from different strains of T. cruzi and mainly evaluate its immunogenic properties at the bioinformatic level. From specialized databases, 15 sequences of the enolase gene were aligned to obtain a consensus sequence, where this sequence was modeled and then evaluated and validated through different bioinformatic programs to learn their immunogenic potential. Finally, chimeric peptides were designed with the most representative epitopes. The results showed high immunogenic potential with six epitopes for MHC-I, and seven epitopes for MHC-II, all of which were highly representative of the enolase present in strains from the American continent as well as five epitopes for B cells. Regarding the computational modeling, molecular docking with Toll-like receptors showed a high affinity and low constant of dissociation, which could lead to an innate-type immune response that helps to eliminate the parasite. In conclusion, the consensus sequence proposed for enolase is capable of providing an ideal immune response; however, the experimental evaluation of this enolase consensus and their chimeric peptides should be a high priority to develop a vaccine against Chagas disease.

Vaccine development to control the rising scourge of antibiotic-resistant Acinetobacter baumannii: a systematic review

Acinetobacter baumannii has emerged as one of major nosocomial pathogen and global emergence of multidrug-resistant strains has become a challenge for developing effective treatment options. A. baumannii has developed resistance to almost all the antibiotics viz. beta-lactams, carbapenems, tigecycline and now colistin, a last resort of antibiotics. The world is on the cusp of post antibiotic era and the evolution of multi-, extreme- and pan–drug-resistant A. baumannii strains is its obvious harbinger. Various combinations of antibiotics have been investigated but no successful treatment option is available. All these failed efforts have led researchers to develop and implement prophylactic vaccination for the prevention of infections caused by this pathogen. In this review, the advantages and disadvantages of active and passive immunization, the types of sub-unit and multi-component vaccine candidates investigated against A. baumannii viz. whole cell organism, outer membrane vesicles, outer membrane complexes, conjugate vaccines and sub-unit vaccines have been discussed. In addition, the benefits of Reverse vaccinology are emphasized here in which the potential vaccine candidates are predicted using bioinformatic online tools prior to in vivo validations.

Designing a Recombinant Vaccine against Providencia rettgeri Using Immunoinformatics Approach

Antibiotic resistance (AR) is the resistance mechanism pattern in bacteria that evolves over some time, thus protecting the bacteria against antibiotics. AR is due to bacterial evolution to make itself fit to changing environmental conditions in a quest for survival of the fittest. AR has emerged due to the misuse and overuse of antimicrobial drugs, and few antibiotics are now left to deal with these superbug infections. To combat AR, vaccination is an effective method, used either therapeutically or prophylactically. In the current study, an in silico approach was applied for the design of multi-epitope-based vaccines against Providencia rettgeri, a major cause of traveler’s diarrhea. A total of six proteins: fimbrial protein, flagellar hook protein (FlgE), flagellar basal body L-ring protein (FlgH), flagellar hook-basal body complex protein (FliE), flagellar basal body P-ring formation protein (FlgA), and Gram-negative pili assembly chaperone domain proteins, were considered as vaccine targets and were utilized for B- and T-cell epitope prediction. The predicted epitopes were assessed for allergenicity, antigenicity, virulence, toxicity, and solubility. Moreover, filtered epitopes were utilized in multi-epitope vaccine construction. The predicted epitopes were joined with each other through specific GPGPG linkers and were joined with cholera toxin B subunit adjuvant via another EAAAK linker in order to enhance the efficacy of the designed vaccine. Docking studies of the designed vaccine construct were performed with MHC-I (PDB ID: 1I1Y), MHC-II (1KG0), and TLR-4 (4G8A). Findings of the docking study were validated through molecular dynamic simulations, which confirmed that the designed vaccine showed strong interactions with the immune receptors, and that the epitopes were exposed to the host immune system for proper recognition and processing. Additionally, binding free energies were estimated, which highlighted both electrostatic energy and van der Waals forces to make the complexes stable. Briefly, findings of the current study are promising and may help experimental vaccinologists to formulate a novel multi-epitope vaccine against P. rettgeri.

Cov19VaxKB: A Web-based Integrative COVID-19 Vaccine Knowledge Base

The development of SARS-CoV-2 vaccines during the COVID-19 pandemic has prompted the emergence of COVID-19 vaccine data. Timely access to COVID-19 vaccine information is crucial to researchers and public. To support more comprehensive annotation, integration, and analysis of COVID-19 vaccine information, we have developed Cov19VaxKB, a knowledge-focused COVID-19 vaccine database (http://www.violinet.org/cov19vaxkb/). Cov19VaxKB features comprehensive lists of COVID-19 vaccines, vaccine formulations, clinical trials, publications, news articles, and vaccine adverse event case reports. A web-based query interface enables comparison of product information and host responses among various vaccines. The knowledge base also includes a vaccine design tool for predicting vaccine targets and a statistical analysis tool that identifies enriched adverse events for FDA-authorized COVID-19 vaccines based on VAERS case report data. To support data exchange, Cov19VaxKB is synchronized with Vaccine Ontology and the Vaccine Investigation and Online Information Network (VIOLIN) database. The data integration and analytical features of Cov19VaxKB can facilitate vaccine research and development while also serving as a useful reference for the public.

Identification of Mycobacterium tuberculosis Antigens with Vaccine Potential Using a Machine Learning-Based Reverse Vaccinology Approach

Tuberculosis (TB) is the leading cause of death of any single infectious agent, having led to 1.4 million deaths in 2019 alone. Moreover, an estimated one-quarter of the global population is latently infected with Mycobacterium tuberculosis (MTB), presenting a huge pool of potential future disease. Nonetheless, the only currently licensed TB vaccine fails to prevent the activation of latent TB infections (LTBI). These facts together illustrate the desperate need for a more effective TB vaccine strategy that can prevent both primary infection and the activation of LTBI. In this study, we employed a machine learning-based reverse vaccinology approach to predict the likelihood that each protein within the proteome of MTB laboratory reference strain H37Rv would be a protective antigen (PAg). The proteins predicted most likely to be a PAg were assessed for their belonging to a protein family of previously established PAgs, the relevance of their biological processes to MTB virulence and latency, and finally the immunogenic potential that they may provide in terms of the number of promiscuous epitopes within each. This study led to the identification of 16 proteins with the greatest vaccine potential for further in vitro and in vivo studies. It also demonstrates the value of computational methods in vaccine development.

Genomic Analysis of Pasteurella atlantica Provides Insight on Its Virulence Factors and Phylogeny and Highlights the Potential of Reverse Vaccinology in Aquaculture

Pasteurellosis in farmed lumpsuckers, Cyclopterus lumpus, has emerged as a serious disease in Norwegian aquaculture in recent years. Genomic characterization of the causative agent is essential in understanding the biology of the bacteria involved and in devising an efficient preventive strategy. The genomes of two clinical Pasteurella atlantica isolates were sequenced (≈2.3 Mbp), and phylogenetic analysis confirmed their position as a novel species within the Pasteurellaceae. In silico analyses revealed 11 genomic islands and 5 prophages, highlighting the potential of mobile elements as driving forces in the evolution of this species. The previously documented pathogenicity of P. atlantica is strongly supported by the current study, and 17 target genes were recognized as putative primary drivers of pathogenicity. The expression level of a predicted vaccine target, an uncharacterized adhesin protein, was significantly increased in both broth culture and following the exposure of P. atlantica to lumpsucker head kidney leucocytes. Based on in silico and functional analyses, the strongest gene target candidates will be prioritized in future vaccine development efforts to prevent future pasteurellosis outbreaks.

Antigenicity and adhesiveness of a Plasmodium vivax VIR-E protein from Brazilian isolates

BACKGROUND

Plasmodium vivax, the major cause of malaria in Latin America, has a large subtelomeric multigene family called vir. In the P. vivax genome, about 20% of its sequences are vir genes. Vir antigens are grouped in subfamilies according to their sequence similarities and have been shown to have distinct roles and subcellular locations. However, little is known about vir subfamilies, especially when comes to their functions.

OBJECTIVE

To evaluate the diversity, antigenicity, and adhesiveness of Plasmodium vivax VIR-E.

METHODS

Vir-E genes were amplified from six P. vivax isolates from Manaus, North of Brazil. The presence of naturally acquired antibodies to recombinant PvBrVIR-E and PvAMA-1 was evaluated by ELISA. Binding capacity of recombinant PvBrVIR-E was assessed by adhesion assay to CHO-ICAM1 cells.

FINDINGS

Despite vir-E sequence diversity, among those identified sequences, a representative one was chosen to be expressed as recombinant protein. The presence of IgM or IgG antibodies to PvBrVIR-E was detected in 23.75% of the study population while the presence of IgG antibodies to PvAMA-1 antigen was 66.25% in the same population. PvBrVIR-E was adhesive to CHO-ICAM1.

MAIN CONCLUSIONS

PvBrVIR-E was antigenic and adhesive to CHO-ICAM1.