A guide to in silico vaccine discovery for eukaryotic pathogens

Reverse vaccinology and immunoinformatics approach to design a chimeric epitope vaccine against Orientia tsutsugamushi

Scrub typhus is a vector-borne infectious disease caused by Orientia tsutsugamushi and it is reportedly associated with up to 20 % of hospitalized cases of febrile illnesses. The major challenge of vaccine development is the lack of identified antigens that can induce both heterotypic and homotypic immunity including the production of antibodies, cytotoxic T lymphocyte, and helper T lymphocytes. We employed a comprehensive immunoinformatic prediction algorithm to identify immunogenic epitopes of the 56-kDa type-specific cell membrane surface antigen and surface cell antigen A of O. tsutsugamushi to select potential candidates for developing vaccines and diagnostic assays. We identified 35 linear and 29 continuous immunogenic B-cell epitopes and 51 and 27 strong-binding T-cell epitopes of major histocompatibility complex class I and class II molecules, respectively, in the conserved and variable regions of the 56-kDa type-specific surface antigen. The predicted B- and T-cell epitopes were used to develop immunogenic multi-epitope candidate vaccines and showed to elicit a broad-range of immune protection. A stable interactions between the multi-epitope vaccines and the host fibronectin protein were observed using docking and simulation methods. Molecular dynamics simulation studies demonstrated that the multi-epitope vaccine constructs and fibronectin docked models were stable during simulation time. Furthermore, the multi-epitope vaccine exhibited properties such as antigenicity, non-allergenicity and ability to induce interferon gamma production and had strong associations with their respective human leukocyte antigen alleles of world-wide population coverage. A correlation of immune simulations and the in-silico predicted immunogenic potential of multi-epitope vaccines implicate for further investigations to accelerate designing of epitope-based vaccine candidates and chimeric antigens for development of serological diagnostic assays for scrub typhus.

Designing a multi-epitope chimeric protein from different potential targets: A potential vaccine candidate against Plasmodium

Malaria is an infectious disease that has been a continuous threat to mankind since the time immemorial. Owing to the complex multi-staged life cycle of the plasmodium parasite, an effective malaria vaccine which is fully protective against the parasite infection is urgently needed to deal with the challenges. In the present study, essential parasite proteins were identified and a chimeric protein with multivalent epitopes was generated. The designed chimeric protein consists of best potential B and T cell epitopes from five different essential parasite proteins. Physiochemical studies of the chimeric protein showed that the modeled vaccine construct was thermo-stable, hydrophilic and antigenic in nature. And the binding of the vaccine construct with Toll-like receptor-4 (TLR-4) as revealed by the molecular docking suggests the possible interaction and role of the vaccine construct in activating the innate immune response. The constructed vaccine being a chimeric protein containing epitopes from different potential candidates could target different stages or pathways of the parasite. Moreover, the approach used in this study is time and cost effective, and can be applied in the discoveries of new potential vaccine targets for other pathogens.

Viral informatics: bioinformatics-based solution for managing viral infections

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

Compilation of parasitic immunogenic proteins from 30 years of published research using machine learning and natural language processing

The World Health Organisation reported in 2020 that six of the top 10 sources of death in low-income countries are parasites. Parasites are microorganisms in a relationship with a larger organism, the host. They acquire all benefits at the host’s expense. A disease develops if the parasitic infection disrupts normal functioning of the host. This disruption can range from mild to severe, including death. Humans and livestock continue to be challenged by established and emerging infectious disease threats. Vaccination is the most efficient tool for preventing current and future threats. Immunogenic proteins sourced from the disease-causing parasite are worthwhile vaccine components (subunits) due to reliable safety and manufacturing capacity. Publications with ‘subunit vaccine’ in their title have accumulated to thousands over the last three decades. However, there are possibly thousands more reporting immunogenicity results without mentioning ‘subunit’ and/or ‘vaccine’. The exact number is unclear given the non-standardised keywords in publications. The study aim is to identify parasite proteins that induce a protective response in an animal model as reported in the scientific literature within the last 30 years using machine learning and natural language processing. Source code to fulfil this aim and the vaccine candidate list obtained is made available.

Establishment and Application of an Indirect Enzyme-Linked Immunosorbent Assay for Measuring GPI-Anchored Protein 52 (P52) Antibodies in Babesia gibsoni-Infected Dogs

Babesia gibsoni is a malaria-like protozoan that parasitizes the red blood cells of canids to cause babesiosis. Due to its high expression and essential function in the survival of parasites, the Glycosylphosphatidylinositol (GPI) anchor protein family is considered an excellent immunodiagnostic marker. Herein, we identified a novel GPI-anchored protein named as BgGPI52-WH with a size of 52 kDa; the recombinant BgGPI52-WH with high antigenicity and immunogenicity was used as a diagnostic antigen to establish a new iELISA method. The iELISA had a sensitivity of 1:400, and no cross-reaction with other apicomplexan parasites occurred. We further demonstrated that the degree of variation was less than 10% using the same samples from the same or different batches of an enzyme-labeled strip. It was found that the method was able to detect early infection (6 days after infection) in the sera of the B. gibsoni-infected experimental dogs in which antibody response to rBgGPI52-WH was evaluated. Clinical sera from pet hospitals were further tested, and the average positive rate was about 11.41% (17/149). The results indicate that BgGPI52-WH is a reliable diagnostic antigen, and the new iELISA could be used as a practical method for the early diagnosis of B. gibsoni.

Machine Learning and Its Applications for Protozoal Pathogens and Protozoal Infectious Diseases

In recent years, massive attention has been attracted to the development and application of machine learning (ML) in the field of infectious diseases, not only serving as a catalyst for academic studies but also as a key means of detecting pathogenic microorganisms, implementing public health surveillance, exploring host-pathogen interactions, discovering drug and vaccine candidates, and so forth. These applications also include the management of infectious diseases caused by protozoal pathogens, such as Plasmodium, Trypanosoma, Toxoplasma, Cryptosporidium, and Giardia, a class of fatal or life-threatening causative agents capable of infecting humans and a wide range of animals. With the reduction of computational cost, availability of effective ML algorithms, popularization of ML tools, and accumulation of high-throughput data, it is possible to implement the integration of ML applications into increasing scientific research related to protozoal infection. Here, we will present a brief overview of important concepts in ML serving as background knowledge, with a focus on basic workflows, popular algorithms (e.g., support vector machine, random forest, and neural networks), feature extraction and selection, and model evaluation metrics. We will then review current ML applications and major advances concerning protozoal pathogens and protozoal infectious diseases through combination with correlative biology expertise and provide forward-looking insights for perspectives and opportunities in future advances in ML techniques in this field.

Identification of anti-horn fly vaccine antigen candidates using a reverse vaccinology approach

Background

The horn fly, Haematobia irritans irritans, causes significant production losses to the cattle industry. Horn fly control relies on insecticides; however, alternative control methods such as vaccines are needed due to the fly's capacity to quickly develop resistance to insecticides, and the pressure for eco-friendly options.

Methods

We used a reverse vaccinology approach comprising three vaccine prediction and 11 annotation tools to evaluate and rank 79,542 translated open reading frames (ORFs) from the horn fly's transcriptome, and selected 10 transcript ORFs as vaccine candidates for expression in Pichia pastoris. The expression of the 10 selected transcripts and the proteins that they encoded were investigated in adult flies by reverse transcription polymerase chain reaction (RT-PCR) and mass spectrometry, respectively. Then, we evaluated the immunogenicity of a vaccine candidate in an immunization trial and the antigen’s effects on horn fly mortality and fecundity in an in vitro feeding assay.

Results

Six of the ten vaccine candidate antigens were successfully expressed in P. pastoris. RT-PCR confirmed the expression of all six ORFs in adult fly RNA. One of the vaccine candidate antigens, BI-HS009, was expressed in sufficient quantity for immunogenicity and efficacy trials. The IgG titers of animals vaccinated with BI-HS009 plus adjuvant were significantly higher than those of animals vaccinated with buffer plus adjuvant only from days 42 to 112, with a peak on day 56. Progeny of horn flies feeding upon blood from animals vaccinated with BI-HS009 plus adjuvant collected on day 56 had 63% lower pupariation rate and 57% lower adult emergence than the control group (ANOVA: F_{(1, 6)} = 8.221, P = 0.028 and F_{(1, 6)} = 8.299, P = 0.028, respectively).

Conclusions

The reverse vaccinology approach streamlined the discovery process by prioritizing possible vaccine antigen candidates. Through a thoughtful process of selection and in vivo and in vitro evaluations, we were able to identify a promising antigen for an anti-horn fly vaccine.

Graphical abstract

Supplementary Information

The online version contains supplementary material available at 10.1186/s13071-021-04938-5.

Predicting Protein Therapeutic Candidates for Bovine Babesiosis Using Secondary Structure Properties and Machine Learning

Bovine babesiosis causes significant annual global economic loss in the beef and dairy cattle industry. It is a disease instigated from infection of red blood cells by haemoprotozoan parasites of the genus Babesia in the phylum Apicomplexa. Principal species are Babesia bovis, Babesia bigemina, and Babesia divergens. There is no subunit vaccine. Potential therapeutic targets against babesiosis include members of the exportome. This study investigates the novel use of protein secondary structure characteristics and machine learning algorithms to predict exportome membership probabilities. The premise of the approach is to detect characteristic differences that can help classify one protein type from another. Structural properties such as a protein’s local conformational classification states, backbone torsion angles ϕ (phi) and ψ (psi), solvent-accessible surface area, contact number, and half-sphere exposure are explored here as potential distinguishing protein characteristics. The presented methods that exploit these structural properties via machine learning are shown to have the capacity to detect exportome from non-exportome Babesia bovis proteins with an 86–92% accuracy (based on 10-fold cross validation and independent testing). These methods are encapsulated in freely available Linux pipelines setup for automated, high-throughput processing. Furthermore, proposed therapeutic candidates for laboratory investigation are provided for B. bovis, B. bigemina, and two other haemoprotozoan species, Babesia canis, and Plasmodium falciparum.

Saponin-adjuvanted recombinant vaccines containing rCP00660, rCP09720 or rCP01850 proteins against Corynebacterium pseudotuberculosis infection in mice

PURPOSE

rCP01850, rCP09729 and rCP00660 proteins from Corynebacterium pseudotuberculosis, predicted as the three best targets to be used in vaccines against Caseous Lymphadenitis in mature epitope density (MED) analysis were tested as vaccinal targets in association to saponin as adjuvant.

METHODOLOGY

rCP00660, rCP09720 and rCP01850 were expressed in E. coli and purified for immunization assay. Balb/c mice were divided into five groups of sixteen animals each. G1 was injected with saline solution (0.9% NaCl), G2 with saponin, G3, G4 and G5 with, respectively, rCP00660, rCP09720 and rCP01850 added by saponin. Two doses were administered within a 21-days interval, and blood samples were collected for IgG quantification. Twenty-one days after the last immunization, ten mice in each group were challenged with virulent C. pseudotuberculosis MIC-6 strain, and mortality was recorded for 40 days. Meanwhile six mice in each group were used for cytokine quantification by qPCR.

RESULTS

G2, G3, G4 and G5 presented protection rates of 10, 30, 40 and 60%, respectively. In spite of levels of total IgG were higher in G4 and G5, production of IgG2a was higher than IgG1 for G5. G3, G4 and G5 presented significant high IFN-γ levels, however, only G5 showed high TNF-α while G3 and G4 showed high IL-17.

CONCLUSION

rCP01850 added by saponin was able to protect efficiently mice against C. pseudotuberculosis challenge, and to induce high IgG, IFN-γ and TNF-α levels. In spite of rCP00660 and rCP09720 had not same adequate protection levels, significant IgG, IFN-γ, and IL-17 levels and further studies aiming to improve protection rates should be conducted.

In silico discovery of antigenic proteins and epitopes of SARS-CoV-2 for the development of a vaccine or a diagnostic approach for COVID-19

In the genome of SARS-CoV-2, the 5′-terminus encodes a polyprotein, which is further cleaved into 15 non-structural proteins whereas the 3′ terminus encodes four structural proteins and eight accessory proteins. Among these 27 proteins, the present study aimed to discover likely antigenic proteins and epitopes to be used for the development of a vaccine or serodiagnostic assay using an in silico approach. For this purpose, after the full genome analysis of SARS-CoV-2 Wuhan isolate and variant proteins that are detected frequently, surface proteins including spike, envelope, and membrane proteins as well as proteins with signal peptide were determined as probable vaccine candidates whereas the remaining were considered as possible antigens to be used during the development of serodiagnostic assays. According to results obtained, among 27 proteins, 26 of them were predicted as probable antigen. In 26 proteins, spike protein was selected as the best vaccine candidate because of having a signal peptide, negative GRAVY value, one transmembrane helix, moderate aliphatic index, a big molecular weight, a long-estimated half-life, beta wrap motifs as well as having stable, soluble and non-allergic features. In addition, orf7a, orf8, and nsp-10 proteins with signal peptide were considered as potential vaccine candidates. Nucleocapsid protein and a highly antigenic GGDGKMKD epitope were identified as ideal antigens to be used in the development of serodiagnostic assays. Moreover, considering MHC-I alleles, highly antigenic KLNDLCFTNV and ITLCFTLKRK epitopes can be used to develop an epitope-based peptide vaccine.

Evaluation of Babesia gibsoni GPI-anchored Protein 47 (BgGPI47-WH) as a Potential Diagnostic Antigen by Enzyme-Linked Immunosorbent Assay

Babesia gibsoni is one of the important pathogens causing severe incurable canine babesiosis, suggesting the necessity to develop a sensitive, specific, and highly automated diagnostic method for clinical application. Surface proteins are ideal candidates for diagnostic targets because they are the primary targets for host immune responses during host-parasite interactions. Glycosylphosphatidylinositol (GPI)-anchored proteins are abundant on the surface of parasites and play an important role in parasite diagnosis. In this study, a GPI-anchored protein named BgGPI47-WH was obtained and mouse anti-rBgGPI47-WH polyclonal antibody was produced by immunizing mice with the purified protein and Freund's adjuvant. Western blot was used to identify the native form and immunogenicity of BgGPI47-WH. An ELISA method was established by using recombinant BgGPI47-WH protein to evaluate its potential as a diagnostic antigen and the established method exhibited high specificity. The antibody response was evaluated by using the B. gibsoni-infected sera collected from different experimental dogs and the established ELISA could recognize antibodies at day 6 until day 101 post infection, indicating the potential use of BgGPI47-WH for early stage diagnosis. The specificity of the established ELISA was further evaluated by using 147 clinical samples collected from animal hospitals and 17.0% (25/147) of the samples were tested positive, with an overall proportion agreement of 86.39% between the results from BgGPI47-WH and BgSA1. Our results indicated that BgGPI47-WH could be used as a reliable diagnostic antigen and this study has proposed a practical method for early diagnosis of B. gibsoni.

Metazoan Parasite Vaccines: Present Status and Future Prospects

Eukaryotic parasites and pathogens continue to cause some of the most detrimental and difficult to treat diseases (or disease states) in both humans and animals, while also continuously expanding into non-endemic countries. Combined with the ever growing number of reports on drug-resistance and the lack of effective treatment programs for many metazoan diseases, the impact that these organisms will have on quality of life remain a global challenge. Vaccination as an effective prophylactic treatment has been demonstrated for well over 200 years for bacterial and viral diseases. From the earliest variolation procedures to the cutting edge technologies employed today, many protective preparations have been successfully developed for use in both medical and veterinary applications. In spite of the successes of these applications in the discovery of subunit vaccines against prokaryotic pathogens, not many targets have been successfully developed into vaccines directed against metazoan parasites. With the current increase in -omics technologies and metadata for eukaryotic parasites, target discovery for vaccine development can be expedited. However, a good understanding of the host/vector/pathogen interface is needed to understand the underlying biological, biochemical and immunological components that will confer a protective response in the host animal. Therefore, systems biology is rapidly coming of age in the pursuit of effective parasite vaccines. Despite the difficulties, a number of approaches have been developed and applied to parasitic helminths and arthropods. This review will focus on key aspects of vaccine development that require attention in the battle against these metazoan parasites, as well as successes in the field of vaccine development for helminthiases and ectoparasites. Lastly, we propose future direction of applying successes in pursuit of next generation vaccines.

Genome-wide identification of novel vaccine candidates for Plasmodium falciparum malaria using integrative bioinformatics approaches

In spite of decades of malaria research and clinical trials, a fully effective and long-lasting preventive vaccine remains elusive. In the present study, 5370 proteins of Plasmodium falciparum genome were screened for the presence of signal peptide/anchor and GPI anchor motifs. Out of 45 screened surface-associated proteins, 22 were consensually predicted as antigens and had no orthologs in human and mouse except circumsporozoite protein (PF3D7_0304600). Among 22 proteins, 19 were identified as new antigens. In the next step, a total of 4944 peptides were predicted as CD8+ T cell epitopes from 22 probable antigens. Of these, the highest scoring 262 epitopes from each antigen were taken for optimization study in the malaria-endemic regions which covered a broad human population (~93.95%). The predicted epitope 13ILFYFFLWV21 of antigen 6-cysteine (PF3D7_1346800) was binding to the HLA-A*0201 allele with the highest fraction (26%) of immunogenicity in the target populations of North-East Asia, South-East Asia, and sub-Saharan Africa. Therefore, these epitopes are proposed to be favored in vaccine designs against malaria.

On the application of reverse vaccinology to parasitic diseases: a perspective on feature selection and ranking of vaccine candidates

Reverse vaccinology has the potential to rapidly advance vaccine development against parasites, but it is unclear which features studied in silico will advance vaccine development. Here we consider Neospora caninum which is a globally distributed protozoan parasite causing significant economic and reproductive loss to cattle industries worldwide. The aim of this study was to use a reverse vaccinology approach to compile a worthy vaccine candidate list for N. caninum, including proteins containing pathogen-associated molecular patterns to act as vaccine carriers. The in silico approach essentially involved collecting a wide range of gene and protein features from public databases or computationally predicting those for every known Neospora protein. This data collection was then analysed using an automated high-throughput process to identify candidates. The final vaccine list compiled was judged to be the optimum within the constraints of available data, current knowledge, and existing bioinformatics programs. We consider and provide some suggestions and experience on how ranking of vaccine candidate lists can be performed. This study is therefore important in that it provides a valuable resource for establishing new directions in vaccine research against neosporosis and other parasitic diseases of economic and medical importance.

Putative virulence factors of Corynebacterium pseudotuberculosis FRC41: vaccine potential and protein expression

BACKGROUND

Corynebacterium pseudotuberculosis, a facultative intracellular bacterial pathogen, is the etiological agent of caseous lymphadenitis (CLA), an infectious disease that affects sheep and goats and it is responsible for significant economic losses. The disease is characterized mainly by bacteria-induced caseous necrosis in lymphatic glands. New vaccines are needed for reliable control and management of CLA. Thus, the putative virulence factors SpaC, SodC, NanH, and PknG from C. pseudotuberculosis FRC41 may represent new target proteins for vaccine development and pathogenicity studies.

RESULTS

SpaC, PknG and NanH presented better vaccine potential than SodC after in silico analyses. A total of 136 B and T cell epitopes were predicted from the four putative virulence factors. A cluster analysis was performed to evaluate the redundancy degree among the sequences of the predicted epitopes; 57 clusters were formed, most of them (34) were single clusters. Two clusters from PknG and one from SpaC grouped epitopes for B and T-cell (MHC I and II). These epitopes can thus potentially stimulate a complete immune response (humoral and cellular) against C. pseudotuberculosis. Several other clusters, including two from NanH, grouped B-cell epitopes with either MHC I or II epitopes. The four target proteins were expressed in Escherichia coli. A purification protocol was developed for PknG expression.

CONCLUSIONS

In silico analyses show that the putative virulence factors SpaC, PknG and NanH present good potential for CLA vaccine development. Target proteins were successfully expressed in E. coli. A protocol for PknG purification is described.

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.

A rational approach to select immunogenic peptides that induce IFN-γ response against Toxoplasma gondii in human leukocytes

The ideal vaccine to prevent toxoplasmosis in humans would comprise antigens that elicit a protective T cell type 1 response with high IFN-γ production. Here, we report the use of a bioinformatics pipeline to discover peptides based on biochemical characteristics that predict strong IFN-γ response by human leukocytes. We selected peptide sequences that previously were reported to induce IFN-γ to identify the biophysical characteristics that will predict HLA-A*02 high-affinity epitopes. We found that the protein motif pattern FL...L..[VL] was common in previously reported highly immunogenic sequences. We have selected new peptides with a length of 9 residues with affinities from 2 to 21 nM with peptide signal and transmembrane domains and predicted to be cleaved at the proteasome to perform ELISPOT assays with human leukocytes. Within 9 peptides with the highest scores for IFN-γ production, four peptides elicited IFN-γ levels in a range from 252 to 1763 SFC/1e6. Our pipeline uncovered Toxoplasma proteins with peptides that are processed by MHC class 1 in humans. Our results suggest that our rational strategy for the selection of immunogenic epitopes could be used to select peptides as candidates for inclusion in epitope-based vaccines.

Improving the gene structure annotation of the apicomplexan parasite Neospora caninum fulfils a vital requirement towards an in silico-derived vaccine

Neospora caninum is an apicomplexan parasite which can cause abortion in cattle, instigating major economic burden. Vaccination has been proposed as the most cost-effective control measure to alleviate this burden. Consequently the overriding aspiration for N. caninum research is the identification and subsequent evaluation of vaccine candidates in animal models. To save time, cost and effort, it is now feasible to use an in silico approach for vaccine candidate prediction. Precise protein sequences, derived from the correct open reading frame, are paramount and arguably the most important factor determining the success or failure of this approach. The challenge is that publicly available N. caninum sequences are mostly derived from gene predictions. Annotated inaccuracies can lead to erroneously predicted vaccine candidates by bioinformatics programs. This study evaluates the current N. caninum annotation for potential inaccuracies. Comparisons with annotation from a closely related pathogen, Toxoplasma gondii, are also made to distinguish patterns of inconsistency. More importantly, a mRNA sequencing (RNA-Seq) experiment is used to validate the annotation. Potential discrepancies originating from a questionable start codon context and exon boundaries were identified in 1943 protein coding sequences. We conclude, where experimental data were available, that the majority of N. caninum gene sequences were reliably predicted. Nevertheless, almost 28% of genes were identified as questionable. Given the limitations of RNA-Seq, the intention of this study was not to replace the existing annotation but to support or oppose particular aspects of it. Ideally, many studies aimed at improving the annotation are required to build a consensus. We believe this study, in providing a new resource on gene structure and annotation, is a worthy contributor to this endeavour.

Enhancing in silico protein-based vaccine discovery for eukaryotic pathogens using predicted peptide-MHC binding and peptide conservation scores

Given thousands of proteins constituting a eukaryotic pathogen, the principal objective for a high-throughput in silico vaccine discovery pipeline is to select those proteins worthy of laboratory validation. Accurate prediction of T-cell epitopes on protein antigens is one crucial piece of evidence that would aid in this selection. Prediction of peptides recognised by T-cell receptors have to date proved to be of insufficient accuracy. The in silico approach is consequently reliant on an indirect method, which involves the prediction of peptides binding to major histocompatibility complex (MHC) molecules. There is no guarantee nevertheless that predicted peptide-MHC complexes will be presented by antigen-presenting cells and/or recognised by cognate T-cell receptors. The aim of this study was to determine if predicted peptide-MHC binding scores could provide contributing evidence to establish a protein's potential as a vaccine. Using T-Cell MHC class I binding prediction tools provided by the Immune Epitope Database and Analysis Resource, peptide binding affinity to 76 common MHC I alleles were predicted for 160 Toxoplasma gondii proteins: 75 taken from published studies represented proteins known or expected to induce T-cell immune responses and 85 considered less likely vaccine candidates. The results show there is no universal set of rules that can be applied directly to binding scores to distinguish a vaccine from a non-vaccine candidate. We present, however, two proposed strategies exploiting binding scores that provide supporting evidence that a protein is likely to induce a T-cell immune response-one using random forest (a machine learning algorithm) with a 72% sensitivity and 82.4% specificity and the other, using amino acid conservation scores with a 74.6% sensitivity and 70.5% specificity when applied to the 160 benchmark proteins. More importantly, the binding score strategies are valuable evidence contributors to the overall in silico vaccine discovery pool of evidence.

EPMLR: sequence-based linear B-cell epitope prediction method using multiple linear regression

Background

B-cell epitopes have been studied extensively due to their immunological applications, such as peptide-based vaccine development, antibody production, and disease diagnosis and therapy. Despite several decades of research, the accurate prediction of linear B-cell epitopes has remained a challenging task.

Results

In this work, based on the antigen’s primary sequence information, a novel linear B-cell epitope prediction model was developed using the multiple linear regression (MLR). A 10-fold cross-validation test on a large non-redundant dataset was performed to evaluate the performance of our model. To alleviate the problem caused by the noise of negative dataset, 300 experiments utilizing 300 sub-datasets were performed. We achieved overall sensitivity of 81.8%, precision of 64.1% and area under the receiver operating characteristic curve (AUC) of 0.728.

Conclusions

We have presented a reliable method for the identification of linear B cell epitope using antigen’s primary sequence information. Moreover, a web server EPMLR has been developed for linear B-cell epitope prediction: http://www.bioinfo.tsinghua.edu.cn/epitope/EPMLR/.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-014-0414-y) contains supplementary material, which is available to authorized users.

Discovering a vaccine against neosporosis using computers: is it feasible?

A vaccine is urgently needed to prevent cattle neosporosis. This infectious disease is caused by the parasite Neospora caninum, a complex biological system with multifaceted life cycles. An in silico vaccine discovery approach attempts to transform digital abstractions of this system into adequate knowledge to predict candidates. Researchers need current information to implement such an approach, such as understanding evasion mechanisms of the immune system, type of immune response to elicit, availability of data and prediction programs, and statistical models to analyze predictions. Taken together, an in silico approach involves assembly of an intricate jigsaw of interdisciplinary and interdependent knowledge. In this review, we focus on the approach influencing vaccine development against Neospora caninum, which can be generalized to other pathogenic apicomplexans.

Vacceed: a high-throughput in silico vaccine candidate discovery pipeline for eukaryotic pathogens based on reverse vaccinology

We present Vacceed, a highly configurable and scalable framework designed to automate the process of high-throughput in silico vaccine candidate discovery for eukaryotic pathogens. Given thousands of protein sequences from the target pathogen as input, the main output is a ranked list of protein candidates determined by a set of machine learning algorithms. Vacceed has the potential to save time and money by reducing the number of false candidates allocated for laboratory validation. Vacceed, if required, can also predict protein sequences from the pathogen's genome.

AVAILABILITY AND IMPLEMENTATION

Vacceed is tested on Linux and can be freely downloaded from https://github.com/sgoodswe/vacceed/releases (includes a worked example with sample data). Vacceed User Guide can be obtained from https://github.com/sgoodswe/vacceed.

A novel strategy for classifying the output from an in silico vaccine discovery pipeline for eukaryotic pathogens using machine learning algorithms

BACKGROUND

An in silico vaccine discovery pipeline for eukaryotic pathogens typically consists of several computational tools to predict protein characteristics. The aim of the in silico approach to discovering subunit vaccines is to use predicted characteristics to identify proteins which are worthy of laboratory investigation. A major challenge is that these predictions are inherent with hidden inaccuracies and contradictions. This study focuses on how to reduce the number of false candidates using machine learning algorithms rather than relying on expensive laboratory validation. Proteins from Toxoplasma gondii, Plasmodium sp., and Caenorhabditis elegans were used as training and test datasets.

RESULTS

The results show that machine learning algorithms can effectively distinguish expected true from expected false vaccine candidates (with an average sensitivity and specificity of 0.97 and 0.98 respectively), for proteins observed to induce immune responses experimentally.

CONCLUSIONS

Vaccine candidates from an in silico approach can only be truly validated in a laboratory. Given any in silico output and appropriate training data, the number of false candidates allocated for validation can be dramatically reduced using a pool of machine learning algorithms. This will ultimately save time and money in the laboratory.

Vaccine design: emerging concepts and renewed optimism

Arguably, vaccination represents the single most effective medical intervention ever developed. Yet, vaccines have failed to provide any or adequate protection against some of the most significant global diseases. The pathogens responsible for these vaccine-recalcitrant diseases have properties that allow them to evade immune surveillance and misdirect or eliminate the immune response. However, genomic and systems biology tools, novel adjuvants and delivery systems, and refined molecular insight into protective immunity have started to redefine the landscape, and results from recent efficacy trials of HIV and malaria vaccines have instilled hope that another golden age of vaccines may be on the horizon.