An Introduction to B-Cell Epitope Mapping and In Silico Epitope Prediction

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.

Prediction, mapping and validation of tick glutathione S-transferase B-cell epitopes

In search of ways to address the increasing incidence of global acaricide resistance, tick control through vaccination is regarded as a sustainable alternative approach. Recently, a novel cocktail antigen tick-vaccine was developed based on the recombinant glutathione S-transferase (rGST) anti-sera cross-reaction to glutathione S-transferases of Rhipicephalus appendiculatus (GST-Ra), Amblyomma variegatum (GST-Av), Haemaphysalis longicornis (GST-Hl), Rhipicephalus decoloratus (GST-Rd) and Rhipicephalus microplus (GST-Rm). Therefore, the current study aimed to predict the shared B-cell epitopes within the GST sequences of these tick species. Prediction of B-cell epitopes and proteasomal cleavage sites were performed using immunoinformatics algorithms. The conserved epitopes predicted within the sequences were mapped on the homodimers of the respective tick GSTs, and the corresponding peptides were independently used for rabbit immunization experiments. Based on the dot blot assay, the immunogenicity of the peptides and their potential to be recognized by corresponding rGST anti-sera raised by rabbit immunization in a previous work were investigated. This study revealed that the predicted conserved B-cell epitopes within the five tick GST sequences were localized on the surface of the respective GST homodimers. The epitopes of GST-Ra, GST-Rd, GST-Av, and GST-Hl were also shown to contain a seven residue-long peptide sequence with no proteasomal cleavage sites, whereas proteasomal digestion of GST-Rm was predicted to yield a 4-residue fragment. Given that a few proteasomal cleavage sites were found within the conserved epitope sequences of the four GSTs, the sequences could also contain a T-cell epitope. Finally, the peptide and rGST anti-sera reacted against the corresponding peptide, confirming their immunogenicity. These data support the claim that the rGSTs, used in the previous study, contain conserved B-cell epitopes, which elucidates why the rGST anti-sera cross-reacted to non-homologous tick GSTs. Taken together, the data suggest that the B-cell epitopes predicted in this study could be useful for constituting epitope-based GST tick vaccines.

Deep learning methods improve linear B-cell epitope prediction

Background

B-cell epitopes play important roles in vaccine design, clinical diagnosis, and antibody production. Although some models have been developed to predict linear or conformational B-cell epitopes, their performance is still unsatisfactory. Hundreds of thousands of linear B-cell epitope data have accumulated in the Immune Epitope Database (IEDB). These data can be explored using the deep learning methods, in order to create better predictive models for linear B-cell epitopes.

Results

After data cleaning, we obtained 240,563 peptide samples with experimental evidence from the IEDB database, including 25,884 linear B-cell epitopes and 214,679 non-epitopes. Based on the peptide center, we adapted each peptide to the same length by trimming or extending. A random portion of the data, with the same amount of epitopes and non-epitopes, were set aside as test dataset. Then a same number of epitopes and non-epitopes were randomly selected from the remaining data to build a classifier with the feedforward deep neural network. We built eleven classifiers to form an ensemble prediction model. The model will report a peptide as an epitope if it was classified as epitope by all eleven classifiers. Then we used the test data set to evaluate the performance of the model using the area value under the receiver operating characteristic (ROC) curve (AUC) as an indicator. We established 40 models to predict linear B-cell epitopes of length from 11 to 50 separately, and found that the AUC value increased with the length and tended to be stable when the length was 38. Repeated results showed that the models constructed by this method were robust. Tested on our and two public test datasets, our models outperformed current major models available.

Conclusions

We applied the feedforward deep neural network to the large amount of linear B-cell epitope data with experimental evidence in the IEDB database, and constructed ensemble prediction models with better performance than the current major models available. We named the models as DLBEpitope and provided web services using the models at http://ccb1.bmi.ac.cn:81/dlbepitope/.

In silico Design of a Multivalent Vaccine Against Candida albicans

Invasive candidiasis (IC) is the most common nosocomial infection and a leading cause of mycoses-related deaths. High-systemic toxicity and emergence of antifungal-resistant species warrant the development of newer preventive approaches against IC. Here, we have adopted an immunotherapeutic peptide vaccine-based approach, to enhance the body's immune response against invasive candida infections. Using computational tools, we screened the entire candida proteome (6030 proteins) and identified the most immunodominant HLA class I, HLA class II and B- cell epitopes. By further immunoinformatic analyses for enhanced vaccine efficacy, we selected the 18- most promising epitopes, which were joined together using molecular linkers to create a multivalent recombinant protein against Candida albicans (mvPC). To increase mvPC's immunogenicity, we added a synthetic adjuvant (RS09) to the mvPC design. The selected mvPC epitopes are homologous against all currently available annotated reference sequences of 22 C. albicans strains, thus offering a higher coverage and greater protective response. A major advantage of the current vaccine approach is mvPC's multivalent nature (recognizing multiple-epitopes), which is likely to provide enhanced protection against complex candida antigens. Here, we describe the computational analyses leading to mvPC design.

Identification of a Novel Linear B-Cell Epitope on the Nucleocapsid Protein of Porcine Deltacoronavirus

Porcine deltacoronavirus (PDCoV), first identified in 2012, is a swine enteropathogen now found in many countries. The nucleocapsid (N) protein, a core component of PDCoV, is essential for virus replication and is a significant candidate in the development of diagnostics for PDCoV. In this study, monoclonal antibodies (mAbs) were generated and tested for reactivity with three truncations of the full protein (N1, N2, N3) that contained partial overlaps; of the five monoclonals chosen tested, each reacted with only the N3 truncation. The antibody designated 4E88 had highest binding affinity with the N protein and was chosen for in-depth examination. The 4E88 epitope was located to amino acids 308-AKPKQQKKPKK-318 by testing the 4E88 monoclonal for reactivity with a series of N3 truncations, then the minimal epitope, 309-KPKQQKKPK-317 (designated EP-4E88), was pinpointed by testing the 4E88 monoclonal for reactivity with a series of synthetic peptides of this region. Homology analysis showed that the EP-4E88 sequence is highly conserved among PDCoV strains, and also shares high similarity with sparrow coronavirus (HKU17), Asian leopard cat coronavirus (ALCCoV), quail coronavirus (UAE-HKU30), and sparrow deltacoronavirus (SpDCoV). Of note, the PDCoV EP-4E88 sequence shared very low similarity (<22.2%) with other porcine coronaviruses (PEDV, TGEV, PRCV, SADS-CoV, PHEV), demonstrating that it is an epitope that can be used for distinguishing PDCoV and other porcine coronavirus. 3D structural analysis revealed that amino acids of EP-4E88 were in close proximity and may be exposed on the surface of the N protein.

Molecular Modelling and Insilico Engineering of PapMV-CP Towards Display and Development of Capripox Viral Like Particles Based on Immunogenic P32 Envelop Protein is the Homologous of the Vaccinia-Viral H3L Gene: An Insilico Approach

Viral-like particles are assembled from capsid protein structural subunits of different viruses and have ability to establish research in biomedicals, like construction of novel safety vaccines, gene therapy vectors by delivering systems for nucleic acids, small biomolecules and diagnostics. Papaya Mosaic Viral nanoparticals can provide as a vaccine candidate helps to increase the immunity by fusing the epitope based peptide antigen. Capripox viruses are the genus comprises Lymphy skin-disease, Sheep and Goat pox Viruses are notified by The World Animal Health Organization (OIE) based on their economic impotence act as a transboundary animal diseases viruses of sheep, goat, and cattle’s respectively. Plant viral based innovative vaccines have been emerged ineffective vaccine development. This research describes the engineering and development of a new vaccine candidate by display immunogenic peptide using the carrier capsid protein of Papaya Mosaic Virus. The Capripox virus P32 immunogenic protein is homologous of the vaccinia virus H3L gene displayed PapMV CP. The antigenicity of P32 protein epitope lowest score among epitopes C-terminally docked epitopes are EP6 > EP3 > EP8 as well the lowest score among epitopes N-terminally docked epitopes are EP8 > EP3 > EP6 presented on the N-terminus of PMV CP region which are found to be suitable for epitope display. And these modelled immunogenic peptide could be used to develop a viral like particles. Epitope based Antibody developed against immunogenic epitopic regions can contribute to a novel and robust protection from infection. As well might be used for developing cost effective detection kits for Transboundary animal disease viruses.

Antigen-Antibody Complexes

In vertebrates, immunoglobulins (Igs), commonly known as antibodies, play an integral role in the armamentarium of immune defense against various pathogens. After an antigenic challenge, antibodies are secreted by differentiated B cells called plasma cells. Antibodies have two predominant roles that involve specific binding to antigens to launch an immune response, along with activation of other components of the immune system to fight pathogens. The ability of immunoglobulins to fight against innumerable and diverse pathogens lies in their intrinsic ability to discriminate between different antigens. Due to this specificity and high affinity for their antigens, antibodies have been a valuable and indispensable tool in research, diagnostics and therapy. Although seemingly a simple maneuver, the association between an antibody and its antigen, to make an antigen-antibody complex, is comprised of myriads of non-covalent interactions. Amino acid residues on the antigen binding site, the epitope, and on the antibody binding site, the paratope, intimately contribute to the energetics needed for the antigen-antibody complex stability. Structural biology methods to study antigen-antibody complexes are extremely valuable tools to visualize antigen-antibody interactions in detail; this helps to elucidate the basis of molecular recognition between an antibody and its specific antigen. The main scope of this chapter is to discuss the structure and function of different classes of antibodies and the various aspects of antigen-antibody interactions including antigen-antibody interfaces-with a special focus on paratopes, complementarity determining regions (CDRs) and other non-CDR residues important for antigen binding and recognition. Herein, we also discuss methods used to study antigen-antibody complexes, antigen recognition by antibodies, types of antigens in complexes, and how antigen-antibody complexes play a role in modern day medicine and human health. Understanding the molecular basis of antigen binding and recognition by antibodies helps to facilitate the production of better and more potent antibodies for immunotherapy, vaccines and various other applications.

Benchmarking the PEPOP methods for mimicking discontinuous epitopes

Background

Computational methods provide approaches to identify epitopes in protein Ags to help characterizing potential biomarkers identified by high-throughput genomic or proteomic experiments. PEPOP version 1.0 was developed as an antigenic or immunogenic peptide prediction tool. We have now improved this tool by implementing 32 new methods (PEPOP version 2.0) to guide the choice of peptides that mimic discontinuous epitopes and thus potentially able to replace the cognate protein Ag in its interaction with an Ab. In the present work, we describe these new methods and the benchmarking of their performances.

Results

Benchmarking was carried out by comparing the peptides predicted by the different methods and the corresponding epitopes determined by X-ray crystallography in a dataset of 75 Ag-Ab complexes. The Sensitivity (Se) and Positive Predictive Value (PPV) parameters were used to assess the performance of these methods. The results were compared to that of peptides obtained either by chance or by using the SUPERFICIAL tool, the only available comparable method.

Conclusion

The PEPOP methods were more efficient than, or as much as chance, and 33 of the 34 PEPOP methods performed better than SUPERFICIAL. Overall, “optimized” methods (tools that use the traveling salesman problem approach to design peptides) can predict peptides that best match true epitopes in most cases.

Reliable Serological Diagnostic Tests for Arboviruses: Feasible or Utopia?

Infections with arthropod-borne viruses are increasing globally as a result of climate and demographic changes, global dispersion of insect vectors, and increased air travel. The similar symptomatology of arboviral diseases and the cocirculation of different arboviruses in Africa, Asia, and South America complicate diagnosis. Despite the high sensitivity and specificity of molecular diagnostic tests, their utility is limited to the short viremic phase of arbovirus infections, and therefore the diagnosis of infection is frequently missed in clinical practice. Conversely, the duration of antibody responses provides a wider window of opportunity, making diagnosis more dependent on IgM/IgG detection. This review discusses the issues underlying the low specificity of antibody-detection assays, and addresses the challenges and strategies for discovering more specific biomarkers to enable a more accurate diagnosis.

Physicochemical determinants of antibody-protein interactions

Antibodies are specialized proteins generated by immune system for high specificity and affinity binding to target antigens. Because of their essential roles in immune system, antibodies have been successfully developed and engineered as biopharmaceuticals for treatment of various diseases. Analysis of antibody-protein interactions is always required to get detailed information on effectivity of such antibody-based therapeutics. Although physicochemical rules cannot be generalized for every antibody-protein interaction, there are some features which should be taken into account during antibody development and engineering efforts. In this chapter, physicochemical analysis of antibody paratope-protein epitope interactions will be discussed to highlight important characteristics. First, paratope and non-paratope regions of antibodies will be described and important roles of these regions on binding and biophysical features of antibodies will be discussed. Then, general features of epitope regions of protein antigens will be introduced along with several computational/experimental tools to identify them. Lastly, a rising star of antibody biopharmaceuticals, nanobodies, will be described to show importance of next-generation antibody fragment based biopharmaceuticals in drug development.

Immunoinformatics prediction of OMP2b and BCSP31 for designing multi-epitope vaccine against Brucella

Brucella poses a serious threat to human health. High quality vaccines for Brucella are urgently needed to effectively reduce the incidence of brucellosis. OMP2b and BCSP31 are important component proteins of the Brucella outer membrane and are highly immunogenic. Here, we used the bioinformatics software ProtParam, SOPMA, SWISS-MODEL, Rasmol, BepiPred, SYFPEITHI and IEDB to analyze the structure of these two proteins and predict the epitopes of T cells and B cells. Through analysis, we predicted three Th cell epitopes, seven CTL epitopes, eight B cell epitopes, and one T-B combined epitope of OMP2b protein. Subsequently, we also obtained three Th cell epitopes, six CTL epitopes, nine B cell epitopes and one T-B combined epitope of BCSP31 protein. The T-B combined epitopes and CTL epitopes of OMP2b and those of BCSP31 were synthesized to detect their immunogenicity. The IFN-γ ELISPOT assay showed that the T-B combined epitope peptides of OMP2b and BCSP31 activated Th cell immune responses. ELISA analysis detected the specific antibodies against the T-B combined epitope peptide of OMP2b and BCSP31 in the serum of Brucellosis patients. Additionally, CTL epitope peptide of OMP2b and BCSP31 proteins promoted the secretion of soluble perforin and granzyme B in the culture supernatant. In conclusion, our study shows that the T-B combined epitopes and CTL epitopes of OMP2b and BCSP31 have immunogenicity and immunoreactivity. Our results may lay a theoretical foundation for the development of vaccines against Brucella.

Arming Filamentous Bacteriophage, a Nature-Made Nanoparticle, for New Vaccine and Immunotherapeutic Strategies

The pharmaceutical use of bacteriophages as safe and inexpensive therapeutic tools is collecting renewed interest. The use of lytic phages to fight antibiotic-resistant bacterial strains is pursued in academic and industrial projects and is the object of several clinical trials. On the other hand, filamentous bacteriophages used for the phage display technology can also have diagnostic and therapeutic applications. Filamentous bacteriophages are nature-made nanoparticles useful for their size, the capability to enter blood vessels, and the capacity of high-density antigen expression. In the last decades, our laboratory focused its efforts in the study of antigen delivery strategies based on the filamentous bacteriophage 'fd', able to trigger all arms of the immune response, with particular emphasis on the ability of the MHC class I restricted antigenic determinants displayed on phages to induce strong and protective cytotoxic responses. We showed that fd bacteriophages, engineered to target mouse dendritic cells (DCs), activate innate and adaptive responses without the need of exogenous adjuvants, and more recently, we described the display of immunologically active lipids. In this review, we will provide an overview of the reported applications of the bacteriophage carriers and describe the advantages of exploiting this technology for delivery strategies.

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

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

A combined view of B-cell epitope features in antigens

B-cell epitope mapping is a promising approach to identify therapeutics and vaccine candidates in antigenic proteins. We used MATLAB programming to view in combination different features such as beta turn region, surface accessibility, antigenicity and hydrophilicity in an antigen sequence to help predict a discontinuous, conformational B-cell epitope. We analyzed, grouped, compared, matched and superposed these features for a combined visualization using MATLAB programming for identifying and illustrating a potential B-cell epitope region in an antigen protein. This protocol finds application in the design and development of an effective B cell epitope candidate.

Diagnostic Performance of Dengue Virus Envelope Domain III in Acute Dengue Infection

Dengue, one of the most prevalent illnesses caused by dengue viruses that are members of the genus Flavivirus, is a significant global health problem. However, similar clinical symptoms and high antigenic homologies with other Flaviviruses in the endemic area pose difficulties for differential diagnosis of dengue from other arbovirus infections. Here, we investigated four types of recombinant envelope protein domain III (DV-rED III) derived from four dengue virus (DENV) serotypes for diagnostic potential in detecting IgM in acute phase (mainly 2–3 days after onset of fever). Each independent DV-1, -3, and -4-rED III-ELISA showed less than 60% sensitivity, but the combined results of DV-1, -3, and -4-rED III-ELISA led to sensitivity of 81.82% (18/22) (95% CI, 59.72 to 94.81) and 100% specificity (46/46) (95% CI, 92.29 to 100.00) as each antigen compensated the other antigen-derived negative result. In conclusion, the independent combination of data derived from each recombinant antigen (DV1-, DV3-, and DV4-rED III) showed comparable efficacy for the detection of IgM in patients with acute-phase dengue infection.

Decreasing the immunogenicity of Erwinia chrysanthemi asparaginase via protein engineering: computational approach

Immunogenicity of therapeutic proteins is one of the main challenges in disease treatment. L-Asparaginase is an important enzyme in cancer treatment which sometimes leads to undesirable side effects such as immunogenic or allergic responses. Here, to decrease Erwinase (Erwinia chrysanthemiL-Asparaginase) immunogenicity, which is the main drawback of the enzyme, firstly conformational B cell epitopes of Erwinase were predicted from three-dimensional structure by three different computational methods. A few residues were defined as candidates for reducing immunogenicity of the protein by point mutation. In addition to immunogenicity and hydrophobicity, stability and binding energy of mutants were also analyzed computationally. In order to evaluate the stability of the best mutant, molecular dynamics simulation was performed. Among mutants, H240A and Q239A presented significant reduction in immunogenicity. In contrast, the immunogenicity scores of D235A slightly decreased according to two servers. Binding affinity of substrate to the active site reduced significantly in K265A and E268A. The final results of molecular dynamics simulation indicated that H240A mutation has not changed the stability, flexibility, and the total structure of desired protein. Overall, point mutation can be used for reducing immunogenicity of therapeutic proteins, in this context, in silico approaches can be used to screen suitable mutants.

PEPOP 2.0: new approaches to mimic non-continuous epitopes

BACKGROUND

Bioinformatics methods are helpful to identify new molecules for diagnostic or therapeutic applications. For example, the use of peptides capable of mimicking binding sites has several benefits in replacing a protein which is difficult to produce, or toxic. Using peptides is less expensive. Peptides are easier to manipulate, and can be used as drugs. Continuous epitopes predicted by bioinformatics tools are commonly used and these sequential epitopes are used as is in further experiments. Numerous discontinuous epitope predictors have been developed but only two bioinformatics tools have been proposed so far to predict peptide sequences: Superficial and PEPOP 2.0. PEPOP 2.0 can generate series of peptide sequences that can replace continuous or discontinuous epitopes in their interaction with their cognate antibody.

RESULTS

We have developed an improved version of PEPOP (PEPOP 2.0) dedicated to answer to experimentalists' need for a tool able to handle proteins and to turn them into peptides. The PEPOP 2.0 web site has been reorganized by peptide prediction category and is therefore better formulated to experimental designs. Since the first version of PEPOP, 32 new methods of peptide design were developed. In total, PEPOP 2.0 proposes 35 methods in which 34 deal specifically with discontinuous epitopes, the most represented epitope type in nature.

CONCLUSION

Through the presentation of its user-friendly, well-structured new web site conceived in close proximity to experimentalists, we report original methods that show how PEPOP 2.0 can assist biologists in dealing with discontinuous epitopes.

In silico identification of epitopes present in human heat shock proteins (HSPs) overexpressed by tumour cells

Although many of heat shock proteins (HSPs) are crucial in homeostasis due to their role in maintaining cellular proteostasis by the integration of two pivotal processes-folding and degradation, several decades of cancer proteomics suggest that HSPs may improve cancer establishment and progression. Therefore, it is imperative to explore how these molecules impact patient outcomes and whether their interaction with the immune systems improves the protumour or antitumour environment. Here, using an immunoinformatics approach were investigated the best probable epitopes from ten HSPs (HSP90α, HSP90β, HSPA1A, HSPA1L, HSPA2, HSPA5, HSPA6, HSPB1, HSPB5 and HSP60/HSP10). To achieve this aim, antigenicity, immunogenicity (prediction of continuous and discontinuous B cell epitopes, binding peptides to HLA class I and HLA class II, and overlapping epitopes), analysis of conservancy and population coverage, and prediction of IgE epitopes were evaluated. According to the physicochemical properties used for their prediction (hydrophilicity, flexibility, accessibility and antigenicity propensity), ten continuous epitopes (one per HSPs) were considered as the best and also several regions of each molecule were identified as B discontinuous epitopes. Interestingly, peptides of HSP90β, HSPA2, HSPB1, and HSPB5 were predicted as both continuous and discontinuous B cell epitopes. For all the HSPs evaluated were identified potential overlapping epitopes ("NTFYSNKEI", "TTYSCVGVF", "TADRWRVSL", "VKHFSPEEL" and "CEFQDAYVL"). Moreover, these peptides were negative for IgE epitopes and showed a large coverage in the human population (HLA-A*02, HLA-B*15, HLA-C*03, and HLA-C*12). Taken together, these data indicate that such epitopes may activate both the humoral and cell-mediated response, and thus serve as therapeutic targets for cancer. However, it must be assessed their efficacy and safety in vitro and in vivo before their translation in clinical trials.

Computational Approaches and Challenges to Developing Universal Influenza Vaccines

The traditional design of effective vaccines for rapidly-evolving pathogens, such as influenza A virus, has failed to provide broad spectrum and long-lasting protection. With low cost whole genome sequencing technology and powerful computing capabilities, novel computational approaches have demonstrated the potential to facilitate the design of a universal influenza vaccine. However, few studies have integrated computational optimization in the design and discovery of new vaccines. Understanding the potential of computational vaccine design is necessary before these approaches can be implemented on a broad scale. This review summarizes some promising computational approaches under current development, including computationally optimized broadly reactive antigens with consensus sequences, phylogenetic model-based ancestral sequence reconstruction, and immunomics to compute conserved cross-reactive T-cell epitopes. Interactions between virus-host-environment determine the evolvability of the influenza population. We propose that with the development of novel technologies that allow the integration of data sources such as protein structural modeling, host antibody repertoire analysis and advanced phylodynamic modeling, computational approaches will be crucial for the development of a long-lasting universal influenza vaccine. Taken together, computational approaches are powerful and promising tools for the development of a universal influenza vaccine with durable and broad protection.

Molecular Design of Peptide-Fc Fusion Drugs

Background:

Peptide-Fc fusion drugs, also known as peptibodies, are a category of biological therapeutics in which the Fc region of an antibody is genetically fused to a peptide of interest. However, to develop such kind of drugs is laborious and expensive. Rational design is urgently needed.

Methods:

We summarized the key steps in peptide-Fc fusion technology and stressed the main computational resources, tools, and methods that had been used in the rational design of peptide-Fc fusion drugs. We also raised open questions about the computer-aided molecular design of peptide-Fc.

Results:

The design of peptibody consists of four steps. First, identify peptide leads from native ligands, biopanning, and computational design or prediction. Second, select the proper Fc region from different classes or subclasses of immunoglobulin. Third, fuse the peptide leads and Fc together properly. At last, evaluate the immunogenicity of the constructs. At each step, there are quite a few useful resources and computational tools.

Conclusion:

Reviewing the molecular design of peptibody will certainly help make the transition from peptide leads to drugs on the market quicker and cheaper.

Assessment of human leukocyte antigen immunogenicity: current methods, challenges and opportunities

Purpose of review

Donor–recipient human leukocyte antigen (HLA) matching improves outcomes after solid-organ transplantation, but current assessment of HLA incompatibility is inadequate as it does not consider the relative immunogenicity of individual HLA mismatches. In this article, we review existing strategies for assessing HLA immunogenicity and discuss current challenges and future opportunities in this field.

Recent findings

Current HLA immunogenicity algorithms focus primarily on the humoral component of the alloimmune response and aim to determine a measure of ‘dissimilarity’ between donor and recipient HLA. This can be achieved by deriving information from comparison of donor and recipient HLA at the amino acid sequence, structural and/or the physicochemical level, accounting for both B-cell and T-cell pathways of alloreactivity. Substantial evidence now supports the superiority of this molecular definition of HLA incompatibility, over conventional enumeration of HLA antigenic differences, for assessing the risk of humoral alloimmunity and for predicting graft outcomes after transplantation.

Summary

Significant progress has been made in developing computational HLA immunogenicity algorithms that offer exciting opportunities for a more rational approach to determining the degree of donor–recipient HLA incompatibility and to defining HLA-related immunological risk. A number of challenges now need to be overcome to enable their implementation into clinical practice.

Highly conserved epitopes of DENV structural and non-structural proteins: Candidates for universal vaccine targets

Dengue is a severe emerging arthropod borne viral disease occurring globally. Around two fifths of the world's population, or up to 3.9 billion people, are at a risk of dengue infection. Infection induces a life-long protective immunity to the homologous serotype but confers only partial and transient protection against subsequent infection caused by other serotypes. Thus, there is a need for a vaccine which is capable of providing a life- long protection against all the serotypes of dengue virus. In our study, comparative genomics of Dengue virus (DENV) was conducted to explore potential candidates for novel vaccine targets. From our analysis we successfully found 100% conserved epitopes in Envelope protein (RCPTQGE); NS3 (SAAQRRGR, PGTSGSPI); NS4A (QRTPQDNQL); NS4B (LQAKATREAQKRA) and NS5 proteins (QRGSGQV) in all DENV serotypes. Some serotype specific conserved motifs were also found in NS1, NS5, Capsid, PrM and Envelope proteins. Using comparative genomics and immunoinformatics approach, we could find conserved epitopes which can be explored as peptide vaccine candidates to combat dengue worldwide. Serotype specific epitopes can also be exploited for rapid diagnostics. All ten proteins are explored to find the conserved epitopes in DENV serotypes, thus making it the most extensively studied viral genome so far.

Immunogenicity of Protein Pharmaceuticals

Protein therapeutics have drastically changed the landscape of treatment for many diseases by providing a regimen that is highly specific and lacks many off-target toxicities. The clinical utility of many therapeutic proteins has been undermined by the potential development of unwanted immune responses against the protein, limiting their efficacy and negatively impacting its safety profile. This review attempts to provide an overview of immunogenicity of therapeutic proteins, including immune mechanisms and factors influencing immunogenicity, impact of immunogenicity, preclinical screening methods, and strategies to mitigate immunogenicity.

Antibody-mediated biorecognition of myelin oligodendrocyte glycoprotein: computational evidence of demyelination-related epitopes

Antigen-antibody interaction is crucial in autoimmune disease pathogenesis, as multiple sclerosis and neuromyelitis optica. Given that, autoantibodies are essential biomolecules, of which the myelin oligodendrocyte glycoprotein (MOG) can figure as a target. Here we combined Molecular Dynamics (MD), Steered Molecular Dynamics (SMD), and Atomic Force Microscope (AFM) to detail MOG recognition by its specific antibody. The complex model consisted of the MOG external domain interacting with an experimental anti-MOG antibody from the Protein Data Bank (1PKQ). Computational data demonstrated thirteen MOG residues with a robust contribution to the antigen-antibody interaction. Comprising five of the thirteen anchor residues (ASP₁₀₂, HIS₁₀₃, SER₁₀₄, TYR₁₀₅, and GLN₁₀₆), the well-known MOG_92–106 peptide in complex with the anti-MOG was analysed by AFM and SMD. These analyses evidenced similar force values of 780 pN and 765 pN for computational and experimental MOG_92–106 and anti-MOG detachment, respectively. MOG_92–106 was responsible for 75% of the total force measured between MOG external domain and anti-MOG, holding the interaction with the antibody. The antigen-antibody binding was confirmed by Surface Plasmon Resonance (SPR) measurements. Combined approaches presented here can conveniently be adjusted to detail novel molecules in diseases research. This can optimize pre-clinical steps, guiding experiments, reducing costs, and animal model usage.

Immunoproteomics Methods and Techniques

The varied landscape of the adaptive immune response is determined by the peptides presented by immune cells, derived from viral or microbial pathogens or cancerous cells. The study of immune biomarkers or antigens is not new, and classical methods such as agglutination, enzyme-linked immunosorbent assay, or Western blotting have been used for many years to study the immune response to vaccination or disease. However, in many of these traditional techniques, protein or peptide identification has often been the bottleneck. Recent progress in genomics and mass spectrometry have led to many of the rapid advances in proteomics approaches. Immunoproteomics describes a rapidly growing collection of approaches that have the common goal of identifying and measuring antigenic peptides or proteins. This includes gel-based, array-based, mass spectrometry-based, DNA-based, or in silico approaches. Immunoproteomics is yielding an understanding of disease and disease progression, vaccine candidates, and biomarkers. This review gives an overview of immunoproteomics and closely related technologies that are used to define the full set of protein antigens targeted by the immune system during disease.

In silico mutagenesis: decreasing the immunogenicity of botulinum toxin type A

Botulinum toxin serotype A is a prominent therapeutic enzyme, for both clinical and cosmetic uses. Since this protein is produced by bacteria, it exhibits an allergenic effect when subjected to human therapy. Protein mutagenesis is one method to improve the characteristics of protein. However, in silico study is needed to give suggestion of which amino acid should be mutated. Hence, a lot of money and time can be saved. This study initially screened which residue of the Botulinum toxin serotype A is B-cell epitopes both linearly and conformationally. By overlapping the B-cell epitopes with the excluded conserve sequence, seven residues were allowed to be mutated. There were two proposed muteins showing a reduction in the antigenicity probability: ΔE147, E510F, T1062F, ΔE1080, N1089M and ΔQ1090; and ΔE147, E510F, T1062F, E1080W, N1089M and ΔQ1090. Molecular dynamics simulation of the 3D proposed muteins indicated an increase of flexibility in both muteins compared to that in the native protein. Both muteins have lower antigenicity. In addition, they are similar in structure, stability and functionality compared to the native protein.

Predicting Humoral Alloimmunity from Differences in Donor and Recipient HLA Surface Electrostatic Potential

In transplantation, development of humoral alloimmunity against donor HLA is a major cause of organ transplant failure, but our ability to assess the immunological risk associated with a potential donor–recipient HLA combination is limited. We hypothesized that the capacity of donor HLA to induce a specific alloantibody response depends on their structural and physicochemical dissimilarity compared with recipient HLA. To test this hypothesis, we first developed a novel computational scoring system that enables quantitative assessment of surface electrostatic potential differences between donor and recipient HLA molecules at the tertiary structure level [three-dimensional electrostatic mismatch score (EMS-3D)]. We then examined humoral alloimmune responses in healthy females subjected to a standardized injection of donor lymphocytes from their male partner. This analysis showed a strong association between the EMS-3D of donor HLA and donor-specific alloantibody development; this relationship was strongest for HLA-DQ alloantigens. In the clinical transplantation setting, the immunogenic potential of HLA-DRB1 and -DQ mismatches expressed on donor kidneys, as assessed by their EMS-3D, was an independent predictor of development of donor-specific alloantibody after graft failure. Collectively, these findings demonstrate the translational potential of our approach to improve immunological risk assessment and to decrease the burden of humoral alloimmunity in organ transplantation.

Innovative Immunization Strategies for Antivenom Development

Snakes, scorpions, and spiders are venomous animals that pose a threat to human health, and severe envenomings from the bites or stings of these animals must be treated with antivenom. Current antivenoms are based on plasma-derived immunoglobulins or immunoglobulin fragments from hyper-immunized animals. Although these medicines have been life-saving for more than 120 years, opportunities to improve envenoming therapy exist. In the later decades, new biotechnological tools have been applied with the aim of improving the efficacy, safety, and affordability of antivenoms. Within the avenues explored, novel immunization strategies using synthetic peptide epitopes, recombinant toxins (or toxoids), or DNA strings as immunogens have demonstrated potential for generating antivenoms with high therapeutic antibody titers and broad neutralizing capacity. Furthermore, these approaches circumvent the need for venom in the production process of antivenoms, thereby limiting some of the complications associated with animal captivity and venom collection. Finally, an important benefit of innovative immunization approaches is that they are often compatible with existing antivenom manufacturing setups. In this review, we compile all reported studies examining venom-independent innovative immunization strategies for antivenom development. In addition, a brief description of toxin families of medical relevance found in snake, scorpion, and spider venoms is presented, as well as how biochemical, bioinformatic, and omics tools could aid the development of next-generation antivenoms.

Identification of B- and T-cell epitopes on HtrA protein of Orientia tsutsugamushi

Orientia tsutsugamushi, a cause of scrub typhus is emerging as an important pathogen in several parts of the tropics. The control of this infection relies on rapid diagnosis, specific treatment, and prevention through vector control. Development of a vaccine for human use would be very important as a public health measure. Antibody and T-cell response have been found to be important in the protection against scrub typhus. This study was undertaken to predict the peptide vaccine that elicits both B- and T-cell immunity. The outer-membrane protein, 47-kDa high-temperature requirement A was used as the target protein for the identification of protective antigen(s). Using BepiPred2 program, the potential B-cell epitope PNSSWGRYGLKMGLR with high conservation among O. tsutsugamushi and the maximum surface exposed residues was identified. Using IEDB, NetMHCpan, and NetCTL programs, T-cell epitopes MLNELTPEL and VTNGIISSK were identified. These peptides were found to have promiscuous class-I major histocompatibility complex (MHC) binding affinity to MHC supertypes and high proteasomal cleavage, transporter associated with antigen processing prediction, and antigenicity scores. In the I-TASSER generated model, the C-score was -0.69 and the estimated TM-score was 0.63 ± 0.14. The location of the epitope in the 3D model was external. Therefore, an antibody to this outer-membrane protein epitope could opsonize the bacterium for clearance by the reticuloendothelial system. The T-cell epitopes would generate T-helper function. The B-cell epitope(s) identified could be evaluated as antigen(s) in immunodiagnostic assays. This cocktail of three peptides would elicit both B- and T-cell immune response with a suitable adjuvant and serve as a vaccine candidate.

Longitudinal evaluation of humoral immune response and merozoite surface antigen diversity in calves naturally infected with Babesia bovis, in São Paulo, Brazil

Babesiosis is an economically important infectious disease affecting cattle worldwide. In order to longitudinally evaluate the humoral immune response against Babesia bovis and the merozoite surface antigen diversity of B. bovis among naturally infected calves in Taiaçu, Brazil, serum and DNA samples from 15 calves were obtained quarterly, from their birth to 12 months of age. Anti-B. bovis IgG antibodies were detected by means of the indirect fluorescent antibody test (IFAT) and enzyme-linked immunosorbent assay (ELISA). The polymerase chain reaction (PCR) was used to investigate the genetic diversity of B. bovis, based on the genes that encode merozoite surface antigens (MSA-1, MSA-2b and MSA-2c). The serological results demonstrated that up to six months of age, all the calves developed active immunity against B. bovis. Among the 75 DNA samples evaluated, 2, 4 and 5 sequences of the genes msa-1, msa-2b and msa-2c were obtained. The present study demonstrated that the msa-1 and msa-2b genes sequences amplified from blood DNA of calves positive to B. bovis from Taiaçu were genetically distinct, and that msa-2c was conserved. All animals were serologically positive to ELISA and IFAT, which used full repertoire of parasite antigens in despite of the genetic diversity of MSAs.

iBCE-EL: A New Ensemble Learning Framework for Improved Linear B-Cell Epitope Prediction

Identification of B-cell epitopes (BCEs) is a fundamental step for epitope-based vaccine development, antibody production, and disease prevention and diagnosis. Due to the avalanche of protein sequence data discovered in postgenomic age, it is essential to develop an automated computational method to enable fast and accurate identification of novel BCEs within vast number of candidate proteins and peptides. Although several computational methods have been developed, their accuracy is unreliable. Thus, developing a reliable model with significant prediction improvements is highly desirable. In this study, we first constructed a non-redundant data set of 5,550 experimentally validated BCEs and 6,893 non-BCEs from the Immune Epitope Database. We then developed a novel ensemble learning framework for improved linear BCE predictor called iBCE-EL, a fusion of two independent predictors, namely, extremely randomized tree (ERT) and gradient boosting (GB) classifiers, which, respectively, uses a combination of physicochemical properties (PCP) and amino acid composition and a combination of dipeptide and PCP as input features. Cross-validation analysis on a benchmarking data set showed that iBCE-EL performed better than individual classifiers (ERT and GB), with a Matthews correlation coefficient (MCC) of 0.454. Furthermore, we evaluated the performance of iBCE-EL on the independent data set. Results show that iBCE-EL significantly outperformed the state-of-the-art method with an MCC of 0.463. To the best of our knowledge, iBCE-EL is the first ensemble method for linear BCEs prediction. iBCE-EL was implemented in a web-based platform, which is available at http://thegleelab.org/iBCE-EL. iBCE-EL contains two prediction modes. The first one identifying peptide sequences as BCEs or non-BCEs, while later one is aimed at providing users with the option of mining potential BCEs from protein sequences.

Biosensor-based epitope mapping of antibodies targeting the hemagglutinin and neuraminidase of influenza A virus

Characterization of the epitopes on antigen recognized by monoclonal antibodies (mAb) is useful for the development of therapeutic antibodies, diagnostic tools, and vaccines. Epitope mapping also provides functional information for sequence-based repertoire analysis of antibody response to pathogen infection and/or vaccination. However, development of mapping strategies has lagged behind mAb discovery. We have developed a site-directed mutagenesis approach that can be used in conjunction with bio-layer interferometry (BLI) biosensors to map mAb epitopes. By generating a panel of single point mutants in the recombinant hemagglutinin (HA) and neuraminidase (NA) proteins of influenza A viruses, we have characterized the epitopes of hundreds of mAbs targeting the H1 and H3 subtypes of HA and the N9 subtype of NA.

In silico-guided sequence modifications of K-ras epitopes improve immunological outcome against G12V and G13D mutant KRAS antigens

Background

Somatic point substitution mutations in the KRAS proto-oncogene primarily affect codons 12/13 where glycine is converted into other amino acids, and are highly prevalent in pancreatic, colorectal, and non-small cell lung cancers. These cohorts are non-responsive to anti-EGFR treatments, and are left with non-specific chemotherapy regimens as their sole treatment options. In the past, the development of peptide vaccines for cancer treatment was reported to have poor AT properties when inducing immune responses. Utilization of bioinformatics tools have since become an interesting approach in improving the design of peptide vaccines based on T- and B-cell epitope predictions.

Methods

In this study, the region spanning exon 2 from the 4th to 18th codon within the peptide sequence of wtKRAS was chosen for sequence manipulation. Mutated G12V and G13D K-ras controls were generated in silico, along with additional single amino acid substitutions flanking the original codon 12/13 mutations. IEDB was used for assessing human and mouse MHC class I/II epitope predictions, as well as linear B-cell epitopes predictions, while RNA secondary structure prediction was performed via CENTROIDFOLD. A scoring and ranking system was established in order to shortlist top mimotopes whereby normalized and reducing weighted scores were assigned to peptide sequences based on seven immunological parameters. Among the top 20 ranked peptide sequences, peptides of three mimotopes were synthesized and subjected to in vitro and in vivo immunoassays. Mice PBMCs were treated in vitro and subjected to cytokine assessment using CBA assay. Thereafter, mice were immunized and sera were subjected to IgG-based ELISA.

Results

In silico immunogenicity prediction using IEDB tools shortlisted one G12V mimotope (68-V) and two G13D mimotopes (164-D, 224-D) from a total of 1,680 candidates. Shortlisted mimotopes were predicted to promote high MHC-II and -I affinities with optimized B-cell epitopes. CBA assay indicated that: 224-D induced secretions of IL-4, IL-5, IL-10, IL-12p70, and IL-21; 164-D triggered IL-10 and TNF-α; while 68-V showed no immunological responses. Specific-IgG sera titers against mutated K-ras antigens from 164-D immunized Balb/c mice were also elevated post first and second boosters compared to wild-type and G12/G13 controls.

Discussion

In silico-guided predictions of mutated K-ras T- and B-cell epitopes were successful in identifying two immunogens with high predictive scores, Th-bias cytokine induction and IgG-specific stimulation. Developments of such immunogens are potentially useful for future immunotherapeutic and diagnostic applications against KRAS(+) malignancies, monoclonal antibody production, and various other research and development initiatives.

Postgenomic Approaches and Bioinformatics Tools to Advance the Development of Vaccines against Bacteria of the Burkholderia cepacia Complex

Bacteria of the Burkholderia cepacia complex (Bcc) remain an important cause of morbidity and mortality among patients suffering from cystic fibrosis. Eradication of these pathogens by antimicrobial therapy often fails, highlighting the need to develop novel strategies to eradicate infections. Vaccines are attractive since they can confer protection to particularly vulnerable patients, as is the case of cystic fibrosis patients. Several studies have identified specific virulence factors and proteins as potential subunit vaccine candidates. So far, no vaccine is available to protect from Bcc infections. In the present work, we review the most promising postgenomic approaches and selected web tools available to speed up the identification of immunogenic proteins with the potential of conferring protection against Bcc infections.

Biochemical analysis and identification of linear B-cell epitopes from recombinant Sm21.7 antigen from Schistosoma mansoni

Schistosoma mansoni tegument is a dynamic host-interactive layer that is an essential source of parasite antigens and a relevant field for schistosome vaccine research. Sm21.7 is a cytoskeleton antigen found in S. mansoni tegument that engenders protection in experimental challenge infection. Because of its crucial role in the parasite tegument and its promising protective capability, Sm21.7 is an exciting target for the development of therapeutic strategies. The present study describes Sm21.7 structural and biophysical features using circular dichroism spectroscopy and identifies linear B-cell epitopes of Sm21.7 using in-silico methods and immunoassay. The Sm21.7 gene was cloned into the pETDEST42 vector, and the recombinant protein was overexpressed in Escherichia coli DE3. The soluble protein was purified by affinity chromatography followed by ion-exchange chromatography. Purified recombinant Sm21.7 was analyzed by circular dichroism spectroscopy which demonstrated that the rSm21.7 structure was comprised of approximately 38% α-helices and its conformation remains stable at temperatures of up to 60 °C. Prediction of rSm21.7 B-cell epitopes was based on amino acid physicochemical properties. Sixteen peptides corresponding to predicted epitopes were synthesized and immunoreactivity assessed by spot peptide array using pooled rSm21.7-immunized mice sera or patients' sera with different clinical forms of S. mansoni infection. Immunoassays revealed that sera from rSm21.7-immunized mice reacted predominantly with peptides located in the dynein-light chain domain (DLC) at the C-terminal region of rSm21.7. Comparative analysis of the antibody response of acute, intestinal and hepatosplenic patients' sera to the Sm21.7 peptides showed that a differential recognition pattern of Sm21.7-derived peptides by intestinal patients' sera might contribute to down-regulate the immune response in chronic intestinal patients. Together, the results may help the development of S. mansoni vaccine strategies based on the rSm21.7 antigen.

A novel synthetic peptide microarray assay detects Chlamydia species-specific antibodies in animal and human sera

Serological analysis of Chlamydia (C.) spp. infections is still mainly based on micro-immunofluorescence and ELISA. To overcome the limitations of conventional serology, we have designed a novel microarray carrying 52 synthetic peptides representing B-cell epitopes from immunodominant proteins of all 11 chlamydial species. The new assay has been validated using monospecific mouse hyperimmune sera. Subsequently, serum samples from cattle, sheep and humans with a known history of chlamydial infection were examined. For instance, the specific humoral response of sheep to treatment with a C. abortus vaccine has been visualized against a background of C. pecorum carriership. In samples from humans, dual infection with C. trachomatis and C. pneumoniae could be demonstrated. The experiments revealed that the peptide microarray assay was capable of simultaneously identifying specific antibodies to each Chlamydia spp. The actual assay represents an open platform test that can be complemented through future advances in Chlamydia proteome research. The concept of the highly parallel multi-antigen microarray proven in this study has the potential to enhance our understanding of antibody responses by defining not only a single quantitative response, but also the pattern of this response. The added value of using peptide antigens will consist in unprecedented serodiagnostic specificity.

Current progress of immunoinformatics approach harnessed for cellular- and antibody-dependent vaccine design

Immunoinformatics plays a pivotal role in vaccine design, immunodiagnostic development, and antibody production. In the past, antibody design and vaccine development depended exclusively on immunological experiments which are relatively expensive and time-consuming. However, recent advances in the field of immunological bioinformatics have provided feasible tools which can be used to lessen the time and cost required for vaccine and antibody development. This approach allows the selection of immunogenic regions from the pathogen genomes. The ideal regions could be developed as potential vaccine candidates to trigger protective immune responses in the hosts. At present, epitope-based vaccines are attractive concepts which have been successfully trailed to develop vaccines which target rapidly mutating pathogens. In this article, we provide an overview of the current progress of immunoinformatics and their applications in the vaccine design, immune system modeling and therapeutics.

Excavating chikungunya genome to design B and T cell multi-epitope subunit vaccine using comprehensive immunoinformatics approach to control chikungunya infection

Chikungunya infection has been a cause of countless deaths worldwide. Due to lack of permanent treatment and prevention of this disease, the mortality rate remains very high. Therefore, we followed an immunoinformatics approach for the development of multi-epitope subunit vaccine which is able to elucidate humoral, cell-mediated and innate immune responses inside the host body. Both structural and non-structural proteins of chikungunya virus were utilized for prediction of B-cell and T-cell binding epitopes along with interferon-γ (IFN-γ) inducing epitopes. The vaccine construct is composed of β-defensin as an adjuvant at the N-terminal followed by Cytotoxic T-Lymphocytes (CTL) and Helper T-Lymphocyte (HTL) epitopes. The same vaccine construct was also utilized for the prediction of B-cell binding epitopes and IFN-γ inducing epitopes. This was followed by the 3D model generation, refinement and validation of the vaccine construct. Later on, the interaction of modeled vaccine with the innate immune receptor (TLR-3) was explored by performing molecular docking and molecular dynamics simulation studies. Also to check the efficiency of expression of this vaccine construct in an expression vector, in silico cloning was performed at the final stage of vaccine development. Further, designed multi-epitope subunit vaccine necessitates experimental and clinical investigation to develop as an immunogenic vaccine candidate.