In silico prediction of binding of promiscuous peptides to multiple MHC class-II molecules identifies the Th1 cell epitopes from secreted and transmembrane proteins of Schistosoma japonicum in BALB/c mice

An Overview of Current Uses and Future Opportunities for Computer-Assisted Design of Vaccines for Neglected Tropical Diseases

Neglected tropical diseases are infectious diseases that impose high morbidity and mortality rates over 1.5 billion people worldwide. Originally restricted to tropical and subtropical regions, changing climate conditions have increased their potential to emerge elsewhere. Control of their impact suffers from shortages like poor epidemiological surveillance or irregular drug distribution, and some NTDs still lack of appropriate diagnostics and/or efficient therapeutics. For these, availability of vaccines to prevent new infections, or the worsening of those already established, would mean a major breakthrough. However, only dengue and rabies count with approved vaccines at present. Herein, we review the state-of-the-art of vaccination strategies for NTDs, setting the focus on third generation vaccines and the concept of reverse vaccinology. Its capability to address pathogens´ biological complexity, likely contributing to save developmental costs is discussed. The use of computational tools is a fundamental aid to analyze increasingly large datasets aimed at designing vaccine candidates with the highest, possibly, opportunities to succeed. Ultimately, we identify and analyze those studies that took an in silico approach to find vaccine candidates, and experimentally assessed their immunogenicity and/or protection capabilities.

Designing and evaluation of an antibody-targeted chimeric recombinant vaccine encoding Shigella flexneri outer membrane antigens

Shigellosis is one of the most common diseases found in the developing countries, especially those countries that are prone flood. The causative agent for this disease is the Shigella species. This organism is one of the third most common enteropathogens responsible for childhood diarrhea. Since Shigella can survive gastric acidity and is an intracellular pathogen, it becomes difficult to treat. Also, uncontrolled use of antibiotics has led to development of resistant strains which poses a threat to public health. Therefore, there is a need for long term control of Shigella infection which can be achieved by designing a proper and effective vaccine. In this study, emphasis was made on designing a candidate that could elicit both B-cell and T-cell immune response. Hence B- and T-cell epitopes of outer membrane channel protein (OM) and putative lipoprotein (PL) from S. flexneri 2a were computationally predicted using immunoinformatics approach and a chimeric construct (chimeric-OP) containing the immunogenic epitopes selected from OM and PL was designed, cloned and expressed in E. coli system. The immunogenicity of the recombinant chimeric-OP was assessed using Shigella antigen infected rabbit antibody. The result showed that the chimeric-OP was a synthetic peptide candidate suitable for the development of vaccine and immunodiagnostics against Shigella infection.

Refining wet lab experiments with in silico searches: A rational quest for diagnostic peptides in visceral leishmaniasis

Background

The search for diagnostic biomarkers has been profiting from a growing number of high quality sequenced genomes and freely available bioinformatic tools. These can be combined with wet lab experiments for a rational search. Improved, point-of-care diagnostic tests for visceral leishmaniasis (VL), early case detection and surveillance are required. Previous investigations demonstrated the potential of IgG1 as a biomarker for monitoring clinical status in rapid diagnostic tests (RDTs), although using a crude lysate antigen (CLA) as capturing antigen. Replacing the CLA by specific antigens would lead to more robust RDTs.

Methodology

Immunoblots revealed L. donovani protein bands detected by IgG1 from VL patients. Upon confident identification of these antigens by mass spectrometry (MS), we searched for evidence of constitutive protein expression and presence of antigenic domains or high accessibility to B-cells. Selected candidates had their linear epitopes mapped with in silico algorithms. Multiple high-scoring predicted epitopes from the shortlisted proteins were screened in peptide arrays. The most promising candidate was tested in RDT prototypes using VL and nonendemic healthy control (NEHC) patient sera.

Results

Over 90% of the proteins identified from the immunoblots did not satisfy the selection criteria and were excluded from the downstream epitope mapping. Screening of predicted epitope peptides from the shortlisted proteins identified the most reactive, for which the sensitivity for IgG1 was 84% (95% CI 60—97%) with Sudanese VL sera on RDT prototypes. None of the sera from NEHCs were positive.

Conclusion

We employed in silico searches to reduce drastically the output of wet lab experiments, focusing on promising candidates containing selected protein features. By predicting epitopes in silico we screened a large number of peptides using arrays, identifying the most promising one, for which IgG1 sensitivity and specificity, with limited sample size, supported this proof of concept strategy for diagnostics discovery, which can be applied to the development of more robust IgG1 RDTs for monitoring clinical status in VL.

Visceral leishmaniasis (VL) is a neglected tropical disease caused by protozoan parasites of the Leishmania donovani complex. Without treatment, VL is fatal. Although diagnostic techniques, mainly based on the detection of anti-Leishmania antibodies are available, invasive procedures such as microscopy from spleen or bone marrow aspirates are still required for the diagnosis of seronegative VL suspects, for the detection of recurrent cases and to confirm cure after successful treatment. Previous investigations showed the potential of IgG1 as a biomarker of post-chemotherapeutic relapse for VL in rapid diagnostic tests (RDTs) sensitised with crude lysate antigen (CLA). Here we employed in silico tools to search for desired protein features in a large number of L. donovani antigens detected by human IgG1 in western blots. We then employed prediction algorithms to profile epitopes from the shortlisted proteins. We screened a panel of high-scoring peptides in a high-throughput manner using arrays, with low reagent consumption. The most reactive peptide was adapted to RDTs, showing promising results of both sensitivity and specificity. This peptide has the potential of replacing the CLAs in IgG1 RDTs. Thus we believe that in silico tools can be used to optimise wet lab experiments for a rational search of biomarkers.

Immune-Informatic Analysis and Design of Peptide Vaccine From Multi-epitopes Against Corynebacterium pseudotuberculosis

Caseous lymphadenitis (CLA) is a disease caused by Corynebacterium pseudotuberculosis bacteria that affects sheep and goats. The absence of a serologic diagnose is a factor that contributes for the disease dissemination, and due to the formation of granuloma, the treatment is very expensive. Therefore, prophylaxis is the approach with best cost-benefit relation; however, it still lacks an effective vaccine. In this sense, this work seeks to apply bioinformatic tools to design an effective vaccine against CLA, using CP40 protein as standard for the design of immunodominant epitopes, from which a total of 6 sequences were obtained, varying from 10 to 16 amino acid residues. The evaluation of different properties of the vaccines showed that the vaccine is a potent and nonallergenic antigen remaining stable in a wide range of temperatures. The initial tertiary structure of the vaccine was then predicted and a model selected. Later, the process of CP40 protein and TLR2 receptor binding was performed, presenting interaction with this receptor, which plays an important role in the activation of the immune response.

Rational selection of immunodominant and preserved epitope Sm043300e from Schistosoma mansoni and design of a chimeric molecule for biotechnological purposes

Human schistosomiasis is a neglected tropical disease of great importance in public health. A large number of people are infected with schistosomiasis, making vaccine development and effective diagnosis important control strategies. A rational epitope prediction workflow using Schistosoma mansoni hypothetical proteins was previously presented by our group, and an improvement to that approach is presented here. Briefly, immunodominant epitopes from parasite membrane proteins were predicted by reverse vaccinology strategy with additional in silico analysis. Furthermore, epitope recognition was evaluated using sera of individuals infected with S. mansoni. The epitope that stood out in both in silico and in vitro assays was used to compose a rational chimeric molecule to improve immune response activation. Out of 2185 transmembrane proteins, four epitopes with high binding affinities for human and mouse MHCII molecules were selected through computational screening. These epitopes were synthesized to evaluate their ability to induce TCD4+ lymphocyte proliferation in mice. Sm204830e and Sm043300e induced significant TCD4+ proliferation. Both epitopes were submitted to enzyme-linked immunosorbent assay to evaluate their recognition by IgG antibodies from the sera of infected individuals, and epitope Sm043300 was significantly recognized in most sera samples. Epitope Sm043300 also showed good affinity for human MHCII molecules in molecular docking, and its sequence is curiously highly conserved in four S. mansoni proteins, all of which are described as G-protein-coupled receptors. In addition, we have demonstrated the feasibility of incorporating this epitope, which showed low similarity to human sequences, into a chimeric molecule. The stability of the molecule was evaluated by molecular modeling aimed at future molecule production for use in diagnosis and vaccination trials.

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.

Major histocompatibility complex linked databases and prediction tools for designing vaccines

Presently, the major histocompatibility complex (MHC) is receiving considerable interest owing to its remarkable role in antigen presentation and vaccine design. The specific databases and prediction approaches related to MHC sequences, structures and binding/nonbinding peptides have been aggressively developed in the past two decades with their own benchmarks and standards. Before using these databases and prediction tools, it is important to analyze why and how the tools are constructed along with their strengths and limitations. The current review presents insights into web-based immunological bioinformatics resources that include searchable databases of MHC sequences, epitopes and prediction tools that are linked to MHC based vaccine design, including population coverage analysis. In T cell epitope forecasts, MHC class I binding predictions are very accurate for most of the identified MHC alleles. However, these predictions could be further improved by integrating proteasome cleavage (in conjugation with transporter associated with antigen processing (TAP) binding) prediction, as well as T cell receptor binding prediction. On the other hand, MHC class II restricted epitope predictions display relatively low accuracy compared to MHC class I. To date, pan-specific tools have been developed, which not only deliver significantly improved predictions in terms of accuracy, but also in terms of the coverage of MHC alleles and supertypes. In addition, structural modeling and simulation systems for peptide-MHC complexes enable the molecular-level investigation of immune processes. Finally, epitope prediction tools, and their assessments and guidelines, have been presented to immunologist for the design of novel vaccine and diagnostics.

Evolution, molecular epidemiology and perspectives on the research of taeniid parasites with special emphasis on Taenia solium

Human cysticercosis is known since old historical times in Greece and China; however, human infections by tapeworms have accompanied human beings for more that hundred thousand years. The disease is tightly bound to poverty and lack of hygiene, and has been eradicated in developed countries, but continues being a public health problem in developing countries of Latin-American, Sub-Saharan Africa and Asia, and is also remerging in a number of non endemic countries. It is considered a neglected disease. Here we revise a number of key scientific contributions on taeniid biology that open new avenues for more effective approaches to the control of cysticercosis. The evolution of flatworms and class Cestoda is analyzed, with special emphasis on the emergence of taeniid parasites and the colonization of the human species by tapeworms. The complex molecular host-parasite interplay in this relationship as result of co-evolution between two distantly related organisms. The relevant host and parasite's factors, in the prospect of identifying species-specific molecular markers useful in epidemiological studies carried out in endemic countries. The new possibilities arising with the characterization of the genomes for several species of tapeworms, including a deeper understanding of these organisms, as well as improved tools for diagnosis, vaccination and drug treatment. The need to revise the current control and management strategies for this tropical neglected disease.

Molecular characterization and identification of Th1 epitopes of a Schistosoma japonicum protein similar to prosaposin

The tegument of schistosomula contains T cell antigens that might simulate the protective mechanisms of the radiation-attenuated vaccine in a mouse model of schistosomiasis. Immune mechanisms mediated by the CD4+ Th1 response are important in the RAV model. To rapidly identify Th1 epitopes in molecules from the Schistosoma japonicum schistosomula tegument, this study analyzed S. japonicum proteomics data. Preliminary experiments identified a protein similar to prosaposin (SjPSAP) from the tegument of schistosomula. We confirmed that SjPSAP was present in the tegument of the parasite using an indirect immunofluorescence assay. We then identified Th cell epitopes in SjPSAP using in silico prediction combined with experimental validation. From the SjPSAP sequence, we used several algorithms to predict 11 promiscuous Th cell epitopes that might bind to both murine and human MHC class II molecules. To validate the in silico predictions, proliferation and cytokine production profiles of spleen lymphocytes from BALB/c mice immunized with the 11 predicted peptides were measured in vitro using a modified methyl thiazolyl tetrazolium assay and flow cytometry. The results showed that 4 of the 11 predicted peptides induced a recall CD4+ Th1 response in vitro. We measured direct binding of the four peptides predicted to induce a response to antigen-presenting cells from BALB/c mice using a fluorometric method and found that the peptides bound to both I-Ad and I-Ed mouse molecules. These results demonstrated that potentially protective Th1-type epitopes in SjPSAP molecules could be identified rapidly by combining in silico prediction with experimental validation. This strategy could be a fast method for identifying Th1 epitopes in a schistosoma antigen with features such as large size or poor expression of recombinant antigens.

Identification of a vaccine against schistosomiasis using bioinformatics and molecular modeling tools

Schistosomiasis is a serious public health problem in Brazil and worldwide. Although the drugs used to treatment schistosomiasis are effective, the disease continues to expand in all endemic countries due to constant reinfection, poor sanitation, and the lack of effective programs for disease control. However, advances generated through genome projects have provided important information that has improved the understanding of the biology of this parasite. These advances, associated with the advent of bioinformatic analysis, are becoming an important tool in reverse vaccinology. Through database access to the DNA and protein sequences of Schistosoma mansoni and the use of bioinformatics programs, fourteen epitopes were identified. Five epitopes were obtained from proteins whose immunogenic potential had already been assessed in other studies (KP), and nine whose immunogenic potential is unknown (UP). To improve stimulation of the host immune system, the selected epitopes were modeled with a sugar moiety. After this addition, all of the epitopes showed structures similar to those observed in the native proteins, but only eleven of the peptides presented thermodynamically stable structures. Prediction analysis and molecular modeling showed that the glycopeptides presented here are important targets in the search for a vaccine against schistosomiasis. Additionally, they suggest that these molecules may be used in immunological assays to evaluate the level of protection, the effect on pathology reduction and the profile of cytokines and antibodies induced by them.

In silico identification of epitopes in Mycobacterium avium subsp. paratuberculosis proteins that were upregulated under stress conditions

Johne's disease in ruminants is caused by Mycobacterium avium subsp. paratuberculosis. Diagnosis of M. avium subsp. paratuberculosis infection is difficult, especially in the early stages. To date, ideal antigen candidates are not available for efficient immunization or immunodiagnosis. This study reports the in silico selection and subsequent analysis of epitopes of M. avium subsp. paratuberculosis proteins that were found to be upregulated under stress conditions as a means to identify immunogenic candidate proteins. Previous studies have reported differential regulation of proteins when M. avium subsp. paratuberculosis is exposed to stressors which induce a response similar to dormancy. Dormancy may be involved in evading host defense mechanisms, and the host may also mount an immune response against these proteins. Twenty-five M. avium subsp. paratuberculosis proteins that were previously identified as being upregulated under in vitro stress conditions were analyzed for B and T cell epitopes by use of the prediction tools at the Immune Epitope Database and Analysis Resource. Major histocompatibility complex class I T cell epitopes were predicted using an artificial neural network method, and class II T cell epitopes were predicted using the consensus method. Conformational B cell epitopes were predicted from the relevant three-dimensional structure template for each protein. Based on the greatest number of predicted epitopes, eight proteins (MAP2698c [encoded by desA2], MAP2312c [encoded by fadE19], MAP3651c [encoded by fadE3_2], MAP2872c [encoded by fabG5_2], MAP3523c [encoded by oxcA], MAP0187c [encoded by sodA], and the hypothetical proteins MAP3567 and MAP1168c) were identified as potential candidates for study of antibody- and cell-mediated immune responses within infected hosts.

The utility and limitations of current Web-available algorithms to predict peptides recognized by CD4 T cells in response to pathogen infection

The ability to track CD4 T cells elicited in response to pathogen infection or vaccination is critical because of the role these cells play in protective immunity. Coupled with advances in genome sequencing of pathogenic organisms, there is considerable appeal for implementation of computer-based algorithms to predict peptides that bind to the class II molecules, forming the complex recognized by CD4 T cells. Despite recent progress in this area, there is a paucity of data regarding the success of these algorithms in identifying actual pathogen-derived epitopes. In this study, we sought to rigorously evaluate the performance of multiple Web-available algorithms by comparing their predictions with our results--obtained by purely empirical methods for epitope discovery in influenza that used overlapping peptides and cytokine ELISPOTs--for three independent class II molecules. We analyzed the data in different ways, trying to anticipate how an investigator might use these computational tools for epitope discovery. We come to the conclusion that currently available algorithms can indeed facilitate epitope discovery, but all shared a high degree of false-positive and false-negative predictions. Therefore, efficiencies were low. We also found dramatic disparities among algorithms and between predicted IC(50) values and true dissociation rates of peptide-MHC class II complexes. We suggest that improved success of predictive algorithms will depend less on changes in computational methods or increased data sets and more on changes in parameters used to "train" the algorithms that factor in elements of T cell repertoire and peptide acquisition by class II molecules.