Design of an epitope-based peptide vaccine against spike protein of human coronavirus: an in silico approach

In silico design and evaluation of a multiepitope vaccine targeting the nucleoprotein of Puumala orthohantavirus

The Puumala orthohantavirus is present in the body of the bank vole (Myodes glareolus). Humans infected with this virus may develop hemorrhagic fever accompanying renal syndrome. In addition, the infection may further lead to the failure of an immune system completely. The present study aimed to propose a possible vaccine by employing bioinformatics techniques to identify B and T-cell antigens. The best multi-epitope of potential immunogenicity was generated by combining epitopes. Additionally, the linkers EAAAK, AAY, and GPGPG were utilized in order to link the epitopes successfully. Further, C-ImmSim was used to perform in silico immunological simulations upon the vaccine. For the purpose of conducting expression tests in Escherichia coli, the chimeric protein construct was cloned using Snapgene into the pET-9c vector. The designed vaccine showed adequate results, evidenced by the global population coverage and favorable immune response. The developed vaccine was found to be highly effective and to have excellent population coverage in a number of computer-based assessments. This work is fully dependent on the development of nucleoprotein-based vaccines, which would constitute a significant step forward if our findings were used in developing a global vaccination to combat the Puumala virus.

Structural, functional, molecular docking analysis of a hypothetical protein from Talaromyces marneffei and its molecular dynamic simulation: an in-silico approach

Telaromyces marneffei (formerly Penicillium marneffei) is an endemic pathogenic fungus in Southern China and Southeast Asia. It can cause disease in patients with travel-related exposure to this organism and high morbidity and mortality in acquired immune deficiency syndrome (AIDS). In this study, we analyzed the structure and function of a hypothetical protein from T. marneffei using several bioinformatics tools and servers to unveil novel pharmacological targets and design a peptide vaccine against specific epitopes. A total of seven functional epitopes were screened on the protein, and 'STGVDMWSV' was the most antigenic, non-allergenic and non-toxic. Molecular docking showed stronger affinity between the CTL epitope 'STGVDMWSV' and the MHC I allele HLA-A*02:01, a higher docking score -234.98 kcal/mol, revealed stable interactions during a 100 ns molecular dynamic simulation. Overall, the results of this study revealed that this hypothetical protein is crucial for comprehending biochemical, physiological pathways and identifying novel therapeutic targets for human health. Communicated by Ramaswamy H. Sarma.

Identification of B and T Cell Epitopes to Design an Epitope-Based Peptide Vaccine against the Cell Surface Binding Protein of Monkeypox Virus: An Immunoinformatics Study

Background

Although the monkeypox virus-associated illness was previously confined to Africa, recently, it has started to spread across the globe and become a significant threat to human lives. Hence, this study was designed to identify the B and T cell epitopes and develop an epitope-based peptide vaccine against this virus's cell surface binding protein through an in silico approach to combat monkeypox-associated diseases.

Results

The analysis revealed that the cell surface binding protein of the monkeypox virus contains 30 B cell and 19 T cell epitopes within the given parameter. Among the T cell epitopes, epitope "ILFLMSQRY" was found to be one of the most potential peptide vaccine candidates. The docking analysis revealed an excellent binding affinity of this epitope with the human receptor HLA-B^∗15:01 with a very low binding energy (-7.5 kcal/mol).

Conclusion

The outcome of this research will aid the development of a T cell epitope-based peptide vaccine, and the discovered B and T cell epitopes will facilitate the creation of other epitope and multi-epitope-based vaccines in the future. This research will also serve as a basis for further in vitro and in vivo analysis to develop a vaccine that is effective against the monkeypox virus.

Bioinformatics and In silico approaches to identify novel biomarkers and key pathways for cancers that are linked to the progression of female infertility: A comprehensive approach for drug discovery

Despite modern treatment, infertility remains one of the most common gynecologic diseases causing severe health effects worldwide. The clinical and epidemiological data have shown that several cancerous risk factors are strongly linked to Female Infertility (FI) development, but the exact causes remain unknown. Understanding how these risk factors affect FI-affected cell pathways might pave the door for the discovery of critical signaling pathways and hub proteins that may be targeted for therapeutic intervention. To deal with this, we have used a bioinformatics pipeline to build a transcriptome study of FI with four carcinogenic risk factors: Endometrial Cancer (EC), Ovarian Cancer (OC), Cervical Cancer (CC), and Thyroid Cancer (TC). We identified FI sharing 97, 211, 87 and 33 differentially expressed genes (DEGs) with EC, OC, CC, and TC, respectively. We have built gene-disease association networks from the identified genes based on the multilayer network and neighbour-based benchmarking. Identified TNF signalling pathways, ovarian infertility genes, cholesterol metabolic process, and cellular response to cytokine stimulus were significant molecular and GO pathways, both of which improved our understanding the fundamental molecular mechanisms of cancers associated with FI progression. For therapeutic intervention, we have targeted the two most significant hub proteins VEGFA and PIK3R1, out of ten proteins based on Maximal Clique Centrality (MCC) value of cytoscape and literature analysis for molecular docking with 27 phytoestrogenic compounds. Among them, sesamin, galangin and coumestrol showed the highest binding affinity for VEGFA and PIK3R1 proteins together with favourable ADMET properties. We recommended that our identified pathway, hub proteins and phytocompounds may be served as new targets and therapeutic interventions for accurate diagnosis and treatment of multiple diseases.

Systems biology and in silico-based analysis of PCOS revealed the risk of metabolic disorders

Background

Polycystic ovarian syndrome (PCOS) is a common condition of hyperandrogenism, chronic ovulation, and polycystic ovaries in females during the reproduction and maturation of the ovum. Although PCOS has been associated with metabolic disorders, including type 2 diabetes (T2D), obesity (OBE), and cardiovascular disease (CVD), Causal connection and molecular features are still unknown.

Purpose

Therefore, we investigated the shared common differentially expressed genes (DEGs), pathways, and networks of associated proteins in PCOS and metabolic diseases with therapeutic intervention.

Methods

We have used a bioinformatics pipeline to analyze transcriptome data for the polycystic ovarian syndrome (PCOS), type 2 diabetes (T2D), obesity (OBE), and cardiovascular diseases (CVD) in female patients. Then we employed gene-disease association network, gene ontology (GO) and signaling pathway analysis, selection of hub genes from protein-protein interaction (PPI) network, molecular docking, and gold benchmarking approach to screen potential hub proteins.

Result

We discovered 2225 DEGs in PCOS patients relative to healthy controls and 34, 91, and 205 significant DEGs with T2D, Obesity, and CVD, respectively. Gene Ontology analysis revealed several significant shared and metabolic pathways from signaling pathway analysis. Furthermore, we identified ten potential hub proteins from PPI analysis that may serve as a therapeutic intervention in the future. Finally, we targeted one significant hub protein, IGF2R (PDB ID: 2V5O), out of ten hub proteins based on the Maximal clique centrality (MCC) algorithm and literature review for molecular docking study. Enzastaurin (-12.5), Kaempferol (-9.1), Quercetin (-9.0), and Coumestrol (-8.9) kcal/mol showed higher binding affinity in the molecular docking approach than 19 drug compounds. We have also found that the selected four compounds displayed favorable ADMET properties compared to the native ligand.

Conclusion

Our in-silico research findings identified a shared molecular etiology between PCOS and metabolic diseases that may suggest new therapeutic targets and warrants future experimental validation of the key targets.

Immunoinformatics approach to epitope-based vaccine design against the SARS-CoV-2 in Bangladeshi patients

BACKGROUND

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the causative agent of the ongoing coronavirus disease 2019 (COVID-19) pandemic which has brought a great challenge to public health. After the first emergence of novel coronavirus SARS-CoV-2 in the city of Wuhan, China, in December 2019. As of March 2020, SARS-CoV-2 was first reported in Bangladesh and since then the country has experienced a steady rise in infections, resulting in 13,355,191 cases and 29,024 deaths as of 27 February 2022. Bioinformatics techniques are used to predict B cell and T cell epitopes from the new SARS-CoV-2 spike glycoprotein in order to build a unique multiple epitope vaccine. The immunogenicity, antigenicity scores, and toxicity of these epitopes were evaluated and chosen based on their capacity to elicit an immune response.

RESULT

The best multi-epitope of the possible immunogenic property was created by combining epitopes. EAAAK, AAY, and GPGPG linkers were used to connect the epitopes. In several computer-based immune response analyses, this vaccine design was found to be efficient, as well as having high population coverage.

CONCLUSION

This research is entirely reliant on the development of epitope-based vaccines, and these in silico findings would represent a major step forward in the development of a vaccine that might eradicate SARS-CoV-2 in Bangladeshi patients.

Structural and Functional Annotation and Molecular Docking Analysis of a Hypothetical Protein from Neisseria gonorrhoeae: An In-Silico Approach

Background

Worldwide, Neisseria gonorrhoeae-related sexually transmitted infections (STIs) continue to be of significant public health concern. This obligate-human pathogen has developed a number of defenses against both innate and adaptive immune responses during infection, some of which are mediated by the pathogen's proteins. Hence, the uncharacterized proteins of N. gonorrhoeae can be annotated to get insight into the unique functions of this organism related to its pathogenicity and to find a more efficient therapeutic target.

Methods

In this study, a hypothetical protein (HP) of N. gonorrhoeae was chosen for analysis and an in-silico approach was used to explore various properties such as physicochemical characteristics, subcellular localization, secondary structure, 3D structures, and functional annotation of that HP. Finally, a molecular docking analysis was performed to design an epitope-based vaccine against that HP.

Results

This study has identified the potential role of the chosen HP of N. gonorrhoeae in plasmid transfer, cell cycle control, cell division, and chromosome partitioning. Acidic nature, thermal stability, cytoplasmic localization of the protein, and some of its other physicochemical properties have also been identified through this study. Molecular docking analysis has demonstrated that one of the T cell epitopes of the protein has a significant binding affinity with the human leukocyte antigen HLA-B∗15 : 01.

Conclusions

The in-silico characterization of this protein will help us understand molecular mechanism of action of N. gonorrhoeae and get an insight into novel therapeutic identification processes. This research will, therefore, enhance our knowledge to find new medications to tackle this potential threat to humankind.

Computational design of a broad-spectrum multi-epitope vaccine candidate against seven strains of human coronaviruses

Spike (S) proteins are an attractive target as it mediates the binding of the SARS-CoV-2 to the host through ACE-2 receptors. We hypothesize that the screening of the S protein sequences of all the seven known HCoVs would result in the identification of potential multi-epitope vaccine candidates capable of conferring immunity against various HCoVs. In the present study, several machine learning-based in-silico tools were employed to design a broad-spectrum multi-epitope vaccine candidate targeting the S protein of seven known strains of human coronaviruses. Herein, multiple B-cell epitopes and T-cell epitopes (CTL and HTL) were predicted from the S protein sequences of all seven known HCoVs. Post-prediction they were linked together with an adjuvant to construct a potential broad-spectrum vaccine candidate. Secondary and tertiary structures were predicted and validated, and the refined 3D-model was docked with an immune receptor. The vaccine candidate was evaluated for antigenicity, allergenicity, solubility, and its ability to achieve high-level expression in bacterial hosts. Finally, the immune simulation was carried out to evaluate the immune response after three vaccine doses. The designed vaccine is antigenic (with or without the adjuvant), non-allergenic, binds well with TLR-3 receptor and might elicit a diverse and strong immune response.

Design of a novel multi-epitope vaccine candidate against hepatitis C virus using structural and nonstructural proteins: An immunoinformatics approach

Hepatitis C virus (HCV) infects the liver and causes chronic infection. Several mutations in the viral genome have been associated with drug resistance development. Currently, there is no approved vaccine against the HCV. The employment of computational biology is the primary and crucial step for vaccine design or antiviral therapy which can substantially reduce the duration and cost of studies. Therefore, in this study, we designed a multi-epitope vaccine using various immunoinformatics tools to elicit the efficient human immune responses against the HCV. Initially, various potential (antigenic, immunogenic, non-toxic and non-allergenic) epitope segments were extracted from viral structural and non-structural protein sequences using multiple screening methods. The selected epitopes were linked to each other properly. Then, toll-like receptors (TLRs) 3 and 4 agonists (50S ribosomal protein L7/L12 and human β-defensin 2, respectively) were added to the N-terminus of the final vaccine sequence to increase its immunogenicity. The 3D structure of the vaccine was modeled. Molecular dynamics simulations studies verified the high stability of final free vaccines and in complex with TLR3 and TLR4. These constructs were also antigenic, non-allergenic, nontoxic and immunogenic. Although the designed vaccine traits were promising as a potential candidate against the HCV infection, experimental studies and clinical trials are required to verify the protective traits and safety of the designed vaccine.

An Immunoinformatics Prediction of Novel Multi-Epitope Vaccines Candidate Against Surface Antigens of Nipah Virus

Nipah virus (NiV) is an emerging zoonotic virus causing outbreaks of encephalitis and respiratory illnesses in humans, with high mortality. NiV is considered endemic in Bangladesh and Southeast Asia. There are no licensed vaccines against NiV. This study aimed at predicting a dual-antigen multi-epitope subunit chimeric vaccine against surface-glycoproteins G and F of NiV. Targeted proteins were subjected to immunoinformatics analyses to predict antigenic B-cell and T-cell epitopes. The proposed vaccine designs were implemented based on the conservancy, population coverage, molecular docking, immune simulations, codon adaptation, secondary mRNA structure, and in-silico cloning. Total 40 T and B-cell epitopes were found to be conserved, antigenic (vaxijen-value > 0.4), non-toxic, non-allergenic, and human non-homologous. Of 12 hypothetical vaccines, two (NiV_BGD_V1 and NiV_BGD_V2) were strongly immunogenic, non-allergenic, and structurally stable. The proposed vaccine candidates show a negative Z-score (- 6.32 and - 6.67) and 83.6% and 89.3% of most rama-favored regions. The molecular docking confirmed the highest affinity of NiV_BGD_V1 and NiV_BGD_V2 with TLR-4 (ΔG = - 30.7) and TLR8 (ΔG = - 20.6), respectively. The vaccine constructs demonstrated increased levels of immunoglobulins and cytokines in humans and could be expressed properly using an adenoviral-based pAdTrack-CMV expression vector. However, more experimental investigations and clinical trials are needed to validate its efficacy and safety.

Supplementary Information

The online version contains supplementary material available at 10.1007/s10989-022-10431-z.

Design of a multi-epitope-based peptide vaccine against the S and N proteins of SARS-COV-2 using immunoinformatics approach

Background

As the new pandemic created by COVID-19 virus created the need of rapid acquisition of a suitable vaccine against SARS-CoV-2 to develop Immunity and to reduce the mortality, the aim of this study was to identify SARS-CoV-2 S protein and N antigenic epitopes by using immunoinformatic methods to design a vaccine against SARS-CoV-2, for which S and N protein-dependent epitopes are predicted. B cell, CTL and HTL were determined based on antigenicity, allergenicity and toxicity that were non-allergenic, non-toxic, and antigenic and were selected for the design of a multi-epitope vaccine structure. Then, in order to increase the safety of Hbd-3 and Hbd-2 as adjuvants, they were connected to the N and C terminals of the vaccine construct, respectively, with a linker. The three-dimensional structure of the structure was predicted and optimized, and its quality was evaluated. The vaccine construct was ligated to MHCI. Finally, after optimizing the codon to increase expression in E. coli K12, the vaccine construct was cloned into pET28a (+) vector.

Results

Epitopes which were used in our survey were based on non-allergenic, non-toxic and antigenic. Therefore, 543-amino-acid-long multi-epitope vaccine formation was invented through linking 9 cytotoxic CTL, 5 HTL and 14 B cell epitopes with appropriate adjuvants and connectors that can control the SARS coronavirus 2 infection and could be more assessed in medical scientific researches.

Conclusion

We believe that the proposed multi-epitope vaccine can effectively evoke an immune response toward SARS-CoV-2.

Supplementary Information

The online version contains supplementary material available at 10.1186/s43042-022-00224-w.

Integrative immunoinformatics paradigm for predicting potential B-cell and T-cell epitopes as viable candidates for subunit vaccine design against COVID-19 virulence

Background

The increase in global mortality rates from SARS-COV2 (COVID-19) infection has been alarming thereby necessitating the continual search for viable therapeutic interventions. Due to minimal microbial components, subunit (peptide-based) vaccines have demonstrated improved efficacies in stimulating immunogenic responses by host B- and T-cells.

Methods

Integrative immunoinformatics algorithms were used to determine linear and discontinuous B-cell epitopes from the S-glycoprotein sequence. End-point selection of the most potential B-cell epitope was based on highly essential physicochemical attributes. NetCTL-I and NetMHC-II algorithms were used to predict probable MHC-I and II T-cell epitopes for globally frequent HLA-A∗O2:01, HLA-B∗35:01, HLA-B∗51:01 and HLA-DRB1∗15:02 molecules. Highly probable T-cell epitopes were selected based on their high propensities for C-terminal cleavage, transport protein (TAP) processing and MHC-I/II binding.

Results

Preferential epitope binding sites were further identified on the HLA molecules using a blind peptide-docking method. Phylogenetic analysis revealed close relativity between SARS-CoV-2 and SARS-CoV S-protein. LALHRSYLTPGDSSSGWTAGAA_242→263 was the most probable B-cell epitope with optimal physicochemical attributes. MHC-I antigenic presentation pathway was highly favourable for YLQPRTFLL_269-277 (HLA-A∗02:01), LPPAYTNSF_24-32 (HLA-B∗35:01) and IPTNFTISV7_14-721 (HLA-B∗51:01). Also, LTDEMIAQYTSALLA_865-881 exhibited the highest binding affinity to HLA-DR B1∗15:01 with core interactions mediated by IAQYTSALL_870-878. COVID-19 YLQPRTFLL_269-277 was preferentially bound to a previously undefined site on HLA-A∗02:01 suggestive of a novel site for MHC-I-mediated T-cell stimulation.

Conclusion

This study implemented combinatorial immunoinformatics methods to model B- and T-cell epitopes with high potentials to trigger immunogenic responses to the S protein of SARS-CoV-2.

Design of a novel multiple epitope-based vaccine: An immunoinformatics approach to combat SARS-CoV-2 strains

BACKGROUND

Since the SARS-CoV-2 outbreak in December 2019 in Wuhan, China, the virus has infected more than 153 million individuals across the world due to its human-to-human transmission. The USA is the most affected country having more than 32-million cases till date. Sudden high fever, pneumonia and organ failure have been observed in infected individuals.

OBJECTIVES

In the current situation of emerging viral disease, there is no specific vaccine, or any therapeutics available for SARS-CoV-2, thus there is a dire need to design a potential vaccine to combat the virus by developing immunity in the population. The purpose of present study was to develop a potential vaccine by targeting B and T-cell epitopes using bioinformatics approaches.

METHODS

B- and T-cell epitopes are predicted from novel M protein-SARS-CoV-2 for the development of a unique multiple epitope vaccine by applying bioinformatics approaches. These epitopes were analyzed and selected for their immunogenicity, antigenicity scores, and toxicity in correspondence to their ability to trigger immune response. In combination to epitopes, best multi-epitope of potential immunogenic property was constructed. The epitopes were joined using EAAAK, AAY and GPGPG linkers.

RESULTS

The constructed vaccine showed good results of worldwide population coverage and promising immune response. This constructed vaccine was subjected to in-silico immune simulations by C-ImmSim. Chimeric protein construct was cloned into PET28a (+) vector for expression study in Escherichia coli using snapgene.

CONCLUSION

This vaccine design proved effective in various computer-based immune response analysis as well as showed good population coverage. This study is solely dependent on developing M protein-based vaccine, and these in silico findings would be a breakthrough in the development of an effective vaccine to eradicate SARS-CoV-2 globally.

The significance of bioengineered nanoplatforms against SARS-CoV-2: From detection to genome editing

COVID-19 outbreak can impose serious negative impacts on the infrastructures of societies including the healthcare systems. Despite the increasing research efforts, false positive or negative results that may be associated with serologic or even RT-PCR tests, inappropriate or variable immune response, and high rates of mutations in coronavirus may negatively affect virus detection process and effectiveness of the vaccines or drugs in development. Nanotechnology-based research attempts via developing state-of-the-art techniques such as nanomechatronics ones and advanced materials including the sensors for detecting the pathogen loads at very low concentrations or site-specific delivery of therapeutics, and real-time protections against the pandemic outbreaks by nanorobots can provide outstanding biomedical breakthroughs. Considering the unique characteristics of pathogens particularly the newly-emerged ones and avoiding the exaggerated optimism or simplistic views on the prophylactic and therapeutic approaches including the one-size-fits-all ones or presenting multiple medications that may be associated with synergistic toxicities rather than enhanced efficiencies might pave the way towards the development of more appropriate treatment strategies with reduced safety concerns. This paper highlights the significance of nanoplatforms against the viral disorders and their capabilities of genome editing that may facilitate taking more appropriate measures against SARS-CoV-2.

Immunoinformatics based prediction of recombinant multi-epitope vaccine for the control and prevention of SARS-CoV-2

The emergence of SARS-CoV-2 has been reported during December 2019, in the city of Wuhan, China. The transmission of this virus via human to human interaction has already been described. The novel virus has become pandemic and declared as a comprehensive emergency worldwide by World Health Organization due to its exponential spread within and outside China. There is a need of time to create a therapeutic agent and a vaccine to cure and control this lethal SARS-CoV-2. Conventionally, the vaccine development process is time taking, tiresome and requires more economical inputs with manpower. However, bioinformatics offers a key solution to compute the possibilities. The present study focuses on the utilization of bioinformatics platforms to forecast B and T cell epitopes that belong to SARS-CoV-2 spike glycoprotein. The protein is thought to have an involvement in triggering of momentous immune response. NCBI database was explored to collect the surface glycoprotein sequence and was analyzed to determine the immunogenic epitopes. This prediction analysis was carried out using IEDB web based server and the prediction of protein structure was done by homology modeling approach. This study resulted in prediction of 5T cell and 13B cell epitopes. Moreover, GPGPG linker was used to make these predicted epitopes a single peptide prior to further analysis. Afterwards, a 3D model of the final vaccine peptide was constructed, and the structure quality of the final construct was checked by Ramachandran Plot analysis and ProSA-web. Moreover, docking analysis highlighted three interactions of epitope against HLA-B7 including Lys 178, Gol 303 and Thr 31 residues. In conclusion, the predicted multi epitope peptide can be suggested as therapeutic or prophylactic candidate vaccine against SARS-CoV-2 after further confirmation by immunological assays.

Identification of T Cell Epitopes in the Spike Glycoprotein of Severe Acute Respiratory Syndrome Coronavirus 2 in Rhesus Macaques

The T cell response is an important detection index in severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) vaccine development. The present study was undertaken to determine the T cell epitopes in the spike (S) protein of SARS-CoV-2 that dominate the T cell responses in SARS-CoV-2-infected patients. PBMCs from rhesus macaques vaccinated with a DNA vaccine encoding the full-length S protein were isolated, and an ELISPOT assay was used to identify the recognized T cell epitopes among a total of 158 18-mer and 10-aa-overlapping peptides spanning the full-length S protein. Six multipeptide-based epitopes located in the S1 region, with four of the six located in the receptor-binding domain, were defined as the most frequently recognized epitopes in macaques. The conservation of the epitopes across species was also verified, and peptide mixtures for T cell response detection were established. Six newly defined T cell epitopes were found in the current study, which may provide a novel potential target for T cell response detection and the diagnosis and vaccine design of SARS-CoV-2 based on multipeptide subunit-based epitopes.

Randomized peptide assemblies for enhancing immune responses to nanomaterials

Biomaterials capable of inducing immune responses with minimal associated inflammation are of interest in applications ranging from tissue repair to vaccines. Here we report the design of self-assembling randomized polypeptide nanomaterials inspired by glatiramoids, an immunomodulatory class of linear random copolymers. We hypothesized that peptide self-assemblies bearing similar randomized polypeptides would similarly raise responses skewed toward Type 2 immunity and T_H2 T-cell responses, additionally strengthening responses to co-assembled peptide epitopes in the absence of adjuvant. We developed a method for synthesizing self-assembling peptides terminated with libraries of randomized polypeptides (termed KEYA) with good batch-to-batch reproducibility. These peptides formed regular nanofibers and raised strong antibody responses without adjuvants. KEYA modifications dramatically improved uptake of peptide nanofibers in vitro by antigen presenting cells, and served as strong B-cell and T-cell epitopes in vivo, enhancing immune responses against epitopes relevant to influenza and chronic inflammation while inducing a KEYA-specific Type 2/T_H2/IL-4 phenotype. KEYA modifications also increased IL-4 production by T cells, extended the residence time of nanofibers, induced no measurable swelling in footpad injections, and decreased overall T cell expansion compared to unmodified nanofibers, further suggesting a T_H2 T-cell response with minimal inflammation. Collectively, this work introduces a biomaterial capable of raising strong Type 2/T_H2/IL-4 immune responses, with potential applications ranging from vaccination to tissue repair.

Vaccinomics approach for scheming potential epitope-based peptide vaccine by targeting l-protein of Marburg virus

Marburg virus is one of the world’s most threatening diseases, causing extreme hemorrhagic fever, with a death rate of up to 90%. The Food and Drug Administration (FDA) currently not authorized any treatments or vaccinations for the hindrance and post-exposure of the Marburg virus. In the present study, the vaccinomics methodology was adopted to design a potential novel peptide vaccine against the Marburg virus, targeting RNA-directed RNA polymerase (l). A total of 48 l-proteins from diverse variants of the Marburg virus were collected from the NCBI GenBank server and used to classify the best antigenic protein leading to predict equally T and B-cell epitopes. Initially, the top 26 epitopes were evaluated for the attraction with major histocompatibility complex (MHC) class I and II alleles. Finally, four prospective central epitopes NLSDLTFLI, FRYEFTRHF, YRLRNSTAL, and YRVRNVQTL were carefully chosen. Among these, FRYEFTRHF and YRVRNVQTL peptides showed 100% conservancy. Though YRLRNSTAL showed 95.74% conservancy, it demonstrated the highest combined score as T cell epitope (2.5461) and population coverage of 94.42% among the whole world population. The epitope was found non-allergenic, and docking interactions with human leukocyte antigens (HLAs) also verified. Finally, in vivo analysis of the recommended peptides might contribute to the advancement of an efficient and exclusively prevalent vaccine that would be an active route to impede the virus spreading.

Immunoinformatics and Molecular Docking Studies Predicted Potential Multiepitope-Based Peptide Vaccine and Novel Compounds against Novel SARS-CoV-2 through Virtual Screening

BACKGROUND

Coronaviruses (CoVs) are enveloped positive-strand RNA viruses which have club-like spikes at the surface with a unique replication process. Coronaviruses are categorized as major pathogenic viruses causing a variety of diseases in birds and mammals including humans (lethal respiratory dysfunctions). Nowadays, a new strain of coronaviruses is identified and named as SARS-CoV-2. Multiple cases of SARS-CoV-2 attacks are being reported all over the world. SARS-CoV-2 showed high death rate; however, no specific treatment is available against SARS-CoV-2.

METHODS

In the current study, immunoinformatics approaches were employed to predict the antigenic epitopes against SARS-CoV-2 for the development of the coronavirus vaccine. Cytotoxic T-lymphocyte and B-cell epitopes were predicted for SARS-CoV-2 coronavirus protein. Multiple sequence alignment of three genomes (SARS-CoV, MERS-CoV, and SARS-CoV-2) was used to conserved binding domain analysis.

RESULTS

The docking complexes of 4 CTL epitopes with antigenic sites were analyzed followed by binding affinity and binding interaction analyses of top-ranked predicted peptides with MHC-I HLA molecule. The molecular docking (Food and Drug Regulatory Authority library) was performed, and four compounds exhibiting least binding energy were identified. The designed epitopes lead to the molecular docking against MHC-I, and interactional analyses of the selected docked complexes were investigated. In conclusion, four CTL epitopes (GTDLEGNFY, TVNVLAWLY, GSVGFNIDY, and QTFSVLACY) and four FDA-scrutinized compounds exhibited potential targets as peptide vaccines and potential biomolecules against deadly SARS-CoV-2, respectively. A multiepitope vaccine was also designed from different epitopes of coronavirus proteins joined by linkers and led by an adjuvant.

CONCLUSION

Our investigations predicted epitopes and the reported molecules that may have the potential to inhibit the SARS-CoV-2 virus. These findings can be a step towards the development of a peptide-based vaccine or natural compound drug target against SARS-CoV-2.

Whole proteome screening and identification of potential epitopes of SARS-CoV-2 for vaccine design-an immunoinformatic, molecular docking and molecular dynamics simulation accelerated robust strategy

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the most cryptic pandemic outbreak of the 21st century, has gripped more than 1.8 million people to death and infected almost eighty six million. As it is a new variant of SARS, there is no approved drug or vaccine available against this virus. This study aims to predict some promising cytotoxic T lymphocyte epitopes in the SARS-CoV-2 proteome utilizing immunoinformatic approaches. Firstly, we identified 21 epitopes from 7 different proteins of SARS-CoV-2 inducing immune response and checked for allergenicity and conservancy. Based on these factors, we selected the top three epitopes, namely KAYNVTQAF, ATSRTLSYY, and LTALRLCAY showing functional interactions with the maximum number of MHC alleles and no allergenicity. Secondly, the 3D model of selected epitopes and HLA-A*29:02 were built and Molecular Docking simulation was performed. Most interestingly, the best two epitopes predicted by docking are part of two different structural proteins of SARS-CoV-2, namely Membrane Glycoprotein (ATSRTLSYY) and Nucleocapsid Phosphoprotein (KAYNVTQAF), which are generally target of choice for vaccine designing. Upon Molecular Docking, interactions between selected epitopes and HLA-A*29:02 were further validated by 50 ns Molecular Dynamics (MD) simulation. Analysis of RMSD, Rg, SASA, number of hydrogen bonds, RMSF, MM-PBSA, PCA, and DCCM from MD suggested that ATSRTLSYY is the most stable and promising epitope than KAYNVTQAF epitope. Moreover, we also identified B-cell epitopes for each of the antigenic proteins of SARS CoV-2. Findings of our work will be a good resource for wet lab experiments and will lessen the timeline for vaccine construction.Communicated by Ramaswamy H. Sarma.

An in silico deep learning approach to multi-epitope vaccine design: a SARS-CoV-2 case study

The rampant spread of COVID-19, an infectious disease caused by SARS-CoV-2, all over the world has led to over millions of deaths, and devastated the social, financial and political entities around the world. Without an existing effective medical therapy, vaccines are urgently needed to avoid the spread of this disease. In this study, we propose an in silico deep learning approach for prediction and design of a multi-epitope vaccine (DeepVacPred). By combining the in silico immunoinformatics and deep neural network strategies, the DeepVacPred computational framework directly predicts 26 potential vaccine subunits from the available SARS-CoV-2 spike protein sequence. We further use in silico methods to investigate the linear B-cell epitopes, Cytotoxic T Lymphocytes (CTL) epitopes, Helper T Lymphocytes (HTL) epitopes in the 26 subunit candidates and identify the best 11 of them to construct a multi-epitope vaccine for SARS-CoV-2 virus. The human population coverage, antigenicity, allergenicity, toxicity, physicochemical properties and secondary structure of the designed vaccine are evaluated via state-of-the-art bioinformatic approaches, showing good quality of the designed vaccine. The 3D structure of the designed vaccine is predicted, refined and validated by in silico tools. Finally, we optimize and insert the codon sequence into a plasmid to ensure the cloning and expression efficiency. In conclusion, this proposed artificial intelligence (AI) based vaccine discovery framework accelerates the vaccine design process and constructs a 694aa multi-epitope vaccine containing 16 B-cell epitopes, 82 CTL epitopes and 89 HTL epitopes, which is promising to fight the SARS-CoV-2 viral infection and can be further evaluated in clinical studies. Moreover, we trace the RNA mutations of the SARS-CoV-2 and ensure that the designed vaccine can tackle the recent RNA mutations of the virus.

Analysis of the COVID-19 Vaccine Development Process: an Exploratory Study of Accelerating Factors and Innovative Environments

Purpose

The pace of the COVID-19 vaccine development process is unprecedented and is challenging the traditional paradigm of vaccinology science. The main pressure comes from the pandemic situation, but what makes it possible is a complex set of factors and innovative environments built along the times, which this manuscript aims to study.

Methods

Through an exploratory study within the scope of innovation management, the present manuscript aims to identify and explore factors that are promoting this accelerated development scenario. The method comprises the monitoring of the strategies adopted by the developers and other stakeholders, as regulatory and humanitarian agencies, specific mechanisms from governments and non-governments bodies, and the background technology that has paved this pathway.

Results

Technology-based and R&D strategy factors are the two main factors identified and explored herein. The breakthrough in the field of biotechnology and molecular biology is considered the main base-science that enables the rapid development of new vaccines. Additionally, new technological platforms can also be pointed out. Relating to R&D strategies, the parallelism of phases and adaptive clinical trials in consonance with regulatory agencies are the most relevant.

Conclusions

The need to rapidly develop a vaccine against COVID-19 occurs at a time of great excitement in basic scientific understanding, as well as strategies learned in the past by industry and optimization of regulatory pathways. It is expected that these factors, arising from the global emergency, may redirect the R&D processes for new drugs, especially in times of pandemic.

Molecular Elucidation and Therapeutic Targeting for combating COVID19: Current Scenario and Future Prospective

A corona virus disease 2019 (COVID-19) is a contagious disease which is caused by a novel corona virus. Human corona virus (HCoV) recognized as one of the most rapidly evolving viruses owing to its high genomic nucleotide substitution rates and recombination. Among the severe acute respiratory syndrome (SARS) and Middle-East respiratory syndrome (MERS), COVID-19 has spread more rapidly and increased the level of globalization and adaptation of the virus in every environmental condition due to their high rate of molecular diversity. The whole article highlights the general characteristics of corona virus, their molecular diversity, and molecular protein targeting against COVID-19 with their newer approaches. Through this review, an attempt has made to critically evaluate the recent advances and future aspects helpful to the treatment of COVID-19 based on the present understanding of SARS-CoV-2 infections, which may help offer new insights and potential therapeutic targets for the treatment of the COVID-19.

Pharmacoinformatics based elucidation and designing of potential inhibitors against Plasmodium falciparum to target importin α/β mediated nuclear importation

Plasmodium falciparum, the prime causative agent of malaria, is responsible for 4, 05,000 deaths per year and fatality rates are higher among the children aged below 5 years. The emerging distribution of the multi-drug resistant P. falciparum becomes a worldwide concern, so the identification of unique targets and novel inhibitors is a prime need now. In the present study, we have employed pharmacoinformatics approaches to analyze 265 lead-like compounds from PubChem databases for virtual screening. Thereafter, 15 lead-like compounds were docked within the active side pocket of importin alpha. Comparative ligand properties and absorption, distribution, metabolism, excretion, and toxicity (ADMET) profile were also assessed. Finally, a novel inhibitor was designed and assessed computationally for its efficacy. From the comparative analysis we have found that our screened compounds possess better results than the existing lead ivermectin; having the highest binding energy of -15.6 kcal/mol, whereas ivermectin has -12.4 kcal/mol. The novel lead compound possessed more fascinating output without deviating any of the rules of Lipinski. It also possessed higher bioavailability and the drug-likeness score of 0.55 and 0.71, respectively compared to ivermectin. Furthermore, the binding study was confirmed by molecular dynamics simulation over 25 ns by evaluating the stability of the complex. Finally, all the screened compounds and the novel compound showed promising ADMET properties likewise. To end, we hope that our proposed screened compounds, as well as the novel compound, might give some advances to treat malaria efficiently in vitro and in vivo.

Nanotheranostics against COVID-19: From multivalent to immune-targeted materials

Destructive impacts of COVID-19 pandemic worldwide necessitates taking more appropriate measures for mitigating virus spread and development of the effective theranostic agents. In general, high heterogeneity of viruses is a major challenging issue towards the development of effective antiviral agents. Regarding the coronavirus, its high mutation rates can negatively affect virus detection process or the efficiency of drugs and vaccines in development or induce drug resistance. Bioengineered nanomaterials with suitable physicochemical characteristics for site-specific therapeutic delivery, highly-sensitive nanobiosensors for detection of very low virus concentration, and real-time protections using the nanorobots can provide roadmaps towards the imminent breakthroughs in theranostics of a variety of diseases including the COVID-19. Besides revolutionizing the classical disinfection procedures, state-of-the-art nanotechnology-based approaches enable providing the analytical tools for accelerated monitoring of coronavirus and associated biomarkers or drug delivery towards the pulmonary system or other affected organs. Multivalent nanomaterials capable of interaction with multivalent pathogens including the viruses could be suitable candidates for viral detection and prevention of further infections. Besides the inactivation or destruction of the virus, functionalized nanoparticles capable of modulating patient's immune response might be of great significance for attenuating the exaggerated inflammatory reactions or development of the effective nanovaccines and medications against the virus pandemics including the COVID-19.

Epitope-Based Immunoinformatics Approach on Nucleocapsid Protein of Severe Acute Respiratory Syndrome-Coronavirus-2

With an increasing fatality rate, severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2) has emerged as a promising threat to human health worldwide. Recently, the World Health Organization (WHO) has announced the infectious disease caused by SARS-CoV-2, which is known as coronavirus disease-2019 (COVID-2019), as a global pandemic. Additionally, the positive cases are still following an upward trend worldwide and as a corollary, there is a need for a potential vaccine to impede the progression of the disease. Lately, it has been documented that the nucleocapsid (N) protein of SARS-CoV-2 is responsible for viral replication and interferes with host immune responses. We comparatively analyzed the sequences of N protein of SARS-CoV-2 for the identification of core attributes and analyzed the ancestry through phylogenetic analysis. Subsequently, we predicted the most immunogenic epitope for the T-cell and B-cell. Importantly, our investigation mainly focused on major histocompatibility complex (MHC) class I potential peptides and NTASWFTAL interacted with most human leukocyte antigen (HLA) that are encoded by MHC class I molecules. Further, molecular docking analysis unveiled that NTASWFTAL possessed a greater affinity towards HLA and also available in a greater range of the population. Our study provides a consolidated base for vaccine design and we hope that this computational analysis will pave the way for designing novel vaccine candidates.

Genome based evolutionary lineage of SARS-CoV-2 towards the development of novel chimeric vaccine

The present study aimed to predict a novel chimeric vaccine by simultaneously targeting four major structural proteins via the establishment of ancestral relationship among different strains of coronaviruses. Conserved regions from the homologous protein sets of spike glycoprotein, membrane protein, envelope protein and nucleocapsid protein were identified through multiple sequence alignment. The phylogeny analyses of whole genome stated that four proteins reflected the close ancestral relation of SARS-CoV-2 to SARS-COV-1 and bat coronavirus. Numerous immunogenic epitopes (both T cell and B cell) were generated from the common fragments which were further ranked on the basis of antigenicity, transmembrane topology, conservancy level, toxicity and allergenicity pattern and population coverage analysis. Top putative epitopes were combined with appropriate adjuvants and linkers to construct a novel multiepitope subunit vaccine against COVID-19. The designed constructs were characterized based on physicochemical properties, allergenicity, antigenicity and solubility which revealed the superiority of construct V3 in terms safety and efficacy. Essential molecular dynamics and normal mode analysis confirmed minimal deformability of the refined model at molecular level. In addition, disulfide engineering was investigated to accelerate the stability of the protein. Molecular docking study ensured high binding affinity between construct V3 and HLA cells, as well as with different host receptors. Microbial expression and translational efficacy of the constructs were checked using pET28a(+) vector of E. coli strain K12. However, the in vivo and in vitro validation of suggested vaccine molecule might be ensured with wet lab trials using model animals for the implementation of the presented data.

Systems Biology Approaches for Therapeutics Development Against COVID-19

Understanding the systems biology approaches for promoting the development of new therapeutic drugs is attaining importance nowadays. The threat of COVID-19 outbreak needs to be vanished for global welfare, and every section of research is focusing on it. There is an opportunity for finding new, quick, and accurate tools for developing treatment options, including the vaccine against COVID-19. The review at this moment covers various aspects of pathogenesis and host factors for exploring the virus target and developing suitable therapeutic solutions through systems biology tools. Furthermore, this review also covers the extensive details of multiomics tools i.e., transcriptomics, proteomics, genomics, lipidomics, immunomics, and in silico computational modeling aiming towards the study of host-virus interactions in search of therapeutic targets against the COVID-19.

Design of novel multiepitope constructs-based peptide vaccine against the structural S, N and M proteins of human COVID-19 using immunoinformatics analysis

The causative agent of severe acute respiratory syndrome (SARS) reported by the Chinese Center for Disease Control (China CDC) has been identified as a novel Betacoronavirus (SARS-CoV-2). A computational approach was adopted to identify multiepitope vaccine candidates against SARS-CoV-2 based on S, N and M proteins being able to elicit both humoral and cellular immune responses. In this study, the sequence of the virus was obtained from NCBI database and analyzed with in silico tools such as NetMHCpan, IEDB, BepiPred, NetCTL, Tap transport/proteasomal cleavage, Pa3P, GalexyPepDock, I-TASSER, Ellipro and ClusPro. To identify the most immunodominant regions, after analysis of population coverage and epitope conservancy, we proposed three different constructs based on linear B-cell, CTL and HTL epitopes. The 3D structure of constructs was assessed to find discontinuous B-cell epitopes. Among CTL predicted epitopes, S257-265, S603-611 and S360-368, and among HTL predicted epitopes, N167-181, S313-330 and S1110-1126 had better MHC binding rank. We found one putative CTL epitope, S360-368 related to receptor-binding domain (RBD) region for S protein. The predicted epitopes were non-allergen and showed a high quality of proteasomal cleavage and Tap transport efficiency and 100% conservancy within four different clades of SARS-CoV-2. For CTL and HTL epitopes, the highest population coverage of the world's population was calculated for S27-37 with 86.27% and for S196-231, S303-323, S313-330, S1009-1030 and N328-349 with 90.33%, respectively. We identified overall 10 discontinuous B-cell epitopes for three multiepitope constructs. All three constructs showed strong interactions with TLRs 2, 3 and 4 supporting the hypothesis of SARS-CoV-2 susceptibility to TLRs 2, 3 and 4 like other Coronaviridae families. These data demonstrated that the novel designed multiepitope constructs can contribute to develop SARS-CoV-2 peptide vaccine candidates. The in vivo studies are underway using several vaccination strategies.

Exploring the out of sight antigens of SARS-CoV-2 to design a candidate multi-epitope vaccine by utilizing immunoinformatics approaches

SARS-CoV-2 causes a severe respiratory disease called COVID-19. Currently, global health is facing its devastating outbreak. However, there is no vaccine available against this virus up to now. In this study, a novel multi-epitope vaccine against SARS-CoV-2 was designed to provoke both innate and adaptive immune responses. The immunodominant regions of six non-structural proteins (nsp7, nsp8, nsp9, nsp10, nsp12 and nsp14) of SARS-CoV-2 were selected by multiple immunoinformatic tools to provoke T cell immune response. Also, immunodominant fragment of the functional region of SARS-CoV-2 spike (400-510 residues) protein was selected for inducing neutralizing antibodies production. The selected regions' sequences were connected to each other by furin-sensitive linker (RVRR). Moreover, the functional region of β-defensin as a well-known agonist for the TLR-4/MD complex was added at the N-terminus of the vaccine using (EAAAK)3 linker. Also, a CD4 + T-helper epitope, PADRE, was used at the C-terminal of the vaccine by GPGPG and A(EAAAK)2A linkers to form the final vaccine construct. The physicochemical properties, allergenicity, antigenicity, functionality and population coverage of the final vaccine construct were analyzed. The final vaccine construct was an immunogenic, non-allergen and unfunctional protein which contained multiple CD8 + and CD4 + overlapping epitopes, IFN-γ inducing epitopes, linear and conformational B cell epitopes. It could form stable and significant interactions with TLR-4/MD according to molecular docking and dynamics simulations. Global population coverage of the vaccine for HLA-I and II were estimated 96.2% and 97.1%, respectively. At last, the final vaccine construct was reverse translated to design the DNA vaccine. Although the designed vaccine exhibited high efficacy in silico, further experimental validation is necessary.

Computational perspectives revealed prospective vaccine candidates from five structural proteins of novel SARS corona virus 2019 (SARS-CoV-2)

Background

The present pandemic COVID-19 is caused by SARS-CoV-2, a single-stranded positive-sense RNA virus from the Coronaviridae family. Due to a lack of antiviral drugs, vaccines against the virus are urgently required.

Methods

In this study, validated computational approaches were used to identify peptide-based epitopes from six structural proteins having antigenic properties. The Net-CTL 1.2 tool was used for the prediction of CD8⁺ T-cell epitopes, while the robust tools Bepi-Pred 2 and LBtope was employed for the identification of linear B-cell epitopes. Docking studies of the identified epitopes were performed using HADDOCK 2.4 and the structures were visualized by Discovery Studio and LigPlot⁺. Antigenicity, immunogenicity, conservancy, population coverage and allergenicity of the predicted epitopes were determined by the bioinformatics tools like VaxiJen v2.0 server, the Immune Epitope Database tools and AllerTOP v.2.0, AllergenFP 1.0 and ElliPro.

Results

The predicted T cell and linear B-cell epitopes were considered as prime vaccine targets in case they passed the requisite parameters like antigenicity, immunogenicity, conservancy, non-allergenicity and broad range of population coverage. Among the predicted CD8+ T cell epitopes, potential vaccine targets from surface glycoprotein were; YQPYRVVVL, PYRVVVLSF, GVYFASTEK, QLTPTWRVY, and those from ORF3a protein were LKKRWQLAL, HVTFFIYNK. Similarly, RFLYIIKLI, LTWICLLQF from membrane protein and three epitopes viz; SPRWYFYYL, TWLTYTGAI, KTFPPTEPK from nucleocapsid phosphoprotein were the superior vaccine targets observed in our study. The negative values of HADDOCK and Z scores obtained for the best cluster indicated the potential of the epitopes as suitable vaccine candidates. Analysis of the 3D and 2D interaction diagrams of best cluster produced by HADDOCK 2.4 displayed the binding interaction of leading T cell epitopes within the MHC-1 peptide binding clefts. On the other hand, among linear B cell epitopes the majority of potential vaccine targets were from nucleocapsid protein, viz; ⁵⁹⁻HGKEDLKFPRGQGVPINTNSSPDDQIGYYRRATRRIRGGDGKMKDLS⁻¹⁰⁵, ²²⁷⁻LNQLE SKMSGKGQQQQGQTVTKKSAAEASKKPRQKRTATK⁻²⁶⁶, ³⁻DNGPQNQRNAPRITFGGP⁻²⁰, ²⁹⁻GERSGARSKQRRPQGL⁻⁴⁵. Two other prime vaccine targets, ³⁷⁰⁻NSASFSTFKCYGVSPTKLNDLCFTNV⁻³⁹⁵ and ²⁶⁰⁻AGAAAYYVGYLQPRT⁻²⁷⁴ were identified in the spike protein. The potential B-cell conformational epitopes were predicted on the basis of a higher protrusion index indicating greater solvent accessibility. These conformational epitopes were of various lengths and belonged to spike, ORF3a, membrane and nucleocapsid proteins.

Conclusions

Taken together, eleven T cell epitopes, seven B cell linear epitopes and ten B cell conformational epitopes were identified from five structural proteins of SARS-CoV-2 using advanced computational tools. These potential vaccine candidates may provide important timely directives for an effective vaccine against SARS-CoV-2.

Design of novel viral attachment inhibitors of the spike glycoprotein (S) of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) through virtual screening and dynamics

To date, the global COVID-19 pandemic has been associated with 11.8 million cases and over 545481 deaths. In this study, we have employed virtual screening approaches and selected 415 lead-like compounds from 103 million chemical substances, based on the existing drugs, from PubChem databases as potential candidates for the S protein-mediated viral attachment inhibition. Thereafter, based on drug-likeness and Lipinski's rules, 44 lead-like compounds were docked within the active side pocket of the viral-host attachment site of the S protein. Corresponding ligand properties and absorption, distribution, metabolism, excretion, and toxicity (ADMET) profile were measured. Furthermore, four novel inhibitors were designed and assessed computationally for efficacy. Comparative analysis showed the screened compounds in this study maintain better results than the proposed mother compounds, VE607 and SSAA09E2. The four designed novel lead compounds possessed more fascinating output without deviating from any of Lipinski's rules. They also showed higher bioavailability and the drug-likeness score was 0.56 and 1.81 compared with VE607 and SSAA09E2, respectively. All the screened compounds and novel compounds showed promising ADMET properties. Among them, the compound AMTM-02 was the best candidate, with a docking score of -7.5 kcal/mol. Furthermore, the binding study was verified by molecular dynamics simulation over 100 ns by assessing the stability of the complex. The proposed screened compounds and the novel compounds may give some breakthroughs for the development of a therapeutic drug to treat SARS-CoV-2 proficiently in vitro and in vivo.

In Silico Design of a Novel Multi-Epitope Peptide Vaccine Against Hepatocellular Carcinoma

Background:

Hepatocellular Carcinoma (HCC) is a prevalent cancer in the world. As yet, there is no medication for complete treatment of HCC.

Objective:

There is a critical need to search for an innovative therapy for HCC. Recently, multiepitope vaccines have been introduced as effective immunotherapy approach against HCC.

Methods:

In this research, several immunoinformatics methods were applied to create an original multi-epitope vaccine against HCC consisting of CD8+ cytolytic T lymphocytes (CTLs) epitopes selected from α- fetoprotein (AFP), glypican-3 (GPC3), aspartyl-β-hydroxylase (ASPH); CD4+ helper T lymphocytes (HTLs) epitopes from tetanus toxin fragment C (TTFC), and finally, two tandem repeats of HSP70407-426 were used which stimulated strong innate and adaptive immune responses. All the mentioned parts were connected together by relevant linkers.

Results:

According to physicochemical, structural, and immunological results, the designed vaccine is stable, non-allergen, antigen; it also has a high-quality 3D structure, and numerous linear and conformational B cell epitopes, whereby this vaccine may stimulate efficient humoral immunity.

Conclusion:

Center on the collected results, the designed vaccine potentially can induce cellular and humoral immune responses in HCC cases; nonetheless, the efficiency of vaccine must be approved within in vitro and in vivo immunological analyzes.

Artificial Intelligence for COVID-19 Drug Discovery and Vaccine Development

SARS-COV-2 has roused the scientific community with a call to action to combat the growing pandemic. At the time of this writing, there are as yet no novel antiviral agents or approved vaccines available for deployment as a frontline defense. Understanding the pathobiology of COVID-19 could aid scientists in their discovery of potent antivirals by elucidating unexplored viral pathways. One method for accomplishing this is the leveraging of computational methods to discover new candidate drugs and vaccines in silico. In the last decade, machine learning-based models, trained on specific biomolecules, have offered inexpensive and rapid implementation methods for the discovery of effective viral therapies. Given a target biomolecule, these models are capable of predicting inhibitor candidates in a structural-based manner. If enough data are presented to a model, it can aid the search for a drug or vaccine candidate by identifying patterns within the data. In this review, we focus on the recent advances of COVID-19 drug and vaccine development using artificial intelligence and the potential of intelligent training for the discovery of COVID-19 therapeutics. To facilitate applications of deep learning for SARS-COV-2, we highlight multiple molecular targets of COVID-19, inhibition of which may increase patient survival. Moreover, we present CoronaDB-AI, a dataset of compounds, peptides, and epitopes discovered either in silico or in vitro that can be potentially used for training models in order to extract COVID-19 treatment. The information and datasets provided in this review can be used to train deep learning-based models and accelerate the discovery of effective viral therapies.

An overview of in silico vaccine design against different pathogens and cancer

INTRODUCTION

Due to overcome the hardness of the vaccine design, computational vaccinology is emerging widely. Prediction of T cell and B cell epitopes, antigen processing analysis, antigenicity analysis, population coverage, conservancy analysis, allergenicity assessment, toxicity prediction, and protein-peptide docking are important steps in the process of designing and developing potent vaccines against various viruses and cancers. In order to perform all of the analyses, several bioinformatics tools and online web servers have been developed. Scientists must take the decision to apply more suitable and precise servers for each part based on their accuracy.

AREAS COVERED

In this review, a wide-range list of different bioinformatics tools and online web servers has been provided. Moreover, some studies were proposed to show the importance of various bioinformatics tools for predicting and developing efficient vaccines against different pathogens including viruses, bacteria, parasites, and fungi as well as cancer.

EXPERT OPINION

Immunoinformatics is the best way to find potential vaccine candidates against different pathogens. Thus, the selection of the most accurate tools is necessary to predict and develop potent preventive and therapeutic vaccines. To further evaluation of the computational and in silico vaccine design, in vitro/in vivo analyses are required to develop vaccine candidates.

An insight into the epitope-based peptide vaccine design strategy and studies against COVID-19

SARS-CoV-2 is a new member of the coronavirus family and caused the pandemic of coronavirus disease 2019 (COVID-19) in 2020. It is crucial to design and produce an effective vaccine for the prevention of rapid transmission and possible deaths wcaused by the disease. Although intensive work and research are being carried out all over the world to develop a vaccine, an effective and approved formulation that can prevent the infection and limit the outbreak has not been announced yet. Among all types of vaccines, epitope-based peptide vaccines outshine with their low-cost production, easy modification in the structure, and safety. In this review, vaccine studies against COVID-19 have been summarized and detailed information about the epitope-based peptide vaccines against COVID-19 has been provided. We have not only compared the peptide vaccine with other types of vaccines but also presented comprehensive literature information about development steps for an effective and protective formulation to give an insight into on-going peptide vaccine studies against SARS-CoV-2.

In silico vaccine design against Chlamydia trachomatis infection

Chlamydia trachomatis, a gram-negative bacterium known to infect the genital sites mainly columnar epithelial cells of the cervix, urethra and rectum in women and causes acute epididymitis, urinary tract inflammation and DNA damage to the sperms in men, hence considered to be one of the major sexually transmitted infections. The infection is asymptomatic in many people and remains untreated leading to serious health complications, including pelvic inflammatory disease, ectopic pregnancy and infertility. The current treatment options include antibiotics, but the pathogen has gained resistance against many antibiotics. The present work involves an in silico reverse vaccinology approach for identifying the immunogens as vaccine candidates that can be effective against reinfections and should be capable of inducing long-term protective immunity against Chlamydial infections. This study identifies the putative vaccine candidates that are membrane bound with high antigenicity properties; antigenicity induces the immunogenicity which involves identification of T-cell and B-cell epitopes that induce both humoral and cell-mediated immunity. The epitopes 'LSWEMELAY', 'LSNTEGYRY', 'TSDLGQMEY', 'FIDLLQAIY' and 'FSNNFSDIY' were predicted as core sequences for class I MHC molecules. The identified epitopes showed promising ability to interact with the human leukocyte antigens (HLA). These epitopes showed maximum population coverage with epitope conservancy above 80%. Molecular docking was performed to test the binding affinities of the identified epitopes with the HLA molecule to study the binding cleft interactions. The vaccine candidate thus identified from this study showed to possess the potential to activate the B- and T-cell immune responses which are more specific and make the body stronger against infections and effective for reinfections.

Understanding the B and T cell epitopes of spike protein of severe acute respiratory syndrome coronavirus-2: A computational way to predict the immunogens

The 2019 novel severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) outbreak has caused a large number of deaths, with thousands of confirmed cases worldwide. The present study followed computational approaches to identify B- and T-cell epitopes for the spike (S) glycoprotein of SARS-CoV-2 by its interactions with the human leukocyte antigen alleles. We identified 24 peptide stretches on the SARS-CoV-2 S protein that are well conserved among the reported strains. The S protein structure further validated the presence of predicted peptides on the surface, of which 20 are surface exposed and predicted to have reasonable epitope binding efficiency. The work could be useful for understanding the immunodominant regions in the surface protein of SARS-CoV-2 and could potentially help in designing some peptide-based diagnostics. Also, identified T-cell epitopes might be considered for incorporation in vaccine designs.

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Determination of variability and average solvent accessibility.

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Identification of the B- and T-cell epitopes for spike glycoprotein of SARS-CoV-2.

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Interactions of B and T cell epitopes with the human leukocyte antigen alleles.

Epitope-Based Peptide Vaccine Against Severe Acute Respiratory Syndrome-Coronavirus-2 Nucleocapsid Protein: An in silico Approach.. [DOI: 10.1101/2020.05.16.100206] [Citation(s) in RCA: 0] [Impact Index Per Article: 0]

With an increasing fatality rate, severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2) has emerged as a promising threat to human health worldwide. SARS-CoV-2 is a member of the Coronaviridae family, which is transmitted from animal to human and because of being contagious, further it transmitted human to human. Recently, the World Health Organization (WHO) has announced the infectious disease caused by SARS-CoV-2, which is known as coronavirus disease-2019 (COVID-2019) as a global pandemic. But, no specific medications are available for the treatment of COVID-19 so far. As a corollary, there is a need for a potential vaccine to impede the progression of the disease. Lately, it has been documented that the nucleocapsid (N) protein of SARS-CoV-2 is responsible for viral replication as well as interferes with host immune responses. We have comparatively analyzed the sequences of N protein of SARS-CoV-2 for the identification of core attributes and analyzed the ancestry through phylogenetic analysis. Subsequently, we have predicted the most immunogenic epitope for T-cell as well as B-cell. Importantly, our investigation mainly focused on major histocompatibility complex (MHC) class I potential peptides and NTASWFTAL interacted with most human leukocyte antigen (HLA) that are encoded by MHC class I molecules. Further, molecular docking analysis unveiled that NTASWFTAL possessed a greater affinity towards HLA and also available in a greater range of the population. Our study provides a consolidated base for vaccine design and we hope that this computational analysis will pave the way for designing novel vaccine candidates.

Drug repurposing for coronavirus (COVID-19): in silico screening of known drugs against coronavirus 3CL hydrolase and protease enzymes

In December 2019, COVID-19 epidemic was described in Wuhan, China, and the infection has spread widely affecting hundreds of thousands. Herein, an effort was made to identify commercially available drugs in order to repurpose them against coronavirus by the means of structure-based virtual screening. In addition, ZINC15 library was used to identify novel leads against main proteases. Human TMPRSS2 3D structure was first generated using homology modeling approach. Our molecular docking study showed four potential inhibitors against Mpro enzyme, two available drugs (Talampicillin and Lurasidone) and two novel drug-like compounds (ZINC000000702323 and ZINC000012481889). Moreover, four promising inhibitors were identified against TMPRSS2; Rubitecan and Loprazolam drugs, and compounds ZINC000015988935 and ZINC000103558522. ADMET profile showed that the hits from our study are safe and drug-like compounds. Furthermore, molecular dynamic (MD) simulation and binding free energy calculation using the MM-PBSA method was performed to calculate the interaction energy of the top-ranked drugs.Communicated by Ramaswamy H. Sarma.

Assessment of a poly-epitope candidate vaccine against Hepatitis B, C, and poliovirus in interaction with monocyte-derived dendritic cells: An ex-vivo study

Design and application of epitope-based polyvalent vaccines have recently garnered attention as an efficient alternative for conventional vaccines. We previously have reported the in silico design of HHP antigen which encompasses the immune-dominant epitopes of Hepatitis B surface antigen (HBsAg), Hepatitis C core protein (HCVcp) and Poliovirus viral proteins (VPs). It has been shown that the HHP has desirable conformation to expose the epitopes, high antigenicity and other desired physicochemical and immunological properties. To confirm the accuracy of these predictions, the ex-vivo immunogenicity of the HHP was assessed. The HHP gene was chemically synthesized in pET28a and expressed in E. coli (BL21). The expressed protein was purified and its immunological potency was evaluated on dendritic cells (DCs) as antigen presenting cells (APCs). Functional analysis was assessed in co-cultivation of autologous T-cells with matured DCs (mDCs). T-cell activation, proliferation and cytokines secretion were evaluated using flowcytometry and ELISA methods. Our results indicated that the HHP could induce the DC maturation. The mDCs were able to trigger T-cell activation and proliferation. In silico design and ex-vivo confirmation of immunological potential could pave the way to introduce efficient immunogens for further analysis. The ability of HHP in DC maturation and T-cell activation makes it an amenable vaccine candidate for further in-vivo studies.

Bioinformatics design and experimental validation of influenza A virus multi-epitopes that induce neutralizing antibodies

Pandemics caused by influenza A virus (IAV) are responsible for the deaths of millions of humans around the world. One of these pandemics occurred in Mexico in 2009. Despite the impact of IAV on human health, there is no effective vaccine. Gene mutations and translocation of genome segments of different IAV subtypes infecting a single host cell make the development of a universal vaccine difficult. The design of immunogenic peptides using bioinformatics tools could be an interesting strategy to increase the success of vaccines. In this work, we used the predicted amino acid sequences of the neuraminidase (NA) and hemagglutinin (HA) proteins of different IAV subtypes to perform multiple alignments, epitope predictions, molecular dynamics simulations, and experimental validation. Peptide selection was based on the following criteria: promiscuity, protein surface exposure, and the degree of conservation among different medically relevant IAV strains. These peptides were tested using immunological assays to test their ability to induce production of antibodies against IAV. We immunized rabbits and mice and measured the levels of IgG and IgA antibodies in serum samples and nasal washes. Rabbit antibodies against the peptides P11 and P14 (both of which are hybrids of NA and HA) recognized HA from both group 1 (H1, H2, and H5) and group 2 (H3 and H7) IAV and also recognized the purified NA protein from the viral stock (influenza A Puerto Rico/916/34). IgG antibodies from rabbits immunized with P11 and P14 were capable of recognizing viral particles and inhibited virus hemagglutination. Additionally, intranasal immunization of mice with P11 and P14 induced specific IgG and IgA antibodies in serum and nasal mucosa, respectively. Interestingly, the IgG antibodies were found to have neutralizing capability. In conclusion, the peptides designed through in silico studies were validated in experimental assays.

Development of HPV16,18,31,45 E5 and E7 peptides-based vaccines predicted by immunoinformatics tools

Objectives

Viral oncoproteins are ideal targets in therapeutic vaccines for functional inhibition of human papillomaviruses (HPVs). Herein, we designed the peptide constructs derived from E5 and E7 oncoproteins of high-risk HPV types 16, 18, 31 and 45 using the bioinformatics tools and investigated their potency in mice.

Results

The framework of the combined in silico/in vivo analysis included (1) to determine physicochemical properties of the designed constructs, (2) to identify potential IFN-γ-inducing epitopes, (3) to assess allergenicity, (4) to recognize linear and discontinuous B cell epitopes using modeling and validation of 3D structure of the designed constructs, and (5) to evaluate immune responses and tumor growth in vivo. Our in silico data determined high potency of the HPV_16,18,31,45 E5 and HPV_16,18,31,45 E7 peptides for trigger B- and T-cell responses, and IFN-γ secretion. In vivo study indicated that the mixture of E5 and E7 immunodominant peptides from four types of high-risk HPV could induce Th1 immune response, and protect completely mice against TC-1 tumor cells.

Conclusion

Generally, the combined in silico/in vivo approaches showed the ability of the designed E5 and E7 peptide constructs from four major high-risk HPV types for development of therapeutic vaccines.

Electronic supplementary material

The online version of this article (10.1007/s10529-020-02792-6) contains supplementary material, which is available to authorized users.

Japanese encephalitis virus: A multi-epitope loaded peptide vaccine formulation using reverse vaccinology approach

Japanese encephalitis (JE) is a serious leading health complication emerging expansively that has severely affected the survival rate of human beings. This fatal disease is caused by JE Virus (JEV). The current study was carried out for designing a multi-epitope loaded peptide vaccine to prevent JEV. Based on reverse vaccinology and in silico approaches, octapeptide B-cell and hexapeptide T-cell epitopes belonging to five proteins, viz. E, prM, NS1, NS3 and NS5 of JEV were determined. Hydrophilicity, antigenicity, immunogenicity and aliphatic amino acids of the epitopes were estimated. Further, the epitopes were analyzed for different physicochemical parameters, e.g. total net charges, amino acid composition and Boman index. Out of all the epitopes, a total of four T-cell epitopes namely KRADSS, KRSRRS, SKRSRR and KECPDE and one B-cell epitope i.e. PKPCSKGD were found to have potential for raising immunity in human against the pathogen. Taking into account the outcome of this study, the pharmaceutical industries could initiate efforts to combine the identified epitopes together with adjuvant or carrier protein to develop a multi-epitope-loaded peptide vaccine against JEV. The peptide vaccine, being cost effective, could be administered as a prophylactic measure and in JEV infected individuals to combat the spread of this virus in human population. However, prior to administration into human beings, the vaccine must pass through several clinical trials.

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Potential T and B-cell epitopes of 5 proteins in Japanese encephalitis virus (JEV) were determined using bioinformatics.

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All epitopes were analyzed for hydrophilicity, immunogenicity, antigenicity and other physicochemical parameters.

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Identified epitopes could be used for developing a multi-epitope-loaded peptide vaccine to combat JEV.

In silico/In vivo analysis of high-risk papillomavirus L1 and L2 conserved sequences for development of cross-subtype prophylactic vaccine

Human papillomavirus (HPV) is the most common sexually transmitted infection in the world and the main cause of cervical cancer. Nowadays, the virus-like particles (VLPs) based on L1 proteins have been considered as the best candidate for vaccine development against HPV infections. Two commercial HPV (Gardasil and Cervarix) are available. These HPV VLP vaccines induce genotype-limited protection. The major impediments such as economic barriers especially gaps in financing obstructed the optimal delivery of vaccines in developing countries. Thus, many efforts are underway to develop the next generation of vaccines against other types of high-risk HPV. In this study, we developed DNA constructs (based on L1 and L2 genes) that were potentially immunogenic and highly conserved among the high-risk HPV types. The framework of analysis include (1) B-cell epitope mapping, (2) T-cell epitope mapping (i.e., CD4⁺ and CD8⁺ T cells), (3) allergenicity assessment, (4) tap transport and proteasomal cleavage, (5) population coverage, (6) global and template-based docking, and (7) data collection, analysis, and design of the L1 and L2 DNA constructs. Our data indicated the 8-epitope candidates for helper T-cell and CTL in L1 and L2 sequences. For the L1 and L2 constructs, combination of these peptides in a single universal vaccine could involve all world population by the rate of 95.55% and 96.33%, respectively. In vitro studies showed high expression rates of multiepitope L1 (~57.86%) and L2 (~68.42%) DNA constructs in HEK-293T cells. Moreover, in vivo studies indicated that the combination of L1 and L2 DNA constructs without any adjuvant or delivery system induced effective immune responses, and protected mice against C3 tumor cells (the percentage of tumor-free mice: ~66.67%). Thus, the designed L1 and L2 DNA constructs would represent promising applications for HPV vaccine development.

Epitope Prediction by Novel Immunoinformatics Approach: A State-of-the-art Review

Immunoinformatics is a science that helps to create significant immunological information using bioinformatics softwares and applications. One of the most important applications of immunoinformatics is the prediction of a variety of specific epitopes for B cell recognition and T cell through MHC class I and II molecules. This method reduces costs and time compared to laboratory tests. In this state-of-the-art review, we review about 50 papers to find the latest and most used immunoinformatic tools as well as their applications for predicting the viral, bacterial and tumoral structural and linear epitopes of B and T cells. In the clinic, the main application of prediction of epitopes is for designing peptide-based vaccines. Peptide-based vaccines are a considerably potential alternative to low-cost vaccines that may reduce the risks related to the production of common vaccines.

Epidemics and Peptide Vaccine Response: A Brief Review

We briefly review the situations arising out of epidemics that erupt rather suddenly, threatening life and livelihoods of humans. Ebola, Zika and the Nipah virus outbreaks are recent examples where the viral epidemics have led to considerably high degree of fatalities or debilitating consequences. The problems are accentuated by a lack of drugs or vaccines effective against the new and emergent viruses, and the inordinate amount of temporal and financial resources that are required to combat the novel pathogens. Progress in computational, biological and informational sciences have made it possible to consider design of synthetic vaccines that can be rapidly developed and deployed to help stem the damages. In this review, we consider the pros and cons of this new paradigm and suggest a new system where the manufacturing process can be decentralized to provide more targeted vaccines to meet the urgent needs of protection in case of a rampaging epidemic.

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.

Expression and serological application of recombinant epitope-repeat protein carrying an immunodominant epitope of Newcastle disease virus nucleoprotein

Purpose

The aim of the present study was to develop a serodiagnostic test for differentiation infected from vaccinated animal (DIVA) strategy accompanying the marker vaccine lacking an immunodominant epitope (IDE) of nucleoprotein of Newcastle disease virus (NDV).

Materials and Methods

Recombinant epitope-repeat protein (rERP) gene encoding eight repeats of the IDE sequence (ETQFLDLMRAVANSMR) by tetra-glycine linker was synthesized. Recombinant baculovirus carrying the rERP gene was generated to express the rERP in insect cells. Specificity and sensitivity of an indirect enzyme-linked immunosorbent assay (ELISA) employing the rERP was evaluated.

Results

The rERP with molecular weight of 20 kDa was successfully expressed by the recombinant baculovirus in an insect-baculovirus system. The rERP was antigenically functional as demonstrated by Western blotting. An indirect ELISA employing the rERP was developed and its specificity and sensitivity was determined. The ELISA test allowed discrimination of NDV infected sera from epitope deletion virus vaccinated sera.

Conclusion

The preliminary results represent rERP ELISA as a promising DIVA diagnostic tool.

Identification of novel HLA-A11-restricted T-cell epitopes in the Ebola virus nucleoprotein

The Ebola virus (EBOV) is a very contagious virus that is highly fatal in humans and animals. The largest epidemic was in West Africa in 2014, in which over 11,000 people died. However, to date, there are no licensed vaccines against it. Studies show that CD4⁺ and CD8⁺ T-cell responses, especially cytotoxic T-lymphocyte (CTL) responses, play key roles in protecting individuals from EBOV infection. Since HLA-restricted epitope vaccines are likely to be effective and safe immunization strategies for infectious diseases, the present study screened for CTL epitopes in the EBOV-nucleoprotein that are restricted by HLA-A11 (a common allele in Chinese people). Predictive computer analysis of the amino-acid sequence of EBOV-nucleoprotein identified ten putative HLA-A11-restricted epitopes. ELISPOT assay of immunized HLA-A11/DR1 transgenic mice showed that five (GR-9, VR-9, EK-9, PK-9, and RK-9) induced effective CTL responses. Additional epitope analyses will aid the design of epitope vaccines against EBOV.