Exploring the Papillomaviral Proteome to Identify Potential Candidates for a Chimeric Vaccine against Cervix Papilloma Using Immunomics and Computational Structural Vaccinology

Immunoinformatic based designing of potential immunogenic novel mRNA and peptide-based prophylactic vaccines against H5N1 and H7N9 avian influenza viruses

Influenza viruses are the most common cause of serious respiratory illnesses worldwide and are responsible for a significant number of annual fatalities. Therefore, it is crucial to look for new immunogenic sites that might trigger an effective immune response. In the present study, bioinformatics tools were used to design mRNA and multiepitope-based vaccines against H5N1 and H7N9 subtypes of avian influenza viruses. Several Immunoinformatic tools were employed to extrapolate T and B lymphocyte epitopes of HA and NA proteins of both subtypes. The molecular docking approach was used to dock the selected HTL and CTL epitopes with the corresponding MHC molecules. Eight (8) CTL, four (4) HTL, and Six (6) linear B cell epitopes were chosen for the structural arrangement of mRNA and of peptide-based prophylactic vaccine designs. Different physicochemical characteristics of the selected epitopes fitted with suitable linkers were analyzed. High antigenic, non-toxic, and non-allergenic features of the designed vaccines were noted at a neutral physiological pH. Codon optimization tool was used to check the GC content and CAI value of constructed MEVC-Flu vaccine, which were recorded to be 50.42% and 0.97 respectively. the GC content and CAI value verify the stable expression of vaccine in pET28a + vector. In-silico immunological simulation the MEVC-Flu vaccine construct revealed a high level of immune responses. The molecular dynamics simulation and docking results confirmed the stable interaction of TLR-8 and MEVC-Flu vaccine. Based on these parameters, vaccine constructs can be regarded as an optimistic choice against H5N1 and H7N9 strains of the influenza virus. Further experimental testing of these prophylactic vaccine designs against pathogenic avian influenza strains may clarify their safety and efficacy.Communicated by Ramaswamy H. Sarma.

Challenges and Opportunities in the Process Development of Chimeric Vaccines

Vaccines are integral to human life to protect them from life-threatening diseases. However, conventional vaccines often suffer limitations like inefficiency, safety concerns, unavailability for non-culturable microbes, and genetic variability among pathogens. Chimeric vaccines combine multiple antigen-encoding genes of similar or different microbial strains to protect against hyper-evolving drug-resistant pathogens. The outbreaks of dreadful diseases have led researchers to develop economical chimeric vaccines that can cater to a large population in a shorter time. The process development begins with computationally aided omics-based approaches to design chimeric vaccines. Furthermore, developing these vaccines requires optimizing upstream and downstream processes for mass production at an industrial scale. Owing to the complex structures and complicated bioprocessing of evolving pathogens, various high-throughput process technologies have come up with added advantages. Recent advancements in high-throughput tools, process analytical technology (PAT), quality-by-design (QbD), design of experiments (DoE), modeling and simulations, single-use technology, and integrated continuous bioprocessing have made scalable production more convenient and economical. The paradigm shift to innovative strategies requires significant attention to deal with major health threats at the global scale. This review outlines the challenges and emerging avenues in the bioprocess development of chimeric vaccines.

Alum and a TLR7 agonist combined with built-in TLR4 and 5 agonists synergistically enhance immune responses against HPV RG1 epitope

To relieve the limitations of the human papillomavirus (HPV) vaccines based on L1 capsid protein, vaccine formulations based on RG1 epitope of HPV L2 using various built-in adjuvants are under study. Herein, we describe design and construction of a rejoined peptide (RP) harboring HPV16 RG1 epitope fused to TLR4/5 agonists and a tetanus toxoid epitope, which were linked by the (GGGS)3 linker in tandem. In silico analyses indicated the proper physicochemical, immunogenic and safety profile of the RP. Docking analyses on predicted 3D model suggested the effective interaction of TLR4/5 agonists within RP with their corresponding TLRs. Expressing the 1206 bp RP-coding DNA in E. coli produced a 46 kDa protein, and immunization of mice by natively-purified RP in different adjuvant formulations indicated the crucial role of the built-in adjuvants for induction of anti-RG1 responses that could be further enhanced by combination of TLR7 agonist/alum adjuvants. While the TLR4/5 agonists contributed in the elicitation of the Th2-polarized immune responses, combination with TLR7 agonist changed the polarization to the balanced Th1/Th2 immune responses. Indeed, RP + TLR7 agonist/alum adjuvants induced the strongest immune responses that could efficiently neutralize the HPV pseudoviruses, and thus might be a promising formulation for an inexpensive and cross-reactive HPV vaccine.

Mapping Potential Vaccine Candidates Predicted by VaxiJen for Different Viral Pathogens between 2017-2021-A Scoping Review

Reverse vaccinology (RV) is a promising alternative to traditional vaccinology. RV focuses on in silico methods to identify antigens or potential vaccine candidates (PVCs) from a pathogen's proteome. Researchers use VaxiJen, the most well-known RV tool, to predict PVCs for various pathogens. The purpose of this scoping review is to provide an overview of PVCs predicted by VaxiJen for different viruses between 2017 and 2021 using Arksey and O'Malley's framework and the Preferred Reporting Items for Systematic Reviews extension for Scoping Reviews (PRISMA-ScR) guidelines. We used the term 'vaxijen' to search PubMed, Scopus, Web of Science, EBSCOhost, and ProQuest One Academic. The protocol was registered at the Open Science Framework (OSF). We identified articles on this topic, charted them, and discussed the key findings. The database searches yielded 1033 articles, of which 275 were eligible. Most studies focused on severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), published between 2020 and 2021. Only a few articles (8/275; 2.9%) conducted experimental validations to confirm the predictions as vaccine candidates, with 2.2% (6/275) articles mentioning recombinant protein expression. Researchers commonly targeted parts of the SARS-CoV-2 spike (S) protein, with the frequently predicted epitopes as PVCs being major histocompatibility complex (MHC) class I T cell epitopes WTAGAAAYY, RQIAPGQTG, IAIVMVTIM, and B cell epitope IAPGQTGKIADY, among others. The findings of this review are promising for the development of novel vaccines. We recommend that vaccinologists use these findings as a guide to performing experimental validation for various viruses, with SARS-CoV-2 as a priority, because better vaccines are needed, especially to stay ahead of the emergence of new variants. If successful, these vaccines could provide broader protection than traditional vaccines.

Prediction of promiscuous epitopes in ORF2 of Hepatitis E virus: an In-Silico approach

Background

Vaccine development against emerging infections is essentially important for saving people from increasing viral infections. In developing countries, Hepatitis E (HEV) is a common infection affecting millions of people worldwide. Based on In-silico analysis, different approaches have been targeted.

Objectives

Rationale of this study is to design an epitope-based vaccine candidates with the help of immunoinformatics that can predict promiscuous B-cell and T-cell epitopes of the most antigenic HEV-ORF2 capsid protein.

Materials & Methods

This study suggests potential T-cell and B-cell epitopes of the highly antigenic HEV ORF2 capsid protein while using various In-silico tools such as NCBI-BLAST, Expassy, CLC workbench, Ellipro and Discotope.

Results

Potential antigenic and immunogenic CD8+ T-cell epitopes were predicted from the global consensus sequence of ORF2-HEV. Furthermore, twenty-two linear B-cell epitopes were predicted. Among these, "SLGAGPV" at position 587-593 and "LEFRNLTPGNTNTRVSRYSS" at position 306-325 were most antigenic with antigenicity score 1.4206 and 1.3600 respectively. Discontinuous B-cell epitopes were found by three-dimensional capsid protein structure. Epitopes predicted in this study reveal high antigenicity and promiscuity for HLA classes.

Conclusion

Collectively, our data suggests promiscuous epitopes that can potentially acts as new candidates for the design of HEV peptide vaccine.

Engineering a novel immunogenic chimera protein utilizing bacterial infections associated with atherosclerosis to induce a deviation in adaptive immune responses via Immunoinformatics approaches

Recent studies have established the role of bacteria including Streptococcus pneumoniae, Helicobacter pylori, Chlamydia pneumonia, Mycobacterium tuberculosis, and Porphyromonas gingivalis in the development of atherosclerosis. These bacteria contribute to plaque formation via promoting Th1 immune responses and speeding up ox-LDL formation. Hence, we employed computational reverse vaccinology (RV) approaches to deviate immune response toward Th2 via engineering a novel immunogenic chimera protein. Prominent atherogenic antigens from related bacteria were identified. Then, machine learning-based servers were employed for predicting CTL and HTL epitopes. We selected epitopes from a wide variety of HLAs. Then, a chimeric protein sequence containing TAT peptide, adjuvant, IL-10 inducer, and linker-separated epitopes was designed. The conformational structure of the vaccine was built via multiple-template homology modelling using MODELLER. The initial structure was refined and validated by Ramachandran plot. The vaccine was also docked with TLR4. After that, molecular dynamics (MD) simulation of the docked vaccine-TLR4 was conducted. Finally, the immune simulation of the vaccine was conducted via the C-ImmSim server. A chimera protein with 629 amino acids was built and, classified as a non-allergenic probable antigen. An improved ERRAT score of 80.95 for the refined structure verified its stability. Additionally, validation via the Ramachandran plot showed 98.09% of the residues were located in the most favorable and permitted regions. MD simulations showed the vaccine-TLR4 complex reached a stable conformation. Also, RMS fluctuations analysis revealed no sign of protein denaturation or unfolding. Finally, immune response simulations indicated a promising response by innate and adaptive immunity. In summary, we built an immunogenic vaccine against atherosclerosis and demonstrated its favorable properties via advanced Immunoinformatics analyses. This study may pave the path for combat against atherosclerosis.

Peptide-Based Nanovaccines in the Treatment of Cervical Cancer: A Review of Recent Advances

Persistent infection with high-risk human papillomaviruses (HPVs), such as HPV-16 and HPV-18, can induce cervical cancer in humans. The disease carries high morbidity and mortality among females worldwide. Inoculation with prophylactic HPV vaccines, such as Gardasil^® or Cervarix^®, is the predominant method of preventing cervical cancer in females 6 to 26 years of age. However, despite the availability of commercial prophylactic HPV vaccines, no therapeutic HPV vaccines to eliminate existing HPV infections have been approved. Peptide-based vaccines, which form one of the most potent vaccine platforms, have been broadly investigated to overcome this shortcoming. Peptide-based vaccines are especially effective in inducing cellular immune responses and eradicating tumor cells when combined with nanoscale adjuvant particles and delivery systems. This review summarizes progress in the development of peptide-based nanovaccines against HPV infection.

The Role of Flagellin B in Vibrio anguillarum-Induced Intestinal Immunity and Functional Domain Identification

Vibrio anguillarum, an opportunistic pathogen of aquatic animals, moves using a filament comprised of polymerised flagellin proteins. Flagellins are essential virulence factors for V. anguillarum infection. Herein, we investigated the effects of flagellins (flaA, flaB, flaC, flaD and flaE) on cell apoptosis, TLR5 expression, and production of IL-8 and TNF-α. FlaB exhibited the strongest immunostimulation effects. To explore the functions of flaB in infection, we constructed a flaB deletion mutant using a two-step recombination method, and in vitro experiments showed a significant decrease in the expression of TLR5 and inflammatory cytokines compared with wild-type cells. However in the in vivo study, expression of inflammatory cytokines and intestinal mucosal structure showed no significant differences between groups. Additionally, flaB induced a significant increase in TLR5 expression based on microscopy analysis of fluorescently labelled TLR5, indicating interactions between the two proteins, which was confirmed by native PAGE and yeast two-hybrid assay. Molecular simulation of interactions between flaB and TLR5 was performed to identify the residues involved in binding, revealing two binding sites. Then, based on molecular dynamics simulations, we carried out thirteen site-directed mutations occurring at the amino acid sites of Q57, N83, N87, R91, D94, E122, D152, N312, R313, N320, L97, H316, I324 in binding regions of flaB protein by TLR5, respectively. Surface plasmon resonance (SPR) was employed to compare the affinities of flaB mutants for TLR5, and D152, D94, I324, N87, R313, N320 and H316 were found to mediate interactions between flaB and TLR5. Our comprehensive and systematic analysis of V. anguillarum flagellins establishes the groundwork for future design of flagellin-based vaccines.

Prioritization of potential vaccine candidates and designing a multiepitope-based subunit vaccine against multidrug-resistant Salmonella Typhi str. CT18: A subtractive proteomics and immunoinformatics approach

Salmonella enterica serovar Typhi (S. Typhi), a causative agent of typhoid fever, is a Gram-negative, human-restricted pathogen that causes significant morbidity and mortality, particularly in developing countries. The currently available typhoid vaccines are not recommended to children below six years of age and have poor long-term efficacy. Due to these limitations and the emerging threat of multidrug-resistance (MDR) strains, the development of a new vaccine is urgently needed. The present study aims to design a multiepitope-based subunit vaccine (MESV) against MDR S. Typhi str. CT18 using a computational-based approach comprising subtractive proteomics and immunoinformatics. Firstly, we investigated the proteome of S. Typhi str. CT18 using subtractive proteomics and identified twelve essential, virulent, host non-homologous, and antigenic outer membrane proteins (OMPs) as potential vaccine candidates with low transmembrane helices (≤1) and molecular weight (≤110 kDa). The OMPs were mapped for cytotoxic T lymphocyte(CTL) epitopes, helper T lymphocyte (HTL) epitopes, and linear B lymphocyte (LBL) epitopes using various immunoinformatics tools and servers. A total of 6, 12, and 11 CTL, HTL, and LBL epitopes were shortlisted, respectively, based on their immunogenicity, antigenicity, allergenicity, toxicity, and hydropathicity potential. Four MESV constructs (MESVCs), MESVC-1, MESVC-2, MESVC-3, and MESVC-4, were designed by linking the CTL, HTL, and LBL epitopes with immune-modulating adjuvants, linkers, and PADRE (Pan HLA DR-binding epitope) sequences. The MESVCs were evaluated for their physicochemical properties, allergenicity, antigenicity, toxicity, and solubility potential to ensure their safety and immunogenic behavior. Secondary and tertiary structures of shortlisted MESVCs (MESVC-1, MESVC-3, and MESVC-4) were predicted, modeled, refined, validated, and then docked with various MHC I, MHC II, and TLR4/MD2 complex. Molecular dynamics (MD) simulation of the final selected MESVC-4 with TLR4/MD2 complex confirms its binding affinity and stability. Codon optimization and in silico cloning verified the translation efficiency and successful expression of MESVC-4 in E. coli str. K12. Finally, the efficiency of MESVC-4 to trigger an effective immune response was assessed by an in silico immune simulation. In conclusion, our findings show that the designed MESVC-4 can elicit humoral and cellular immune responses, implying that it may be used for prophylactic or therapeutic purposes. Therefore, it should be subjected to further experimental validations.

In silico designing of vaccine candidate against Clostridium difficile

Clostridium difficile is a spore-forming gram-positive bacterium, recognized as the primary cause of antibiotic-associated nosocomial diarrhoea. Clostridium difficile infection (CDI) has emerged as a major health-associated infection with increased incidence and hospitalization over the years with high mortality rates. Contamination and infection occur after ingestion of vegetative spores, which germinate in the gastro-intestinal tract. The surface layer protein and flagellar proteins are responsible for the bacterial colonization while the spore coat protein, is associated with spore colonization. Both these factors are the main concern of the recurrence of CDI in hospitalized patients. In this study, the CotE, SlpA and FliC proteins are chosen to form a multivalent, multi-epitopic, chimeric vaccine candidate using the immunoinformatics approach. The overall reliability of the candidate vaccine was validated in silico and the molecular dynamics simulation verified the stability of the vaccine designed. Docking studies showed stable vaccine interactions with Toll‐Like Receptors of innate immune cells and MHC receptors. In silico codon optimization of the vaccine and its insertion in the cloning vector indicates a competent expression of the modelled vaccine in E. coli expression system. An in silico immune simulation system evaluated the effectiveness of the candidate vaccine to trigger a protective immune response.

Bioinformatics analyses for the designation of a hybrid protein against urinary tract infections caused by Pseudomonas aeruginosa and investigation of the presence of pre-existing antibodies in infected humans

Pseudomonas aeruginosa is an important pathogen causing urinary tract infections (UTIs) and resistance to antibiotics has increased the need for a vaccine against this bacterium. P. aeruginosa V-antigen (PcrV), which is a component of the type III secretion system, delivers exoenzymes such as exoenzyme S (ExoS) into the host cells. In the present study, we aimed to design and express a hybrid protein composed of PcrV and ExoS from P. aeruginosa using bioinformatics. Finally, pre-existing antibodies were evaluated in sera collected from patients with UTI. The prediction results showed that the hybrid protein ExoS.PcrV had a C-score of -0.85 and Z-score of -5.55 versus C-score of -2.93 and Z-score of -2.65 for PcrV.ExoS. Based on BepiPred and ABCpred, 15 and 14 linear B-cell epitopes, as well as five conformational epitopes were identified in ExoS.PcrV. The interaction between the protein and immune receptor was validated in silico. Molecular docking indicated that the hybrid protein interacted strongly with Toll-like receptor 2. ExoS.PcrV was expressed in pET28a-BL21 and purified with a size of 57 kD by Nickel resins. The protein reacted with all sera collected from humans infected with P. aeruginosa following Western blot. The infected patients produced significantly higher IgG levels against the protein compared to the control as indicated by ELISA. The protein ExoS.PcrV was determined as a promising candidate against UTI caused by P. aeruginosa and the presence of pre-existing antibodies indicated the advantage of the hybrid protein. Evaluation of the efficacy of hybrid protein is ongoing in mice model. Communicated by Ramaswamy H. Sarma.

Association of human papillomavirus genotype distribution and cervical cytology: a cross-sectional study

The present study attempted to analyse human papillomavirus (HPV) genotype distribution and its association with cervical cytology results in women in western China. The present retrospective analysis was performed in 1089 female outpatients with a positive HPV test result who had undergone a cervical cytology test at the gynaecological clinic, West China Second Hospital, Sichuan University, China, between January 2014 and December 2016. Of the 1089 patients with HPV infection, multiple HPV genotypes were detected in 220 patients (20.20%). Among the 1368 HPV genotypes detected, 1145 (83.70%) were high-risk subtypes. The most common genotypes were HPV-52 (18.64%), HPV-16 (16.59%), HPV-58 (13.23%), HPV-18 (6.80%), HPV-56 (5.56%) and HPV-59 (5.56%). Cervical cytology revealed abnormal cells in 430 (39.49%) patients. The most common diagnoses were atypical squamous cells of undetermined significance (ASC-US; 236 cases, 54.88%), low-grade squamous intraepithelial lesions (LSIL; 151 cases, 35.12%), high-grade squamous intraepithelial lesions (HSIL; 63 cases, 14.65%) and atypical glandular cells (AGC; 21 cases, 4.88%). HPV-66 was significantly associated (P = 0.037) with ASC; HPV-52 and HPV-56 were significantly associated with LSIL (P = 0.009 and 0.026, respectively); HPV-16 (P < 0.001), HPV-33 (P = 0.014) and HPV-58 (P = 0.003) were significantly associated with HSIL; and HPV-16 (P = 0.005) was significantly associated with AGC. HPV-16, HPV-52 and HPV-58 are associated with different diagnoses in patients with positive cervical cytological findings.

Advances in Current Diabetes Proteomics: From the Perspectives of Label- free Quantification and Biomarker Selection

BACKGROUND

Due to its prevalence and negative impacts on both the economy and society, the diabetes mellitus (DM) has emerged as a worldwide concern. In light of this, the label-free quantification (LFQ) proteomics and diabetic marker selection methods have been applied to elucidate the underlying mechanisms associated with insulin resistance, explore novel protein biomarkers, and discover innovative therapeutic protein targets.

OBJECTIVE

The purpose of this manuscript is to review and analyze the recent computational advances and development of label-free quantification and diabetic marker selection in diabetes proteomics.

METHODS

Web of Science database, PubMed database and Google Scholar were utilized for searching label-free quantification, computational advances, feature selection and diabetes proteomics.

RESULTS

In this study, we systematically review the computational advances of label-free quantification and diabetic marker selection methods which were applied to get the understanding of DM pathological mechanisms. Firstly, different popular quantification measurements and proteomic quantification software tools which have been applied to the diabetes studies are comprehensively discussed. Secondly, a number of popular manipulation methods including transformation, pretreatment (centering, scaling, and normalization), missing value imputation methods and a variety of popular feature selection techniques applied to diabetes proteomic data are overviewed with objective evaluation on their advantages and disadvantages. Finally, the guidelines for the efficient use of the computationbased LFQ technology and feature selection methods in diabetes proteomics are proposed.

CONCLUSION

In summary, this review provides guidelines for researchers who will engage in proteomics biomarker discovery and by properly applying these proteomic computational advances, more reliable therapeutic targets will be found in the field of diabetes mellitus.

Bioinformatics Approaches for Anti-cancer Drug Discovery

Drug discovery is important in cancer therapy and precision medicines. Traditional approaches of drug discovery are mainly based on in vivo animal experiments and in vitro drug screening, but these methods are usually expensive and laborious. In the last decade, omics data explosion provides an opportunity for computational prediction of anti-cancer drugs, improving the efficiency of drug discovery. High-throughput transcriptome data were widely used in biomarkers' identification and drug prediction by integrating with drug-response data. Moreover, biological network theory and methodology were also successfully applied to the anti-cancer drug discovery, such as studies based on protein-protein interaction network, drug-target network and disease-gene network. In this review, we summarized and discussed the bioinformatics approaches for predicting anti-cancer drugs and drug combinations based on the multi-omic data, including transcriptomics, toxicogenomics, functional genomics and biological network. We believe that the general overview of available databases and current computational methods will be helpful for the development of novel cancer therapy strategies.

Vaccination Strategies for the Control and Treatment of HPV Infection and HPV-Associated Cancer

Human papillomavirus (HPV) is the most common sexually transmitted infection, currently affecting close to 80 million Americans. Importantly, HPV infection is recognized as the etiologic factor for numerous cancers, including cervical, vulval, vaginal, penile, anal, and a subset of oropharyngeal cancers. The prevalence of HPV infection and its associated diseases are a significant problem, affecting millions of individuals worldwide. Likewise, the incidence of HPV infection poses a significant burden on individuals and the broader healthcare system. Between 2011 and 2015, there were an estimated 42,700 new cases of HPV-associated cancers each year in the United States alone. Similarly, the global burden of HPV is high, with around 630,000 new cases of HPV-associated cancer occurring each year. In the last decade, a total of three preventive major capsid protein (L1) virus-like particle-based HPV vaccines have been licensed and brought to market as a means to prevent the spread of HPV infection. These prophylactic vaccines have been demonstrated to be safe and efficacious in preventing HPV infection. The most recent iteration of the preventive HPV vaccine, a nanovalent, L1-VLP vaccine, protects against a total of nine HPV types (seven high-risk and two low-risk HPV types), including the high-risk types HPV16 and HPV18, which are responsible for causing the majority of HPV-associated cancers. Although current prophylactic HPV vaccines have demonstrated huge success in preventing infection, existing barriers to vaccine acquisition have limited their widespread use, especially in low- and middle-income countries, where the burden of HPV-associated diseases is highest. Prophylactic vaccines are unable to provide protection to individuals with existing HPV infections or HPV-associated diseases. Instead, therapeutic HPV vaccines capable of generating T cell-mediated immunity against HPV infection and associated diseases are needed to ameliorate the burden of disease in individuals with existing HPV infection. To generate a cell-mediated immune response against HPV, most therapeutic vaccines target HPV oncoproteins E6 and E7. Several types of therapeutic HPV vaccine candidates have been developed including live-vector, protein, peptide, dendritic cell, and DNA-based vaccines. This chapter will review the commercially available prophylactic HPV vaccines and discuss the recent progress in the development of therapeutic HPV vaccines.

Design and optimization of a subunit vaccine targeting COVID-19 molecular shreds using an immunoinformatics framework

COVID-19 has been declared as a global health emergency and exposed the world to a deadly virus, which has dramatically changed the lives of humans for an unknown period of time. In the battleground with the virus, we have employed an immunoinformatics framework to design a robust vaccine as an insurance plan for the future. The pathogenic sequence with cryptic epitope taken from patients in Wuhan, China, was harnessed to design a promiscuous cytotoxic T-lymphocyte, helper T-lymphocyte, and B-cell epitope based subunit vaccine, engineered with adjuvants and conformational linkers. The reported vaccine has high antigenicity and immunogenicity profiles with potential TAP affinity, which ensures elevated antigen processing capability. It has strong binding with major histocompatibility complex (MHC) receptors (MHC-1 and MHC-2) and virus-specific membrane receptor TLR-2, with scores of -1010.7, -1035.7, and -1076.3 kcal mol-1, respectively. Molecular dynamics simulation analysis was used to assess the stable binding with TLR-2 with minimal atomic motions through a deformation plot, covariance matrix, and elastic network. Importantly, an in silico immunization assay showed the reliable elicitation of key players in terms of immune cells together with memory cells to evoke adaptive immune responses upon administration of the construct. In view of favorable outcomes, we also propose a plausible vaccine mechanism to elicit an immune response to fight coronavirus.

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.

A candidate multi-epitope vaccine against SARS-CoV-2

In the past two decades, 7 coronaviruses have infected the human population, with two major outbreaks caused by SARS-CoV and MERS-CoV in the year 2002 and 2012, respectively. Currently, the entire world is facing a pandemic of another coronavirus, SARS-CoV-2, with a high fatality rate. The spike glycoprotein of SARS-CoV-2 mediates entry of virus into the host cell and is one of the most important antigenic determinants, making it a potential candidate for a vaccine. In this study, we have computationally designed a multi-epitope vaccine using spike glycoprotein of SARS-CoV-2. The overall quality of the candidate vaccine was validated in silico and Molecular Dynamics Simulation confirmed the stability of the designed vaccine. Docking studies revealed stable interactions of the vaccine with Toll-Like Receptors and MHC Receptors. The in silico cloning and codon optimization supported the proficient expression of the designed vaccine in E. coli expression system. The efficiency of the candidate vaccine to trigger an effective immune response was assessed by an in silico immune simulation. The computational analyses suggest that the designed multi-epitope vaccine is structurally stable which can induce specific immune responses and thus, can be a potential vaccine candidate against SARS-CoV-2.

Pathways involved in viral oncogenesis: New perspectives from virus-host protein interactomics

Oncogenic viruses are among the apparent causes of cancer-associated mortality. It was estimated that 12% to 15% of human malignancies are linked to oncoviruses. Although modernist strategies and traditional genetic studies have defined host-pathogen interactions of the oncoviruses, their host functions which are critical for the establishment of infection still remain mysterious. However, over the last few years, it has become clear that infections hijack and modify cellular pathways for their benefit. In this context, we constructed the virus-host protein interaction networks of seven oncoviruses (EBV, HBV, HCV, HTLV-1, HHV8, HPV16, and HPV18), and revealed cellular pathways hijacking as a result of oncogenic virus infection. Several signaling pathways/processes such as TGF-β signaling, cell cycle, retinoblastoma tumor suppressor protein, and androgen receptor signaling were mutually targeted by viruses to induce oncogenesis. Besides, cellular pathways specific to a certain virus were detected. By this study, we believe that we improve the understanding of the molecular pathogenesis of viral oncogenesis and provide information in setting new targets for treatment, prognosis, and diagnosis.

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.

COVID-19 Coronavirus spike protein analysis for synthetic vaccines, a peptidomimetic antagonist, and therapeutic drugs, and analysis of a proposed achilles' heel conserved region to minimize probability of escape mutations and drug resistance

This paper continues a recent study of the spike protein sequence of the COVID-19 virus (SARS-CoV-2). It is also in part an introductory review to relevant computational techniques for tackling viral threats, using COVID-19 as an example. Q-UEL tools for facilitating access to knowledge and bioinformatics tools were again used for efficiency, but the focus in this paper is even more on the virus. Subsequence KRSFIEDLLFNKV of the S2′ spike glycoprotein proteolytic cleavage site continues to appear important. Here it is shown to be recognizable in the common cold coronaviruses, avian coronaviruses and possibly as traces in the nidoviruses of reptiles and fish. Its function or functions thus seem important to the coronaviruses. It might represent SARS-CoV-2 Achilles’ heel, less likely to acquire resistance by mutation, as has happened in some early SARS vaccine studies discussed in the previous paper. Preliminary conformational analysis of the receptor (ACE2) binding site of the spike protein is carried out suggesting that while it is somewhat conserved, it appears to be more variable than KRSFIEDLLFNKV. However compounds like emodin that inhibit SARS entry, apparently by binding ACE2, might also have functions at several different human protein binding sites. The enzyme 11β-hydroxysteroid dehydrogenase type 1 is again argued to be a convenient model pharmacophore perhaps representing an ensemble of targets, and it is noted that it occurs both in lung and alimentary tract. Perhaps it benefits the virus to block an inflammatory response by inhibiting the dehydrogenase, but a fairly complex web involves several possible targets.

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This paper “drills down” into the studies of the author's previous COVID-19 paper.

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Designing vaccine and drugs must seek to avoid escape mutations.

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Subsequence KRSFIEDLLFNKV seems recognizable across many coronaviruses.

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The ACE2 binding domain is a target, but shows variation.

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A steroid dehydrogenase is argued to remain an interesting model pharmacophore.

Computers and viral diseases. Preliminary bioinformatics studies on the design of a synthetic vaccine and a preventative peptidomimetic antagonist against the SARS-CoV-2 (2019-nCoV, COVID-19) coronavirus

This paper concerns study of the genome of the Wuhan Seafood Market isolate believed to represent the causative agent of the disease COVID-19. This is to find a short section or sections of viral protein sequence suitable for preliminary design proposal for a peptide synthetic vaccine and a peptidomimetic therapeutic, and to explore some design possibilities. The project was originally directed towards a use case for the Q-UEL language and its implementation in a knowledge management and automated inference system for medicine called the BioIngine, but focus here remains mostly on the virus itself. However, using Q-UEL systems to access relevant and emerging literature, and to interact with standard publically available bioinformatics tools on the Internet, did help quickly identify sequences of amino acids that are well conserved across many coronaviruses including 2019-nCoV. KRSFIEDLLFNKV was found to be particularly well conserved in this study and corresponds to the region around one of the known cleavage sites of the SARS virus that are believed to be required for virus activation for cell entry. This sequence motif and surrounding variations formed the basis for proposing a specific synthetic vaccine epitope and peptidomimetic agent. The work can, nonetheless, be described in traditional bioinformatics terms, and readily reproduced by others, albeit with the caveat that new data and research into 2019-nCoV is emerging and evolving at an explosive pace. Preliminary studies using molecular modeling and docking, and in that context the potential value of certain known herbal extracts, are also described.

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Bioinformatics studies are carried out on the COVID-19 virus.

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A sequence motif KRSFIEDLLFNKV is of particular interest.

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Based on the above, synthetic peptides are designed.

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Preliminary considerations are also given to non-peptide organic molecules.

Multiepitope Subunit Vaccine to Evoke Immune Response against Acute Encephalitis

Acute encephalitis syndrome outbreak has emerged as a major health concern on both national and international scales. Brain inflammation/infections caused by Japanese encephalitis virus (JEV) can lead to death. The cases are growing in numbers globally, and this emergent health concern requires an effective and viable vaccine to strengthen the body's immune system against this deadly virus. Proteomic analyses of JEV revealed the envelope protein as a potential target for vaccine development by patient samples analysis. Hence, in this study, we aimed to design a multiepitope subunit vaccine for acute encephalitis using the advanced structural biology and immunoinformatics approaches. We report the multiepitope subunit vaccine consisted of the putative T-cell epitope (MHC-1 and MHC-2 restricted) and B-cell epitope and with high antigenicity and immunogenicity. The TAP affinity epitopes along with adjuvants were engineered to the vaccine, to ensure the ease transportation inside the host and elicitation of a strong immune response. The specificity of vaccine construct was evaluated by molecular docking with major histocompatibility complex (MHC) receptors and host membrane receptor TLR2. High docking scores and a close interaction to the binding groove of receptors confirmed the potency and specificity of the vaccine. Also, molecular dynamics simulation studies confirmed the stable interaction of vaccine with TLR2 for a long run (100 ns), which showed the prolonged elicitation of the strong immune response. Peptide dynamics studies showed the flexible, strong, and stable binding of vaccine with minimal deviation in root-mean-square deviation (RMSD), root-mean-square fluctuation (RMSF), and secondary structure estimation (SSE) plots till 100 ns simulation run. The in silico immune simulation approach based on the position-specific scoring matrix and machine learning methods resulted in the strong immune response reinforcement statistics of immune cells (T-cells, B-cells population, and memory cells) in response to vaccine candidate. The favorable results and well-correlated data of varied in silico techniques paved for a potent multiepitope vaccine and helped us to propose the mechanism of action of designed vaccine and generation of the immune response against acute encephalitis syndrome.

Prediction and validation of potent peptides against herpes simplex virus type 1 via immunoinformatic and systems biology approach

The human herpes simplex virus type 1 (HSV-1) is an extremely rampant human pathogen, and its infection could cause life-long diseases, including the central nervous system disorders. The glycoproteins of HSV-1 such as glycoprotein B, glycoprotein C, glycoprotein D, glycoprotein H, and glycoprotein L are highly involved in mediating the viral attachment and infection of the host cell. Therefore, immunoinformatic approaches followed by molecular dynamics simulation and systems biology has been used to analyze these glycoproteins in order to propose effective peptide-based vaccine candidates against the HSV-1 infection. The ElliPro and NetCTL.1.2 online tools were employed to forecast the B- and T-lymphocyte (CTL) epitopes for gB, gC, gD, gH, and gL. The 3D coordinates of these epitopes were modeled and docked against the human major histocompatibility complex molecule-1. The outcomes obtained from postdocking analysis along with TAP (Transporter associated with antigen processing), MHC binding, and C-terminal cleavage score assisted in the selection of potential epitopes. These epitopes were further subjected to molecular dynamics simulation and systems biology approach which showed significant results. On the basis of these substantial outcomes, peptides are proposed that could be used to provoke immunity against the HSV-1 infection.

Immunomics Datasets and Tools: To Identify Potential Epitope Segments for Designing Chimeric Vaccine Candidate to Cervix Papilloma

Immunomics tools and databases play an important role in the designing of prophylactic or therapeutic vaccines against pathogenic bacteria and viruses. Therefore, we aimed to illustrate the different immunological databases and web servers used to design a chimeric vaccine candidate against human cervix papilloma. Initially, cellular immunity inducing major histocompatibility complex class I and II epitopes from L2 protein of papilloma 58 strain were predicted using the IEDB, NetMHC, and Tepi tools. Then, the overlapped segments from the above analysis were used to calculate efficiency on interferon-gamma and humoral immunity production. In addition, the allergenicity, antigenicity, cross-reactivity with human proteomes, and epitope conservancy of elite segments were determined. The chimeric vaccine candidate (SGD58) was constructed with two different overlapped peptide segments (23–36) and (29–42), adjuvants (flagellin and RS09), two Th epitopes, and amino acid linkers. The results of homology modeling demonstrated that SGD58 have 88.6% of favored regions based on Ramachandran plot. Protein–protein docking with Swarm Dock reveals SGD58 with receptor complex have −54.74 kcal/mol of binding energy with more than 20 interacting residues. Docked complex are stable in 100ns of molecular dynamic simulation. Further, coding sequences of SGD58 also show elevated gene expression in E. coli. In conclusion, SGD58 may prompt vaccine against cervix papilloma. This study provides insight of vaccine design against different pathogenic microbes as well.

Viewing the Emphasis on State-of-the-Art Magnetic Nanoparticles: Synthesis, Physical Properties, and Applications in Cancer Theranostics

Cancer-related mortality is a leading cause of death among both men and women around the world. Target-specific therapeutic drugs, early diagnosis, and treatment are crucial to reducing the mortality rate. One of the recent trends in modern medicine is "Theranostics," a combination of therapeutics and diagnosis. Extensive interest in magnetic nanoparticles (MNPs) and ultrasmall superparamagnetic iron oxide nanoparticles (NPs) has been increasing due to their biocompatibility, superparamagnetism, less-toxicity, enhanced programmed cell death, and auto-phagocytosis on cancer cells. MNPs act as a multifunctional, noninvasive, ligand conjugated nano-imaging vehicle in targeted drug delivery and diagnosis. In this review, we primarily discuss the significance of the crystal structure, magnetic properties, and the most common method for synthesis of the smaller sized MNPs and their limitations. Next, the recent applications of MNPs in cancer therapy and theranostics are discussed, with certain preclinical and clinical experiments. The focus is on implementation and understanding of the mechanism of action of MNPs in cancer therapy through passive and active targeting drug delivery (magnetic drug targeting and targeting ligand conjugated MNPs). In addition, the theranostic application of MNPs with a dual and multimodal imaging system for early diagnosis and treatment of various cancer types including breast, cervical, glioblastoma, and lung cancer is reviewed. In the near future, the theranostic potential of MNPs with multimodality imaging techniques may enhance the acuity of personalized medicine in the diagnosis and treatment of individual patients.