Jenner-predict server: prediction of protein vaccine candidates (PVCs) in bacteria based on host-pathogen interactions

An MCDM approach for Reverse vaccinology model to predict bacterial protective antigens

Reverse vaccinology (RV) is a significant step in sensible vaccine design. In recent years, many machine learning (ML) methods have been used to improve RV prediction accuracy. However, there are still issues with prediction accuracy and programme accessibility in ML-based RV. This paper presents a supervised ML-based method to classify bacterial protective antigens (BPAgs) and identify the model(s) that consistently perform well for the training dataset. Six ML classifiers are used for testing with physiochemical features extracted from a comprehensive training dataset. Selecting the best performing model from different performance metrics (accuracy, precision, recall, F1-score, and AUC-ROC) has not been easy, because all the metrics has the same importance to predict BPAgs. To fix this issue, we propose a soft and hard ranking model based on multi-criteria decision-making (MCDM) approach for selecting the best performing ML method that classifies BPAgs. First, our proposed model uses homologous proteins (positive and negative samples) from Protegen and Uniprot databases. Second, we applied four strategies of Synthetic Minority Oversampling Technique and Edited Nearest Neighbour (SMOTE-ENN) to handle the data imbalance problem and train the model using ML methods. Third, we consider MCDM-based technique for order preference by similarity to the ideal solution (TOPSIS) method integrated with soft and hard ranking model. The entropy is used to obtain weighted evaluation criteria for ranking the models. Our experimental evaluations show that the proposed method with best performing models (Random Forest and Extreme Gradient Boosting) outperforms compared to existing open-source RV methods using benchmark datasets.

In Vivo Validation of Novel Synthetic tbp1 Peptide-Based Vaccine Candidates against Haemophilus influenzae Strains in BALB/c Mice

Haemophilus influenzae is a Gram-negative bacterium characterized as a small, nonmotile, facultative anaerobic coccobacillus. It is a common cause of a variety of invasive and non-invasive infections. Among six serotypes (a-f), H. influenzae type b (Hib) is the most familiar and predominant mostly in children and immunocompromised individuals. Following Hib vaccination, infections due to other serotypes have increased in number, and currently, there is no suitable effective vaccine to induce cross-strain protective antibody responses. The current study was aimed to validate the capability of two 20-mer highly conserved synthetic tbp1 (transferrin-binding protein 1) peptide-based vaccine candidates (tbp1-E1 and tbp1-E2) predicted using in silico approaches to induce immune responses against H. influenzae strains. Cytokine induction ability, immune simulations, and molecular dynamics (MD) simulations were performed to confirm the candidacy of epitopic docked complexes. Synthetic peptide vaccine formulations in combination with two different adjuvants, BGs (Bacterial Ghosts) and CFA/IFA (complete/incomplete Freund's adjuvant), were used in BALB/c mouse groups in three booster shots at two-week intervals. An indirect ELISA was performed to determine endpoint antibody titers using the Student's t-distribution method. The results revealed that the synergistic use of both peptides in combination with BG adjuvants produced better results. Significant differences in absorbance values were observed in comparison to the rest of the peptide-adjuvant combinations. The findings of this study indicate that these tbp1 peptide-based vaccine candidates may present a preliminary set of peptides for the development of an effective cross-strain vaccine against H. influenzae in the future due to their highly conserved nature.

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.

Fungal Vaccine Development: State of the Art and Perspectives Using Immunoinformatics

Fungal infections represent a serious global health problem, causing damage to health and the economy on the scale of millions. Although vaccines are the most effective therapeutic approach used to combat infectious agents, at the moment, no fungal vaccine has been approved for use in humans. However, the scientific community has been working hard to overcome this challenge. In this sense, we aim to describe here an update on the development of fungal vaccines and the progress of methodological and experimental immunotherapies against fungal infections. In addition, advances in immunoinformatic tools are described as an important aid by which to overcome the difficulty of achieving success in fungal vaccine development. In silico approaches are great options for the most important and difficult questions regarding the attainment of an efficient fungal vaccine. Here, we suggest how bioinformatic tools could contribute, considering the main challenges, to an effective fungal vaccine.

Prediction of Bacterial Immunogenicity by Machine Learning Methods

Prediction of bacterial immunogens is a prerequisite for the process of vaccine development through reverse vaccinology. The application of in silico methods allows significant reduction in time and cost for the discovery of potential vaccine candidates among proteins of a bacterial species. The steps in the prediction algorithm include collection of protein sequence datasets of known bacterial immunogens and non-immunogens, data preprocessing to transform the protein sequences into numerical matrices suitable for use as training and test sets for various machine learning methods, and derivation of predictive models. The performance of the derived models is evaluated by means of classification metrics.In this chapter, we present a protocol for predicting bacterial immunogenicity by applying machine learning methods. The protocol describes the process of model development from data collection and manipulation to training and validation of the derived models.

Vaxi-DL: An Artificial Intelligence-Enabled Platform for Vaccine Development

Vaccine development is a complex and long process. It involves several steps, including computational studies, experimental analyses, animal model system studies, and clinical trials. This process can be accelerated by using in silico antigen screening to identify potential vaccine candidates. In this chapter, we describe a deep learning-based technique which utilizes 18 biological and 9154 physicochemical properties of proteins for finding potential vaccine candidates. Using this technique, a new web-based system, named Vaxi-DL, was developed which helped in finding new vaccine candidates from bacteria, protozoa, viruses, and fungi. Vaxi-DL is available at: https://vac.kamalrawal.in/vaxidl/ .

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.

SILVI, an open-source pipeline for T-cell epitope selection

High-throughput screening of available genomic data and identification of potential antigenic candidates have promoted the development of epitope-based vaccines and therapeutics. Several immunoinformatic tools are available to predict potential epitopes and other immunogenicity-related features, yet it is still challenging and time-consuming to compare and integrate results from different algorithms. We developed the R script SILVI (short for: from in silico to in vivo), to assist in the selection of the potentially most immunogenic T-cell epitopes from Human Leukocyte Antigen (HLA)-binding prediction data. SILVI merges and compares data from available HLA-binding prediction servers, and integrates additional relevant information of predicted epitopes, namely BLASTp alignments with host proteins and physical-chemical properties. The two default criteria applied by SILVI and additional filtering allow the fast selection of the most conserved, promiscuous, strong binding T-cell epitopes. Users may adapt the script at their discretion as it is written in open-source R language. To demonstrate the workflow and present selection options, SILVI was used to integrate HLA-binding prediction results of three example proteins, from viral, bacterial and parasitic microorganisms, containing validated epitopes included in the Immune Epitope Database (IEDB), plus the Human Papillomavirus (HPV) proteome. Applying different filters on predicted IC50, hydrophobicity and mismatches with host proteins allows to significantly reduce the epitope lists with favourable sensitivity and specificity to select immunogenic epitopes. We contemplate SILVI will assist T-cell epitope selections and can be continuously refined in a community-driven manner, helping the improvement and design of peptide-based vaccines or immunotherapies. SILVI development version is available at: github.com/JoanaPissarra/SILVI2020 and https://doi.org/10.5281/zenodo.6865909.

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.

Challenges in Serologic Diagnostics of Neglected Human Systemic Mycoses: An Overview on Characterization of New Targets

Systemic mycoses have been viewed as neglected diseases and they are responsible for deaths and disabilities around the world. Rapid, low-cost, simple, highly-specific and sensitive diagnostic tests are critical components of patient care, disease control and active surveillance. However, the diagnosis of fungal infections represents a great challenge because of the decline in the expertise needed for identifying fungi, and a reduced number of instruments and assays specific to fungal identification. Unfortunately, time of diagnosis is one of the most important risk factors for mortality rates from many of the systemic mycoses. In addition, phenotypic and biochemical identification methods are often time-consuming, which has created an increasing demand for new methods of fungal identification. In this review, we discuss the current context of the diagnosis of the main systemic mycoses and propose alternative approaches for the identification of new targets for fungal pathogens, which can help in the development of new diagnostic tests.

Vaxi-DL: A web-based deep learning server to identify potential vaccine candidates

The development of a new vaccine is a challenging exercise involving several steps including computational studies, experimental work, and animal studies followed by clinical studies. To accelerate the process, in silico screening is frequently used for antigen identification. Here, we present Vaxi-DL, web-based deep learning (DL) software that evaluates the potential of protein sequences to serve as vaccine target antigens. Four different DL pathogen models were trained to predict target antigens in bacteria, protozoa, fungi, and viruses that cause infectious diseases in humans. Datasets containing antigenic and non-antigenic sequences were derived from known vaccine candidates and the Protegen database. Biological and physicochemical properties were computed for the datasets using publicly available bioinformatics tools. For each of the four pathogen models, the datasets were divided into training, validation, and testing subsets and then scaled and normalised. The models were constructed using Fully Connected Layers (FCLs), hyper-tuned, and trained using the training subset. Accuracy, sensitivity, specificity, precision, recall, and AUC (Area under the Curve) were used as metrics to assess the performance of these models. The models were benchmarked using independent datasets of known target antigens against other prediction tools such as VaxiJen and Vaxign-ML. We also tested Vaxi-DL on 219 known potential vaccine candidates (PVC) from 37 different pathogens. Our tool predicted 175 PVCs correctly out of 219 sequences. We also tested Vaxi-DL on different datasets obtained from multiple resources. Our tool has demonstrated an average sensitivity of 93% and will thus be a useful tool for prioritising PVCs for preclinical studies.

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.

An Update on "Reverse Vaccinology": The Pathway from Genomes and Epitope Predictions to Tailored, Recombinant Vaccines

In this chapter, we review the computational approaches that have led to a new generation of vaccines in recent years. There are many alternative routes to develop vaccines based on the concept of reverse vaccinology. They all follow the same basic principles-mining available genome and proteome information for antigen candidates, and recombinantly expressing them for vaccine production. Some of the same principles have been used successfully for cancer therapy approaches. In this review, we focus on infectious diseases, describing the general workflow from bioinformatic predictions of antigens and epitopes down to examples where such predictions have been used successfully for vaccine development.

Plant-Based Vaccines: Antigen Design, Diversity, and Strategies for High Level Production

Vaccines for human use have conventionally been developed by the production of (1) microbial pathogens in eggs or mammalian cells that are then inactivated, or (2) by the production of pathogen proteins in mammalian and insect cells that are purified for vaccine formulation, as well as, more recently, (3) by using RNA or DNA fragments from pathogens. Another approach for recombinant antigen production in the last three decades has been the use of plants as biofactories. Only have few plant-produced vaccines been evaluated in clinical trials to fight against diseases, of which COVID-19 vaccines are the most recent to be FDA approved. In silico tools have accelerated vaccine design, which, combined with transitory antigen expression in plants, has led to the testing of promising prototypes in pre-clinical and clinical trials. Therefore, this review deals with a description of immunoinformatic tools and plant genetic engineering technologies used for antigen design (virus-like particles (VLP), subunit vaccines, VLP chimeras) and the main strategies for high antigen production levels. These key topics for plant-made vaccine development are discussed and perspectives are provided.

Designing and development of epitope-based vaccines against Helicobacter pylori

Helicobacter pylori infection is the principal cause of serious diseases (e.g. gastric cancer and peptic ulcers). Antibiotic therapy is an inadequate strategy in H. pylori eradication because of which vaccination is an inevitable approach. Despite the presence of countless vaccine candidates, current vaccines in clinical trials have performed with poor efficacy which makes vaccination extremely challenging. Remarkable advancements in immunology and pathogenic biology have provided an appropriate opportunity to develop various epitope-based vaccines. The fusion of proper antigens involved in different aspects of H. pylori colonization and pathogenesis as well as peptide linkers and built-in adjuvants results in producing epitope-based vaccines with excellent therapeutic efficacy and negligible adverse effects. Difficulties of the in vitro culture of H. pylori, high genetic variation, and unfavourable immune responses against feeble epitopes in the complete antigen are major drawbacks of current vaccine strategies that epitope-based vaccines may overcome. Besides decreasing the biohazard risk, designing precise formulations, saving time and cost, and induction of maximum immunity with minimum adverse effects are the advantages of epitope-based vaccines. The present article is a comprehensive review of strategies for designing and developing epitope-based vaccines to provide insights into the innovative vaccination against H. pylori.

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.

Identification of Vibrio parahaemolyticus and Vibrio spp. Specific Outer Membrane Proteins by Reverse Vaccinology and Surface Proteome

The discovery of outer membrane proteins (OMPs) with desirable specificity and surface availability is a fundamental challenge to develop accurate immunodiagnostic assay and multivalent vaccine of pathogenic Vibrio species in food and aquaculture. Herein 101 OMPs were systemically screened from 4,831 non-redundant proteins of Vibrio parahaemolyticus by bioinformatical predication of signaling peptides, transmembrane (TM) α-helix, and subcellular location. The sequence homology analysis with 32 species of Vibrio spp. and all the non-Vibrio strains revealed that 15 OMPs were conserved in at least 23 Vibrio species, including BamA (VP2310), GspD (VP0133), Tolc (VP0425), OmpK (VP2362), OmpW (VPA0096), LptD (VP0339), Pal (VP1061), flagellar L-ring protein (VP0782), flagellar protein MotY (VP2111), hypothetical protein (VP1713), fimbrial assembly protein (VP2746), VacJ lipoprotein (VP2214), agglutination protein (VP1634), and lipoprotein (VP1267), Chitobiase (VP0755); high adhesion probability of flgH, LptD, OmpK, and OmpW indicated they were potential multivalent Vibrio vaccine candidates. V. parahaemolyticus OMPs were found to share high homology with at least one or two Vibrio species, 19 OMPs including OmpA like protein (VPA073), CsuD (VPA1504), and MtrC (VP1220) were found relatively specific to V. parahaemolyticus. The surface proteomic study by enzymatical shaving the cells showed the capsular polysaccharides most likely limited the protease action, while the glycosidases improved the availability of OMPs to trypsin. The OmpA (VPA1186, VPA0248, VP0764), Omp (VPA0166), OmpU (VP2467), BamA (VP2310), TolC (VP0425), GspD (VP0133), OmpK (VP2362), lpp (VPA1469), Pal (VP1061), agglutination protein (VP1634), and putative iron (III) compound receptor (VPA1435) have better availability on the cell surface.

Bacterial Immunogenicity Prediction by Machine Learning Methods

The identification of protective immunogens is the most important and vigorous initial step in the long-lasting and expensive process of vaccine design and development. Machine learning (ML) methods are very effective in data mining and in the analysis of big data such as microbial proteomes. They are able to significantly reduce the experimental work for discovering novel vaccine candidates. Here, we applied six supervised ML methods (partial least squares-based discriminant analysis, k nearest neighbor (kNN), random forest (RF), support vector machine (SVM), random subspace method (RSM), and extreme gradient boosting) on a set of 317 known bacterial immunogens and 317 bacterial non-immunogens and derived models for immunogenicity prediction. The models were validated by internal cross-validation in 10 groups from the training set and by the external test set. All of them showed good predictive ability, but the xgboost model displays the most prominent ability to identify immunogens by recognizing 84% of the known immunogens in the test set. The combined RSM-kNN model was the best in the recognition of non-immunogens, identifying 92% of them in the test set. The three best performing ML models (xgboost, RSM-kNN, and RF) were implemented in the new version of the server VaxiJen, and the prediction of bacterial immunogens is now based on majority voting.

The combination of artificial intelligence and systems biology for intelligent vaccine design

INTRODUCTION

A new body of evidence depicts the applications of artificial intelligence and systems biology in vaccine design and development. The combination of both approaches shall revolutionize healthcare, accelerating clinical trial processes and reducing the costs and time involved in drug research and development.

AREAS COVERED

This review explores the basics of artificial intelligence and systems biology approaches in the vaccine development pipeline. The topics include a detailed description of epitope prediction tools for designing epitope-based vaccines and agent-based models for immune system response prediction, along with a focus on their potentiality to facilitate clinical trial phases.

EXPERT OPINION

Artificial intelligence and systems biology offer the opportunity to avoid the inefficiencies and failures that arise in the classical vaccine development pipeline. One promising solution is the combination of both methodologies in a multiscale perspective through an accurate pipeline. We are entering an 'in silico era' in which scientific partnerships, including a more and more increasing creation of an 'ecosystem' of collaboration and multidisciplinary approach, are relevant for addressing the long and risky road of vaccine discovery and development. In this context, regulatory guidance should be developed to qualify the in silico trials as evidence for intelligent vaccine development.

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.

Immunoinformatics and Vaccine Development: An Overview

The use of vaccines have resulted in a remarkable improvement in global health. It has saved several lives, reduced treatment costs and raised the quality of animal and human lives. Current traditional vaccines came empirically with either vague or completely no knowledge of how they modulate our immune system. Even at the face of potential vaccine design advance, immune-related concerns (as seen with specific vulnerable populations, cases of emerging/re-emerging infectious disease, pathogens with complex lifecycle and antigenic variability, need for personalized vaccinations, and concerns for vaccines' immunological safety -specifically vaccine likelihood to trigger non-antigen-specific responses that may cause autoimmunity and vaccine allergy) are being raised. And these concerns have driven immunologists toward research for a better approach to vaccine design that will consider these challenges. Currently, immunoinformatics has paved the way for a better understanding of some infectious disease pathogenesis, diagnosis, immune system response and computational vaccinology. The importance of this immunoinformatics in the study of infectious diseases is diverse in terms of computational approaches used, but is united by common qualities related to host–pathogen relationship. Bioinformatics methods are also used to assign functions to uncharacterized genes which can be targeted as a candidate in vaccine design and can be a better approach toward the inclusion of women that are pregnant into vaccine trials and programs. The essence of this review is to give insight into the need to focus on novel computational, experimental and computation-driven experimental approaches for studying of host–pathogen interactions and thus making a case for its use in vaccine development.

ReVac: a reverse vaccinology computational pipeline for prioritization of prokaryotic protein vaccine candidates

Background

Reverse vaccinology accelerates the discovery of potential vaccine candidates (PVCs) prior to experimental validation. Current programs typically use one bacterial proteome to identify PVCs through a filtering architecture using feature prediction programs or a machine learning approach. Filtering approaches may eliminate potential antigens based on limitations in the accuracy of prediction tools used. Machine learning approaches are heavily dependent on the selection of training datasets with experimentally validated antigens (positive control) and non-protective-antigens (negative control). The use of one or few bacterial proteomes does not assess PVC conservation among strains, an important feature of vaccine antigens.

Results

We present ReVac, which implements both a panoply of feature prediction programs without filtering out proteins, and scoring of candidates based on predictions made on curated positive and negative control PVCs datasets. ReVac surveys several genomes assessing protein conservation, as well as DNA and protein repeats, which may result in variable expression of PVCs. ReVac’s orthologous clustering of conserved genes, identifies core and dispensable genome components. This is useful for determining the degree of conservation of PVCs among the population of isolates for a given pathogen. Potential vaccine candidates are then prioritized based on conservation and overall feature-based scoring. We present the application of ReVac, applied to 69 Moraxella catarrhalis and 270 non-typeable Haemophilus influenzae genomes, prioritizing 64 and 29 proteins as PVCs, respectively.

Conclusion

ReVac’s use of a scoring scheme ranks PVCs for subsequent experimental testing. It employs a redundancy-based approach in its predictions of features using several prediction tools. The protein’s features are collated, and each protein is ranked based on the scoring scheme. Multi-genome analyses performed in ReVac allow for a comprehensive overview of PVCs from a pan-genome perspective, as an essential pre-requisite for any bacterial subunit vaccine design. ReVac prioritized PVCs of two human respiratory pathogens, identifying both novel and previously validated PVCs.

Comparative transcriptome analysis of different stages of Plasmodium falciparum to explore vaccine and drug candidates

Malaria is a life-threatening disease causes huge burden on human health. Every year >200 million cases of malaria are reported globally. Researchers have carried out research on transcriptome of different stages of Plasmodium species to understand complex pathology of pathogens and to discover therapeutics. Researchers are targeting different stages of Plasmodium falciparum separately. Hence, to target all stages of Plasmodium simultaneously comparative transcriptome analysis of different stages was carried out and 44 commonly expressed proteins from different stages of Plasmodium were identified. These proteins were analyzed for their drug target and vaccine potential in different analysis. Conservation of these proteins in human infecting Plasmodium species was also studied. Current approach is also justified because few of these proteins were found to be known vaccine and drug target candidates in different infectious diseases. These proteins can be taken as drug targets and/or vaccine candidates in further experimentation against malaria.

PanRV: Pangenome-reverse vaccinology approach for identifications of potential vaccine candidates in microbial pangenome

Background

A revolutionary diversion from classical vaccinology to reverse vaccinology approach has been observed in the last decade. The ever-increasing genomic and proteomic data has greatly facilitated the vaccine designing and development process. Reverse vaccinology is considered as a cost-effective and proficient approach to screen the entire pathogen genome. To look for broad-spectrum immunogenic targets and analysis of closely-related bacterial species, the assimilation of pangenome concept into reverse vaccinology approach is essential. The categories of species pangenome such as core, accessory, and unique genes sets can be analyzed for the identification of vaccine candidates through reverse vaccinology.

Results

We have designed an integrative computational pipeline term as “PanRV” that employs both the pangenome and reverse vaccinology approaches. PanRV comprises of four functional modules including i) Pangenome Estimation Module (PGM) ii) Reverse Vaccinology Module (RVM) iii) Functional Annotation Module (FAM) and iv) Antibiotic Resistance Association Module (ARM). The pipeline is tested by using genomic data from 301 genomes of Staphylococcus aureus and the results are verified by experimentally known antigenic data.

Conclusion

The proposed pipeline has proved to be the first comprehensive automated pipeline that can precisely identify putative vaccine candidates exploiting the microbial pangenome. PanRV is a Linux based package developed in JAVA language. An executable installer is provided for ease of installation along with a user manual at https://sourceforge.net/projects/panrv2/.

Electronic supplementary material

The online version of this article (10.1186/s12859-019-2713-9) contains supplementary material, which is available to authorized users.

Comparison of Open-Source Reverse Vaccinology Programs for Bacterial Vaccine Antigen Discovery

Reverse Vaccinology (RV) is a widely used approach to identify potential vaccine candidates (PVCs) by screening the proteome of a pathogen through computational analyses. Since its first application in Group B meningococcus (MenB) vaccine in early 1990's, several software programs have been developed implementing different flavors of the first RV protocol. However, there has been no comprehensive review to date on these different RV tools. We have compared six of these applications designed for bacterial vaccines (NERVE, Vaxign, VaxiJen, Jenner-predict, Bowman-Heinson, and VacSol) against a set of 11 pathogens for which a curated list of known bacterial protective antigens (BPAs) was available. We present results on: (1) the comparison of criteria and programs used for the selection of PVCs (2) computational runtime and (3) performances in terms of fraction of proteome identified as PVC, fraction and enrichment of BPA identified in the set of PVCs. This review demonstrates that none of the programs was able to recall 100% of the tested set of BPAs and that the output lists of proteins are in poor agreement suggesting in the process of prioritize vaccine candidates not to rely on a single RV tool response. Singularly the best balance in terms of fraction of a proteome predicted as good candidate and recall of BPAs has been observed by the machine-learning approach proposed by Bowman (1) and enhanced by Heinson (2). Even though more performing than the other approaches it shows the disadvantage of limited accessibility to non-experts users and strong dependence between results and a-priori training dataset composition. In conclusion we believe that to significantly enhance the performances of next RV methods further studies should focus on the enhancement of accuracy of the existing protein annotation tools and should leverage on the assets of machine-learning techniques applied to biological datasets expanded also through the incorporation and curation of bacterial proteins characterized by negative experimental results.

Vaccines Meet Big Data: State-of-the-Art and Future Prospects. From the Classical 3Is ("Isolate-Inactivate-Inject") Vaccinology 1.0 to Vaccinology 3.0, Vaccinomics, and Beyond: A Historical Overview

Vaccines are public health interventions aimed at preventing infections-related mortality, morbidity, and disability. While vaccines have been successfully designed for those infectious diseases preventable by preexisting neutralizing specific antibodies, for other communicable diseases, additional immunological mechanisms should be elicited to achieve a full protection. “New vaccines” are particularly urgent in the nowadays society, in which economic growth, globalization, and immigration are leading to the emergence/reemergence of old and new infectious agents at the animal–human interface. Conventional vaccinology (the so-called “vaccinology 1.0”) was officially born in 1796 thanks to the contribution of Edward Jenner. Entering the twenty-first century, vaccinology has shifted from a classical discipline in which serendipity and the Pasteurian principle of the three Is (isolate, inactivate, and inject) played a major role to a science, characterized by a rational design and plan (“vaccinology 3.0”). This shift has been possible thanks to Big Data, characterized by different dimensions, such as high volume, velocity, and variety of data. Big Data sources include new cutting-edge, high-throughput technologies, electronic registries, social media, and social networks, among others. The current mini-review aims at exploring the potential roles as well as pitfalls and challenges of Big Data in shaping the future vaccinology, moving toward a tailored and personalized vaccine design and administration.

Comparative transcriptomics uncovers differences in photoautotrophic versus photoheterotrophic modes of nutrition in relation to secondary metabolites biosynthesis in Swertia chirayita

Swertia chirayita is a high-value medicinal herb exhibiting antidiabetic, hepatoprotective, anticancer, antiediematogenic and antipyretic properties. Scarcity of its plant material has necessitated in vitro production of therapeutic metabolites; however, their yields were low compared to field grown plants. Possible reasons for this could be differences in physiological and biochemical processes between plants grown in photoautotrophic versus photoheterotrophic modes of nutrition. Comparative transcriptomes of S. chirayita were generated to decipher the crucial molecular components associated with the secondary metabolites biosynthesis. Illumina HiSeq sequencing yielded 57,460 and 43,702 transcripts for green house grown (SCFG) and tissue cultured (SCTC) plants, respectively. Biological role analysis (GO and COG assignments) revealed major differences in SCFG and SCTC transcriptomes. KEGG orthology mapped 351 and 341 transcripts onto secondary metabolites biosynthesis pathways for SCFG and SCTC transcriptomes, respectively. Nineteen out of 30 genes from primary metabolism showed higher in silico expression (FPKM) in SCFG versus SCTC, possibly indicating their involvement in regulating the central carbon pool. In silico data were validated by RT-qPCR using a set of 16 genes, wherein 10 genes showed similar expression pattern across both the methods. Comparative transcriptomes identified differentially expressed transcription factors and ABC-type transporters putatively associated with secondary metabolism in S. chirayita. Additionally, functional classification was performed using NCBI Biosystems database. This study identified the molecular components implicated in differential modes of nutrition (photoautotrophic vs. photoheterotrophic) in relation to secondary metabolites production in S. chirayita.

Potential Outer Membrane Protein Candidates for Vaccine Development Against the Pathogen Vibrio anguillarum: A Reverse Vaccinology Based Identification

Reverse vaccinology is a widely used approach that has facilitated the rapid identification of vaccine candidates suitable in vaccine development for pathogens. Vibrio anguillarum is a major pathogen responsible for vibriosis in fish and shellfish leading to huge economic losses to the aquaculture industry. Although commercial vaccines are available for fish against this bacterium they have their own limitations. In this study, we used the reverse vaccinology strategy to screen and identify V. anguillarum outer membrane proteins (OMPs) that could serve as vaccine candidates. Our analysis identified 23 antigenic outer membrane proteins which were highly conserved (>98% identity) across serovars of this bacterium. Of the 23, two were identified as outer membrane lipoproteins. Among the OMPs identified 18 were novel to this study and conserved across several Vibrio spp. with an identity of 21-93%. While the least (>48%) identity was observed for V. anguillarum ferrichrome-iron transporter protein, the highest identity (>80%) was seen for outer membrane proteins OmpK, BamA, OmpU, Fatty acid transporter, and two hypothetical proteins. These potential vaccine targets identified could contribute to the development of effective vaccine not only against V. anguillarum but also across other Vibrio spp. In addition, several B-cell and T-cell epitopes were predicted for the novel OMPs in this study which could aid in narrowing down peptide selection in designing a suitable epitope-based vaccine.

Identification of New Features from Known Bacterial Protective Vaccine Antigens Enhances Rational Vaccine Design

With many protective vaccine antigens reported in the literature and verified experimentally, how to use the knowledge mined from these antigens to support rational vaccine design and study underlying design mechanism remains unclear. In order to address the problem, a systematic bioinformatics analysis was performed on 291 Gram-positive and Gram-negative bacterial protective antigens with experimental evidence manually curated in the Protegen database. The bioinformatics analyses evaluated included subcellular localization, adhesin probability, peptide signaling, transmembrane α-helix and β-barrel, conserved domain, Clusters of Orthologous Groups, and Gene Ontology functional annotations. Here we showed the critical role of adhesins, along with subcellular localization, peptide signaling, in predicting secreted extracellular or surface-exposed protective antigens, with mechanistic explanations supported by functional analysis. We also found a significant negative correlation of transmembrane α-helix to antigen protectiveness in Gram-positive and Gram-negative pathogens, while a positive correlation of transmembrane β-barrel was observed in Gram-negative pathogens. The commonly less-focused cytoplasmic and cytoplasmic membrane proteins could be potentially predicted with the help of other selection criteria such as adhesin probability and functional analysis. The significant findings in this study can support rational vaccine design and enhance our understanding of vaccine design mechanisms.

Subcellular localization based comparative study on radioresistant bacteria: A novel approach to mine proteins involve in radioresistance

Radioresistant bacteria (RRB) are among the most radioresistant organisms and has a unique role in evolution. Along with the evolutionary role, radioresistant organisms play important role in paper industries, bioremediation, vaccine development and possibility in anti-aging and anti-cancer treatment. The study of radiation resistance in RRB was mainly focused on cytosolic mechanisms such as DNA repair mechanism, cell cleansing activity and high antioxidant activity. Although it was known that protein localized on outer areas of cell play role in resistance towards extreme condition but the mechanisms/proteins localized on the outer area of cells are not studied for radioresistance. Considering the fact that outer part of cell is more exposed to radiations and proteins present in outer area of the cell may have role in radioresistance. Localization based comparative study of proteome from RRB and non-radio resistant bacteria was carried out. In RRB 20 unique proteins have been identified. Further domain, structural, and pathway analysis of selected proteins were carried out. Out of 20 proteins, 8 proteins were direct involvement in radioresistance and literature study strengthens this, however, 1 proteins had assumed relation in radioresistance. Selected radioresistant proteins may be helpful for optimal use of RRB in industry and health care.

VacSol: a high throughput in silico pipeline to predict potential therapeutic targets in prokaryotic pathogens using subtractive reverse vaccinology

Background

With advances in reverse vaccinology approaches, a progressive improvement has been observed in the prediction of putative vaccine candidates. Reverse vaccinology has changed the way of discovery and provides a mean to propose target identification in reduced time and labour. In this regard, high throughput genomic sequencing technologies and supporting bioinformatics tools have greatly facilitated the prompt analysis of pathogens, where various predicted candidates have been found effective against certain infections and diseases. A pipeline, VacSol, is designed here based on a similar approach to predict putative vaccine candidates both rapidly and efficiently.

Results

VacSol, a new pipeline introduced here, is a highly scalable, multi-mode, and configurable software designed to automate the high throughput in silico vaccine candidate prediction process for the identification of putative vaccine candidates against the proteome of bacterial pathogens. Vaccine candidates are screened using integrated, well-known and robust algorithms/tools for proteome analysis, and the results from the VacSol software are presented in five different formats by taking proteome sequence as input in FASTA file format. The utility of VacSol is tested and compared with published data and using the Helicobacter pylori 26695 reference strain as a benchmark.

Conclusion

VacSol rapidly and efficiently screens the whole bacterial pathogen proteome to identify a few predicted putative vaccine candidate proteins. This pipeline has the potential to save computational costs and time by efficiently reducing false positive candidate hits. VacSol results do not depend on any universal set of rules and may vary based on the provided input. It is freely available to download from: https://sourceforge.net/projects/vacsol/.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-017-1540-0) contains supplementary material, which is available to authorized users.

Enhancing the Biological Relevance of Machine Learning Classifiers for Reverse Vaccinology

Reverse vaccinology (RV) is a bioinformatics approach that can predict antigens with protective potential from the protein coding genomes of bacterial pathogens for subunit vaccine design. RV has become firmly established following the development of the BEXSERO^® vaccine against Neisseria meningitidis serogroup B. RV studies have begun to incorporate machine learning (ML) techniques to distinguish bacterial protective antigens (BPAs) from non-BPAs. This research contributes significantly to the RV field by using permutation analysis to demonstrate that a signal for protective antigens can be curated from published data. Furthermore, the effects of the following on an ML approach to RV were also assessed: nested cross-validation, balancing selection of non-BPAs for subcellular localization, increasing the training data, and incorporating greater numbers of protein annotation tools for feature generation. These enhancements yielded a support vector machine (SVM) classifier that could discriminate BPAs (n = 200) from non-BPAs (n = 200) with an area under the curve (AUC) of 0.787. In addition, hierarchical clustering of BPAs revealed that intracellular BPAs clustered separately from extracellular BPAs. However, no immediate benefit was derived when training SVM classifiers on data sets exclusively containing intra- or extracellular BPAs. In conclusion, this work demonstrates that ML classifiers have great utility in RV approaches and will lead to new subunit vaccines in the future.

Cattle Immunized with a Recombinant Subunit Vaccine Formulation Exhibits a Trend towards Protection against Histophilus somni Bacterial Challenge

Histophilosis, a mucosal and septicemic infection of cattle is caused by the Gram negative pathogen Histophilus somni (H. somni). As existing vaccines against H. somni infection have shown to be of limited efficacy, we used a reverse vaccinology approach to identify new vaccine candidates. Three groups (B, C, D) of cattle were immunized with subunit vaccines and a control group (group A) was vaccinated with adjuvant alone. All four groups were challenged with H. somni. The results demonstrate that there was no significant difference in clinical signs, joint lesions, weight change or rectal temperature between any of the vaccinated groups (B,C,D) vs the control group A. However, the trend to protection was greatest for group C vaccinates. The group C vaccine was a pool of six recombinant proteins. Serum antibody responses determined using ELISA showed significantly higher titers for group C, with P values ranging from < 0.0148 to < 0.0002, than group A. Even though serum antibody titers in group B (5 out of 6 antigens) and group D were significantly higher compared to group A, they exerted less of a trend towards protection. In conclusion, the vaccine used in group C exhibits a trend towards protective immunity in cattle and would be a good candidate for further analysis to determine which proteins were responsible for the trend towards protection.

iVAX: An integrated toolkit for the selection and optimization of antigens and the design of epitope-driven vaccines

Computational vaccine design, also known as computational vaccinology, encompasses epitope mapping, antigen selection and immunogen design using computational tools. The iVAX toolkit is an integrated set of tools that has been in development since 1998 by De Groot and Martin. It comprises a suite of immunoinformatics algorithms for triaging candidate antigens, selecting immunogenic and conserved T cell epitopes, eliminating regulatory T cell epitopes, and optimizing antigens for immunogenicity and protection against disease. iVAX has been applied to vaccine development programs for emerging infectious diseases, cancer antigens and biodefense targets. Several iVAX vaccine design projects have had success in pre-clinical studies in animal models and are progressing toward clinical studies. The toolkit now incorporates a range of immunoinformatics tools for infectious disease and cancer immunotherapy vaccine design. This article will provide a guide to the iVAX approach to computational vaccinology.

A review of reverse vaccinology approaches for the development of vaccines against ticks and tick borne diseases

The field of reverse vaccinology developed as an outcome of the genome sequence revolution. Following the introduction of live vaccinations in the western world by Edward Jenner in 1798 and the coining of the phrase 'vaccine', in 1881 Pasteur developed a rational design for vaccines. Pasteur proposed that in order to make a vaccine that one should 'isolate, inactivate and inject the microorganism' and these basic rules of vaccinology were largely followed for the next 100 years leading to the elimination of several highly infectious diseases. However, new technologies were needed to conquer many pathogens which could not be eliminated using these traditional technologies. Thus increasingly, computers were used to mine genome sequences to rationally design recombinant vaccines. Several vaccines for bacterial and viral diseases (i.e. meningococcus and HIV) have been developed, however the on-going challenge for parasite vaccines has been due to their comparatively larger genomes. Understanding the immune response is important in reverse vaccinology studies as this knowledge will influence how the genome mining is to be conducted. Vaccine candidates for anaplasmosis, cowdriosis, theileriosis, leishmaniasis, malaria, schistosomiasis, and the cattle tick have been identified using reverse vaccinology approaches. Some challenges for parasite vaccine development include the ability to address antigenic variability as well the understanding of the complex interplay between antibody, mucosal and/or T cell immune responses. To understand the complex parasite interactions with the livestock host, there is the limitation where algorithms for epitope mining using the human genome cannot directly be adapted for bovine, for example the prediction of peptide binding to major histocompatibility complex motifs. As the number of genomes for both hosts and parasites increase, the development of new algorithms for pan-genomic mining will continue to impact the future of parasite and ricketsial (and other tick borne pathogens) disease vaccine development.

Reverse vaccinology as an approach for developing Histophilus somni vaccine candidates

Histophilosis of cattle is caused by the Gram negative bacterial pathogen Histophilus somni (H. somni) which is also associated with the bovine respiratory disease (BRD) complex. Existing vaccines for H. somni include either killed cells or bacteria-free outer membrane proteins from the organism which have proven to be moderately successful. In this study, reverse vaccinology was used to predict potential H. somni vaccine candidates from genome sequences. In turn, these may protect animals against new strains circulating in the field. Whole genome sequencing of six recent clinical H. somni isolates was performed using an Illumina MiSeq and compared to six genomes from the 1980's. De novo assembly of crude whole genomes was completed using Geneious 6.1.7. Protein coding regions was predicted using Glimmer3. Scores from multiple web-based programs were utilized to evaluate the antigenicity of these predicted proteins which were finally ranked based on their surface exposure scores. A single new strain was selected for future vaccine development based on conservation of the protein candidates among all 12 isolates. A positive signal with convalescent serum for these antigens in western blots indicates in vivo recognition. In order to test the protective capacity of these antigens bovine animal trials are ongoing.

Structural and Computational Biology in the Design of Immunogenic Vaccine Antigens

Vaccination is historically one of the most important medical interventions for the prevention of infectious disease. Previously, vaccines were typically made of rather crude mixtures of inactivated or attenuated causative agents. However, over the last 10–20 years, several important technological and computational advances have enabled major progress in the discovery and design of potently immunogenic recombinant protein vaccine antigens. Here we discuss three key breakthrough approaches that have potentiated structural and computational vaccine design. Firstly, genomic sciences gave birth to the field of reverse vaccinology, which has enabled the rapid computational identification of potential vaccine antigens. Secondly, major advances in structural biology, experimental epitope mapping, and computational epitope prediction have yielded molecular insights into the immunogenic determinants defining protective antigens, enabling their rational optimization. Thirdly, and most recently, computational approaches have been used to convert this wealth of structural and immunological information into the design of improved vaccine antigens. This review aims to illustrate the growing power of combining sequencing, structural and computational approaches, and we discuss how this may drive the design of novel immunogens suitable for future vaccines urgently needed to increase the global prevention of infectious disease.

Next-generation sequencing (NGS) transcriptomes reveal association of multiple genes and pathways contributing to secondary metabolites accumulation in tuberous roots of Aconitum heterophyllum Wall

The transcriptomes of Aconitum heterophyllum were assembled and characterized for the first time to decipher molecular components contributing to biosynthesis and accumulation of metabolites in tuberous roots. Aconitum heterophyllum Wall., popularly known as Atis, is a high-value medicinal herb of North-Western Himalayas. No information exists as of today on genetic factors contributing to the biosynthesis of secondary metabolites accumulating in tuberous roots, thereby, limiting genetic interventions towards genetic improvement of A. heterophyllum. Illumina paired-end sequencing followed by de novo assembly yielded 75,548 transcripts for root transcriptome and 39,100 transcripts for shoot transcriptome with minimum length of 200 bp. Biological role analysis of root versus shoot transcriptomes assigned 27,596 and 16,604 root transcripts; 12,340 and 9398 shoot transcripts into gene ontology and clusters of orthologous group, respectively. KEGG pathway mapping assigned 37 and 31 transcripts onto starch-sucrose metabolism while 329 and 341 KEGG orthologies associated with transcripts were found to be involved in biosynthesis of various secondary metabolites for root and shoot transcriptomes, respectively. In silico expression profiling of the mevalonate/2-C-methyl-D-erythritol 4-phosphate (non-mevalonate) pathway genes for aconites biosynthesis revealed 4 genes HMGR (3-hydroxy-3-methylglutaryl-CoA reductase), MVK (mevalonate kinase), MVDD (mevalonate diphosphate decarboxylase) and HDS (1-hydroxy-2-methyl-2-(E)-butenyl 4-diphosphate synthase) with higher expression in root transcriptome compared to shoot transcriptome suggesting their key role in biosynthesis of aconite alkaloids. Five genes, GMPase (geranyl diphosphate mannose pyrophosphorylase), SHAGGY, RBX1 (RING-box protein 1), SRF receptor kinases and β-amylase, implicated in tuberous root formation in other plant species showed higher levels of expression in tuberous roots compared to shoots. A total of 15,487 transcription factors belonging to bHLH, MYB, bZIP families and 399 ABC transporters which regulate biosynthesis and accumulation of bioactive compounds were identified in root and shoot transcriptomes. The expression of 5 ABC transporters involved in tuberous root development was validated by quantitative PCR analysis. Network connectivity diagrams were drawn for starch-sucrose metabolism and isoquinoline alkaloid biosynthesis associated with tuberous root growth and secondary metabolism, respectively, in root transcriptome of A. heterophyllum. The current endeavor will be of practical importance in planning a suitable genetic intervention strategy for the improvement of A. heterophyllum.

The promise of reverse vaccinology

Reverse vaccinology (RV) is a computational approach that aims to identify putative vaccine candidates in the protein coding genome (proteome) of pathogens. RV has primarily been applied to bacterial pathogens to identify proteins that can be formulated into subunit vaccines, which consist of one or more protein antigens. An RV approach based on a filtering method has already been used to construct a subunit vaccine against Neisseria meningitidis serogroup B that is now registered in several countries (Bexsero). Recently, machine learning methods have been used to improve the ability of RV approaches to identify vaccine candidates. Further improvements related to the incorporation of epitope-binding annotation and gene expression data are discussed. In the future, it is envisaged that RV approaches will facilitate rapid vaccine design with less reliance on conventional animal testing and clinical trials in order to curb the threat of antibiotic resistance or newly emerged outbreaks of bacterial origin.

Nonclassically secreted proteins as possible antigens for vaccine development: a reverse vaccinology approach

Reverse vaccinology strategies have already been applied to a variety of microorganisms and have contributed significantly to vaccine development. However, most of the studies focused on an individual organism or on proteins with signature sequence motifs commonly found in known secreted proteins from bacteria. In this work, we applied a reverse vaccinology strategy based on conservation, virulence, and nonclassically surface exposure criterions to identify potential antigens in two microorganisms with significant degree of genomic plasticity among isolates (Streptococcus pneumoniae and Leptospira spp.), which imposes a major limitation to the production of a multistrain component vaccine. PSORTb 3.0.2 was run to predict the subcellular localization of the proteins. OrthoMCL was run to identify groups of the most conserved proteins between strains. Virulence prediction was done for the most conserved proteins, and SecretomeP was run to predict the nonclassically secreted proteins among the potential virulence factors. Based on the above criteria, we identified 37 proteins conserved between 16 genomes of S. pneumoniae and 12 proteins conserved between 5 leptospiral genomes as potential vaccine candidates.

Computational peptide vaccinology

Immunoinformatics focuses on modeling immune responses for better understanding of the immune system and in many cases for proposing agents able to modify the immune system. The most classical of these agents are vaccines derived from living organisms such as smallpox or polio. More modern vaccines comprise recombinant proteins, protein domains, and in some cases peptides. Generating a vaccine from peptides however requires technologies and concepts very different from classical vaccinology. Immunoinformatics therefore provides the computational tools to propose peptides suitable for formulation into vaccines. This chapter introduces the essential biological concepts affecting design and efficacy of peptide vaccines and discusses current methods and workflows applied to design successful peptide vaccines using computers.

Ontology-supported research on vaccine efficacy, safety and integrative biological networks

While vaccine efficacy and safety research has dramatically progressed with the methods of in silico prediction and data mining, many challenges still exist. A formal ontology is a human- and computer-interpretable set of terms and relations that represent entities in a specific domain and how these terms relate to each other. Several community-based ontologies (including Vaccine Ontology, Ontology of Adverse Events and Ontology of Vaccine Adverse Events) have been developed to support vaccine and adverse event representation, classification, data integration, literature mining of host-vaccine interaction networks, and analysis of vaccine adverse events. The author further proposes minimal vaccine information standards and their ontology representations, ontology-based linked open vaccine data and meta-analysis, an integrative One Network ('OneNet') Theory of Life, and ontology-based approaches to study and apply the OneNet theory. In the Big Data era, these proposed strategies provide a novel framework for advanced data integration and analysis of fundamental biological networks including vaccine immune mechanisms.

Common antigens prediction in bacterial bioweapons: a perspective for vaccine design

Bioweapons (BWs) are a serious threat to mankind and the lack of efficient vaccines against bacterial bioweapons (BBWs) further worsens the situation in face of BW attack. Experts believe that difficulties in detection and ease in dissemination of deadly pathogens make BW a better option for attack compared to nuclear weapons. Molecular biology techniques facilitate the use of genetically modified BBWs thus creating uncertainty on which bacteria will be used for BW attack. In the present work, available resources such as proteomic sequences of BBWs, protective antigenic proteins (PAPs) reported in Protegen database and VaxiJen dataset, and immunogenic epitopes in immune epitope database (IEDB) were used to predict potential broad-specific vaccine candidates against BBWs. Comparison of proteomes sequences of BBWs and their analyses using in-house PERL scripts identified 44 conserved proteins and many of them were known to be immunogenic. Comparison of conserved proteins against PAPs identified six either as PAPs or their homologues with a potential of providing protection against multiple pathogens. Similarly, mapping of conserved proteins against experimentally known IEDB epitopes identified six epitopes which had exact epitope match in four proteins including three from earlier predicted six PAPs. These epitopes were also reported to provide protection against several pathogens. In the backdrop of conserved heat shock GroEL protein from Salmonella enterica providing protection against five diverse bacterial pathogens involved in different diseases, and synthetic proteins produced by combination of epitopes from Mycobacterium tuberculosis and 4 viruses providing protection against both bacterium and viruses, the identified putative immunogenic conserved proteins and immune-protective epitopes can further be explored for their potential as broad-specific vaccine candidates against BBWs.

Updates on the web-based VIOLIN vaccine database and analysis system

The integrative Vaccine Investigation and Online Information Network (VIOLIN) vaccine research database and analysis system (http://www.violinet.org) curates, stores, analyses and integrates various vaccine-associated research data. Since its first publication in NAR in 2008, significant updates have been made. Starting from 211 vaccines annotated at the end of 2007, VIOLIN now includes over 3240 vaccines for 192 infectious diseases and eight noninfectious diseases (e.g. cancers and allergies). Under the umbrella of VIOLIN, >10 relatively independent programs are developed. For example, Protegen stores over 800 protective antigens experimentally proven valid for vaccine development. VirmugenDB annotated over 200 'virmugens', a term coined by us to represent those virulence factor genes that can be mutated to generate successful live attenuated vaccines. Specific patterns were identified from the genes collected in Protegen and VirmugenDB. VIOLIN also includes Vaxign, the first web-based vaccine candidate prediction program based on reverse vaccinology. VIOLIN collects and analyzes different vaccine components including vaccine adjuvants (Vaxjo) and DNA vaccine plasmids (DNAVaxDB). VIOLIN includes licensed human vaccines (Huvax) and veterinary vaccines (Vevax). The Vaccine Ontology is applied to standardize and integrate various data in VIOLIN. VIOLIN also hosts the Ontology of Vaccine Adverse Events (OVAE) that logically represents adverse events associated with licensed human vaccines.