Computer-aided biotechnology: from immuno-informatics to reverse vaccinology

Evaluation of vaccine candidates against Rhodococcus equi in BALB/c mice infection model: cellular and humoral immune responses

Rhodococcus equi (R. equi) is a zoonotic opportunistic pathogen that mainly causes fatal lung and extrapulmonary abscesses in foals and immunocompromised individuals. To date, no commercial vaccine against R. equi exists. We previously screened all potential vaccine candidates from the complete genome of R. equi using a reverse vaccinology approach. Five of these candidates, namely ABC transporter substrate-binding protein (ABC transporter), penicillin-binding protein 2 (PBD2), NlpC/P60 family protein (NlpC/P60), esterase family protein (Esterase), and M23 family metallopeptidase (M23) were selected for the evaluation of immunogenicity and immunoprotective effects in BALB/c mice model challenged with R. equi. The results showed that all five vaccine candidate-immunized mice experienced a significant increase in spleen antigen-specific IFN-γ- and TNF-α-positive CD4 + and CD8 + T lymphocytes and generated robust Th1- and Th2-type immune responses and antibody responses. Two weeks after the R. equi challenge, immunization with the five vaccine candidates reduced the bacterial load in the lungs and improved the pathological damage to the lungs and livers compared with those in the control group. NlpC/P60, Esterase, and M23 were more effective than the ABC transporter and PBD2 in inducing protective immunity against R. equi challenge in mice. In addition, these vaccine candidates have the potential to induce T lymphocyte memory immune responses in mice. In summary, these antigens are effective candidates for the development of protective vaccines against R. equi. The R. equi antigen library has been expanded and provides new ideas for the development of multivalent vaccines.

Discovery and development of COVID-19 vaccine from laboratory to clinic

The world has recently experienced one of the biggest and most severe public health disasters with severe acute respiratory syndrome coronavirus (SARS-CoV-2). SARS-CoV-2 is responsible for the coronavirus disease of 2019 (COVID-19) which is one of the most widespread and powerful infections affecting human lungs. Current figures show that the epidemic had reached 216 nations, where it had killed about 6,438,926 individuals and infected 590,405,710. WHO proclaimed the outbreak of the Ebola virus disease (EVD), in 2014 that killed hundreds of people in West Africa. The development of vaccines for SARS-CoV-2 becomes more difficult due to the viral mutation in its non-structural proteins (NSPs) especially NSP2 and NSP3, S protein, and RNA-dependent RNA polymerase (RdRp). Continuous monitoring of SARS-CoV-2, dynamics of the genomic sequence, and spike protein mutations are very important for the successful development of vaccines with good efficacy. Hence, the vaccine development for SARS-CoV-2 faces specific challenges starting from viral mutation. The requirement of long-term immunity development, safety, efficacy, stability, vaccine allocation, distribution, and finally, its cost is discussed in detail. Currently, 169 vaccines are in the clinical development stage, while 198 vaccines are in the preclinical development stage. The majority of these vaccines belong to the Ps-Protein subunit type which has 54, and the minor BacAg-SPV (Bacterial antigen-spore expression vector) type, at least 1 vaccination. The use of computational methods and models for vaccine development has revolutionized the traditional methods of vaccine development. Further, this updated review highlights the upcoming vaccine development strategies in response to the current pandemic and post-pandemic era, in the field of vaccine development.

Identification of vaccine candidates against rhodococcus equi by combining pangenome analysis with a reverse vaccinology approach

Rhodococcus equi (R. equi) is a zoonotic opportunistic pathogen that can cause life-threatening infections. The rapid evolution of multidrug-resistant R. equi and the fact that there is no currently licensed effective vaccine against R. equi warrant the need for vaccine development. Reverse vaccinology (RV), which involves screening a pathogen's entire genome and proteome using various web-based prediction tools, is considered one of the most effective approaches for identifying vaccine candidates. Here, we performed a pangenome analysis to determine the core proteins of R. equi. We then used the RV approach to examine the subcellular localization, host and gut flora homology, antigenicity, transmembrane helices, physicochemical properties, and immunogenicity of the core proteins to select potential vaccine candidates. The vaccine candidates were then subjected to epitope mapping to predict the exposed antigenic epitopes that possess the ability to bind with major histocompatibility complex I/II (MHC I/II) molecules. These vaccine candidates and epitopes will form a library of elements for the development of a polyvalent or universal vaccine against R. equi. Sixteen R. equi complete proteomes were found to contain 6,238 protein families, and the core proteins consisted of 3,969 protein families (∼63.63% of the pangenome), reflecting a low degree of intraspecies genomic variability. From the pool of core proteins, 483 nonhost homologous membrane and extracellular proteins were screened, and 12 vaccine candidates were finally identified according to their antigenicity, physicochemical properties and other factors. These included four cell wall/membrane/envelope biogenesis proteins; four amino acid transport and metabolism proteins; one cell cycle control, cell division and chromosome partitioning protein; one carbohydrate transport and metabolism protein; one secondary metabolite biosynthesis, transport and catabolism protein; and one defense mechanism protein. All 12 vaccine candidates have an experimentally validated 3D structure available in the protein data bank (PDB). Epitope mapping of the candidates showed that 16 MHC I epitopes and 13 MHC II epitopes with the strongest immunogenicity were exposed on the protein surface, indicating that they could be used to develop a polypeptide vaccine. Thus, we utilized an analytical strategy that combines pangenome analysis and RV to generate a peptide antigen library that simplifies the development of multivalent or universal vaccines against R. equi and can be applied to the development of other vaccines.

Engineering protein-based therapeutics through structural and chemical design

Protein-based therapeutics have led to new paradigms in disease treatment. Projected to be half of the top ten selling drugs in 2023, proteins have emerged as rivaling and, in some cases, superior alternatives to historically used small molecule-based medicines. This review chronicles both well-established and emerging design strategies that have enabled this paradigm shift by transforming protein-based structures that are often prone to denaturation, degradation, and aggregation in vitro and in vivo into highly effective therapeutics. In particular, we discuss strategies for creating structures with increased affinity and targetability, enhanced in vivo stability and pharmacokinetics, improved cell permeability, and reduced amounts of undesired immunogenicity.

Immunization of broiler chickens with five newly identified surface-exposed proteins unique to Clostridium perfringens causing necrotic enteritis

Since the ban or reduction on the use of antibiotic growth promoters (AGPs) in commercial broiler chickens in many countries, avian necrotic enteritis (NE) caused by Clostridium perfringens has re-emerged as one of the biggest threats for the poultry industry worldwide. While the toolbox for controlling NE in the absence of antibiotics consists of a limited number of alternatives for which the overall effectiveness has yet proven to be suboptimal, an effective vaccine would represent the best control strategy for this often-deadly disease. Using a comparative and subtractive reverse vaccinology approach, we previously identified 14 putative antigenic proteins unique to NE-causing strains of C. perfringens. In the current work, the in silico findings were confirmed by PCR and sequencing, and five vaccine candidate proteins were produced and purified subsequently. Among them, two candidates were hypothetical proteins, two candidates were prepilin proteins which are predicted to form the subunits of a pilus structure, and one candidate was a non-heme iron protein. Western blotting and ELISA results showed that immunization of broiler chickens with five of these proteins raised antibodies which can specifically recognize both the recombinant and native forms of the protein in pathogenic C. perfringens.

The race to understand immunopathology in COVID-19: Perspectives on the impact of quantitative approaches to understand within-host interactions

The COVID-19 pandemic has revealed the need for the increased integration of modelling and data analysis to public health, experimental, and clinical studies. Throughout the first two years of the pandemic, there has been a concerted effort to improve our understanding of the within-host immune response to the SARS-CoV-2 virus to provide better predictions of COVID-19 severity, treatment and vaccine development questions, and insights into viral evolution and the impacts of variants on immunopathology. Here we provide perspectives on what has been accomplished using quantitative methods, including predictive modelling, population genetics, machine learning, and dimensionality reduction techniques, in the first 26 months of the COVID-19 pandemic approaches, and where we go from here to improve our responses to this and future pandemics.

COVID-19 Vaccines: Computational tools and Development

The 2019 coronavirus outbreak, also known as COVID-19, poses a serious threat to global health and has already had widespread, devastating effects around the world. Scientists have been working tirelessly to develop vaccines to stop the virus from spreading as much as possible, as its cure has not yet been found. As of December 2022, 651,918,402 cases and 6,656,601 deaths had been reported. Globally, over 13 billion doses of vaccine have been administered, representing 64.45% of the world's population that has received the vaccine. To expedite the vaccine development process, computational tools have been utilized. This paper aims to analyze some computational tools that aid vaccine development by presenting positive evidence for proving the efficacy of these vaccines to suppress the spread of the virus and for the use of computational tools in the development of vaccines for emerging diseases.

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.

Vaccinomics to Design a Multiepitope Vaccine against Legionella pneumophila

Legionella pneumophila is found in the natural aquatic environment and can resist a wide range of environmental conditions. There are around fifty species of Legionella, at least twenty-four of which are directly linked to infections in humans. L. pneumophila is the cause of Legionnaires' disease, a potentially lethal form of pneumonia. By blocking phagosome-lysosome fusion, L. pneumophila lives and proliferates inside macrophages. For this disease, there is presently no authorized multiepitope vaccine available. For the multi-epitope-based vaccine (MEBV), the best antigenic candidates were identified using immunoinformatics and subtractive proteomic techniques. Several immunoinformatics methods were utilized to predict B and T cell epitopes from vaccine candidate proteins. To construct an in silico vaccine, epitopes (07 CTL, 03 HTL, and 07 LBL) were carefully selected and docked with MHC molecules (MHC-I and MHC-II) and human TLR4 molecules. To increase the immunological response, the vaccine was combined with a 50S ribosomal adjuvant. To maximize vaccine protein expression, MEBV was cloned and reverse-translated in Escherichia coli. To prove the MEBV's efficacy, more experimental validation is required. After its development, the resulting vaccine is greatly hoped to aid in the prevention of L. pneumophila infections.

SILVI, an open-source pipeline for T-cell epitope selection

High-throughput screening of available genomic data and identification of potential antigenic candidates have promoted the development of epitope-based vaccines and therapeutics. Several immunoinformatic tools are available to predict potential epitopes and other immunogenicity-related features, yet it is still challenging and time-consuming to compare and integrate results from different algorithms. We developed the R script SILVI (short for: from in silico to in vivo), to assist in the selection of the potentially most immunogenic T-cell epitopes from Human Leukocyte Antigen (HLA)-binding prediction data. SILVI merges and compares data from available HLA-binding prediction servers, and integrates additional relevant information of predicted epitopes, namely BLASTp alignments with host proteins and physical-chemical properties. The two default criteria applied by SILVI and additional filtering allow the fast selection of the most conserved, promiscuous, strong binding T-cell epitopes. Users may adapt the script at their discretion as it is written in open-source R language. To demonstrate the workflow and present selection options, SILVI was used to integrate HLA-binding prediction results of three example proteins, from viral, bacterial and parasitic microorganisms, containing validated epitopes included in the Immune Epitope Database (IEDB), plus the Human Papillomavirus (HPV) proteome. Applying different filters on predicted IC50, hydrophobicity and mismatches with host proteins allows to significantly reduce the epitope lists with favourable sensitivity and specificity to select immunogenic epitopes. We contemplate SILVI will assist T-cell epitope selections and can be continuously refined in a community-driven manner, helping the improvement and design of peptide-based vaccines or immunotherapies. SILVI development version is available at: github.com/JoanaPissarra/SILVI2020 and https://doi.org/10.5281/zenodo.6865909.

B.1.1.7 (Alpha) variant is the most antigenic compared to Wuhan strain, B.1.351, B.1.1.28/triple mutant and B.1.429 variants

The rapid spread of the SARS-CoV-2 virus and its variants has created a catastrophic impact worldwide. Several variants have emerged, including B.1.351 (Beta), B.1.1.28/triple mutant (P.1), B.1.1.7 (Alpha), and B.1.429 (Epsilon). We performed comparative and comprehensive antigenicity mapping of the total S-glycoprotein using the Wuhan strain and the other variants and identified 9-mer, 15-mer, and 20-mer CTL epitopes through in silico analysis. The study found that 9-mer CTL epitope regions in the B.1.1.7 variant had the highest antigenicity and an average of the three epitope types. Cluster analysis of the 9-mer CTL epitopes depicted one significant cluster at the 70% level with two nodes (KGFNCYFPL and EGFNCYFPL). The phage-displayed peptides showed mimic 9-mer CTL epitopes with three clusters. CD spectra analysis showed the same band pattern of S-glycoprotein of Wuhan strain and all variants other than B.1.429. The developed 3D model of the superantigen (SAg)-like regions found an interaction pattern with the human TCR, indicating that the SAg-like component might interact with the TCR beta chain. The present study identified another partial SAg-like region (ANQFNSAIGKI) from the S-glycoprotein. Future research should examine the molecular mechanism of antigen processing for CD8⁺ T cells, especially all the variants’ antigens of S-glycoprotein.

Promising Acinetobacter baumannii Vaccine Candidates and Drug Targets in Recent Years

In parallel to the uncontrolled use of antibiotics, the emergence of multidrug-resistant bacteria, like Acinetobacter baumannii, has posed a severe threat. A. baumannii predominates in the nosocomial setting due to its ability to persist in hospitals and survive antibiotic treatment, thereby eventually leading to an increasing prevalence and mortality due to its infection. With the increasing spectra of drug resistance and the incessant collapse of newly discovered antibiotics, new therapeutic countermeasures have been in high demand. Hence, recent research has shown favouritism towards the long-term solution of designing vaccines. Therefore, being a realistic alternative strategy to combat this pathogen, anti-A. Baumannii vaccines research has continued unearthing various antigens with variable results over the last decade. Again, other approaches, including pan-genomics, subtractive proteomics, and reverse vaccination strategies, have shown promise for identifying promiscuous core vaccine candidates that resulted in chimeric vaccine constructs. In addition, the integration of basic knowledge of the pathobiology of this drug-resistant bacteria has also facilitated the development of effective multiantigen vaccines. As opposed to the conventional trial-and-error approach, incorporating the in silico methods in recent studies, particularly network analysis, has manifested a great promise in unearthing novel vaccine candidates from the A. baumannii proteome. Some studies have used multiple A. baumannii data sources to build the co-functional networks and analyze them by k-shell decomposition. Additionally, Whole Genomic Protein Interactome (GPIN) analysis has utilized a rational approach for identifying essential proteins and presenting them as vaccines effective enough to combat the deadly pathogenic threats posed by A. baumannii. Others have identified multiple immune nodes using network-based centrality measurements for synergistic antigen combinations for different vaccination strategies. Protein-protein interactions have also been inferenced utilizing structural approaches, such as molecular docking and molecular dynamics simulation. Similar workflows and technologies were employed to unveil novel A. baumannii drug targets, with a similar trend in the increasing influx of in silico techniques. This review integrates the latest knowledge on the development of A. baumannii vaccines while highlighting the in silico methods as the future of such exploratory research. In parallel, we also briefly summarize recent advancements in A. baumannii drug target research.

Vaccine development to control the rising scourge of antibiotic-resistant Acinetobacter baumannii: a systematic review

Acinetobacter baumannii has emerged as one of major nosocomial pathogen and global emergence of multidrug-resistant strains has become a challenge for developing effective treatment options. A. baumannii has developed resistance to almost all the antibiotics viz. beta-lactams, carbapenems, tigecycline and now colistin, a last resort of antibiotics. The world is on the cusp of post antibiotic era and the evolution of multi-, extreme- and pan–drug-resistant A. baumannii strains is its obvious harbinger. Various combinations of antibiotics have been investigated but no successful treatment option is available. All these failed efforts have led researchers to develop and implement prophylactic vaccination for the prevention of infections caused by this pathogen. In this review, the advantages and disadvantages of active and passive immunization, the types of sub-unit and multi-component vaccine candidates investigated against A. baumannii viz. whole cell organism, outer membrane vesicles, outer membrane complexes, conjugate vaccines and sub-unit vaccines have been discussed. In addition, the benefits of Reverse vaccinology are emphasized here in which the potential vaccine candidates are predicted using bioinformatic online tools prior to in vivo validations.

Review on Approved and Inprogress COVID-19 Vaccines

The last generation of Coronavirus named COVID-19 is responsible for the recent worldwide outbreak. Concerning the widespread and quick predominance, there is a critical requirement for designing appropriate vaccines to surmount this grave problem. Correspondingly, in this revision, COVID-19 vaccines (which are being developed until March 29th, 2021) are classified into specific and non-specific categories. Specific vaccines comprise genetic-based vaccines (mRNA, DNA), vector-based, protein/recombinant protein vaccines, inactivated viruses, live-attenuated vaccines, and novel strategies including microneedle arrays (MNAs), and nanoparticles vaccines. Moreover, specific vaccines such as BCG, MRR, and a few other vaccines are considered Non-specific. What is more, according to the significance of Bioinformatic sciences in the cutting-edge vaccine design and rapid outbreak of COVID-19, herein, Bioinformatic principles including reverse vaccinology, epitopes prediction/selection and, their further applications in the design of vaccines are discussed. Last but not least, safety, challenges, advantages, and future prospects of COVID-19 vaccines are highlighted.

Modified mRNA-Based Vaccines Against Coronavirus Disease 2019

The pandemic of coronavirus disease 2019 (COVID-19) continuously causes deaths worldwide, representing a considerable challenge to health care and economic systems with a new precedent in human history. Many therapeutic medicines primarily focused on preventing severe organ damage and complications, which can be fatal in some confirmed cases. The synthesized modified mRNA (modRNA) represents a nonviral, integration-free, zero-footprint, efficient, and safe strategy for vaccine discovery. modRNA-based technology has facilitated the rapid development of the first COVID-19 vaccines due to its cost- and time-saving properties, thus initiating a new era of prophylactic vaccines against infectious diseases. Recently, COVID-19 modRNA vaccines were approved, and a large-scale vaccination campaign began worldwide. To date, results suggest that the modRNA vaccines are highly effective against virus infection, which causes COVID-19. Although short-term studies have reported that their safety is acceptable, long-term safety and protective immunity remain unclear. In this review, we describe two major approved modRNA vaccines and discuss their potential myocarditis complications.

Evaluation and Designing of Epitopic-Peptide Vaccine Against Bunyamwera orthobunyavirus Using M-Polyprotein Target Sequences

Bunyamwera orthobunyavirus and its serogroup can cause several diseases in humans, cattle, ruminants, and birds. The viral M-polyprotein helps the virus to enter the host body. Therefore, this protein might serve as a potential vaccine target against Bunyamwera orthobunyavirus. The present study applied the immunoinformatics technique to design an epitopic vaccine component that could protect against Bunyamwera infection. Phylogenetic analysis revealed the presence of conserved patterns of M-polyprotein within the viral serogroup. Three epitopes common for both B-cell and T-cell were identified, i.e., YQPTELTRS, YKAHDKEET, and ILGTGTPKF merged with a specific linker peptide to construct an active vaccine component. The low atomic contact energy value of docking complex between human TLR4 (TLR4/MD2 complex) and vaccine construct confirms the elevated protein–protein binding interaction. Molecular dynamic simulation and normal mode analysis illustrate the docking complex’s stability, especially by the higher Eigenvalue. In silico cloning of the vaccine construct was applied to amplify the desired vaccine component. Structural allocation of both the vaccine and epitopes also show the efficacy of the developed vaccine. Hence, the computational research design outcomes support that the peptide-based vaccine construction is a crucial drive target to limit the infection of Bunyamwera orthobunyavirus to an extent.

Predicting Protein Therapeutic Candidates for Bovine Babesiosis Using Secondary Structure Properties and Machine Learning

Bovine babesiosis causes significant annual global economic loss in the beef and dairy cattle industry. It is a disease instigated from infection of red blood cells by haemoprotozoan parasites of the genus Babesia in the phylum Apicomplexa. Principal species are Babesia bovis, Babesia bigemina, and Babesia divergens. There is no subunit vaccine. Potential therapeutic targets against babesiosis include members of the exportome. This study investigates the novel use of protein secondary structure characteristics and machine learning algorithms to predict exportome membership probabilities. The premise of the approach is to detect characteristic differences that can help classify one protein type from another. Structural properties such as a protein’s local conformational classification states, backbone torsion angles ϕ (phi) and ψ (psi), solvent-accessible surface area, contact number, and half-sphere exposure are explored here as potential distinguishing protein characteristics. The presented methods that exploit these structural properties via machine learning are shown to have the capacity to detect exportome from non-exportome Babesia bovis proteins with an 86–92% accuracy (based on 10-fold cross validation and independent testing). These methods are encapsulated in freely available Linux pipelines setup for automated, high-throughput processing. Furthermore, proposed therapeutic candidates for laboratory investigation are provided for B. bovis, B. bigemina, and two other haemoprotozoan species, Babesia canis, and Plasmodium falciparum.

Immunoinformatic Approach for the Identification of Potential Epitopes Against Stenotrophomonas maltophilia: A Global Opportunistic Pathogen

Background:

Stenotrophomonas maltophilia is an aerobic, non-fermentative, gram negative, multidrug resistant and opportunistic nosocomial pathogen. It is associated with high morbidity and mortality in severely immunocompromised paediatric patients, including neonates. Immunoinformatic analysis paved a new way to design epitope-based vaccines which resulted in a potential immunogen with advantages such as lower cost, specific immunity, ease of production, devoid of side effects, and less time consumption than conventional vaccines. Till date, there is no development in the vaccines or antibody-based treatments for S. maltophilia-associated infections.

Introduction:

Currently, epitope-based peptide vaccines against pathogenic bacteria have grasped more attention. In our present study, we have utilized various immunoinformatic tools to find a prominent epitope that interacts with the maximum number of HLA alleles and also with the maximum population coverage for developing a vaccine against Stenotrophomonas maltophilia.

Methods:

This study has incorporated an immunoinformatic based screening approach to explore potential epitope-based vaccine candidates in Stenotrophomonas maltophilia proteome. In this study, 4365 proteins of the Stenotrophomonas maltophilia K279a proteome were screened to identify potential antigens that could be used as a good candidate for the vaccine. Various immunoinformatic tools were used to predict the binding of the promiscuous epitopes with Major Histocompatibility Complex (MHC) class I molecules. Other properties such as allergenicity, physiochemical properties, adhesion properties, antigenicity, population coverage, epitope conservancy and toxicity were analysed for the predicted epitope.

Results:

This study helps in finding the prominent epitope in Stenotrophomonas infections. Hence, the main objective in this research was to screen complete Stenotrophomonas maltophilia proteome to recognize putative epitope candidates for vaccine design. Using computational vaccinology and immunoinformatic tools approach, several aspects are obligatory to be fulfilled by an epitope to be considered as a vaccine candidate. Our findings were promising and showed that the predicted epitopes were non-allergenic and fulfilled other parameters required for being a suitable candidate based on certain physio-chemical, antigenic and adhesion properties.

Conclusion:

The epitopes LLFVLCWPL and KSGEGKCGA have shown the highest binding score of −103 and −78.1 kcal/mol with HLA-A*0201 and HLA-B*0702 MHC class I allele, respectively. They were also predicted to be immunogenic and non-allergenic. Further various immunological tests, both in vivo and in vitro methods, should be performed for finding the efficiency of the predicted epitope in the development of a targeted vaccine against Stenotrophomonas maltophilia infection.

Computational design of a new multi-epitope vaccine using immunoinformatics approach against mastitis disease

Mastitis disease causes significant economic losses in dairy farms by reducing milk production, increasing production costs, and reducing milk quality. Streptococcus agalactiae continues to be a major cause of mastitis in dairy cattle. To date, there has been no approved multi-epitope vaccine against this pathogen in the market. In the present study, an efficient multi-epitope vaccine against S. agalactiae, the causative agent of mastitis, was designed using various immonoinformtics approaches. Potential epitopes were selected from Sip protein to improve vaccine immunogenicity. The designed vaccine is more antigenic in nature. Then, linkers and profilin adjuvant were added to enhance the immunity of vaccines. The designed vaccine was evaluated in terms of molecular weight, PI, immunogenicity, Toxicity, and allergenicity. Prediction of three-dimensional (3 D) structure of multi-epitope vaccine, followed by refinement and validation, was conducted to obtain a high-quality 3 D structure of the designed multi-epitope vaccine. The designed vaccine was then subjected to molecular docking with Toll-like receptor 11 (TLR11) receptor to evaluate its binding efficiency followed by dynamic simulation for stable interaction. In silico cloning approach was carried out to improve the expression of the vaccine construct. These analyses indicate that the designed multi-epitope vaccine may produce particular immune responses against S. agalactiae and may be further helpful to control mastitis after in vitro and in vivo immunological assays.

Immunoinformatics Approach for the Identification and Characterization of T Cell and B Cell Epitopes towards the Peptide-Based Vaccine against SARS-CoV-2

Presently, immunoinformatics is playing a significant role in epitope identification and vaccine designing for various critical diseases. Using immunoinformatics, several scientists are trying to identify and characterize T cell and B cell epitopes as well as design peptide-based vaccine against SARS-CoV-2. In this review article, we have tried to discuss the importance in adaptive immunity and its significance for designing the SARS-CoV-2 vaccine. Moreover, we have attempted to illustrate several significant key points for utilizing immunoinformatics for vaccine designing, such as the criteria for selection and identification of epitopes, T cell epitope, and B cell epitope prediction and different emerging tools/databases for immunoinformatics. In the current scenario, a few immunoinformatics studies have been performed for various infectious pathogens and related diseases. Thus, we have also summarized and included these current immunoinformatics studies in this review article. Finally, we have discussed about the probable T cell and B cell epitopes and their identification and characterization for vaccine designing against SARS-CoV-2.

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.

Designing an efficient multi-epitope vaccine displaying interactions with diverse HLA molecules for an efficient humoral and cellular immune response to prevent COVID-19 infection

Background

The novel SARS-CoV-2 coronavirus, the causative agent of the ongoing pandemic COVID-19 disease continues to infect people globally and has infected millions of humans worldwide. However, no effective vaccine against this virus exists.

Method

Using Immunoinformatics, epitopic sequences from multiple glycoproteins that play crucial role in pathogenesis were identified. Particularly, epitopes were mapped from conserved receptor-binding domain of spike protein which have been experimentally validated in SARS-CoV-1 as a promising target for vaccine development.

Results

A multi-epitopic vaccine construct comprising of B-cell, CTL, HTL epitopes was developed along with fusion of adjuvant and linkers. The epitopes identified herein are reported for the first time and were predicted to be highly antigenic, stable, nonallergen, nontoxic and displayed conservation across several SARS-CoV-2 isolates from different countries. Additionally, the epitopes associated with maximum HLA alleles and population coverage analysis shows the proposed epitopes would be a relevant representative of large proportion of the world population. A reliable three-dimensional structure of the vaccine construct was developed. Consequently, docking and molecular-dynamics simulation ensured the stable interaction between vaccine and innate-immune receptor.

Computational tools for modern vaccine development

Vaccines play an essential role in controlling the rates of fatality and morbidity. Vaccines not only arrest the beginning of different diseases but also assign a gateway for its elimination and reduce toxicity. This review gives an overview of the possible uses of computational tools for vaccine design. Moreover, we have described the initiatives of utilizing the diverse computational resources by exploring the immunological databases for developing epitope-based vaccines, peptide-based drugs, and other resources of immunotherapeutics. Finally, the applications of multi-graft and multivalent scaffolding, codon optimization and antibodyomics tools in identifying and designing in silico vaccine candidates are described.

Computational assembly of a human Cytomegalovirus vaccine upon experimental epitope legacy

Background

Human Cytomegalovirus (HCMV) is a ubiquitous herpesvirus affecting approximately 90% of the world population. HCMV causes disease in immunologically naive and immunosuppressed patients. The prevention, diagnosis and therapy of HCMV infection are thus crucial to public health. The availability of effective prophylactic and therapeutic treatments remain a significant challenge and no vaccine is currently available. Here, we sought to define an epitope-based vaccine against HCMV, eliciting B and T cell responses, from experimentally defined HCMV-specific epitopes.

Results

We selected 398 and 790 experimentally validated HCMV-specific B and T cell epitopes, respectively, from available epitope resources and apply a knowledge-based approach in combination with immunoinformatic predictions to ensemble a universal vaccine against HCMV. The T cell component consists of 6 CD8 and 6 CD4 T cell epitopes that are conserved among HCMV strains. All CD8 T cell epitopes were reported to induce cytotoxic activity, are derived from early expressed genes and are predicted to provide population protection coverage over 97%. The CD4 T cell epitopes are derived from HCMV structural proteins and provide a population protection coverage over 92%. The B cell component consists of just 3 B cell epitopes from the ectodomain of glycoproteins L and H that are highly flexible and exposed to the solvent.

Conclusions

We have defined a multiantigenic epitope vaccine ensemble against the HCMV that should elicit T and B cell responses in the entire population. Importantly, although we arrived to this epitope ensemble with the help of computational predictions, the actual epitopes are not predicted but are known to be immunogenic.

Epitope-Based Vaccine Designing of Nocardia asteroides Targeting the Virulence Factor Mce-Family Protein by Immunoinformatics Approach

Nocardia asteroides is the main causative agent responsible for nocardiosis disease in immunocompromised patient viz. Acquired Immunodeficiency Syndrome (AIDS), malignancy, diabetic, organ recipient and genetic disorders. The virulence factor and outer membrane protein pertains immense contribution towards the designing of epitopic vaccine and limiting the robust outbreak of diseases. While epitopic based vaccine element carrying B and T cell epitope along with adjuvant is highly immunoprophylactic in nature. Present research equips immunoinformatics to figure out the suitable epitopes for effective vaccine designing. The selected epitopes VLGSSVQTA, VNIELKPEF and VVPSNLFAV amino acids sequence are identified by HLA-DRB alleles of both MHC class (MHC-I and II) molecules. Simultaneously, these also accessible to B-cell, confirmed through the ABCPred server. Antigenic property expression is validated by the Vaxijen antigenic prediction web portal. Molecular docking between the epitopes and T cell receptor delta chain authenticate the accurate interaction between epitope and receptor with significantly low binding energy. Easy access of epitopes to immune system also be concluded as transmembrane nature of the protein verified by using of TMHMM server. Appropriate structural identity of the virulence factor Mce-family protein generated through Phyre2 server and subsequently validated by ProSA and PROCHECK program suite. The structural configuration of theses epitopes also shaped using DISTILL web server. Both the structure of epitopes and protein will contribute a significant step in designing of epitopic vaccine against N. asteroides. Therefore, such immunoinformatics based computational drive definitely provides a conspicuous impel towards the development of epitopic vaccine as a promising remedy of nocardiosis.

Epitope-based immunoinformatics approach on RNA-dependent RNA polymerase (RdRp) protein complex of Nipah virus (NiV)

Persistent outbreaks of Nipah virus (NiV) with severe case fatality throw a major challenge on researchers to develop a drug or vaccine to combat the disease. With little knowledge of its molecular mechanisms, we utilized the proteome data of NiV to evaluate the potency of three major proteins (phosphoprotein, polymerase, and nucleocapsid protein) in the RNA-dependent RNA polymerase complex to count as a possible candidate for epitope-based vaccine design. Profound computational analysis was used on the above proteins individually to explore the T-cell immune properties like antigenicity, immunogenicity, binding to major histocompatibility complex class I and class II alleles, conservancy, toxicity, and population coverage. Based on these predictions the peptide 'ELRSELIGY' of phosphoprotein and 'YPLLWSFAM' of nulceocapsid protein were identified as the best-predicted T-cell epitopes and molecular docking with human leukocyte antigen-C (HLA-C*12:03) molecule was effectuated followed by validation with molecular dynamics simulation. The B-cell epitope predictions suggest that the sequence positions 421 to 471 in phosphoprotein, 606 to 640 in polymerase and 496 to 517 in nucleocapsid protein are the best-predicted regions for B-cell immune response. However, the further experimental circumstance is required to test and validate the efficacy of the subunit peptide for potential candidacy against NiV.

PanRV: Pangenome-reverse vaccinology approach for identifications of potential vaccine candidates in microbial pangenome

BACKGROUND

A revolutionary diversion from classical vaccinology to reverse vaccinology approach has been observed in the last decade. The ever-increasing genomic and proteomic data has greatly facilitated the vaccine designing and development process. Reverse vaccinology is considered as a cost-effective and proficient approach to screen the entire pathogen genome. To look for broad-spectrum immunogenic targets and analysis of closely-related bacterial species, the assimilation of pangenome concept into reverse vaccinology approach is essential. The categories of species pangenome such as core, accessory, and unique genes sets can be analyzed for the identification of vaccine candidates through reverse vaccinology.

RESULTS

We have designed an integrative computational pipeline term as "PanRV" that employs both the pangenome and reverse vaccinology approaches. PanRV comprises of four functional modules including i) Pangenome Estimation Module (PGM) ii) Reverse Vaccinology Module (RVM) iii) Functional Annotation Module (FAM) and iv) Antibiotic Resistance Association Module (ARM). The pipeline is tested by using genomic data from 301 genomes of Staphylococcus aureus and the results are verified by experimentally known antigenic data.

CONCLUSION

The proposed pipeline has proved to be the first comprehensive automated pipeline that can precisely identify putative vaccine candidates exploiting the microbial pangenome. PanRV is a Linux based package developed in JAVA language. An executable installer is provided for ease of installation along with a user manual at https://sourceforge.net/projects/panrv2/ .

A multiepitopic theoretical fusion construct based on in-silico epitope screening of known vaccine candidates for protection against wide range of enterobacterial pathogens

Enterobacterial pathogens that have acquired antibiotic resistance genes are a leading cause of community and hospital acquired infections. In such a situation vaccination is considered as a better option to prevent such infections. In the current study reverse vaccinology approach has been used to select peptides from already known immunogenic proteins to design a chimeric construct. We selected Yersiniabactin receptor of Escherichia coli UMN026 and Flagellin of Stenotrophomonas maltophila. B-cell linear epitopes were predicted using Bepipred prediction tool. Peptide binding with reference sets of 27 alleles of MHC class I and class II was also analyzed. The predicted peptides-MHC complexes were further validated using simulation dynamics. The in-silico construction of chimera was done by restriction mapping and codon optimization. Chimera was evaluated using the immunoinformatic approach as done for the selected proteins. From the 673 amino acids of FyuA protein, a region from 1 to 492 was selected for containing more linear epitopes and the processing scores obtained were significant for MHC class I and class II binding. Similarly, from Flagellin, a region between 60 and 328 amino acids was selected and the peptides present in the selected region showed lower percentile ranks for binding with MHC molecules. The simulation studies validated the predictions of peptide-MHC complexes. The selected gene fragments accommodating maximum part of these peptides were used to design a chimaeric construct of 2454 bp. From the immunoinformatic analysis, the chimera was found to be more immunogenic in terms of increased number of B-cell and T-cell epitopes along with increased coverage of global populations with allelic variability.

Development of a novel clustering tool for linear peptide sequences

Epitopes identified in large-scale screens of overlapping peptides often share significant levels of sequence identity, complicating the analysis of epitope-related data. Clustering algorithms are often used to facilitate these analyses, but available methods are generally insufficient in their capacity to define biologically meaningful epitope clusters in the context of the immune response. To fulfil this need we developed an algorithm that generates epitope clusters based on representative or consensus sequences. This tool allows the user to cluster peptide sequences on the basis of a specified level of identity by selecting among three different method options. These include the 'clique method', in which all members of the cluster must share the same minimal level of identity with each other, and the 'connected graph method', in which all members of a cluster must share a defined level of identity with at least one other member of the cluster. In cases where it is not possible to define a clear consensus sequence with the connected graph method, a third option provides a novel 'cluster-breaking algorithm' for consensus sequence driven sub-clustering. Herein we demonstrate the tool's clustering performance and applicability using (i) a selection of dengue virus epitopes for the 'clique method', (ii) sets of allergen-derived peptides from related species for the 'connected graph method' and (iii) large data sets of eluted ligand, major histocompatibility complex binding and T-cell recognition data captured within the Immune Epitope Database (IEDB) with the newly developed 'cluster-breaking algorithm'. This novel clustering tool is accessible at http://tools.iedb.org/cluster2/.

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

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

Overlapping CD8+ and CD4+ T-cell epitopes identification for the progression of epitope-based peptide vaccine from nucleocapsid and glycoprotein of emerging Rift Valley fever virus using immunoinformatics approach

Rift Valley fever virus (RVFV) is an emergent arthropod-borne zoonotic infectious viral pathogen which causes fatal diseases in the humans and ruminants. Currently, no effective and licensed vaccine is available for the prevention of RVFV infection in endemic as well as in non-endemic regions. So, an immunoinformatics-driven genome-wide screening approach was performed for the identification of overlapping CD8+ and CD4+ T-cell epitopes and also linear B-cell epitopes from the conserved sequences of the nucleocapsid (N) and glycoprotein (G) of RVFV. We identified overlapping 99.39% conserved 1 CD8+ T-cell epitope (MMHPSFAGM) from N protein and 100% conserved 7 epitopes (AVFALAPVV, LAVFALAPV, FALAPVVFA, VFALAPVVF, IAMTVLPAL, FFDWFSGLM, and FLLIYLGRT) from G protein and also identified IL-4 and IFN-γ induced (99.39% conserved) 1 N protein CD4+ T-cell epitope (HMMHPSFAGMVDPSL) and 100% conserved 5 G protein CD4+ T-cell epitopes (LPALAVFALAPVVFA, PALAVFALAPVVFAE, GIAMTVLPALAVFAL, GSWNFFDWFSGLMSW, and FFLLIYLGRTGLSKM). The overlapping CD8+ and CD4+ T-cell epitopes were bound with most conserved HLA-C*12:03 and HLA-DRB1*01:01, respectively with the high binding affinity (kcal/mol). The combined population coverage analysis revealed that the allele frequencies of these epitopes are high in endemic and non-endemic regions. Besides, we found 100% conserved and non-allergenic 2 decamer B-cell epitopes, GVCEVGVQAL and RVFNCIDWVH of G protein had the sequence similarity with the nonamer CD8+ T-cell epitopes, VCEVGVQAL and RVFNCIDWV, respectively. Consequently, these epitopes may be used for the development of epitope-based peptide vaccine against emerging RVFV. However, in vivo and in vitro experiments are required for their efficient use as a vaccine.

In silico identification and characterization of common epitope-based peptide vaccine for Nipah and Hendra viruses

OBJECTIVE

To explore a common B- and T-cell epitope-based vaccine that can elicit an immune response against encephalitis causing genus Henipaviruses, Hendra virus (HeV) and Nipah virus (NiV).

METHODS

Membrane proteins F, G and M of HeV and NiV were retrieved from the protein database and subjected to different bioinformatics tools to predict antigenic B-cell epitopes. Best B-cell epitopes were then analyzed to predict their T-cell antigenic potentiality. Antigenic B- and T-cell epitopes that shared maximum identity with HeV and NiV were selected. Stability of the selected epitopes was predicted. Finally, the selected epitopes were subjected to molecular docking simulation with HLA-DR to confirm their antigenic potentiality in silico.

RESULTS

One epitope from G proteins, one from M proteins and none from F proteins were selected based on their antigenic potentiality. The epitope from the G proteins was stable whereas that from M was unstable. The M-epitope was made stable by adding flanking dipeptides. The 15-mer G-epitope (VDPLRVQWRNNSVIS) showed at least 66% identity with all NiV and HeV G protein sequences, while the 15-mer M-epitope (GKLEFRRNNAIAFKG) with the dipeptide flanking residues showed 73% identity with all NiV and HeV M protein sequences available in the database. Molecular docking simulation with most frequent MHC class-II (MHC II) and class-I (MHC I) molecules showed that these epitopes could bind within HLA binding grooves to elicit an immune response.

CONCLUSIONS

Data in our present study revealed the notion that the epitopes from G and M proteins might be the target for peptide-based subunit vaccine design against HeV and NiV. However, the biochemical analysis is necessary to experimentally validate the interaction of epitopes individually with the MHC molecules through elucidation of immunity induction.

Towards developing a vaccine for rheumatic heart disease

Rheumatic heart disease (RHD) is the most serious manifestations of rheumatic fever, which is caused by group A Streptococcus (GAS or Streptococcus pyogenes) infection. RHD is an auto immune sequelae of GAS pharyngitis, rather than the direct bacterial infection of the heart, which leads to chronic heart valve damage. Although antibiotics like penicillin are effective against GAS infection, improper medical care such as poor patient compliance, overcrowding, poverty, and repeated exposure to GAS, leads to acute rheumatic fever and RHD. Thus, efforts have been put forth towards developing a vaccine. However, a potential global vaccine is yet to be identified due to the widespread diversity of S. pyogenes strains and cross reactivity of streptococcal proteins with host tissues. In this review, we discuss the available vaccine targets of S. pyogenes and the significance of in silico approaches in designing a vaccine for RHD.

Immunoinformatics as a Tool for New Antifungal Vaccines

Immunoinformatics aids in screening for vaccine candidates, which can be experimentally tested for their efficacy. This chapter describes methods to use immunoinformatics to screen fungal vaccines candidates. Surface-localized molecules called adhesins could elicit immune response and serve as efficient vaccine candidates. The screening process is patterned on two steps, namely, a First Layer screen mostly used for value addition and prioritization based on characteristics of known antigens and a Second Layer highly focussed on core immunoinformatics analysis involving the binding and interactions of the molecules of the immune system. Together they offer a comprehensive objective evaluation of vaccine candidates selection in silico for fungal pathogens.

The quantitative prediction of HLA-B*2705 peptide binding affinities using Support Vector Regression to gain insights into its role for the Spondyloarthropathies

Computational methods are increasingly utilised in many immunoinformatics problems such as the prediction of binding affinity of peptides. The peptides could provide valuable insight into the drug design and development such as vaccines. Moreover, they can be used to diagnose diseases. The presence of human class I MHC allele HLA-B*2705 is one of the strong hypothesis that would lead spondyloarthropathies. In this paper, Support Vector Regression is used in order to predict binding affinity of peptides with the aid of experimentally determined peptide-MHC binding affinities of 222 peptides to HLA-B*2705 to get more insight into this problematic disease. The results yield a high correlation coefficient as much as 0.65 and the SVR-based predictive models can be considered as a useful tool in order to predict the binding affinities for newly discovered peptides.

A review of reverse vaccinology approaches for the development of vaccines against ticks and tick borne diseases

The field of reverse vaccinology developed as an outcome of the genome sequence revolution. Following the introduction of live vaccinations in the western world by Edward Jenner in 1798 and the coining of the phrase 'vaccine', in 1881 Pasteur developed a rational design for vaccines. Pasteur proposed that in order to make a vaccine that one should 'isolate, inactivate and inject the microorganism' and these basic rules of vaccinology were largely followed for the next 100 years leading to the elimination of several highly infectious diseases. However, new technologies were needed to conquer many pathogens which could not be eliminated using these traditional technologies. Thus increasingly, computers were used to mine genome sequences to rationally design recombinant vaccines. Several vaccines for bacterial and viral diseases (i.e. meningococcus and HIV) have been developed, however the on-going challenge for parasite vaccines has been due to their comparatively larger genomes. Understanding the immune response is important in reverse vaccinology studies as this knowledge will influence how the genome mining is to be conducted. Vaccine candidates for anaplasmosis, cowdriosis, theileriosis, leishmaniasis, malaria, schistosomiasis, and the cattle tick have been identified using reverse vaccinology approaches. Some challenges for parasite vaccine development include the ability to address antigenic variability as well the understanding of the complex interplay between antibody, mucosal and/or T cell immune responses. To understand the complex parasite interactions with the livestock host, there is the limitation where algorithms for epitope mining using the human genome cannot directly be adapted for bovine, for example the prediction of peptide binding to major histocompatibility complex motifs. As the number of genomes for both hosts and parasites increase, the development of new algorithms for pan-genomic mining will continue to impact the future of parasite and ricketsial (and other tick borne pathogens) disease vaccine development.

DockTope: a Web-based tool for automated pMHC-I modelling

The immune system is constantly challenged, being required to protect the organism against a wide variety of infectious pathogens and, at the same time, to avoid autoimmune disorders. One of the most important molecules involved in these events is the Major Histocompatibility Complex class I (MHC-I), responsible for binding and presenting small peptides from the intracellular environment to CD8⁺ T cells. The study of peptide:MHC-I (pMHC-I) molecules at a structural level is crucial to understand the molecular mechanisms underlying immunologic responses. Unfortunately, there are few pMHC-I structures in the Protein Data Bank (PDB) (especially considering the total number of complexes that could be formed combining different peptides), and pMHC-I modelling tools are scarce. Here, we present DockTope, a free and reliable web-based tool for pMHC-I modelling, based on crystal structures from the PDB. DockTope is fully automated and allows any researcher to construct a pMHC-I complex in an efficient way. We have reproduced a dataset of 135 non-redundant pMHC-I structures from the PDB (Cα RMSD below 1 Å). Modelling of pMHC-I complexes is remarkably important, contributing to the knowledge of important events such as cross-reactivity, autoimmunity, cancer therapy, transplantation and rational vaccine design.

Improving the gene structure annotation of the apicomplexan parasite Neospora caninum fulfils a vital requirement towards an in silico-derived vaccine

Neospora caninum is an apicomplexan parasite which can cause abortion in cattle, instigating major economic burden. Vaccination has been proposed as the most cost-effective control measure to alleviate this burden. Consequently the overriding aspiration for N. caninum research is the identification and subsequent evaluation of vaccine candidates in animal models. To save time, cost and effort, it is now feasible to use an in silico approach for vaccine candidate prediction. Precise protein sequences, derived from the correct open reading frame, are paramount and arguably the most important factor determining the success or failure of this approach. The challenge is that publicly available N. caninum sequences are mostly derived from gene predictions. Annotated inaccuracies can lead to erroneously predicted vaccine candidates by bioinformatics programs. This study evaluates the current N. caninum annotation for potential inaccuracies. Comparisons with annotation from a closely related pathogen, Toxoplasma gondii, are also made to distinguish patterns of inconsistency. More importantly, a mRNA sequencing (RNA-Seq) experiment is used to validate the annotation. Potential discrepancies originating from a questionable start codon context and exon boundaries were identified in 1943 protein coding sequences. We conclude, where experimental data were available, that the majority of N. caninum gene sequences were reliably predicted. Nevertheless, almost 28% of genes were identified as questionable. Given the limitations of RNA-Seq, the intention of this study was not to replace the existing annotation but to support or oppose particular aspects of it. Ideally, many studies aimed at improving the annotation are required to build a consensus. We believe this study, in providing a new resource on gene structure and annotation, is a worthy contributor to this endeavour.

Enhancing in silico protein-based vaccine discovery for eukaryotic pathogens using predicted peptide-MHC binding and peptide conservation scores

Given thousands of proteins constituting a eukaryotic pathogen, the principal objective for a high-throughput in silico vaccine discovery pipeline is to select those proteins worthy of laboratory validation. Accurate prediction of T-cell epitopes on protein antigens is one crucial piece of evidence that would aid in this selection. Prediction of peptides recognised by T-cell receptors have to date proved to be of insufficient accuracy. The in silico approach is consequently reliant on an indirect method, which involves the prediction of peptides binding to major histocompatibility complex (MHC) molecules. There is no guarantee nevertheless that predicted peptide-MHC complexes will be presented by antigen-presenting cells and/or recognised by cognate T-cell receptors. The aim of this study was to determine if predicted peptide-MHC binding scores could provide contributing evidence to establish a protein's potential as a vaccine. Using T-Cell MHC class I binding prediction tools provided by the Immune Epitope Database and Analysis Resource, peptide binding affinity to 76 common MHC I alleles were predicted for 160 Toxoplasma gondii proteins: 75 taken from published studies represented proteins known or expected to induce T-cell immune responses and 85 considered less likely vaccine candidates. The results show there is no universal set of rules that can be applied directly to binding scores to distinguish a vaccine from a non-vaccine candidate. We present, however, two proposed strategies exploiting binding scores that provide supporting evidence that a protein is likely to induce a T-cell immune response-one using random forest (a machine learning algorithm) with a 72% sensitivity and 82.4% specificity and the other, using amino acid conservation scores with a 74.6% sensitivity and 70.5% specificity when applied to the 160 benchmark proteins. More importantly, the binding score strategies are valuable evidence contributors to the overall in silico vaccine discovery pool of evidence.

Discovering a vaccine against neosporosis using computers: is it feasible?

A vaccine is urgently needed to prevent cattle neosporosis. This infectious disease is caused by the parasite Neospora caninum, a complex biological system with multifaceted life cycles. An in silico vaccine discovery approach attempts to transform digital abstractions of this system into adequate knowledge to predict candidates. Researchers need current information to implement such an approach, such as understanding evasion mechanisms of the immune system, type of immune response to elicit, availability of data and prediction programs, and statistical models to analyze predictions. Taken together, an in silico approach involves assembly of an intricate jigsaw of interdisciplinary and interdependent knowledge. In this review, we focus on the approach influencing vaccine development against Neospora caninum, which can be generalized to other pathogenic apicomplexans.

Computational design of protein therapeutics

Computation is increasingly used to guide protein therapeutic designs. Some of the potential applications for computational, structure-based protein design include antibody affinity maturation, modulation of protein-protein interaction, stability improvement and minimization of protein aggregation. The versatility of a computational approach is that different biophysical properties can be analyzed on a common framework. Developing a coherent strategy to address various protein engineering objectives will promote synergy and exploration. Advances in computational structural analysis will thus have a transformative impact on how protein therapeutics are engineered in the future.:

A cognitive computational model inspired by the immune system response

The immune system has a cognitive ability to differentiate between healthy and unhealthy cells. The immune system response (ISR) is stimulated by a disorder in the temporary fuzzy state that is oscillating between the healthy and unhealthy states. However, modeling the immune system is an enormous challenge; the paper introduces an extensive summary of how the immune system response functions, as an overview of a complex topic, to present the immune system as a cognitive intelligent agent. The homogeneity and perfection of the natural immune system have been always standing out as the sought-after model we attempted to imitate while building our proposed model of cognitive architecture. The paper divides the ISR into four logical phases: setting a computational architectural diagram for each phase, proceeding from functional perspectives (input, process, and output), and their consequences. The proposed architecture components are defined by matching biological operations with computational functions and hence with the framework of the paper. On the other hand, the architecture focuses on the interoperability of main theoretical immunological perspectives (classic, cognitive, and danger theory), as related to computer science terminologies. The paper presents a descriptive model of immune system, to figure out the nature of response, deemed to be intrinsic for building a hybrid computational model based on a cognitive intelligent agent perspective and inspired by the natural biology. To that end, this paper highlights the ISR phases as applied to a case study on hepatitis C virus, meanwhile illustrating our proposed architecture perspective.

A Dataset of Experimental HLA-B*2705 Peptide Binding Affinities

T-cell epitopes form the basis of many vaccines, diagnostics, and reagents. Current methods for the in silico identification of T-cell epitopes rely, in the main, on the accurate quantitative prediction of peptide-Major Histocompatibility Complex (pMHC) affinity using data-driven computational approaches. Here, we describe a dataset of experimentally determined pMHC binding affinities for the problematic human class I allele HLA-B*2705. Using an in-house, FACS-based, MHC stabilization assay, we measured binding of 223 peptides. This dataset includes both nonbinding and binding peptides, with measured affinities (expressed as −log10 of the half-maximal binding level) ranging from 1.2 to 7.4. This dataset should provide a useful independent benchmark for new and existing methods for predicting peptide binding to HLA-B*2705.

Integrative immunoinformatics for Mycobacterial diseases in R platform

The sequencing of genomes of the pathogenic Mycobacterial species causing pulmonary and extrapulmonary tuberculosis, leprosy and other atypical mycobacterial infections, offer immense opportunities for discovering new therapeutics and identifying new vaccine candidates. Enhanced RV, which uses additional algorithms to Reverse Vaccinology (RV), has increased potential to reduce likelihood of undesirable features including allergenicity and immune cross reactivity to host. The starting point for MycobacRV database construction includes collection of known vaccine candidates and a set of predicted vaccine candidates identified from the whole genome sequences of 22 mycobacterium species and strains pathogenic to human and one non-pathogenic Mycobacterium tuberculosis H37Ra strain. These predicted vaccine candidates are the adhesins and adhesin-like proteins obtained using SPAAN at Pad > 0.6 and screening for putative extracellular or surface localization characteristics using PSORTb v.3.0 at very stringent cutoff. Subsequently, these protein sequences were analyzed through 21 publicly available algorithms to obtain Orthologs, Paralogs, BetaWrap Motifs, Transmembrane Domains, Signal Peptides, Conserved Domains, and similarity to human proteins, T cell epitopes, B cell epitopes, Discotopes and potential Allergens predictions. The Enhanced RV information was analysed in R platform through scripts following well structured decision trees to derive a set of nonredundant 233 most probable vaccine candidates. Additionally, the degree of conservation of potential epitopes across all orthologs has been obtained with reference to the M. tuberculosis H37Rv strain, the most commonly used strain in M. tuberculosis studies. Utilities for the vaccine candidate search and analysis of epitope conservation across the orthologs with reference to M. tuberculosis H37Rv strain are available in the mycobacrvR package in R platform accessible from the "Download" tab of MycobacRV webserver. MycobacRV an immunoinformatics database of known and predicted mycobacterial vaccine candidates has been developed and is freely available at http://mycobacteriarv.igib.res.in.

Immunoinformatics: a brief review

A large volume of data relevant to immunology research has accumulated due to sequencing of genomes of the human and other model organisms. At the same time, huge amounts of clinical and epidemiologic data are being deposited in various scientific literature and clinical records. This accumulation of the information is like a goldmine for researchers looking for mechanisms of immune function and disease pathogenesis. Thus the need to handle this rapidly growing immunological resource has given rise to the field known as immunoinformatics. Immunoinformatics, otherwise known as computational immunology, is the interface between computer science and experimental immunology. It represents the use of computational methods and resources for the understanding of immunological information. It not only helps in dealing with huge amount of data but also plays a great role in defining new hypotheses related to immune responses. This chapter reviews classical immunology, different databases, and prediction tool. Further, it briefly describes applications of immunoinformatics in reverse vaccinology, immune system modeling, and cancer diagnosis and therapy. It also explores the idea of integrating immunoinformatics with systems biology for the development of personalized medicine. All these efforts save time and cost to a great extent.

Prediction of IL4 inducing peptides

The secretion of Interleukin-4 (IL4) is the characteristic of T-helper 2 responses. IL4 is a cytokine produced by CD4+ T cells in response to helminthes and other extracellular parasites. It has a critical role in guiding antibody class switching, hematopoiesis and inflammation, and the development of appropriate effector T-cell responses. In this study, it is the first time an attempt has been made to understand whether it is possible to predict IL4 inducing peptides. The data set used in this study comprises 904 experimentally validated IL4 inducing and 742 noninducing MHC class II binders. Our analysis revealed that certain types of residues are preferred at certain positions in IL4 inducing peptides. It was also observed that IL4 inducing and noninducing epitopes differ in compositional and motif pattern. Based on our analysis we developed classification models where the hybrid method of amino acid pairs and motif information performed the best with maximum accuracy of 75.76% and MCC of 0.51. These results indicate that it is possible to predict IL4 inducing peptides with reasonable precession. These models would be useful in designing the peptides that may induce desired Th2 response.