Analysis of known bacterial protein vaccine antigens reveals biased physical properties and amino acid composition

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.

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.

A novel vaccine platform using glucan particles for induction of protective responses against Francisella tularensis and other pathogens

Vaccines are considered the bedrock of preventive medicine. However, for many pathogens, it has been challenging to develop vaccines that stimulate protective, long-lasting immunity. We have developed a novel approach using β-1,3-D-glucans (BGs), natural polysaccharides abundantly present in fungal cell walls, as a biomaterial platform for vaccine delivery. BGs simultaneously provide for receptor-targeted antigen delivery to specialized antigen-presenting cells together with adjuvant properties to stimulate antigen-specific and trained non-specific immune responses. This review focuses on various approaches of using BG particles (GPs) to develop bacterial and fungal vaccine candidates. A special case history for the development of an effective GP tularaemia vaccine candidate is highlighted.

Subcellular antigen localization in commensal E. coli is critical for T cell activation and induction of specific tolerance

Oral tolerance to soluble antigens is critically important for the maintenance of immunological homeostasis in the gut. The mechanisms of tolerance induction to antigens of the gut microbiota are still less well understood. Here, we investigate whether the subcellular localization of antigens within non-pathogenic E. coli has a role for its ability to induce antigen-specific tolerance. E. coli that express an ovalbumin (OVA) peptide in the cytoplasm, at the outer membrane or as secreted protein were generated. Intestinal colonization of mice with non-pathogenic E. coli expressing OVA at the membrane induced the expansion of antigen-specific Foxp3⁺ T_regs and mediated systemic immune tolerance. In contrast, cytoplasmic OVA was ignored by antigen-specific CD4⁺ T cells and failed to induce tolerance. In vitro experiments revealed that surface-displayed OVA of viable E. coli was about two times of magnitude more efficient to activate antigen-specific CD4⁺ T cells than soluble antigens, surface-displayed antigens of heat-killed E. coli or cytoplasmic antigen of viable or heat-killed E. coli. This effect was independent of the antigen uptake efficiency in dendritic cells. In summary, our results show that subcellular antigen localization in viable E. coli strongly influences antigen-specific CD4⁺ cell expansion and tolerance induction upon intestinal colonization.

Protection induced by a Francisella tularensis subunit vaccine delivered by glucan particles

Francisella tularensis is an intracellular pathogen causing the disease tularemia, and an organism of concern to biodefence. There is no licensed vaccine available. Subunit approaches have failed to induce protection, which requires both humoral and cellular immune memory responses, and have been hampered by a lack of understanding as to which antigens are immunoprotective. We undertook a preliminary in silico analysis to identify candidate protein antigens. These antigens were then recombinantly expressed and encapsulated into glucan particles (GPs), purified Saccharomyces cerevisiae cell walls composed primarily of β-1,3-glucans. Immunological profiling in the mouse was used to down-selection to seven lead antigens: FTT1043 (Mip), IglC, FTT0814, FTT0438, FTT0071 (GltA), FTT0289, FTT0890 (PilA) prior to transitioning their evaluation to a Fischer 344 rat model for efficacy evaluation. F344 rats were vaccinated with the GP protein antigens co-delivered with GP-loaded with Francisella LPS. Measurement of cell mediated immune responses and computational epitope analysis allowed down-selection to three promising candidates: FTT0438, FTT1043 and FTT0814. Of these, a GP vaccine delivering Francisella LPS and the FTT0814 protein was able to induce protection in rats against an aerosol challenge of F. tularensis SchuS4, and reduced organ colonisation and clinical signs below that which immunisation with a GP-LPS alone vaccine provided. This is the first report of a protein supplementing protection induced by LPS in a Francisella vaccine. This paves the way for developing an effective, safe subunit vaccine for the prevention of inhalational tularemia, and validates the GP platform for vaccine delivery where complex immune responses are required for prevention of infections by intracellular pathogens.

A systems biology approach for diagnostic and vaccine antigen discovery in tropical infectious diseases

PURPOSE OF REVIEW

There is a need for improved diagnosis and for more rapidly assessing the presence, prevalence, and spread of newly emerging or reemerging infectious diseases. An approach to the pathogen-detection strategy is based on analyzing host immune response to the infection. This review focuses on a protein microarray approach for this purpose.

RECENT FINDINGS

Here we take a protein microarray approach to profile the humoral immune response to numerous infectious agents, and to identify the complete antibody repertoire associated with each disease. The results of these studies lead to the identification of diagnostic markers and potential subunit vaccine candidates. These results from over 30 different organisms can also provide information about common trends in the humoral immune response.

SUMMARY

This review describes the implications of the findings for clinical practice or research. A systems biology approach to identify the antibody repertoire associated with infectious diseases challenge using protein microarray has become a powerful method in identifying diagnostic markers and potential subunit vaccine candidates, and moreover, in providing information on proteomic feature (functional and physically properties) of seroreactive and serodiagnostic antigens. Combining the detection of the pathogen with a comprehensive assessment of the host immune response will provide a new understanding of the correlations between specific causative agents, the host response, and the clinical manifestations of the disease.

Software tools for simultaneous data visualization and T cell epitopes and disorder prediction in proteins

We have developed EpDis and MassPred, extendable open source software tools that support bioinformatic research and enable parallel use of different methods for the prediction of T cell epitopes, disorder and disordered binding regions and hydropathy calculation. These tools offer a semi-automated installation of chosen sets of external predictors and an interface allowing for easy application of the prediction methods, which can be applied either to individual proteins or to datasets of a large number of proteins. In addition to access to prediction methods, the tools also provide visualization of the obtained results, calculation of consensus from results of different methods, as well as import of experimental data and their comparison with results obtained with different predictors. The tools also offer a graphical user interface and the possibility to store data and the results obtained using all of the integrated methods in the relational database or flat file for further analysis. The MassPred part enables a massive parallel application of all integrated predictors to the set of proteins. Both tools can be downloaded from http://bioinfo.matf.bg.ac.rs/home/downloads.wafl?cat=Software. Appendix A includes the technical description of the created tools and a list of supported predictors.

Identification of Candidate Vaccine Antigens In Silico

The identification of immunogenic whole-protein antigens is fundamental to the successful discovery of candidate subunit vaccines and their rapid, effective, and efficient transformation into clinically useful, commercially successful vaccine formulations. In the wider context of the experimental discovery of vaccine antigens, with particular reference to reverse vaccinology, this chapter adumbrates the principal computational approaches currently deployed in the hunt for novel antigens: genome-level prediction of antigens, antigen identification through the use of protein sequence alignment-based approaches, antigen detection through the use of subcellular location prediction, and the use of alignment-independent approaches to antigen discovery. Reference is also made to the recent emergence of various expert systems for protein antigen identification.

Systems biology approach predicts antibody signature associated with Brucella melitensis infection in humans

A complete understanding of the factors that determine selection of antigens recognized by the humoral immune response following infectious agent challenge is lacking. Here we illustrate a systems biology approach to identify the antibody signature associated with Brucella melitensis (Bm) infection in humans and predict proteomic features of serodiagnostic antigens. By taking advantage of a full proteome microarray expressing previously cloned 1406 and newly cloned 1640 Bm genes, we were able to identify 122 immunodominant antigens and 33 serodiagnostic antigens. The reactive antigens were then classified according to annotated functional features (COGs), computationally predicted features (e.g., subcellular localization, physical properties), and protein expression estimated by mass spectrometry (MS). Enrichment analyses indicated that membrane association and secretion were significant enriching features of the reactive antigens, as were proteins predicted to have a signal peptide, a single transmembrane domain, and outer membrane or periplasmic location. These features accounted for 67% of the serodiagnostic antigens. An overlay of the seroreactive antigen set with proteomic data sets generated by MS identified an additional 24%, suggesting that protein expression in bacteria is an additional determinant in the induction of Brucella-specific antibodies. This analysis indicates that one-third of the proteome contains enriching features that account for 91% of the antigens recognized, and after B. melitensis infection the immune system develops significant antibody titers against 10% of the proteins with these enriching features. This systems biology approach provides an empirical basis for understanding the breadth and specificity of the immune response to B. melitensis and a new framework for comparing the humoral responses against other microorganisms.

ImmunoGrid: towards agent-based simulations of the human immune system at a natural scale

The ultimate aim of the EU-funded ImmunoGrid project is to develop a natural-scale model of the human immune system-that is, one that reflects both the diversity and the relative proportions of the molecules and cells that comprise it-together with the grid infrastructure necessary to apply this model to specific applications in the field of immunology. These objectives present the ImmunoGrid Consortium with formidable challenges in terms of complexity of the immune system, our partial understanding about how the immune system works, the lack of reliable data and the scale of computational resources required. In this paper, we explain the key challenges and the approaches adopted to overcome them. We also consider wider implications for the present ambitious plans to develop natural-scale, integrated models of the human body that can make contributions to personalized health care, such as the European Virtual Physiological Human initiative. Finally, we ask a key question: How long will it take us to resolve these challenges and when can we expect to have fully functional models that will deliver health-care benefits in the form of personalized care solutions and improved disease prevention?

The antigenome: from protein subunit vaccines to antibody treatments of bacterial infections?

New strategies are needed to master infectious diseases. The so-called "passive vaccination", i.e., prevention and treatment with specific antibodies, has a proven record and potential in the management of infections and entered the medical arena more than 100 years ago. Progress in the identification of specific antigens has become the hallmark in the development of novel subunit vaccines that often contain only a single immunogen, frequently proteins, derived from the microbe in order to induce protective immunity. On the other hand, the monoclonal antibody technology has enabled biotechnology to produce antibody species in unlimited quantities and at reasonable costs that are more or less identical to their human counterparts and bind with high affinity to only one specific site of a given antigen. Although, this technology has provided a robust platform for launching novel and successful treatments against a variety of devastating diseases, it is up till now only exceptionally employed in therapy of infectious diseases. Monoclonal antibodies engaged in the treatment of specific cancers seem to work by a dual mode; they mark the cancerous cells for decontamination by the immune system, but also block a function that intervenes with cell growth. The availability of the entire genome sequence of pathogens has strongly facilitated the identification of highly specific protein antigens that are suitable targets for neutralizing antibodies, but also often seem to play an important role in the microbe's life cycle. Thus, the growing repertoire of well-characterized protein antigens will open the perspective to develop monoclonal antibodies against bacterial infections, at least as last resort treatment, when vaccination and antibiotics are no options for prevention or therapy. In the following chapter we describe and compare various technologies regarding the identification of suitable target antigens and the foundation of cognate monoclonal antibodies and discuss their possible applications in the treatment of bacterial infections together with an overview of current efforts.

A Burkholderia pseudomallei protein microarray reveals serodiagnostic and cross-reactive antigens

Understanding the way in which the immune system responds to infection is central to the development of vaccines and many diagnostics. To provide insight into this area, we fabricated a protein microarray containing 1,205 Burkholderia pseudomallei proteins, probed it with 88 melioidosis patient sera, and identified 170 reactive antigens. This subset of antigens was printed on a smaller array and probed with a collection of 747 individual sera derived from 10 patient groups including melioidosis patients from Northeast Thailand and Singapore, patients with different infections, healthy individuals from the USA, and from endemic and nonendemic regions of Thailand. We identified 49 antigens that are significantly more reactive in melioidosis patients than healthy people and patients with other types of bacterial infections. We also identified 59 cross-reactive antigens that are equally reactive among all groups, including healthy controls from the USA. Using these results we were able to devise a test that can classify melioidosis positive and negative individuals with sensitivity and specificity of 95% and 83%, respectively, a significant improvement over currently available diagnostic assays. Half of the reactive antigens contained a predicted signal peptide sequence and were classified as outer membrane, surface structures or secreted molecules, and an additional 20% were associated with pathogenicity, adaptation or chaperones. These results show that microarrays allow a more comprehensive analysis of the immune response on an antigen-specific, patient-specific, and population-specific basis, can identify serodiagnostic antigens, and contribute to a more detailed understanding of immunogenicity to this pathogen.

Immunization against Clostridium perfringens cells elicits protection against Clostridium tetani in mouse model: identification of cross-reactive proteins using proteomic methodologies

Background

Clostridium tetani and Clostridium perfringens are among the medically important clostridial pathogens causing diseases in man and animals. Several homologous open reading frames (ORFs) have been identified in the genomes of the two pathogens by comparative genomic analysis. We tested a likelihood of extensive sharing of common epitopes between homologous proteins of these two medically important pathogens and the possibility of cross-protection using active immunization.

Results

Eight predominant cross-reactive spots were identified by mass spectrometry and had hits in the C. tetani E88 proteome with significant MOWSE scores. Most of the cross-reactive proteins of C. tetani shared 65–78% sequence similarity with their closest homologues in C. perfringens ATCC13124. Electron transfer flavoprotein beta-subunit (CT3) was the most abundant protein (43.3%), followed by methylaspartate ammonia-lyase (36.8%) and 2-phosphoglycerate dehydratase (35.6%). All the proteins were predicted to be cytoplasmic by PSORT protein localization algorithm. Active immunization with C. perfringens whole cells elicited cross-protective immunity against C. tetani infection in a mouse model.

Conclusion

Most of the dominant cross-reactive proteins of C. tetani belonged to the cluster of orthologous group (COG) functional category, either of posttranslational modification, protein turnover, and chaperones (O) or energy production and conversion (C). The homologs of the identified proteins have been shown to play role in pathogenesis in other Gram-positive pathogenic bacteria. Our findings provide basis for the search of potential vaccine candidates with broader coverage, encompassing more than one pathogenic clostridial species.

Identifying candidate subunit vaccines using an alignment-independent method based on principal amino acid properties

Subunit vaccine discovery is an accepted clinical priority. The empirical approach is time- and labor-consuming and can often end in failure. Rational information-driven approaches can overcome these limitations in a fast and efficient manner. However, informatics solutions require reliable algorithms for antigen identification. All known algorithms use sequence similarity to identify antigens. However, antigenicity may be encoded subtly in a sequence and may not be directly identifiable by sequence alignment. We propose a new alignment-independent method for antigen recognition based on the principal chemical properties of protein amino acid sequences. The method is tested by cross-validation on a training set of bacterial antigens and external validation on a test set of known antigens. The prediction accuracy is 83% for the cross-validation and 80% for the external test set. Our approach is accurate and robust, and provides a potent tool for the in silico discovery of medically relevant subunit vaccines.

Machine learning approaches for prediction of linear B-cell epitopes on proteins

Identification and characterization of antigenic determinants on proteins has received considerable attention utilizing both, experimental as well as computational methods. For computational routines mostly structural as well as physicochemical parameters have been utilized for predicting the antigenic propensity of protein sites. However, the performance of computational routines has been low when compared to experimental alternatives. Here we describe the construction of machine learning based classifiers to enhance the prediction quality for identifying linear B-cell epitopes on proteins. Our approach combines several parameters previously associated with antigenicity, and includes novel parameters based on frequencies of amino acids and amino acid neighborhood propensities. We utilized machine learning algorithms for deriving antigenicity classification functions assigning antigenic propensities to each amino acid of a given protein sequence. We compared the prediction quality of the novel classifiers with respect to established routines for epitope scoring, and tested prediction accuracy on experimental data available for HIV proteins. The major finding is that machine learning classifiers clearly outperform the reference classification systems on the HIV epitope validation set.