Characterization of Plasmodium falciparum Proteome at Asexual Blood Stages for Screening of Effective Vaccine Candidates: An Immunoinformatics Approach

Screening and characterization of hypothetical proteins of Plasmodium falciparum as novel vaccine candidates in the fight against malaria using reverse vaccinology

BACKGROUND

Plasmodium falciparum is the most deadly and leading cause of morbidity and mortality in Africa. About 90% of all malaria deaths in the world today occur in Sub-Saharan Africa especially in children aged < 5 years. In 2018, it was reported that there were 228 million malaria cases that resulted in 405,000 deaths from 91 countries. Currently, a fully effective and long-lasting preventive malaria vaccine is still elusive therefore more effort is needed to identify better effective vaccine candidates. The aim of this study was to identify and characterize hypothetical proteins as vaccine candidates derived from Plasmodium falciparum 3D7 genome by reverse vaccinology.

RESULTS

Of the 23 selected hypothetical proteins, 5 were predicted on the extracellular localization by WoLFPSORTv.2.0 program and all the 5 had less than 2 transmembrane regions that were predicted by TMHMMv2.0 and HMMTOP programs at default settings. Two out of the five proteins lacked secretory signal peptides as predicted by SignalP program. Among the 5 extracellular proteins, 3 were predicted to be antigenic by VaxiJen (score ≥ 0.5) and had negative GRAVY values ranging from - 1.156 to - 0.440. B cell epitope prediction by ABCpred and BCpred programs revealed a total of 15 antigenic epitopes. A total of 13 cytotoxic T cells were predicted from the 3 proteins using CTLPred online server. Only 2 out of the 13 CTL were antigenic, immunogenic, non-allergenic, and non-toxic using VaxiJen, IEDB, AllergenFp, and Toxinpred servers respectively in that order. Five HTL peptides from XP_001351030.1 protein are predicted inducers of all the three cytokines. STRING protein-protein network analysis of HPs revealed XP_001350955.1 closely interacts with nucleoside diphosphate kinase (PF13-0349) at 0.704, XP_001351030.1 interacts with male development protein1 (Mdv-1) at 0.645, and XP_001351047.1 with an uncharacterized protein (MAL8P1.53) at 0.400.

CONCLUSION

Reverse vaccinology is a promising strategy for the screening and identification of antigenic antigens with potential capacity to elicit cellular and humoral immune responses against P. falciparum infection. In this study, potential vaccine candidates of Plasmodium falciparum were identified and screened using standard bioinformatics tools. The vaccine candidates contained antigenic and immunogenic epitopes which could be considered for novel and effective vaccine targets. However, we strongly recommend in vivo and in vitro experiments to validate their immunogenicity and protective efficacy to completely decipher the vaccine targets against malaria.

Exploitation of reverse vaccinology and immunoinformatics as promising platform for genome-wide screening of new effective vaccine candidates against Plasmodium falciparum

Background

In the current scenario, designing of world-wide effective malaria vaccine against Plasmodium falciparum remain challenging despite the significant progress has been made in last few decades. Conventional vaccinology (isolate, inactivate and inject) approaches are time consuming, laborious and expensive; therefore, the use of computational vaccinology tools are imperative, which can facilitate the design of new and promising vaccine candidates.

Results

In current investigation, initially 5548 proteins of P. falciparum genome were carefully chosen for the incidence of signal peptide/ anchor using SignalP4.0 tool that resulted into 640 surface linked proteins (SLP). Out of these SLP, only 17 were predicted to contain GPI-anchors using PredGPI tool in which further 5 proteins were considered as malarial antigenic adhesins by MAAP and VaxiJen programs, respectively. In the subsequent step, T cell epitopes of 5 genome derived predicted antigenic adhesins (GDPAA) and 5 randomly selected known malarial adhesins (RSKMA) were analysed employing MHC class I and II tools of IEDB analysis resource. Finally, VaxiJen scored T cell epitopes from each antigen were considered for prediction of population coverage (PPC) analysis in the world-wide population including malaria endemic regions. The validation of the present in silico strategy was carried out by comparing the PPC of combined (MHC class I and II) predicted epitope ensemble among GDPAA (99.97%), RSKMA (99.90%) and experimentally known epitopes (EKE) of P. falciparum (97.72%) pertaining to world-wide human population.

Conclusions

The present study systematically screened 5 potential protective antigens from P. falciparum genome using bioinformatics tools. Interestingly, these GDPAA, RSKMA and EKE of P. falciparum epitope ensembles forecasted to contain highly promiscuous T cell epitopes, which are potentially effective for most of the world-wide human population with malaria endemic regions. Therefore, these epitope ensembles could be considered in near future for novel and significantly effective vaccine candidate against malaria.

Electronic supplementary material

The online version of this article (10.1186/s12859-018-2482-x) contains supplementary material, which is available to authorized users.

Proteome-scale identification of Leishmania infantum for novel vaccine candidates: A hierarchical subtractive approach

Vaccines are one of the most significant achievements in medical science. However, vaccine design is still challenging at all stages. The selection of antigenic peptides as vaccine candidates is the first and most important step for vaccine design. Experimental selection of antigenic peptides for the design of vaccines is a time-consuming, labor-intensive and expensive procedure. More recently, in the light of computer-aided biotechnology and reverse vaccinology, the precise selection of antigenic peptides and rational vaccine design against many pathogens have developed. In this study, the whole proteome of Leishmania infantum was analyzed using a pipeline of algorithms. From the set of 8045 proteins of L. infantum, sixteen novel antigenic proteins were derived using a hierarchical proteome subtractive analysis. These novel vaccine targets can be utilized as top candidates for designing the new prophylactic or therapeutic vaccines against visceral leishmaniasis. Significantly, all the sixteen novel vaccine candidates are non-allergen antigenic proteins that have not been used for the design of vaccines against visceral leishmaniasis until now.

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.

Genome-Wide Prediction of Vaccine Candidates for Leishmania major: An Integrated Approach

Despite the wealth of information regarding genetics of the causative parasite and experimental immunology of the cutaneous leishmaniasis, there is currently no licensed vaccine against it. In the current study, a two-level data mining strategy was employed, to screen the Leishmania major genome for promising vaccine candidates. First, we screened a set of 25 potential antigens from 8312 protein coding sequences, based on presence of signal peptides, GPI anchors, and consensus antigenicity predictions. Second, we conducted a comprehensive immunogenic analysis of the 25 antigens based on epitopes predicted by NetCTL tool. Interestingly, results revealed that candidate antigen number 1 (LmjF.03.0550) had greater number of potential T cell epitopes, as compared to five well-characterized control antigens (CSP-Plasmodium falciparum, M1 and NP-Influenza A virus, core protein-Hepatitis B virus, and PSTA1-Mycobacterium tuberculosis). In order to determine an optimal set of epitopes among the highest scoring predicted epitopes, the OptiTope tool was employed for populations susceptible to cutaneous leishmaniasis. The epitope (127SLWSLLAGV) from antigen number 1, found to bind with the most prevalent allele HLA-A⁎0201 (25% frequency in Southwest Asia), was predicted as most immunogenic for all the target populations. Thus, our study reasserts the potential of genome-wide screening of pathogen antigens and epitopes, for identification of promising vaccine candidates.

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.