An assessment on epitope prediction methods for protozoa genomes

Synthetic Peptides Selected by Immunoinformatics as Potential Tools for the Specific Diagnosis of Canine Visceral Leishmaniasis

Diagnosing canine visceral leishmaniasis (CVL) in Brazil faces challenges due to the limitations regarding the sensitivity and specificity of the current diagnostic protocol. Therefore, it is urgent to map new antigens or enhance the existing ones for future diagnostic techniques. Immunoinformatic tools are promising in the identification of new potential epitopes or antigen candidates. In this study, we evaluated peptides selected by epitope prediction for CVL serodiagnosis in ELISA assays. Ten B-cell epitopes were immunogenic in silico, but two peptides (peptides No. 45 and No. 48) showed the best performance in vitro. The selected peptides, both individually and in combination, were highly diagnostically accurate, with sensitivities ranging from 86.4% to 100% and with a specificity of approximately 90%. We observed that the combination of peptides showed better performance when compared to peptide alone, by detecting all asymptomatic dogs, showing lower cross-reactivity in sera from dogs with other canine infections, and did not detect vaccinated animals. Moreover, our data indicate the potential use of immunoinformatic tools associated with ELISA assays for the selection and evaluation of potential new targets, such as peptides, applied to the diagnosis of CVL.

Technological advancements in viral vector designing and optimization for therapeutic applications

Viral vector engineering is critical to the advancement of several sectors of biotechnology, gene therapy, and vaccine development. These vectors were produced from viruses, were employed to deliver therapeutic genes or to alter biological processes. The potential for viral vectors to improve the precision, safety, and efficiency of therapeutic interventions has boosted their demand. The dynamic interplay between technological advancements and computational tools in establishing the landscape of viral vector engineering and vector optimization for therapeutic reasons is discussed in this chapter. It also emphasizes the importance of in silico techniques in maximizing vector potential for therapeutics and many phases of viral vector engineering, from genomic analysis to computer modelling and advancements to improve precise gene delivery. High-throughput screening propels the expedited process of vector selection, and computational techniques to analyze complex omics data to further enhance vector capabilities have been discussed. As in silico models reveal insights into off-target effects and integration sites, vector safety (biodistribution and toxicity) remains a crucial part and bridges the gap between preclinical and clinical investigations. Despite the limitations, this chapter depicts a future in which technology and computing merge to catapult viral vector therapy into an era of boundless possibilities.

Quantitative label-free proteomic analysis of excretory-secretory proteins in different developmental stages of Trichinella spiralis

Trichinella spiralis (T. spiralis) is a zoonotic parasitic nematode with a unique life cycle, as all developmental stages are contained within a single host. Excretory-secretory (ES) proteins are the main targets of the interactions between T. spiralis and the host at different stages of development and are essential for parasite survival. However, the ES protein profiles of T. spiralis at different developmental stages have not been characterized. The proteomes of ES proteins from different developmental stages, namely, muscle larvae (ML), intestinal infective larvae (IIL), preadult (PA) 6 h, PA 30 h, adult (Ad) 3 days post-infection (dpi) and Ad 6 dpi, were characterized via label-free mass spectrometry analysis in combination with bioinformatics. A total of 1217 proteins were identified from 9341 unique peptides in all developmental stages, 590 of which were quantified and differentially expressed. GO classification and KEGG pathway analysis revealed that these proteins were important for the growth of the larvae and involved in energy metabolism. Moreover, the heat shock cognate 71 kDa protein was the centre of protein interactions at different developmental stages. The results of this study provide comprehensive proteomic data on ES proteins and reveal that these ES proteins were differentially expressed at different developmental stages. Differential proteins are associated with parasite survival and the host immune response and may be potential early diagnostic antigen or antiparasitic vaccine candidates.

Prediction of an immunogenic peptide ensemble and multi-subunit vaccine for Visceral leishmaniasis using bioinformatics approaches

Visceral Leishmaniasis (VL) is a neglected tropical disease of public health importance in the Indian subcontinent. Despite consistent elimination initiatives, the disease has not yet been eliminated and there is an increased risk of resurgence from active VL reservoirs including asymptomatic, post kala azar dermatitis leishmaniasis (PKDL) and HIV-VL co-infected individuals. To achieve complete elimination and sustain it in the long term, a prophylactic vaccine, which can elicit long lasting immunity, is desirable. In this study, we employed immunoinformatic tools to design a multi-subunit epitope vaccine for the Indian population by targeting antigenic secretory proteins screened from the Leishmania donovani proteome. Out of 8014 proteins, 277 secretory proteins were screened for their cellular location and proteomic evidence. Through NCBI BlastP, unique fragments of the proteins were cropped, and their antigenicity was evaluated. B-cell, HTL and CTL epitopes as well as IFN-ɣ, IL-17, and IL-10 inducers were predicted, manually mapped to the fragments and common regions were tabulated forming a peptide ensemble. The ensemble was evaluated for Class I MHC immunogenicity and toxicity. Further, immunogenic peptides were randomly selected and used to design vaccine constructs. Eight vaccine constructs were generated by linking random peptides with GS linkers. Synthetic TLR-4 agonist, RS09 was used as an adjuvant and linked with the constructs using EAAK linkers. The predicted population coverage of the constructs was ∼99.8 % in the Indian as well as South Asian populations. The most antigenic, nontoxic, non-allergic construct was chosen for the prediction of secondary and tertiary structures. The 3D structures were refined and analyzed using Ramachandran plot and Z-scores. The construct was docked with TLR-4 receptor. Molecular dynamic simulation was performed to check for the stability of the docked complex. Comparative in silico immune simulation studies showed that the predicted construct elicited humoral and cell-mediated immunity in human host comparable to that elicited by Leish-F3, which is a promising vaccine candidate for human VL.

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.

CD8TCEI-EukPath: A Novel Predictor to Rapidly Identify CD8+ T-Cell Epitopes of Eukaryotic Pathogens Using a Hybrid Feature Selection Approach

Computational prediction to screen potential vaccine candidates has been proven to be a reliable way to provide guarantees for vaccine discovery in infectious diseases. As an important class of organisms causing infectious diseases, pathogenic eukaryotes (such as parasitic protozoans) have evolved the ability to colonize a wide range of hosts, including humans and animals; meanwhile, protective vaccines are urgently needed. Inspired by the immunological idea that pathogen-derived epitopes are able to mediate the CD8⁺ T-cell-related host adaptive immune response and with the available positive and negative CD8⁺ T-cell epitopes (TCEs), we proposed a novel predictor called CD8TCEI-EukPath to detect CD8⁺ TCEs of eukaryotic pathogens. Our method integrated multiple amino acid sequence-based hybrid features, employed a well-established feature selection technique, and eventually built an efficient machine learning classifier to differentiate CD8⁺ TCEs from non-CD8⁺ TCEs. Based on the feature selection results, 520 optimal hybrid features were used for modeling by utilizing the LightGBM algorithm. CD8TCEI-EukPath achieved impressive performance, with an accuracy of 79.255% in ten-fold cross-validation and an accuracy of 78.169% in the independent test. Collectively, CD8TCEI-EukPath will contribute to rapidly screening epitope-based vaccine candidates, particularly from large peptide-coding datasets. To conduct the prediction of CD8⁺ TCEs conveniently, an online web server is freely accessible (http://lab.malab.cn/∼hrs/CD8TCEI-EukPath/).

Prediction of B cell and T-helper cell epitopes candidates of bovine leukaemia virus (BLV) by in silico approach

The bovine leukaemia virus (BLV) is a retrovirus responsible for enzootic bovine leukaemia (EBL) disease, the most common cattle disease leading to high annual economic losses to the cattle breeding industry. Virus monitoring among the sheep and cattle herds is usually done by vaccination. Inactivated virus vaccines can partially protect the livestock from viral challenge. However, vaccinated animals are likely to be infected. So, there is an essential need for producing vaccine by other methods. Gp60 SU, encoded by Env gene, is the surface glycoprotein of BLV detected to be the major target for the host immunity against the virus. Different stages were performed to predict the potential B and T-helper cell epitopes. The general framework of the method includes retrieving the amino acid sequence of gp60 SU, conducting the sequence alignment, getting the entropy plot, retrieving the previously found epitopes, predicting the hydropathy parameters, modelling the tertiary structure of the glycoprotein, minimizing the structure energy, validating the model by Ramachandran plot, predicting the linear and discontinuous epitopes by various servers and eventually choosing the consensus immunogenic regions. Ramachandran plot scrutiny has demonstrated that the modelled prediction is accurate and suitable. By surveying overlaps of various results, 4 and 2 immunogenic regions were selected as linear and conformational epitopes respectively. Amino acids 35-53, 67-97, 288-302 and 410-421 and those of numbers 37-58 and 72-100 were the regions selected as linear and conformational epitopes respectively. The tertiary structure of the final epitope was modelled as well. A comparison of the predicted epitopes structure with that of gp60 SU envelope, illustrated that the tertiary structure of these epitopes does not change after being separated from the primary complete one. The present achievements will lead to a better interpretation of the antigen-antibody interactions against gp60 in the designing process of safe and efficient vaccines.

Recent advances and new strategies in Leishmaniasis diagnosis

Leishmaniasis is a set of complex and multifaceted syndromes, with different clinical manifestations, caused by different species of the genus Leishmania spp. that can be characterized by at least four syndromes: visceral leishmaniasis (VL, also known as kala-azar), post-kala-azar dermal leishmaniasis (PKDL), cutaneous leishmaniasis (CL), and mucocutaneous leishmaniasis (MCL). Among the most serious clinical forms, VL stands out, which causes the death of around 59,000 people annually. Fast and accurate diagnosis in VL is essential to reduce the disease's morbidity and mortality. There are a large number of diagnostic tests for leishmaniasis, however they do cross-react with other protozoa and their sensitivity changes according to the clinical form of the disease. Thus, it is essential and necessary to provide a diagnosis that is sufficiently sensitive to detect asymptomatic infected individuals and specific to discriminate individuals with other infectious and parasitic diseases, thus enabling more accurate diagnostic tools than those currently used. In this context, the aim of this review is to summarize the conventional diagnostic tools and point out the new advances and strategies on visceral and cutaneous leishmaniasis diagnosis.

Computational B-cell epitope identification and production of neutralizing murine antibodies against Atroxlysin-I

Epitope identification is essential for developing effective antibodies that can detect and neutralize bioactive proteins. Computational prediction is a valuable and time-saving alternative for experimental identification. Current computational methods for epitope prediction are underused and undervalued due to their high false positive rate. In this work, we targeted common properties of linear B-cell epitopes identified in an individual protein class (metalloendopeptidases) and introduced an alternative method to reduce the false positive rate and increase accuracy, proposing to restrict predictive models to a single specific protein class. For this purpose, curated epitope sequences from metalloendopeptidases were transformed into frame-shifted Kmers (3 to 15 amino acid residues long). These Kmers were decomposed into a matrix of biochemical attributes and used to train a decision tree classifier. The resulting prediction model showed a lower false positive rate and greater area under the curve when compared to state-of-the-art methods. Our predictions were used for synthesizing peptides mimicking the predicted epitopes for immunization of mice. A predicted linear epitope that was previously undetected by an experimental immunoassay was able to induce neutralizing-antibody production in mice. Therefore, we present an improved prediction alternative and show that computationally identified epitopes can go undetected during experimental mapping.

Peptide Vaccines for Leishmaniasis

Due to an increase in the incidence of leishmaniases worldwide, the development of new strategies such as prophylactic vaccines to prevent infection and decrease the disease have become a high priority. Classic vaccines against leishmaniases were based on live or attenuated parasites or their subunits. Nevertheless, the use of whole parasite or their subunits for vaccine production has numerous disadvantages. Therefore, the use of Leishmania peptides to design more specific vaccines against leishmaniases seems promising. Moreover, peptides have several benefits in comparison with other kinds of antigens, for instance, good stability, absence of potentially damaging materials, antigen low complexity, and low-cost to scale up. By contrast, peptides are poor immunogenic alone, and they need to be delivered correctly. In this context, several approaches described in this review are useful to solve these drawbacks. Approaches, such as, peptides in combination with potent adjuvants, cellular vaccinations, adenovirus, polyepitopes, or DNA vaccines have been used to develop peptide-based vaccines. Recent advancements in peptide vaccine design, chimeric, or polypeptide vaccines and nanovaccines based on particles attached or formulated with antigenic components or peptides have been increasingly employed to drive a specific immune response. In this review, we briefly summarize the old, current, and future stands on peptide-based vaccines, describing the disadvantages and benefits associated with them. We also propose possible approaches to overcome the related weaknesses of synthetic vaccines and suggest future guidelines for their development.

Small Myristoylated Protein-3, Identified as a Potential Virulence Factor in Leishmania amazonensis, Proves to be a Protective Antigen against Visceral Leishmaniasis

In a proteomics approach conducted with Leishmania amazonensis, parasite proteins showed either an increase or a decrease in their expression content during extensive in vitro cultivation, and were related to the survival and the infectivity of the parasites, respectively. In the current study, a computational screening was performed to predict virulence factors among these molecules. Three proteins were selected, one of which presented no homology to human proteins. This candidate, namely small myristoylated protein-3 (SMP-3), was cloned, and its recombinant version (rSMP-3) was used to stimulate peripheral blood mononuclear cells (PBMCs) from healthy subjects living in an endemic area of leishmaniasis and from visceral leishmaniasis patients. Results showed high interferon-γ (IFN-γ) production and low levels of interleukin 10 (IL-10) in the cell supernatants. An in vivo experiment was then conducted on BALB/c mice, which were immunized with rSMP-3/saponin and later challenged with Leishmania infantum promastigotes. The rSMP-3/saponin combination induced high production of protein-specific IFN-γ, IL-12, and granulocyte-macrophage colony-stimulating factor (GM-CSF) by the spleen cells of the immunized mice. This pattern was associated with protection, which was characterized by a significant reduction in the parasite load in distinct organs of the animals. Altogether, these results have revealed that this new virulence factor is immunogenic in both mice and humans, and have proven its protective efficacy against visceral leishmaniasis in a murine model.

Selecting targets for the diagnosis of Schistosoma mansoni infection: An integrative approach using multi-omic and immunoinformatics data

In order to effectively control and monitor schistosomiasis, new diagnostic methods are essential. Taking advantage of computational approaches provided by immunoinformatics and considering the availability of Schistosoma mansoni predicted proteome information, candidate antigens of schistosomiasis were selected and used in immunodiagnosis tests based on Enzime-linked Immunosorbent Assay (ELISA). The computational selection strategy was based on signal peptide prediction; low similarity to human proteins; B- and T-cell epitope prediction; location and expression in different parasite life stages within definitive host. Results of the above-mentioned analysis were parsed to extract meaningful biological information and loaded into a relational database developed to integrate them. In the end, seven proteins were selected and one B-cell linear epitope from each one of them was selected using B-cell epitope score and the presence of intrinsically disordered regions (IDRs). These predicted epitopes generated synthetic peptides that were used in ELISA assays to validate the rational strategy of in silico selection. ELISA was performed using sera from residents of areas of low endemicity for S. mansoni infection and also from healthy donors (HD), not living in an endemic area for schistosomiasis. Discrimination of negative (NEG) and positive (INF) individuals from endemic areas was performed using parasitological and molecular methods. All infected individuals were treated with praziquantel, and serum samples were obtained from them 30 and 180 days post-treatment (30DPT and 180DPT). Results revealed higher IgG levels in INF group than in HD and NEG groups when peptides 1, 3, 4, 5 and 7 were used. Moreover, using peptide 5, ELISA achieved the best performance, since it could discriminate between individuals living in an endemic area that were actively infected from those that were not (NEG, 30DPT, 180DPT groups). Our experimental results also indicate that the computational prediction approach developed is feasible for identifying promising candidates for the diagnosis of schistosomiasis and other diseases.

In silico analysis and in vitro evaluation of immunogenic and immunomodulatory properties of promiscuous peptides derived from Leishmania infantum eukaryotic initiation factor

It is generally considered as imperative the ability to control leishmaniasis through the development of a protective vaccine capable of inducing long-lasting and protective cell-mediated immune responses. In this current study, we demonstrated potential epitopes that bind to H2 MHC class I and II molecules by conducting the in silico analysis of Leishmania infantum eukaryotic Initiation Factor (LieIF) protein, using online available algorithms. Moreover, we synthesized five peptides (16-18 amino acids long) which are part of the N-terminal portion of LieIF and contain promising MHC class I and II-restricted epitopes and afterwards, their predicted immunogenicity was evaluated in vitro by monitoring peptide-specific T-cell responses. Additionally, the immunomodulatory properties of these peptides were investigated in vitro by exploring their potential of inducing phenotypic maturation and functional differentiation of murine Bone-Marrow derived Dendritic Cells (BM-DCs). It was revealed by our data that all the synthetic peptides predicted for H2 alleles; present the property of immunogenicity. Among the synthetic peptides which contained T-cell epitopes, the peptide 52-68 aa (LieIF_2) exhibited immunomodulatory properties with the larger potential. LieIF_2-pulsed BM-DCs up-regulated the expression of the co-stimulatory surface molecules CD80 and CD86, as well as the production of the proinflammatory cytokine TNF-α and of the Th1-polarizing cytokines IL-12 and IFN-γ. The aforementioned data suggest that selected parts of LieIF could be used to develop innovative subunit protective vaccines able to induce effective immunity mediated by MHC class I-restricted as well as class II-restricted T-cell responses.

Immunoinformatics Features Linked to Leishmania Vaccine Development: Data Integration of Experimental and In Silico Studies

Leishmaniasis is a wide-spectrum disease caused by parasites from Leishmania genus. There is no human vaccine available and it is considered by many studies as apotential effective tool for disease control. To discover novel antigens, computational programs have been used in reverse vaccinology strategies. In this work, we developed a validation antigen approach that integrates prediction of B and T cell epitopes, analysis of Protein-Protein Interaction (PPI) networks and metabolic pathways. We selected twenty candidate proteins from Leishmania tested in murine model, with experimental outcome published in the literature. The predictions for CD4⁺ and CD8⁺ T cell epitopes were correlated with protection in experimental outcomes. We also mapped immunogenic proteins on PPI networks in order to find Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways associated with them. Our results suggest that non-protective antigens have lowest frequency of predicted T CD4⁺ and T CD8⁺ epitopes, compared with protective ones. T CD4⁺ and T CD8⁺ cells are more related to leishmaniasis protection in experimental outcomes than B cell predicted epitopes. Considering KEGG analysis, the proteins considered protective are connected to nodes with few pathways, including those associated with ribosome biosynthesis and purine metabolism.

Combination of In Silico Methods in the Search for Potential CD4(+) and CD8(+) T Cell Epitopes in the Proteome of Leishmania braziliensis

The leishmaniases are neglected tropical diseases widespread throughout the globe, which are caused by protozoans from the genus Leishmania and are transmitted by infected phlebotomine flies. The development of a safe and effective vaccine against these diseases has been seen as the best alternative to control and reduce the number of cases. To support vaccine development, this work has applied an in silico approach to search for high potential peptide epitopes able to bind to different major histocompatibility complex Class I and Class II (MHC I and MHC II) molecules from different human populations. First, the predicted proteome of Leishmania braziliensis was compared and analyzed by modern linear programs to find epitopes with the capacity to trigger an immune response. This approach resulted in thousands of epitopes derived from 8,000 proteins conserved among different Leishmania species. Epitopes from proteins similar to those found in host species were excluded, and epitopes from proteins conserved between different Leishmania species and belonging to surface proteins were preferentially selected. The resulting epitopes were then clustered, to avoid redundancies, resulting in a total of 230 individual epitopes for MHC I and 2,319 for MHC II. These were used for molecular modeling and docking with MHC structures retrieved from the Protein Data Bank. Molecular docking then ranked epitopes based on their predicted binding affinity to both MHC I and II. Peptides corresponding to the top 10 ranked epitopes were synthesized and evaluated in vitro for their capacity to stimulate peripheral blood mononuclear cells (PBMC) from post-treated cutaneous leishmaniasis patients, with PBMC from healthy donors used as control. From the 10 peptides tested, 50% showed to be immunogenic and capable to stimulate the proliferation of lymphocytes from recovered individuals.

Post-Genomics and Vaccine Improvement for Leishmania

Leishmaniasis is a parasitic disease that primarily affects Asia, Africa, South America, and the Mediterranean basin. Despite extensive efforts to develop an effective prophylactic vaccine, no promising vaccine is available yet. However, recent advancements in computational vaccinology on the one hand and genome sequencing approaches on the other have generated new hopes in vaccine development. Computational genome mining for new vaccine candidates is known as reverse vaccinology and is believed to further extend the current list of Leishmania vaccine candidates. Reverse vaccinology can also reduce the intrinsic risks associated with live attenuated vaccines. Individual epitopes arranged in tandem as polytopes are also a possible outcome of reverse genome mining. Here, we will briefly compare reverse vaccinology with conventional vaccinology in respect to Leishmania vaccine, and we will discuss how it influences the aforementioned topics. We will also introduce new in vivo models that will bridge the gap between human and laboratory animal models in future studies.

Epitope mapping and identification of amino acids critical for mouse IgG-binding to linear epitopes on Gly m Bd 28K

Gly m Bd 28K is one of the major allergens in soybeans, but there is limited information on its IgG-binding epitopes. Thirty-four overlapping peptides that covered the entire sequence of Gly m Bd 28K were synthesized, and 3 monoclonal antibodies against Gly m Bd 28K were utilized to identify the IgG-binding regions of Gly m Bd 28K. Three dominant peptides corresponding to (28)GDKKSPKSLFLMSNS(42)(G28-S42), (56)LKSHGGRIFYRHMHI(70)(L56-I70), and (154)ETFQSFYIGGGANSH(168)(E154-H168) were recognized. L56-I70 is the most important epitope, and a competitive ELISA indicated that it could inhibit the binding of monoclonal antibody to Gly m Bd 28K protein. Alanine scanning of L56-I70 documented that F64, Y65, and R66 were the critical amino acids of this epitope. Two bioinformatics tools, ABCpred and BepiPred, were used to predict the epitopes of Gly m Bd 28K, and the predictions were compared with the epitopes that we had located by monoclonal antibodies.

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.

Prediction of CD8+ Epitopes in Leishmania braziliensis Proteins Using EPIBOT: In Silico Search and In Vivo Validation

Background

Leishmaniasis is caused by intracellular Leishmania parasites that induce a T-cell mediated response associated with recognition of CD4⁺ and CD8⁺ T cell Line 1Lineepitopes. Identification of CD8⁺ antigenic determinants is crucial for vaccine and therapy development. Herein, we developed an open-source software dedicated to search and compile data obtained from currently available on line prediction algorithms.

Methodology/Principal Findings

We developed a two-phase algorithm and implemented in an open source software called EPIBOT, that consolidates the results obtained with single prediction algorithms, generating a final output in which epitopes are ranked. EPIBOT was initially trained using a set of 831 known epitopes from 397 proteins from IEDB. We then screened 63 Leishmania braziliensis vaccine candidates with the EPIBOT trained tool to search for CD8⁺ T cell epitopes. A proof-of-concept experiment was conducted with the top eight CD8⁺ epitopes, elected by EPIBOT. To do this, the elected peptides were synthesized and validated for their in vivo cytotoxicity. Among the tested epitopes, three were able to induce lysis of pulsed-target cells.

Conclusion

Our results show that EPIBOT can successfully search across existing prediction tools, generating a compiled list of candidate CD8⁺ epitopes. This software is fast and a simple search engine that can be customized to search over different MHC alleles or HLA haplotypes.

An overview of bioinformatics tools for epitope prediction: implications on vaccine development

Exploitation of recombinant DNA and sequencing technologies has led to a new concept in vaccination in which isolated epitopes, capable of stimulating a specific immune response, have been identified and used to achieve advanced vaccine formulations; replacing those constituted by whole pathogen-formulations. In this context, bioinformatics approaches play a critical role on analyzing multiple genomes to select the protective epitopes in silico. It is conceived that cocktails of defined epitopes or chimeric protein arrangements, including the target epitopes, may provide a rationale design capable to elicit convenient humoral or cellular immune responses. This review presents a comprehensive compilation of the most advantageous online immunological software and searchable, in order to facilitate the design and development of vaccines. An outlook on how these tools are supporting vaccine development is presented. HIV and influenza have been taken as examples of promising developments on vaccination against hypervariable viruses. Perspectives in this field are also envisioned.

From genome screening to creation of vaccine against Trypanosoma cruzi by use of immunoinformatics

Chagas disease is caused by the protozoan parasite Trypanosoma cruzi, and activation of CD8(+) T cells is crucial for a protective immune response. Therefore, the identification of antigens with major histocompatibility complex class I epitopes is a crucial step for vaccine development against T. cruzi. Our aim was to identify novel antigens and epitopes by immunoinformatics analysis of the parasite proteome (12 969 proteins) and to validate their immunotherapeutic potential in infected mice. We identified 172 predicted epitopes, using NetMHC and RANKPEP. The corresponding protein sequences were reanalyzed to generate a consensus prediction, and 26 epitopes were selected for in vivo validation. The interferon γ (IFN-γ) recall response of splenocytes from T. cruzi-infected mice confirmed that 10 of 26 epitopes (38%) induced IFN-γ production. The immunotherapeutic potential of a mixture of all 10 peptides was evaluated in infected mice. The therapeutic vaccine was able to control T. cruzi infection, as evidenced by reduced parasitemia, cardiac tissue inflammation, and parasite burden and increased survival. These findings illustrate the benefits of this approach for the rapid development of a vaccine against pathogens with large genomes. The identified peptides and the proteins from which they are derived are excellent candidates for the development of a vaccine against T. cruzi.

Leishmania spp. Proteome Data Sets: A Comprehensive Resource for Vaccine Development to Target Visceral Leishmaniasis

Visceral leishmaniasis is a neglected infectious disease caused primarily by Leishmania donovani and Leishmania infantum protozoan parasites. A significant number of infections take a fatal course. Drug therapy is available but still costly and parasites resistant to first line drugs are observed. Despite many years of trial no commercial vaccine is available to date. However, development of a cost effective, needle-independent vaccine remains a high priority. Reverse vaccinology has attracted much attention since the term has been coined and the approach tested by Rappuoli and colleagues. This in silico selection of antigens from genomic and proteomic data sets was also adapted to aim at developing an anti-Leishmania vaccine. Here, an analysis of the efforts is attempted and the challenges to be overcome by these endeavors are discussed. Strategies that led to successful identification of antigens will be illustrated. Furthermore, these efforts are viewed in the context of anticipated modes of action of effective anti-Leishmania immune responses to highlight possible advantages and shortcomings.

Building classifier ensembles for B-cell epitope prediction

Identification of B-cell epitopes in target antigens is a critical step in epitope-driven vaccine design, immunodiagnostic tests, and antibody production. B-cell epitopes could be linear, i.e., a contiguous amino acid sequence fragment of an antigen, or conformational, i.e., amino acids that are often not contiguous in the primary sequence but appear in close proximity within the folded 3D antigen structure. Numerous computational methods have been proposed for predicting both types of B-cell epitopes. However, the development of tools for reliably predicting B-cell epitopes remains a major challenge in immunoinformatics.Classifier ensembles a promising approach for combining a set of classifiers such that the overall performance of the resulting ensemble is better than the predictive performance of the best individual classifier. In this chapter, we show how to build a classifier ensemble for improved prediction of linear B-cell epitopes. The method can be easily adapted to build classifier ensembles for predicting conformational epitopes.