Revisiting the dimensions of universal vaccine with special focus on COVID-19: Efficacy versus methods of designing.

Even though the use of SARS-CoV-2 vaccines during the COVID-19 pandemic showed unprecedented success in a short time, it also exposed a flaw in the current vaccine design strategy to offer broad protection against emerging variants of concern. However, developing broad-spectrum vaccines is still a challenge for immunologists. The development of universal vaccines against emerging pathogens and their variants appears to be a practical solution to mitigate the economic and physical effects of the pandemic on society. Very few reports are available to explain the basic concept of universal vaccine design and development. This review provides an overview of the innate and adaptive immune responses generated against vaccination and essential insight into immune mechanisms helpful in designing universal vaccines targeting influenza viruses and coronaviruses. In addition, the characteristics, safety, and factors affecting the efficacy of universal vaccines have been discussed. Furthermore, several advancements in methods worthy of designing universal vaccines are described, including chimeric immunogens, heterologous prime-boost vaccines, reverse vaccinology, structure-based antigen design, pan-reactive antibody vaccines, conserved neutralizing epitope-based vaccines, mosaic nanoparticle-based vaccines, etc. In addition to the several advantages, significant potential constraints, such as defocusing the immune response and subdominance, are also discussed.

Computational analysis of spike protein of SARS-CoV-2 (Omicron variant) for development of peptide-based therapeutics and diagnostics.

In the last few years, the worldwide population has suffered from the SARS-CoV-2 pandemic. The WHO dashboard indicated that around 504,079,039 people were infected and 6,204,155 died from COVID-19 caused by different variants of SARS-CoV-2. Recently, a new variant of SARS-CoV-2 (B.1.1.529) was reported by South Africa known as Omicron. The high transmissibility rate and resistance towards available anti-SARS-CoV-2 drugs/vaccines/monoclonal antibodies, make Omicron a variant of concern. Because of various mutations in spike protein, available diagnostic and therapeutic treatments are not reliable. Therefore, the present study explored the development of some therapeutic peptides that can inhibit the SARS-CoV-2 virus interaction with host ACE2 receptors and can also be used for diagnostic purposes. The screened linear B cell epitopes derived from receptor-binding domain of spike protein of Omicron variant were evaluated as peptide inhibitor/vaccine candidates through different bioinformatics tools including molecular docking and simulation to analyze the interaction between Omicron peptide and human ACE2 receptor. Overall, in-silico studies revealed that Omicron peptides OP1-P12, OP14, OP20, OP23, OP24, OP25, OP26, OP27, OP28, OP29, and OP30 have the potential to inhibit Omicron interaction with ACE2 receptor. Moreover, Omicron peptides OP20, OP22, OP23, OP24, OP25, OP26, OP27, and OP30 have shown potential antigenic and immunogenic properties that can be used in design and development vaccines against Omicron. Although the in-silico validation was performed by comparative analysis with the control peptide inhibitor, further validation through wet lab experimentation is required before its use as therapeutic peptides.Communicated by Ramaswamy H. Sarma.

Screening of potential antigens from whole proteome and development of multi-epitope vaccine against Rhizopus delemar using immunoinformatics approaches.

Mucormycosis is a deadly fungal disease mainly caused by Rhizopus oryzae (strain 99-880), also known as Rhizopus delemar. Previously, mucormycosis occurs in immunocompromised patients of diabetes mellitus, cancer, organ transplant, etc. But there was a drastic increase in mucormycosis cases in the ongoing COVID-19 pandemic. Despite several available therapies and antifungal treatments, the mortality rate of mucormycosis is about more than 50%. Currently, there is no vaccine available in the market for mucormycosis that urgently needs to develop a potential vaccine against mucormycosis with high efficacy. In the present study, we have screened 4 genome-derived predicted antigens (GDPA) through sequential filtration of the whole proteome of R. delemar using different benchmarked bioinformatics tools. These 4 GDPA along with 4 randomly selected experimentally reported antigens (ERA) were sourced for prediction of B- and T- cell epitopes and utilized in designing of two potential multi-epitope vaccine candidates which can induce both innate and adaptive immunity against R. delemar. Besides these, comparative immune simulation studies and in silico cloning were performed using L. lactis as an expression system for their possible uses as oral vaccines. This is the first multi-epitope vaccine designed against R. delemar through systematic pipelined reverse vaccinology and immunoinformatic approaches. Although the wet-lab based experimental validation of designed vaccines is required before testing in the preclinical model, the current study will significantly help in reducing the cost of experimentation as well as improving the efficacy of vaccine therapy against mucormycosis and other pathogenic diseases.Communicated by Ramaswamy H. Sarma.

Microstructure, pathophysiology, and potential therapeutics of COVID-19: A comprehensive review.

There have been over seven million cases and almost 413 372 deaths globally due to the novel coronavirus (2019-nCoV) associated disease COVID-19, as of 11 June 2020. Phylogenetic analysis suggests that there is a common source for these infections. The overall sequence similarities between the spike protein of 2019-nCoV and that of SARS-CoV are known to be around 76% to 78% and 73% to 76% for the whole protein and receptor-binding domain (RBD), respectively. Thus, they have the potential to serve as the drug and/or vaccine candidate. However, the individual response against 2019-nCoV differs due to genetic variations in the human population. Understanding the variations in angiotensin-converting enzyme 2 (ACE2) and human leukocyte antigen (HLA) that may affect the severity of 2019-nCoV infection could help in identifying individuals at a higher risk from the COVID-19. A number of potential drugs/vaccines as well as antibody/cytokine-based therapeutics are in various developmental stages of preclinical/clinical trials against SARS-CoV, MERS-CoV, and 2019-nCoV with substantial cross-reactivity, and may be used against COVID-19. For diagnosis, the reverse-transcription polymerase chain reaction is the gold standard test for initial diagnosis of COVID-19. A kit based on serological tests are also recommended for investigating the spread of COVID-19 but this is challenging due to the antibodies cross-reactivity. This review comprehensively summarizes the recent reports available regarding the host-pathogen interaction, morphological and genomic structure of the virus, and the diagnostic techniques as well as the available potential therapeutics against COVID-19.

A cutting-edge immunoinformatics approach for design of multi-epitope oral vaccine against dreadful human malaria.

Human malaria is a pathogenic disease mainly caused by Plasmodium falciparum, which was responsible for about 405,000 deaths globally in the year 2018. To date, several vaccine candidates have been evaluated for prevention, which failed to produce optimal output at various preclinical/clinical stages. This study is based on designing of polypeptide vaccines (PVs) against human malaria that cover almost all stages of life-cycle of Plasmodium and for the same 5 genome derived predicted antigenic proteins (GDPAP) have been used. For the development of a multi-immune inducer, 15 PVs were initially designed using T-cell epitope ensemble, which covered >99% human population as well as linear B-cell epitopes with or without adjuvants. The immune simulation of PVs showed higher levels of T-cell and B-cell activities compared to positive and negative vaccine controls. Furthermore, in silico cloning of PVs and codon optimization followed by enhanced expression within Lactococcus lactis host system was also explored. Although, the study has sound theoretical and in silico findings, the in vitro/in vivo evaluation seems imperative to warrant the immunogenicity and safety of PVs towards management of P. falciparum infection in the future.

Designing of precise vaccine construct against visceral leishmaniasis through predicted epitope ensemble: A contemporary approach.

Visceral leishmaniasis (VL) caused by Leishmania donovani is a fatal parasitic disease affecting primarily the poor population in endemic countries. Increasing number of deaths as well as resistant to existing drugs necessitates the development of an effective vaccine for successful treatment of VL. The present study employed a combinatorial approach for designing monomer vaccine construct against L. donovani by applying forecasted B- and T- cell epitopes from 4 genome derived antigenic proteins having secretory signal peptides and glycophosphatidylinositol (GPI) anchors with ≤ 1 transmembrane helix. The forecasted population coverage of chosen T cell epitope ensemble (combined HLA class I and II) cover 99.14 % of world-wide human population. The predicted 3D structure of vaccine constructs (VC1/VC2) were modeled using homology modeling approach and docked to innate immune receptors TLR-2 and TLR-4 with respective docking energies -1231.4/-910.3 and -1119.4/-1476 kcal/mol. Overall, the aforementioned designed vaccine constructs were found appropriate for including in self-assembly protein nanoparticles (SAPN) for further study in developing cutting-edge precision vaccine against VL in short duration with cost-effective manner.

Genome based screening of epitope ensemble vaccine candidates against dreadful visceral leishmaniasis using immunoinformatics approach.

Visceral leishmaniasis (VL) is a dreadful protozoan disease caused by Leishmania donovani that severely affects huge populations in tropical and sub-tropical regions. The present study reports an unbiased genome based screening of 4 potent vaccine antigens against 8023 L. donovani proteins by following the criteria of presence of signal peptides, GPI-anchors and ≤1 transmembrane helix using advanced bioinformatics tools viz. SignalP4.0, PredGPI and TMHMM2.0, respectively. They are designated as genome based predicted signal peptide antigens (GBPSPA). The antigenicity/immunogenicity of chosen vaccine antigens (GBPSPA) with 4 randomly selected known leishmanial antigens (RSKLA) was compared by simulation study employing C-ImmSim software for human immune responses. This revealed better immunological responses. These antigens were further evaluated for the presence of B- and T-cell epitopes using immune epitope database (IEDB) based recommended consensus method of MHC class I and II tools. It was found to forecast CD4+ and CD8+ T-cell responses in genetically diverse human population worldwide as well as different endemic regions through IEDB based predicted population coverage (PPC) analysis tool. The worldwide percent PPC value of combined (HLA class I and II) epitope ensemble forecast was found to be 99.98, 99.96 and 50.04, respectively for GBPSPA, RSKLA and experimentally known epitopes (EKE) of L. donovani. Therefore, these potential antigens/epitope ensembles could favor the design of prospective and novel vaccine constructs like self-assembled epitopes as nano vaccine formulations against VL. Overall, the present study will serve as a model framework that might improve the effectiveness of designed vaccine against L. donovani and other related pathogens.

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.

Biomedical applications of microbially engineered polyhydroxyalkanoates: an insight into recent advances, bottlenecks, and solutions.

Biopolymeric polyhydroxyalkanoates (PHAs) are fabricated and accumulated by microbes under unbalanced growth conditions, primarily by diverse genera of bacteria. Over the last two decades, microbially engineered PHAs gained substantial interest worldwide owing to their promising wide-range uses in biomedical field as biopolymeric biomaterials. Because of non-hazardous disintegration products, preferred surface alterations, inherent biocompatibility, modifiable mechanical properties, cultivation support for cells, adhesion devoid of carcinogenic impacts, and controllable biodegradability, the PHAs like poly-3-hydroxybutyrate, 3-hydroxybutyrate and 3-hydroxyvalerate co-polymers, 3-hydroxybutyrate and 4-hydroxybutyrate co-polymers, etc., are available for various medical applications. These PHAs have been exploited to design in vivo implants like sutures as well as valves for direct tissue repairing as well as in regeneration devices like bone graft substitutes, nerve guides as well as cardiovascular patches, etc. Furthermore, they are also emerged as attractive candidates for developing effective/novel drug delivery systems because of their biocompatibility and biodegradability with the ability to deliver and release the drugs at a specific site in a controllable manner and, therefore widen the therapeutic window with reduced side effects. However, there still remain some bottlenecks related to PHA purity, mechanical properties, biodegradability, etc., that are need to be addressed so as to make PHAs a realistic biomaterial. In addition, innovative approaches like PHAs co-production with other value-added products, etc., must be developed currently for economical PHA production. This review provides an insight toward the recent advances, bottlenecks, and potential solutions for prospective biomedical applications of PHAs with conclusion that relatively little research/study has been performed presently toward the viability of PHAs as realistic biopolymeric biomaterials.

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.

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.

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.

Identification and characterization of merozoite surface protein 1 epitope.

Malaria is an important tropical infection which urgently requires intervention of an effective vaccine. Antigenic variations of the parasite and allelic diversity of the host are main problems in the development of an effective malaria vaccine. Cytotoxic T lymphocytes (CTL) directed against Plasmodium falciparum-derived antigens are shown to play an important role for the protection against malaria. The merozoite surface protein 1 (MSP1) is expressed in all the four life-cycle stages of Plasmodium falciparum and did not find any sequence similarity to human and mouse reference proteins. MSP1 is a known target of the immune response and a single CTL epitope binding to the HLA-A*0201 is available for merozoite form. Here, we report the results from the computational characterization of MSP1, precursor (1720 residue) and screening of highest scoring potential CTL epitopes for 1712 overlapping peptides binding to thirty four HLA class-I alleles and twelve HLA class-I supertypes (5 HLA-A and 7 HLA-B) using bioinformatics tools. Supertypes are the clustered groups of HLA class-I molecules, representing a sets of molecules that share largely overlapping peptide binding specificity. The prediction results for MSP1 as adhesin and adhesin-like in terms of probability is 1.0. Results also show that MSP1 has orthologs to other related species as well as having non allergenicity and single transmembrane properties demonstrating its suitability as a vaccine candidate. The predicted peptides are expected to be useful in the design of multi-epitope vaccines without compromising the human population coverage.

Ranking of binding and nonbinding peptides to MHC class I molecules using inverse folding approach: implications for vaccine design.

T cell recognition of the peptide-MHC complex initiates a cascade of immunological events necessary for immune responses. Accurate T-cell epitope prediction is an important part of the vaccine designing. Development of predictive algorithms based on sequence profile requires a very large number of experimental binding peptide data to major histocompatibility complex (MHC) molecules. Here we used inverse folding approach to study the peptide specificity of MHC Class-I molecule with the aim of obtaining a better differentiation between binding and nonbinding sequence. Overlapping peptides, spanning the entire protein sequence, are threaded through the backbone coordinates of a known peptide fold in the MHC groove, and their interaction energies are evaluated using statistical pairwise contact potentials. We used the Miyazawa & Jernigan and Betancourt & Thirumalai tables for pairwise contact potentials, and two distance criteria (Nearest atom >> 4.0 A & C-beta >> 7.0 A) for ranking the peptides in an ascending order according to their energy values, and in most cases, known antigenic peptides are highly ranked. The predictions from threading improved when used multiple templates and average scoring scheme. In general, when structural information about a protein-peptide complex is available, the current application of the threading approach can be used to screen a large library of peptides for selection of the best binders to the target protein. The proposed scheme may significantly reduce the number of peptides to be tested in wet laboratory for epitope based vaccine design.

Prediction of MHC binding peptide using Gibbs motif sampler, weight matrix and artificial neural network.

The identification of MHC restricted epitopes is an important goal in peptide based vaccine and diagnostic development. As wet lab experiments for identification of MHC binding peptide are expensive and time consuming, in silico tools have been developed as fast alternatives, however with low performance. In the present study, we used IEDB training and blind validation datasets for the prediction of peptide binding to fourteen human MHC class I and II molecules using Gibbs motif sampler, weight matrix and artificial neural network methods. As compare to MHC class I predictor based on sequence weighting (Aroc=0.95 and CC=0.56) and artificial neural network (Aroc=0.73 and CC=0.25), MHC class II predictor based on Gibbs sampler did not perform well (Aroc=0.62 and CC=0.19). The predictive accuracy of Gibbs motif sampler in identifying the 9-mer cores of a binding peptide to DRB1 alleles are also limited (40 cent), however above the random prediction (14 cent). Therefore, the size of dataset (training and validation) and the correct identification of the binding core are the two main factors limiting the performance of MHC class-II binding peptide prediction. Overall, these data suggest that there is substantial room to improve the quality of the core predictions using novel approaches that capture distinct features of MHC-peptide interactions than the current approaches.