Bioinformatics resources and tools for conformational B-cell epitope prediction

Deciphering the Cross-Reactivity of Tropomyosin across Three Molluscan Species: Insights into the Role of Conserved T-Cell and B-Cell Epitopes

Haliotis discus hannai tropomyosin (HTM), Alectryonella plicatula tropomyosin (ATM), and Mimachlamys nobilis tropomyosin (MTM) were reported as significant seafood allergens capable of eliciting severe allergic reactions. However, studies of cross-reactivity and epitope mapping among them are notably limited. This study discovered their cross-reactivity property through inhibition of IgG/IgE binding capacity and promotion of specific CD4+T-cell proliferation. For T-cell epitopes, 3, 4, and 3 epitopes were identified from the splenocytes of mice immunized with HTM, ATM, and MTM, respectively. In terms of B-cell epitopes, a combination of bioinformatics techniques and serological assays identified 4, 3, and 4 epitopes for HTM, ATM, and MTM, respectively. Sequence logo analysis revealed 2 conserved T-cell epitopes and 5 conserved B-cell epitopes, which may be critical for the observed cross-reactivity property. In conclusion, these findings provide crucial molecular evidence that could enhance the prevention of allergic reactions to aquatic mollusks.

Designing a multi-epitope vaccine against Pseudomonas aeruginosa via integrating reverse vaccinology with immunoinformatics approaches

Pseudomonas aeruginosa is a typically opportunistic pathogen responsible for a wide range of nosocomial infections. In this study, we designed two multi-epitope vaccines targeting P. aeruginosa proteins, incorporating cytotoxic T lymphocyte (CTL), helper T lymphocyte (HTL), and linear B lymphocyte (LBL) epitopes identified using reverse vaccinology and immunoinformatics approaches. The vaccines exhibited favorable physicochemical properties, including stability, solubility, and optimal molecular weight, suggesting their potential as viable candidates for vaccine development. Molecular docking studies revealed strong binding affinity to Toll-like receptors 1 (TLR1) and 2 (TLR2). Furthermore, molecular dynamics simulations confirmed the stability of the vaccine-TLR complexes over time. Immune simulation analyses indicated that the vaccines could induce robust humoral and cellular immune responses, providing a promising new approach for combating P. aeruginosa infections, particularly in the face of increasing antibiotic resistance.

Deep learning of antibody epitopes using positional permutation vectors

Background

The accurate computational prediction of B cell epitopes can vastly reduce the cost and time required for identifying potential epitope candidates for the design of vaccines and immunodiagnostics. However, current computational tools for B cell epitope prediction perform poorly and are not fit-for-purpose, and there remains enormous room for improvement and the need for superior prediction strategies.

Results

Here we propose a novel approach that improves B cell epitope prediction by encoding epitopes as binary positional permutation vectors that represent the position and structural properties of the amino acids within a protein antigen sequence that interact with an antibody. This approach supersedes the traditional method of defining epitopes as scores per amino acid on a protein sequence, where each score reflects each amino acids predicted probability of partaking in a B cell epitope antibody interaction. In addition to defining epitopes as binary positional permutation vectors, the approach also uses the 3D macrostructure features of the unbound protein structures, and in turn uses these features to train another deep learning model on the corresponding antibody-bound protein 3D structures. This enables the algorithm to learn the key structural and physiochemical features of the unbound protein and embedded epitope that initiate the antibody binding process helping to eliminate "induced fit" biases in the training data. We demonstrate that the strategy predicts B cell epitopes with improved accuracy compared to the existing tools. Additionally, we show that this approach reliably identifies the majority of experimentally verified epitopes on the spike protein of SARS-CoV-2 not seen by the model during training and generalizes in a very robust manner on dissimilar data not seen by the model during training.

Conclusions

With the approach described herein, a primary protein sequence and a query positional permutation vector encoding a putative epitope is sufficient to predict B cell epitopes in a reliable manner, potentially advancing the use of computational prediction of B cell epitopes in biomedical research applications.

Generation of nanobodies with conformational specificity for tau oligomers that recognize tau aggregates from human Alzheimer's disease samples

Tauopathies are neurodegenerative diseases that involve tau misfolding and aggregation in the brain. These diseases, including Alzheimer's disease (AD), are some of the least understood and most difficult to treat neurodegenerative disorders. Antibodies and antibody fragments that target tau oligomers, which are especially toxic forms of tau, are promising options for immunotherapies and diagnostic tools for tauopathies. In this study, we have developed conformational, tau oligomer-specific nanobodies, or single-domain antibodies. We demonstrate that these nanobodies, OT2.4 and OT2.6, are highly specific for tau oligomers relative to tau monomers and fibrils. We used epitope mapping to verify that these nanobodies bind to discontinuous epitopes on tau and to support the idea that they interact with a conformation present in the oligomeric, and not monomeric or fibrillar, forms of tau. We show that these nanobodies interact with tau oligomers in brain samples from AD patients and from healthy older adults with primary age-related tauopathy. Our results demonstrate the potential of these nanobodies as tau oligomer-specific binding reagents and future tauopathy therapeutics and diagnostics.

Genomic comparison between Mycobacterium bovis and Mycobacterium microti and in silico analysis of peptide-based biomarkers for serodiagnosis

In recent years, there has been an increase in the number of reported cases of Mycobacterium microti infection in various animals, which can interfere with the ante-mortem diagnosis of animal tuberculosis caused by Mycobacterium bovis. In this study, whole genome sequencing (WGS) was used to search for protein-coding genes to distinguish M. microti from M. bovis. In addition, the population structure of the available M. microti genomic WGS datasets is described, including three novel Belgian isolates from infections in alpacas. Candidate genes were identified by examining the presence of the regions of difference and by a pan-genome analysis of the available WGS data. A total of 80 genes showed presence-absence variation between the two species, including genes encoding Proline-Glutamate (PE), Proline-Proline-Glutamate (PPE), and Polymorphic GC-Rich Sequence (PE-PGRS) proteins involved in virulence and host interaction. Filtering based on predicted subcellular localization, sequence homology and predicted antigenicity resulted in 28 proteins out of 80 that were predicted to be potential antigens. As synthetic peptides are less costly and variable than recombinant proteins, an in silico approach was performed to identify linear and discontinuous B-cell epitopes in the selected proteins. From the 28 proteins, 157 B-cell epitope-based peptides were identified that discriminated between M. bovis and M. microti species. Although confirmation by in vitro testing is still required, these candidate synthetic peptides containing B-cell epitopes could potentially be used in serological tests to differentiate cases of M. bovis from M. microti infection, thus reducing misdiagnosis in animal tuberculosis surveillance.

Designing and development of efficient multi-epitope-based peptide vaccine candidate against emerging avian rotavirus strains: A vaccinomic approach

BACKGROUND

Enteric avian rotavirus (ARV) is the etiological agent of several health problems that pose a global threat to commercial chickens. Therefore, to avoid these widespread epidemics and high mortality rates, only vaccine and strict biosecurity are required.

METHOD

The present study employs computational techniques to design a unique multi-epitope-based vaccine candidate that successfully activates immune cells against the ARV by combining adjuvant, linker, and B and T-cell epitopes. Starting, homologous sequences in the various ARV serotypes were revealed in the NCBI BLAST database, and then the two surface proteins (VP4 and VP7) of the ARV were retrieved from the UniprotKB database. The Clustal Omega server was then used to identify the conserved regions among the homologous sequences, and the B and T-cell epitopes were predicted using IEDB servers. Then, superior epitopes-2 MHC-1 epitopes, 2 MHC-2 epitopes, and 3B-cell epitopes-were combined with various adjuvants to create a total of four unique vaccine candidates. Afterward, the designed vaccine candidates underwent computational validation to assess their antigenicity, allergenicity, and stability. The vaccine candidate (V2) that demonstrated non-antigenicity, a high VaxiJen score, and non-allergenicity was ultimately chosen for molecular docking and dynamic simulation.

RESULTS

Although the V2 and V4 vaccine candidates were highly immunogenic, V2 had a higher solubility rate. The predicted values of the aliphatic index and GRAVY value were 30.4 and 0.417, respectively. In terms of binding energy, V2 outperformed V4. Being successfully docked with TLRs, V2 was praised as the finest. After adaptation, the sequence's 50.73 % GC content outside of the BglII or ApaI restriction sites indicated that it was equivalently safe to clone. The chosen sequence was then inserted into the pET28a(+) vector within the BglII and ApaI restriction sites. This resulted in a final clone that was 4914 base pairs long, with the inserted sequence accounting for 478 bp and the vector accounting for the remainder.

CONCLUSIONS

The immune-mediated simulation results for the selected vaccine construct showed significant response; thus, the study confirmed that the selected V2 vaccine candidate could enhance the immune response against ARV.

Computational Methods to Predict Conformational B-Cell Epitopes

Accurate computational prediction of B-cell epitopes can greatly enhance biomedical research and rapidly advance efforts to develop therapeutics, monoclonal antibodies, vaccines, and immunodiagnostic reagents. Previous research efforts have primarily focused on the development of computational methods to predict linear epitopes rather than conformational epitopes; however, the latter is much more biologically predominant. Several conformational B-cell epitope prediction methods have recently been published, but their predictive performances are weak. Here, we present a review of the latest computational methods and assess their performances on a diverse test set of 29 non-redundant unbound antigen structures. Our results demonstrate that ISPIPab performs better than most methods and compares favorably with other recent antigen-specific methods. Finally, we suggest new strategies and opportunities to improve computational predictions of conformational B-cell epitopes.

In silico design of a novel multi-epitope vaccine against HCV infection through immunoinformatics approaches

Infection with the hepatitis C virus (HCV) is one of the causes of liver cancer, which is the world's sixth most prevalent and third most lethal cancer. The current treatments do not prevent reinfection; because they are expensive, their usage is limited to developed nations. Therefore, a prophylactic vaccine is essential to control this virus. Hence, in this study, an immunoinformatics method was applied to design a multi-epitope vaccine against HCV. The best B- and T-cell epitopes from conserved regions of the E2 protein of seven HCV genotypes were joined with the appropriate linkers to design a multi-epitope vaccine. In addition, cholera enterotoxin subunit B (CtxB) was included as an adjuvant in the vaccine construct. This study is the first to present this epitopes-adjuvant combination. The vaccine had acceptable physicochemical characteristics. The vaccine's 3D structure was predicted and validated. The vaccine's binding stability with Toll-like receptor 2 (TLR2) and TLR4 was confirmed using molecular docking and molecular dynamics (MD) simulation. The immune simulation revealed the vaccine's efficacy by increasing the population of B and T cells in response to vaccination. In silico expression in Escherichia coli (E. coli) was also successful.

Effect of lactic acid bacteria fermentation on cow milk allergenicity and antigenicity: A review

Cow milk is a major allergenic food. The potential prevention and treatment effects of lactic acid bacteria (LAB)-fermented dairy products on allergic symptoms have garnered considerable attention. Cow milk allergy (CMA) is mainly attributed to extracellular and/or cell envelope proteolytic enzymes with hydrolysis specificity. Numerous studies have demonstrated that LAB prevents the risk of allergies by modulating the development and regulation of the host immune system. Specifically, LAB and its effectors can enhance intestinal barrier function and affect immune cells by interfering with humoral and cellular immunity. Fermentation hydrolysis of allergenic epitopes is considered the main mechanism of reducing CMA. This article reviews the linear epitopes of allergens in cow milk and the effect of LAB on these allergens and provides insight into the means of predicting allergenic epitopes by conventional laboratory analysis methods combined with molecular simulation. Although LAB can reduce CMA in several ways, the mechanism of action remains partially clarified. Therefore, this review additionally attempts to summarize the main mechanism of LAB fermentation to provide guidance for establishing an effective preventive and treatment method for CMA and serve as a reference for the screening, research, and application of LAB-based intervention.

CRM197-conjugated peptides vaccine of HCMV pp65 and gH induce maturation of DC and effective viral-specific T cell responses

Human cytomegalovirus (HCMV) infection is prevalent worldwide, and there is currently no licenced HCMV vaccine to control it. Therefore, developing an effective HCMV vaccine is a significant priority. Because of their excellent immunogenicity, the crucial components of HCMV, phosphoprotein 65 (pp65) and glycoproteins H (gH) are potential target proteins for HCMV vaccine design. In this study, we predicted and screened the dominant antigenic epitopes of B and T cells from pp65 and gH conjugated with the carrier protein cross-reacting material 197 (CRM197) to form three peptide-CRM197 vaccines (pp65-CRM197, gH-CRM197, and pp65-CRM197+gH-CRM197). Furthermore, the immunogenicity of the peptide-CRM197 vaccines and their effects on dendritic cells (DCs) were explored. The results showed that three peptide-CRM197 vaccines could induce maturation of DCs through the p38 MAPK signalling pathway and promote the release of proinflammatory factors, such as TNF-α and interleukin (IL) -6. Meanwhile, the peptide-CRM197 vaccines could effectively activate T cell and humoral immunity, which were far better than the inactivated HCMV vaccine. In conclusion, we constructed three peptide-CRM197 vaccines, which could induce multiple immune effects, providing a novel approach for HCMV vaccine design.

Finding and engineering the newly found bacterial superoxide dismutase enzyme to increase its thermostability and decrease the immunogenicity: a computational and experimental research

Superoxide dismutase (SOD) is one of the most important antioxidant enzymes that can reduce oxidative stress in the cell environment. Nowadays, bacterial sources of enzyme are commercially applicable in the cosmetics and pharmaceutical industries, but the allergenic effect of proteins from non-human sources has been mentioned as disadvantage of these kinds of enzymes. In this study, to find the suitable bacterial SOD candidate for decreasing immunogenicity, the sequences of five thermophilic bacteria were selected as reference species. Then, linear and conformational B-cell epitopes of the SOD were analyzed by different servers. The stability and immunogenicity of mutant positions were also evaluated. The mutant gene was inserted into the pET-23a expression vector and transformed into E. Coli BL21 (DE3) for expression of the recombinant enzyme. Afterward, the expression of the mutant enzyme was evaluated by SDS-PAGE analysis and the recombinant enzyme activity was assessed. Anoxybacillus gonensis was selected as a reasonable SOD source according to BLAST search, physicochemical properties analysis, and prediction of allergenic features. Regarding our results, five residues including E84, E142, K144, G147, and M148 were predicted as candidates for mutagenesis. Finally, the K144A was chosen as the final modification due to the increase in the stability of the enzyme and decreased immunogenicity of the enzyme as well. The enzyme activity was 240 U/ml at room temperature. Alternation in K144 to alanine caused increased stability of the enzyme. In silico studies confirmed non-antigenic protein after mutation.

Recent Developments and Future Perspectives of Vaccines and Therapeutic Agents against SARS-CoV2 Using the BCOV_S1_CTD of the S Protein

Since the onset of the coronavirus disease 2019 (COVID-19) pandemic, the virus kept developing and mutating into different variants over time, which also gained increased transmissibility and spread in populations at a higher pace, culminating in successive waves of COVID-19 cases. The scientific community has developed vaccines and antiviral agents against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) disease. Realizing that growing SARS-CoV-2 variations significantly impact the efficacy of antiviral therapies and vaccines, we summarize the appearance and attributes of SARS-CoV-2 variants for future perspectives in drug design, providing up-to-date insights for developing therapeutic agents targeting the variants. The Omicron variant is among the most mutated form; its strong transmissibility and immune resistance capacity have prompted international worry. Most mutation sites currently being studied are in the BCOV_S1_CTD of the S protein. Despite this, several hurdles remain, such as developing vaccination and pharmacological treatment efficacies for emerging mutants of SARS-CoV-2 strains. In this review, we present an updated viewpoint on the current issues faced by the emergence of various SARS-CoV-2 variants. Furthermore, we discuss the clinical studies conducted to assist the development and dissemination of vaccines, small molecule therapeutics, and therapeutic antibodies having broad-spectrum action against SARS-CoV-2 strains.

Serum albumin: a pharmacokinetic marker for optimizing treatment outcome of immune checkpoint blockade

As we look forward to the bright future of immune checkpoint blockade (ICB) therapy, there is still lacking a pharmacokinetic marker to understand the inter-individual differences in ICB response. ICB therapy is based on IgG antibodies that share the same homeostatic pathway with serum albumin. Therefore, serum albumin level could reflect IgG catabolic rate that directly impacts the clearance of therapeutic IgG antibodies. Through interrogating a large, clinically representative pan-cancer cohort of 1,479 ICB-treated patients, this study found that higher baseline albumin levels were significantly associated with stepwise improvements in overall survival (OS), progression-free survival (PFS), and objective response rate (ORR) (p<0.001), with the variability and reproducibility confirmed in 1,000 bootstrap-resampled cohorts. Furthermore, these findings were also confirmed in most subgroups defined by patient demographics, baseline characteristics, treatments, and cancer types, even in those with low ICB-responsive cancer types and low tumor mutation burden (TMB) (TMB≤10 mut/Mb) that most of which have not been approved by the US Food and Drug Administration (FDA) for ICB therapy. In summary, this study highlights the importance of pretreatment pharmacokinetic modeling for predicting ICB treatment outcomes. Based on serum albumin-an inexpensive, non-invasive, and easily accessible biomarker of IgG pharmacokinetics, we could take a step further towards optimizing ICB therapy.

First identification of Nocardia seriolae GapA adhesion function and its three B-cell epitopes with cell-binding activity

Fish nocardiosis mainly caused by Nocardia seriolae (N. seriolae) is a serious threat to aquaculture. Bacterial adhesion to host cells mediated by adhesin is an initial step of pathogenesis. But it is not clear whether glyceraldehyde-3-phosphate dehydrogenase (GapA) is an adhesin of N. seriolae. Here, recombinant GapA protein (rGapA) was prokaryotic expressed, and its role in the bacterial adhesion to Ctenopharyngodon idella kidney cells was investigated by indirect immunofluorescence, protein-binding assay and adhesion inhibition assay. The results showed that an obvious green fluorescence was observed on the surface of the cells co-incubated with rGapA protein; the cytomembrane proteins of the cells pretreated with rGapA could react with anti-rGapA antibody; and the antibody significantly inhibited the adhesion ability of the bacteria. Subsequently, B-cell linear epitopes of GapA protein were identified by using a immunoinformatics approach combined with peptide ELISA and Western blot for the first time. It was found that four predicted epitopes (Ep_58-69 , Ep_139-150 , Ep_186-197 , Ep_318-329 ) could all react with anti-rGapA antibody and obviously inhibit the immunoreactivity between rGapA and anti-rGapA antibody, and they were confirmed as indeed B-cell linear epitopes of the protein. Furthermore, flow cytometry analysis found the percentage of positive cells co-incubated with FITC-labelled epitope peptides (Ep_139-150 , Ep_186-197 , Ep_318-329 ) was significantly higher than those in the FITC-labelled Ep_58-69 , unrelated control peptide and cell control. Collectively, GapA is an adhesin of N. seriolae, and epitope peptides (Ep_139-150 , Ep_186-197 , Ep_318-329 ) possess cell-binding activity, which are potential candidates for developing a multiple epitopes-based adhesin vaccine against fish nocardiosis.

A Promising Tool in Serological Diagnosis: Current Research Progress of Antigenic Epitopes in Infectious Diseases

Infectious diseases, caused by various pathogens in the clinic, threaten the safety of human life, are harmful to physical and mental health, and also increase economic burdens on society. Infections are a complex mechanism of interaction between pathogenic microorganisms and their host. Identification of the causative agent of the infection is vital for the diagnosis and treatment of diseases. Etiological laboratory diagnostic tests are therefore essential to identify pathogens. However, due to its rapidity and automation, the serological diagnostic test is among the methods of great significance for the diagnosis of infections with the basis of detecting antigens or antibodies in body fluids clinically. Epitopes, as a special chemical group that determines the specificity of antigens and the basic unit of inducing immune responses, play an important role in the study of immune responses. Identifying the epitopes of a pathogen may contribute to the development of a vaccine to prevent disease, the diagnosis of the corresponding disease, and the determination of different stages of the disease. Moreover, both the preparation of neutralizing antibodies based on useful epitopes and the assembly of several associated epitopes can be used in the treatment of disease. Epitopes can be divided into B cell epitopes and T cell epitopes; B cell epitopes stimulate the body to produce antibodies and are therefore commonly used as targets for the design of serological diagnostic experiments. Meanwhile, epitopes can fall into two possible categories: linear and conformational. This article reviews the role of B cell epitopes in the clinical diagnosis of infectious diseases.

Protective antigenic epitopes revealed by immunosignatures after three doses of inactivated SARS-CoV-2 vaccine

Background

SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) has infected millions of people around the world. Vaccination is a pillar in the strategy to control transmission of the SARS-CoV-2 spread. Immune responses to vaccination require elucidation.

Methods

The immune responses to vaccination with three doses of inactivated SARS-CoV-2 vaccine were followed in a cohort of 37 healthy adults (18–59 years old). Blood samples were collected at multiple time points and submitted to peptide array, machine learning modeling, and sequence alignment analyses, the results of which were used to generate vaccine-induced antibody-binding region (VIABR) immunosignatures (Registration number: ChiCTR2200058571).

Results

Antibody spectrum signals showed vaccination stimulated antibody production. Sequence alignment analyses revealed that a third vaccine dose generated a new highly represented VIABR near the A570D mutation, and the whole process of inoculation enhanced the VIABR near the N501Y mutation. In addition, the antigen conformational epitopes varied between short- and long-term samples. The amino acids with the highest scores in the short-term samples were distributed primarily in the receptor binding domain (RBD) and N-terminal domain regions of spike (S) protein, while in the long-term samples (12 weeks after the 2^nd dose), some new conformational epitopes (CEs) were localized to crevices within the head of the S protein trimer.

Conclusion

Protective antigenic epitopes were revealed by immunosignatures after three doses of inactivated SARS-CoV-2 vaccine inoculation. A third dose results in a new top-10 VIABR near the A570D mutation site of S protein, and the whole process of inoculation enhanced the VIABR near the N501Y mutation, thus potentially providing protection from strains that have gained invasion and immune escape abilities through these mutation.

A multi-epitope vaccine designed against blood-stage of malaria: an immunoinformatic and structural approach

Malaria is a complex disease caused by parasites of the genus Plasmodium and is the leading cause of morbidity and mortality worldwide. The most severe form of malaria disease is caused by Plasmodium falciparum. Thus, a combination of different approaches is needed to control malaria. Resistance to first-line drugs and insecticides, on the other hand, makes the need for an effective vaccination more urgent than ever. Because erythrocyte parasites cause the most clinical symptoms, developing a vaccination for this stage of infection might be highly beneficial. In this research, we employed various bioinformatics methods to create an efficient multi-epitope vaccine that induces antibodies against the blood stage of malaria infection. For this purpose, we selected the malaria PfGARP protein as the target here. The B, HTL epitopes, and epitope conservation were predicted. The predicted epitopes (including 5 B and 5 HTL epitopes) were connected using suitable linkers, and the flagellin molecule was used as an adjuvant to improve its immunogenicity. The final construct vaccine with 414 amino acids long was designed. The vaccine's allergenicity, antigenicity, solubility, physicochemical characteristics, 2D and 3D structure modeling, molecular docking, molecular dynamics simulation, in silico cloning, and immunological simulation were tested. In silico immune simulation results showed significantly elevated IgG1 and IgM and T helper cells, INF γ, IL 2, and B-cell populations after the injection of the designed vaccine. These significant computational analyses indicated that our proposed vaccine candidate might activate suitable immune responses against malaria. However, in vitro and in vivo studies are essential for further validation.

Identifying vaccine escape sites via statistical comparisons of short-term molecular dynamics

The identification of viral mutations that confer escape from antibodies is crucial for understanding the interplay between immunity and viral evolution. We describe a molecular dynamics (MD)-based approach that goes beyond contact mapping, scales well to a desktop computer with a modern graphics processor, and enables the user to identify functional protein sites that are prone to vaccine escape in a viral antigen. We first implement our MD pipeline to employ site-wise calculation of Kullback-Leibler divergence in atom fluctuation over replicate sets of short-term MD production runs thus enabling a statistical comparison of the rapid motion of influenza hemagglutinin (HA) in both the presence and absence of three well-known neutralizing antibodies. Using this simple comparative method applied to motions of viral proteins, we successfully identified in silico all previously empirically confirmed sites of escape in influenza HA, predetermined via selection experiments and neutralization assays. Upon the validation of our computational approach, we then surveyed potential hotspot residues in the receptor binding domain of the SARS-CoV-2 virus in the presence of COVOX-222 and S2H97 antibodies. We identified many single sites in the antigen-antibody interface that are similarly prone to potential antibody escape and that match many of the known sites of mutations arising in the SARS-CoV-2 variants of concern. In the Omicron variant, we find only minimal adaptive evolutionary shifts in the functional binding profiles of both antibodies. In summary, we provide an inexpensive and accurate computational method to monitor hotspots of functional evolution in antibody binding footprints.

In silico epitope-based vaccine design against influenza a neuraminidase protein: Computational analysis established on B- and T-cell epitope predictions

Objective

Influenza A virus belongs to the most studied virus and its mutant initiates epidemic and pandemics outbreaks. Inoculation is the significant foundation to diminish the risk of infection. To prevent an incidence of influenza from the transmission, various practical approaches require more advancement and progress. More efforts and research must take in front to enhance vaccine efficacy.

Methods

The present research emphasizes the development and expansion of a universal vaccine for the influenza virus. Research focuses on vaccine design with high efficacy. In this study, numerous computational approaches were used, covering a wide range of elements and ideas in bioinformatics methodology. Various B and T-cell epitopic peptides derived from the Neuraminidase protein N1 are recognized by these approaches. With the implementation of numerous obtained databases and bioinformatics tools, the different immune framework methods of the conserved sequences of N1 neuraminidase were analyzed. NCBI databases were employed to retrieve amino acid sequences. The antigenic nature of the neuraminidase sequence was achieved by the VaxiJen server and Kolaskar and Tongaonkar method. After screening of various B and T cell epitopes, one efficient peptide each from B cell epitope and T cell epitopes was assessed for their antigenic determinant vaccine efficacy. Identical two B cell epitopes were recognized from the N1 protein when analyzed using B-cell epitope prediction servers. The detailed examination of amino acid sequences for interpretation of B and T cell epitopes was achieved with the help of the ABCPred and Immune Epitope Database.

Results

Computational immunology via immunoinformatic study exhibited RPNDKTG as having its high conservancy efficiency and demonstrated as a good antigenic, accessible surface hydrophilic B-cell epitope. Among T cell epitope analysis, YVNISNTNF was selected for being a conserved epitope. T cell epitope was also analyzed for its allergenicity and cytotoxicity evaluation. YVNISNTNF epitope was found to be a non-allergen and not toxic for cells as well. This T-cell epitope with maximum world populace coverages was scrutinized for its association with the HLA-DRB1*0401 molecule. Results from docking simulation analyses showed YVNISNTNF having lower binding energy, the radius of gyration (Rg), RMSD values, and RMSE values which make the protein structure more stable and increase its ability to become an epitopic peptide for influenza virus vaccination.

Conclusions

We propose that this epitope analysis may be successfully used as a measurement tool for the robustness of an antigen-antibody reaction between mutant strains in the annual design of the influenza vaccine.

Shotgun Immunoproteomic Approach for the Discovery of Linear B-Cell Epitopes in Biothreat Agents Francisella tularensis and Burkholderia pseudomallei

Peptide-based subunit vaccines are coming to the forefront of current vaccine approaches, with safety and cost-effective production among their top advantages. Peptide vaccine formulations consist of multiple synthetic linear epitopes that together trigger desired immune responses that can result in robust immune memory. The advantages of linear compared to conformational epitopes are their simple structure, ease of synthesis, and ability to stimulate immune responses by means that do not require complex 3D conformation. Prediction of linear epitopes through use of computational tools is fast and cost-effective, but typically of low accuracy, necessitating extensive experimentation to verify results. On the other hand, identification of linear epitopes through experimental screening has been an inefficient process that requires thorough characterization of previously identified full-length protein antigens, or laborious techniques involving genetic manipulation of organisms. In this study, we apply a newly developed generalizable screening method that enables efficient identification of B-cell epitopes in the proteomes of pathogenic bacteria. As a test case, we used this method to identify epitopes in the proteome of Francisella tularensis (Ft), a Select Agent with a well-characterized immunoproteome. Our screen identified many peptides that map to known antigens, including verified and predicted outer membrane proteins and extracellular proteins, validating the utility of this approach. We then used the method to identify seroreactive peptides in the less characterized immunoproteome of Select Agent Burkholderia pseudomallei (Bp). This screen revealed known Bp antigens as well as proteins that have not been previously identified as antigens. Although B-cell epitope prediction tools Bepipred 2.0 and iBCE-EL classified many of our seroreactive peptides as epitopes, they did not score them significantly higher than the non-reactive tryptic peptides in our study, nor did they assign higher scores to seroreactive peptides from known Ft or Bp antigens, highlighting the need for experimental data instead of relying on computational epitope predictions alone. The present workflow is easily adaptable to detecting peptide targets relevant to the immune systems of other mammalian species, including humans (depending upon the availability of convalescent sera from patients), and could aid in accelerating the discovery of B-cell epitopes and development of vaccines to counter emerging biological threats.

A systematic bioinformatics approach for large-scale identification and characterization of host-pathogen shared sequences

Background

Biology has entered the era of big data with the advent of high-throughput omics technologies. Biological databases provide public access to petabytes of data and information facilitating knowledge discovery. Over the years, sequence data of pathogens has seen a large increase in the number of records, given the relatively small genome size and their important role as infectious and symbiotic agents. Humans are host to numerous pathogenic diseases, such as that by viruses, many of which are responsible for high mortality and morbidity. The interaction between pathogens and humans over the evolutionary history has resulted in sharing of sequences, with important biological and evolutionary implications.

Results

This study describes a large-scale, systematic bioinformatics approach for identification and characterization of shared sequences between the host and pathogen. An application of the approach is demonstrated through identification and characterization of the Flaviviridae-human share-ome. A total of 2430 nonamers represented the Flaviviridae-human share-ome with 100% identity. Although the share-ome represented a small fraction of the repertoire of Flaviviridae (~ 0.12%) and human (~ 0.013%) non-redundant nonamers, the 2430 shared nonamers mapped to 16,946 Flaviviridae and 7506 human non-redundant protein sequences. The shared nonamer sequences mapped to 125 species of Flaviviridae, including several with unclassified genus. The majority (~ 68%) of the shared sequences mapped to Hepacivirus C species; West Nile, dengue and Zika viruses of the Flavivirus genus accounted for ~ 11%, ~ 7%, and ~ 3%, respectively, of the Flaviviridae protein sequences (16,946) mapped by the share-ome. Further characterization of the share-ome provided important structural-functional insights to Flaviviridae-human interactions.

Conclusion

Mapping of the host-pathogen share-ome has important implications for the design of vaccines and drugs, diagnostics, disease surveillance and the discovery of unknown, potential host-pathogen interactions. The generic workflow presented herein is potentially applicable to a variety of pathogens, such as of viral, bacterial or parasitic origin.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12864-021-07657-4.

Immunoinformatics aided design of peptide-based vaccines against ebolaviruses

Ebolaviruses are at the forefront of emerging viruses and present a very perceptible threat to global peace and harmony. In the last decade, Ebola virus disease has claimed more than 90% of total lives since its inception in 1976. Owing to multiple host immune evasion methods employed by the virus and the limitations of traditional vaccine development approaches, finding a globally effective and reliable counter measure against Ebola virus remains a challenge. Highly conserved peptide fragments belonging to critical viral proteins and containing multiple epitopes which have the capacity to interact with a wide array of HLA molecules present a viable solution. Immunoinformatics or computational immunology enables rapid screening and shortlisting of plausible epitopes with a high immunogenic potential, thus, supporting expeditious elucidation of efficacious vaccine candidates. In light of above facts, we describe a computational methodology in this chapter for identification of potent peptide vaccine candidates against human infecting viruses. By applying this stringent methodology, we were able to identify multiple, immunogenic ebolavirus peptide fragments which, after verification in animal models, might be considered as part of future synthetic Ebola vaccine.

Risk of all-cause mortality associated with chronic obstructive pulmonary disease and the role of healthy ageing trajectories: a population-based study of middle-aged and older adults

OBJECTIVES

The aims were to study the risk of all-cause mortality associated with chronic obstructive pulmonary disease (COPD) and healthy ageing trajectories (HAT) in three birth cohorts and to determine the moderating role of HAT in the association between COPD and all-cause mortality.

DESIGN

Prospective cohort study.

SETTING

Data from waves 1 to 5 of The Survey of Health, Ageing and Retirement in Europe.

PARTICIPANTS

The total sample was 28 857 community-dwelling individuals aged 50+ years.

MAIN OUTCOME

All-cause mortality associated with COPD and HAT adjusting for covariates. We performed Aalen additive hazards models to explore these associations. Interactions between COPD and HAT were also explored. Analyses were conducted separately in three birth cohorts (>1945, 1936-1945 and ≤1935). Latent class growth analysis was used to classify participants into HAT.

RESULTS

Three parallel HAT were found in the three birth cohorts ('low', 'medium' and 'high' healthy ageing). Participants with COPD had an increased mortality risk, but this effect was no longer significant after adjusting for covariates. The 'low' HAT was associated with increased mortality risk in the three subsamples, although this effect was lower after adjustment. The interaction between COPD and HAT was significant only in the ≤1935 birth cohort, indicating that those with COPD and a 'low' trajectory had a greater risk of mortality.

CONCLUSIONS

The healthy ageing scale may be a suitable tool to identify patients at higher risk to mitigate disease burden and improve patients' quality of life.

Reverse vaccinology approach for the identifications of potential vaccine candidates against Salmonella

Salmonella is a leading cause of foodborne pathogen which causes intestinal and systemic diseases across the world. Vaccination is the most effective protection against Salmonella, but the identification and design of an effective broad-spectrum vaccine is still a great challenge, because of the multi-serotypes of Salmonella. Reverse vaccinology is a new tool to discovery and design vaccine antigens combining human immunology, structural biology and computational biology with microbial genomics. In this study, reverse vaccinology, an in-silico approach was established to screen appropriate immunogen targets by calculating the immunogenicity score of 583 non-redundant outer membrane and secreted proteins of Salmonella. Herein among 100 proteins identified with top-ranked scores, 15 representative antigens were selected randomly. Applying the sequence conservation test, four proteins (FliK, BcsZ, FhuA and FepA) remained as potential vaccine candidates for in vivo evaluation of immunogenicity and immunoprotection. All four candidates were capable to trigger the immune response and stimulate the production of antiserum in mice. Furthermore, top-ranked proteins including FliK and BcsZ provided wide antigenic coverage among the multi-serotype of Salmonella. The S. Typhimurium LT2 challenge model used in mice immunized with FliK and BcsZ showed a high relative percentage survival (RPS) of 52.74 % and 64.71 % respectively. In conclusion, this study constructed an in-silico pipeline able to successfully pre-screen the vaccine targets characterized by high immunogenicity and protective immunity. We show that reverse vaccinology allowed screening of appropriate broad-spectrum vaccines for Salmonella.

Conformational epitope matching and prediction based on protein surface spiral features

Background

A conformational epitope (CE) is composed of neighboring amino acid residues located on an antigenic protein surface structure. CEs bind their complementary paratopes in B-cell receptors and/or antibodies. An effective and efficient prediction tool for CE analysis is critical for the development of immunology-related applications, such as vaccine design and disease diagnosis.

Results

We propose a novel method consisting of two sequential modules: matching and prediction. The matching module includes two main approaches. The first approach is a complete sequence search (CSS) that applies BLAST to align the sequence with all known antigen sequences. Fragments with high epitope sequence identities are identified and the predicted residues are annotated on the query structure. The second approach is a spiral vector search (SVS) that adopts a novel surface spiral feature vector for large-scale surface patch detection when queried against a comprehensive epitope database. The prediction module also contains two proposed subsystems. The first system is based on knowledge-based energy and geometrical neighboring residue contents, and the second system adopts combinatorial features, including amino acid contents and physicochemical characteristics, to formulate corresponding geometric spiral vectors and compare them with all spiral vectors from known CEs. An integrated testing dataset was generated for method evaluation, and our two searching methods effectively identified all epitope regions. The prediction results show that our proposed method outperforms previously published systems in terms of sensitivity, specificity, positive predictive value, and accuracy.

Conclusions

The proposed method significantly improves the performance of traditional epitope prediction. Matching followed by prediction is an efficient and effective approach compared to predicting directly on specific surfaces containing antigenic characteristics.

Protein-Based Immunome Wide Association Studies (PIWAS) for the Discovery of Significant Disease-Associated Antigens

Identification of the antigens associated with antibodies is vital to understanding immune responses in the context of infection, autoimmunity, and cancer. Discovering antigens at a proteome scale could enable broader identification of antigens that are responsible for generating an immune response or driving a disease state. Although targeted tests for known antigens can be straightforward, discovering antigens at a proteome scale using protein and peptide arrays is time consuming and expensive. We leverage Serum Epitope Repertoire Analysis (SERA), an assay based on a random bacterial display peptide library coupled with next generation sequencing (NGS), to power the development of Protein-based Immunome Wide Association Study (PIWAS). PIWAS uses proteome-based signals to discover candidate antibody-antigen epitopes that are significantly elevated in a subset of cases compared to controls. After demonstrating statistical power relative to the magnitude and prevalence of effect in synthetic data, we apply PIWAS to systemic lupus erythematosus (SLE, n=31) and observe known autoantigens, Smith and Ribosomal protein P, within the 22 highest scoring candidate protein antigens across the entire human proteome. We validate the magnitude and location of the SLE specific signal against the Smith family of proteins using a cohort of patients who are positive by predicate anti-Sm tests. To test the generalizability of the method in an additional autoimmune disease, we identified and validated autoantigenic signals to SSB, CENPA, and keratin proteins in a cohort of individuals with Sjogren’s syndrome (n=91). Collectively, these results suggest that PIWAS provides a powerful new tool to discover disease-associated serological antigens within any known proteome.

Immuno-informatics design of a multimeric epitope peptide based vaccine targeting SARS-CoV-2 spike glycoprotein

Developing an efficacious vaccine for SARS-CoV-2 infection is critical to stemming COVID-19 fatalities and providing the global community with immune protection. We have used a bioinformatic approach to aid in designing an epitope peptide-based vaccine against the spike protein of the virus. Five antigenic B cell epitopes with viable antigenicity and a total of 27 discontinuous B cell epitopes were mapped out structurally in the spike protein for antibody recognition. We identified eight CD8+ T cell 9-mers and 12 CD4+ T cell 14-15-mer as promising candidate epitopes putatively restricted by a large number of MHC I and II alleles, respectively. We used this information to construct an in silico chimeric peptide vaccine whose translational rate was highly expressed when cloned in pET28a (+) vector. With our In silico test, the vaccine construct was predicted to elicit high antigenicity and cell-mediated immunity when given as a homologous prime-boost, triggering of toll-like receptor 5 by the adjuvant linker. The vaccine was also characterized by an increase in IgM and IgG and an array of Th1 and Th2 cytokines. Upon in silico challenge with SARS-CoV-2, there was a decrease in antigen levels using our immune simulations. We, therefore, propose that potential vaccine designs consider this approach.

Predicting Immunogenicity Risk in Biopharmaceuticals

The assessment of immunogenicity of biopharmaceuticals is a crucial step in the process of their development. Immunogenicity is related to the activation of adaptive immunity. The complexity of the immune system manifests through numerous different mechanisms, which allows the use of different approaches for predicting the immunogenicity of biopharmaceuticals. The direct experimental approaches are sometimes expensive and time consuming, or their results need to be confirmed. In this case, computational methods for immunogenicity prediction appear as an appropriate complement in the process of drug design. In this review, we analyze the use of various In silico methods and approaches for immunogenicity prediction of biomolecules: sequence alignment algorithms, predicting subcellular localization, searching for major histocompatibility complex (MHC) binding motifs, predicting T and B cell epitopes based on machine learning algorithms, molecular docking, and molecular dynamics simulations. Computational tools for antigenicity and allergenicity prediction also are considered.

Lower risk of subarachnoid haemorrhage in diabetes: a nationwide population-based cohort study

Background and purpose

Diabetes mellitus (DM) is a common metabolic disorder with increased risk of cardiovascular and cerebrovascular complications. However, its relationship with risk of subarachnoid haemorrhage (SAH), the most devastating form of stroke, remains controversial.

Methods

To evaluate the relationship between DM and risk of SAH, we performed a retrospective cohort study using a nationwide, population‐based, health screening database in Korea. We included participants without history of stroke who underwent a nationwide health screening programme between 2003 and 2004. Primary outcome was occurrence of SAH. Participants were followed up until development of SAH or December 2015. Multivariate Cox proportional hazards regression analysis was performed with adjustments for age, sex, systolic blood pressure, total cholesterol, body mass index, physical activity, smoking status, alcohol habit, household income and treatment with antihypertensive agents and statins.

Results

Among 421 768 study participants, prevalence of DM was 9.6%. During a mean follow-up period of 11.6±1.9 years, 1039 patients developed SAH. Presence of DM was significantly associated with decreased risk of SAH (adjusted HR 0.68; 95% CI 0.53 to 0.86; p<0.001). Elevated level of fasting blood glucose was also negatively associated with risk of SAH (adjusted HR per 1 mmol/L increase 0.90; 95% CI 0.86 to 0.95; p<0.001).

Conclusion

DM and elevated level of fasting blood glucose were inversely associated with risk of SAH. Further studies may elucidate the possibly protective, pathophysiological role played by hyperglycaemia in patients at risk of SAH.

T and B cell Epitope analysis of SARS-CoV-2 S protein based on immunoinformatics and experimental research

COVID-19 caused by SARS-CoV-2 is pandemic with a severe morbidity and mortality rate across the world. Despite the race for effective vaccine and drug against further expansion and fatality rate of this novel coronavirus, there is still lack of effective antiviral therapy. To this effect, we deemed it necessary to identify potential B and T cell epitopes from the envelope S protein. This can be used as potential targets to develop anti-SARS-CoV-2 vaccine preparations. In this study, we used immunoinformatics to identify conservative B and T cell epitopes for S proteins of SARS-CoV-2, which might play roles in the initiation of SARS-CoV-2 infection. We identified the B cell and T cell peptide epitopes of S protein and their antigenicity, as well as the interaction between the peptide epitopes and human leucocyte antigen (HLA). Among the B cell epitopes, 'EILDITPCSFGGVS' has the highest score of antigenicity and great immunogenicity. In T cell epitopes, MHC-I peptide 'KIADYNYKL' and MHC-II peptide 'LEILDITPC' were identified as high antigens. Besides, docking analysis showed that the predicted peptide 'KIADYNYKL' was closely bound to the HLA-A*0201. The results of molecular dynamics simulation through GROMACS software showed that 'HLA-A*0201~peptide' complex was very stable. And the peptide we selected could induce the T cell response similar to that of SARS-CoV-2 infection. Moreover, the predicted peptides were highly conserved in different isolates from different countries. The antigenic epitopes presumed in this study were effective new vaccine targets to prevent SARS-CoV-2 infection.

Prediction and evolution of B cell epitopes of surface protein in SARS-CoV-2

BACKGROUND

In order to obtain antibodies that recognize natural proteins, it is possible to predict the antigenic determinants of natural proteins, which are eventually embodied as polypeptides. The polypeptides can be coupled with corresponding vectors to stimulate the immune system to produce corresponding antibodies, which is also a simple and effective vaccine development method. The discovery of epitopes is helpful to the development of SARS-CoV-2 vaccine.

METHODS

The analyses were related to epitopes on 3 proteins, including spike (S), envelope (E) and membrane (M) proteins, which are located on the lipid envelope of the SARS-CoV-2. Based on the NCBI Reference Sequence: NC_045512.2, the conformational and linear B cell epitopes of the surface protein were predicted separately by various prediction methods. Furthermore, the conservation of the epitopes, the adaptability and other evolutionary characteristics were also analyzed, the sequences of the whole genome of SARS-CoV-2 were obtained from the GISAID.

RESULTS

7 epitopes were predicted, including 6 linear epitopes and 1 conformational epitope. One of the linear and one of the conformational consist of identical sequence, but represent different forms of epitopes. It is worth mentioning that all 6 identified epitopes were conserved in nearly 3500 SARS-CoV-2 genomes, showing that it is helpful to obtain stable and long-acting epitopes under the condition of high frequency of amino acid mutation, which deserved further study at the experiment level.

CONCLUSION

The findings would facilitate the vaccine development, had the potential to be directly applied on the prevention in this disease, but also have the potential to prevent the possible threats caused by other types of coronavirus.

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

Background

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

Methods

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

Results

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

Conclusions

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

Reverse vaccinology assisted designing of multiepitope-based subunit vaccine against SARS-CoV-2

BACKGROUND

Coronavirus disease 2019 (COVID-19) linked with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) cause severe illness and life-threatening pneumonia in humans. The current COVID-19 pandemic demands an effective vaccine to acquire protection against the infection. Therefore, the present study was aimed to design a multiepitope-based subunit vaccine (MESV) against COVID-19.

METHODS

Structural proteins (Surface glycoprotein, Envelope protein, and Membrane glycoprotein) of SARS-CoV-2 are responsible for its prime functions. Sequences of proteins were downloaded from GenBank and several immunoinformatics coupled with computational approaches were employed to forecast B- and T- cell epitopes from the SARS-CoV-2 highly antigenic structural proteins to design an effective MESV.

RESULTS

Predicted epitopes suggested high antigenicity, conserveness, substantial interactions with the human leukocyte antigen (HLA) binding alleles, and collective global population coverage of 88.40%. Taken together, 276 amino acids long MESV was designed by connecting 3 cytotoxic T lymphocytes (CTL), 6 helper T lymphocyte (HTL) and 4 B-cell epitopes with suitable adjuvant and linkers. The MESV construct was non-allergenic, stable, and highly antigenic. Molecular docking showed a stable and high binding affinity of MESV with human pathogenic toll-like receptors-3 (TLR3). Furthermore, in silico immune simulation revealed significant immunogenic response of MESV. Finally, MEV codons were optimized for its in silico cloning into the Escherichia coli K-12 system, to ensure its increased expression.

CONCLUSION

The MESV developed in this study is capable of generating immune response against COVID-19. Therefore, if designed MESV further investigated experimentally, it would be an effective vaccine candidate against SARS-CoV-2 to control and prevent COVID-19.

Vaccine design of coronavirus spike (S) glycoprotein in chicken: immunoinformatics and computational approaches

BACKGROUND

Infectious bronchitis (IB) is a highly contagious respiratory disease in chickens and produces economic loss within the poultry industry. This disease is caused by a single stranded RNA virus belonging to Cronaviridae family. This study aimed to design a potential multi-epitopes vaccine against infectious bronchitis virus spike protein (S). Protein characterization was also performed for IBV spike protein.

METHODS

The present study used various tools in Immune Epitope Database (IEDB) to predict conserved B and T cell epitopes against IBV spike (S) protein that may perform a significant role in provoking the resistance response to IBV infection.

RESULTS

In B cell prediction methods, three epitopes ( ₁₁₃₉ KKSSYY ₁₁₄₄ , ₁₁₄₀ KSSYYT ₁₁₄₅ , ₁₁₄₁ SSYYT ₁₁₄₅ ) were selected as surface, linear and antigenic epitopes.Many MHCI and MHCII epitopes were predicted for IBV S protein. Among them ₉₈₂YYITARDMY₉₉₀ and ₉₈₃ YITARDMYM ₉₉₁ epitopes displayed high antigenicity, no allergenicity and no toxicity as well as great linkage with MHCI and MHCII alleles. Moreover, docking analysis of MHCI epitopes produced strong binding affinity with BF₂ alleles.

CONCLUSION

Five conserved epitopes were expected from spike glycoprotein of IBV as the best B and T cell epitopes due to high antigenicity, no allergenicity and no toxicity. In addition, MHC epitopes showed great linkage with MHC alleles as well as strong interaction with BF2 alleles. These epitopes should be designed and incorporated and then tested as multi-epitope vaccine against IBV.

Association between brachial-ankle pulse wave velocity and risk of type 2 diabetes mellitus: results from a cohort study

INTRODUCTION

Brachial-ankle pulse wave velocity (ba-PWV), as a simple and easily measured marker of arterial stiffness, has not been prospectively explored for its role in type 2 diabetes mellitus (T2DM) risk among the general population. This study aimed to explore the association between baseline ba-PWV value and new-onset T2DM among Chinese adults.

RESEARCH DESIGN AND METHODS

Using data from Xiaotangshan Hospital, we conducted a prospective cohort study among those who underwent annual or biennial health check-up examinations and who had their ba-PWV measured from 2009 to 2016. We explored the risk of new-onset T2DM across ba-PWV tertiles using Cox proportional-hazards regression analysis.

RESULTS

Of 6122 adults (68.9% male; mean age: 51.0 (SD 13.0) years) without T2DM and with ba-PWV measured at baseline, 599 participants developed T2DM during an average of 3.8 (SD 2.3) years of follow-up. After multivariable adjustment, ba-PWV was positively related to T2DM risk (p for trend=0.008). Compared with the lowest ba-PWV tertile, the HRs and their 95% CIs were 1.57 (1.18 to 2.10) for the second and 1.66 (1.24 to 2.22) for the third tertile. The risk across ba-PWV tertiles increased steadily from 1000 cm/s to 1400 cm/s and then reached a plateau. Subgroup analyses indicated a significantly higher risk among those aged <65 years and current smokers (p for interactions: <0.001 and 0.006).

CONCLUSIONS

Our findings suggest that ba-PWV might be a useful and independent predictor of new-onset T2DM with ba-PWV ranging between 1000 cm/s and 1400 cm/s, especially among younger individuals and current smokers.

Epitope-based sieve analysis of Plasmodium falciparum sequences from a FMP2.1/AS02A vaccine trial is consistent with differential vaccine efficacy against immunologically relevant AMA1 variants

To prevent premature dismissal of promising vaccine programs, it is critical to determine if lack of efficacy in the field is due to allele specific-efficacy, rather than to the lack of immunogenicity of the candidate antigen. Here we use samples collected during a field trial of the AMA1-based FMP2.1/AS02_A malaria vaccine, which incorporates the AMA1 variant encoded by the reference Plasmodium falciparum 3D7 strain, to assess the usefulness of epitope-based sieve analysis for the detection of vaccine-induced allele-specific immune responses. The samples used are from volunteers who received the malaria vaccine FMP2.1/AS02_A or a control (rabies vaccine), during a vaccine efficacy field trial, and who later developed malaria. In a previous study, P. falciparum DNA was extracted from all samples, and the ama1 locus amplified and sequenced. Here, a sieve analysis was used to measure T and B-cell escape, and difference in 3D7-like epitopes in the two treatment arms. Overall, no difference was observed in mean amino acid distance to the 3D7 AMA1 variant between sequences from vaccinees and controls in B-cell epitopes. However, we found a significantly greater proportion of 3D7-like T-cell epitopes that map to the AMA1 cluster one loop (c1L) region in the control vs. the vaccinee group (p = 0.02), consistent with allele-specific vaccine efficacy. Interestingly, AMA1 epitopes in infections from vaccinees had higher mean IC50, and consequently lower binding affinity, than epitopes generated from the control group (p = 0.01), suggesting that vaccine-induced selection impacted the immunological profile of the strains that pass through the sieve imposed by the vaccine-induced protection. These findings are consistent with a vaccine-derived sieve effect on the c1L region of AMA1 and suggest that sieve analyses of malaria vaccine trial samples targeted to epitopes identified in silico can help identify protective malaria antigens that may be efficacious if combined in a multivalent vaccine.

A novel recombinant multiepitope protein candidate for the diagnosis of brucellosis: A pilot study

Brucellosis is a zoonotic disease transmitted to humans from infected animals. As a systemic disease, it can harm any organ or system of the host body. Human brucellosis presents with various clinical symptoms, which makes diagnosis challenging. Serological diagnosis of brucellosis is based on ELISA or agglutination tests, which use colorimetry to detect antibodies generated against lipopolysaccharide (LPS) or extracts from whole-cell bacteria. To construct a protein that can specifically recognize Brucella, we analyzed hydrophilicity, accessibility, flexibility, antigenicity, and β-turns using a protein network server. Then, we chose the most abundant immunodominant epitopes of the outer membrane proteins omp31, BP26, omp2b and omp16. Based on the sequences of these major epitopes, fifteen major immunodominant epitopes were selected to construct a synthetic Brucella recombinant multiepitope outer membrane protein (rOmp) gene. This recombinant gene was expressed in E. coli, and the produced protein was purified by Ni-NTA affinity purification. The purified protein was tested in an indirect ELISA assay, demonstrating a high level of sensitivity and specificity. This technique is creating a unique antigen that, coupled with overexpression and low-cost purification, offers a promising diagnosis of both human and animal brucellosis, with the potential to avoid the disadvantages of whole brucellosis-antigen-based assays.

Designs of Antigen Structure and Composition for Improved Protein-Based Vaccine Efficacy

Today, vaccinologists have come to understand that the hallmark of any protective immune response is the antigen. However, it is not the whole antigen that dictates the immune response, but rather the various parts comprising the whole that are capable of influencing immunogenicity. Protein-based antigens hold particular importance within this structural approach to understanding immunity because, though different molecules can serve as antigens, only proteins are capable of inducing both cellular and humoral immunity. This fact, coupled with the versatility and customizability of proteins when considering vaccine design applications, makes protein-based vaccines (PBVs) one of today's most promising technologies for artificially inducing immunity. In this review, we follow the development of PBV technologies through time and discuss the antigen-specific receptors that are most critical to any immune response: pattern recognition receptors, B cell receptors, and T cell receptors. Knowledge of these receptors and their ligands has become exceptionally valuable in the field of vaccinology, where today it is possible to make drastic modifications to PBV structure, from primary to quaternary, in order to promote recognition of target epitopes, potentiate vaccine immunogenicity, and prevent antigen-associated complications. Additionally, these modifications have made it possible to control immune responses by modulating stability and targeting PBV to key immune cells. Consequently, careful consideration should be given to protein structure when designing PBVs in the future in order to potentiate PBV efficacy.

Development and Application of Computational Methods in Phage Display Technology

BACKGROUND

Phage display is a powerful and versatile technology for the identification of peptide ligands binding to multiple targets, which has been successfully employed in various fields, such as diagnostics and therapeutics, drug-delivery and material science. The integration of next generation sequencing technology with phage display makes this methodology more productive. With the widespread use of this technique and the fast accumulation of phage display data, databases for these data and computational methods have become an indispensable part in this community. This review aims to summarize and discuss recent progress in the development and application of computational methods in the field of phage display.

METHODS

We undertook a comprehensive search of bioinformatics resources and computational methods for phage display data via Google Scholar and PubMed. The methods and tools were further divided into different categories according to their uses.

RESULTS

We described seven special or relevant databases for phage display data, which provided an evidence-based source for phage display researchers to clean their biopanning results. These databases can identify and report possible target-unrelated peptides (TUPs), thereby excluding false-positive data from peptides obtained from phage display screening experiments. More than 20 computational methods for analyzing biopanning data were also reviewed. These methods were classified into computational methods for reporting TUPs, for predicting epitopes and for analyzing next generation phage display data.

CONCLUSION

The current bioinformatics archives, methods and tools reviewed here have benefitted the biopanning community. To develop better or new computational tools, some promising directions are also discussed.

Epitope-based peptide vaccine design and target site depiction against Middle East Respiratory Syndrome Coronavirus: an immune-informatics study

Background

Middle East Respiratory Syndrome Coronavirus (MERS-COV) is the main cause of lung and kidney infections in developing countries such as Saudi Arabia and South Korea. This infectious single-stranded, positive (+) sense RNA virus enters the host by binding to dipeptidyl-peptide receptors. Since no vaccine is yet available for MERS-COV, rapid case identification, isolation, and infection prevention strategies must be used to combat the spreading of MERS-COV infection. Additionally, there is a desperate need for vaccines and antiviral strategies.

Methods

The present study used immuno-informatics and computational approaches to identify conserved B- and T cell epitopes for the MERS-COV spike (S) protein that may perform a significant role in eliciting the resistance response to MERS-COV infection.

Results

Many conserved cytotoxic T-lymphocyte epitopes and discontinuous and linear B-cell epitopes were predicted for the MERS-COV S protein, and their antigenicity and interactions with the human leukocyte antigen (HLA) B7 allele were estimated. Among B-cell epitopes, QLQMGFGITVQYGT displayed the highest antigenicity-score, and was immensely immunogenic. Among T-cell epitopes, MHC class-I peptide YKLQPLTFL and MHC class-II peptide YCILEPRSG were identified as highly antigenic. Furthermore, docking analyses revealed that the predicted peptides engaged in strong bonding with the HLA-B7 allele.

Conclusion

The present study identified several MERS-COV S protein epitopes that are conserved among various isolates from different countries. The putative antigenic epitopes may prove effective as novel vaccines for eradication and combating of MERS-COV infection.

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.

Immunoinformatics: In Silico Approaches and Computational Design of a Multi-epitope, Immunogenic Protein

Immunoinformatics is a new critical field with several tools and databases that conduct the eyesight of experimental selection and facilitate analysis of the great amount of immunologic data obtained from experimental researches and helps to design and introducing new hypothesis. Given these visages, immunoinformatics seems to be the way that develop and progress the immunological research. Bioinformatics methods and applications are successfully employed in vaccine informatics to assist different sites of the preclinical, clinical, and post-licensure vaccine enterprises. On the other hand, the progression of molecular biology and immunology caused epitope vaccines have become the focus of research on molecular vaccines. Moreover, reverse vaccinology could improve vaccine production and vaccination protocols by in silico prediction of protein-vaccine candidates from genome sequences. B- and T-cell immune epitopes could be predicted by immunoinformatics algorithms and computational methods to improve the vaccine design, protective immunity analysis, assessment of vaccine safety and efficacy, and immunization modeling. This review aims to discuss the power of computational approaches in vaccine design and their relevance to the development of effective vaccines. Furthermore, the various divisions of this field and available tools in each item are introduced and reviewed.

Immune-orthogonal orthologues of AAV capsids and of Cas9 circumvent the immune response to the administration of gene therapy

Protein-based therapeutics can activate the adaptive immune system and lead to the production of neutralizing antibodies and to cytotoxic-T-cell-mediated clearance of the treated cells. Here, we show that the sequential use of immune-orthogonal orthologues of the CRISPR-associated protein 9 (Cas9) and of adeno-associated viruses (AAVs) eludes adaptive immune responses and enables effective gene editing from repeated dosing. We compared total sequence similarities and predicted binding strengths to class-I and class-II major-histocompatibility-complex proteins for 284 DNA-targeting and 84 RNA-targeting CRISPR effectors, and for 167 AAV VP1-capsid-protein orthologues. We predict the absence of cross-reactive immune responses for 79% of the DNA-targeting Cas orthologs, which we validate for three Cas9 orthologs in mice, yet anticipate broad immune cross-reactivity among the AAV serotypes. We also show that efficacious in vivo gene editing is uncompromised when using multiple dosing with orthologues of AAVs and Cas9 in mice previously immunized against the AAV vector and the Cas9 payload. Multiple dosing with protein orthologues may allow for sequential regimens of protein therapeutics that circumvent pre-existing immunity or induced immunity.

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.

Glycoprotein D peptide-based diagnostic approach for the detection of avian infectious laryngotracheitis antibodies

Infectious laryngotracheitis (ILT) is a highly contagious respiratory disease of chickens, pheasants, and peafowl. It is caused by the alpha herpesvirus, infectious laryngotracheitis virus (ILTV). Glycoprotein D (gD) of ILTV is immunogenic and helps in its binding to the susceptible host cell receptor. In the present study, a recombinant gD protein was expressed in a prokaryotic system to develop a single serum dilution ELISA. In addition, two immunogenic peptides, corresponding to regions 77-89 and 317-328, were identified in gD protein. The peptides were synthesized using solid-phase peptide synthesis, purified using reversed-phase HPLC, and characterized using mass spectrometry. The peptides displayed a good titre and were found to be promising antigens to coat the ELISA plate to detect the ILTV antibodies in the serum sample. The developed ELISA showed 96.9% sensitivity, 87.5% specificity, and 95.3% accuracy as compared to OIE referenced standard indirect ILTV ELISA (whole viral coated). The assay may not differentiate vaccinated from infected birds when the flocks are administered with live attenuated vaccines. However, the assay could be useful to detect the disease condition in birds vaccinated with recombinant vaccine expressing glycoproteins other than gD. The developed ILTV single serum dilution ELISA could be an alternative to the existing diagnostics for the detection of ILTV antibodies.

MPLs-Pred: Predicting Membrane Protein-Ligand Binding Sites Using Hybrid Sequence-Based Features and Ligand-Specific Models

Membrane proteins (MPs) are involved in many essential biomolecule mechanisms as a pivotal factor in enabling the small molecule and signal transport between the two sides of the biological membrane; this is the reason that a large portion of modern medicinal drugs target MPs. Therefore, accurately identifying the membrane protein-ligand binding sites (MPLs) will significantly improve drug discovery. In this paper, we propose a sequence-based MPLs predictor called MPLs-Pred, where evolutionary profiles, topology structure, physicochemical properties, and primary sequence segment descriptors are combined as features applied to a random forest classifier, and an under-sampling scheme is used to enhance the classification capability with imbalanced samples. Additional ligand-specific models were taken into consideration in refining the prediction. The corresponding experimental results based on our method achieved an appreciable performance, with 0.63 MCC (Matthews correlation coefficient) as the overall prediction precision, and those values were 0.604, 0.7, and 0.692, respectively, for the three main types of ligands: drugs, metal ions, and biomacromolecules. MPLs-Pred is freely accessible at http://icdtools.nenu.edu.cn/.

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.

Immunoinformatics Approach for Epitope-Based Peptide Vaccine Design and Active Site Prediction against Polyprotein of Emerging Oropouche Virus

Oropouche virus (OROV) is an emerging pathogen which causes Oropouche fever and meningitis in humans. Several outbreaks of OROV in South America, especially in Brazil, have changed its status as an emerging disease, but no vaccine or specific drug target is available yet. Our approach was to identify the epitope-based vaccine candidates as well as the ligand-binding pockets through the use of immunoinformatics. In this report, we identified both T-cell and B-cell epitopes of the most antigenic OROV polyprotein with the potential to induce both humoral and cell-mediated immunity. Eighteen highly antigenic and immunogenic CD8⁺ T-cell epitopes were identified, including three 100% conserved epitopes (TSSWGCEEY, CSMCGLIHY, and LAIDTGCLY) as the potential vaccine candidates. The selected epitopes showed 95.77% coverage for the mixed Brazilian population. The docking simulation ensured the binding interaction with high affinity. A total of five highly conserved and nontoxic linear B-cell epitopes "NQKIDLSQL," "HPLSTSQIGDRC," "SHCNLEFTAITADKIMSL," "PEKIPAKEGWLTFSKEHTSSW," and "HHYKPTKNLPHVVPRYH" were selected as potential vaccine candidates. The predicted eight conformational B-cell epitopes represent the accessibility for the entered virus. In the posttherapeutic strategy, ten ligand-binding pockets were identified for effective inhibitor design against emerging OROV infection. Collectively, this research provides novel candidates for epitope-based peptide vaccine design against OROV.

In Silico Methods in Antibody Design

Antibody therapies with high efficiency and low toxicity are becoming one of the major approaches in antibody therapeutics. Based on high-throughput sequencing and increasing experimental structures of antibodies/antibody-antigen complexes, computational approaches can predict antibody/antigen structures, engineering the function of antibodies and design antibody-antigen complexes with improved properties. This review summarizes recent progress in the field of in silico design of antibodies, including antibody structure modeling, antibody-antigen complex prediction, antibody stability evaluation, and allosteric effects in antibodies and functions. We listed the cases in which these methods have helped experimental studies to improve the affinities and physicochemical properties of antibodies. We emphasized how the molecular dynamics unveiled the allosteric effects during antibody-antigen recognition and antibody-effector recognition.

Phage display peptide libraries: deviations from randomness and correctives

Peptide-expressing phage display libraries are widely used for the interrogation of antibodies. Affinity selected peptides are then analyzed to discover epitope mimetics, or are subjected to computational algorithms for epitope prediction. A critical assumption for these applications is the random representation of amino acids in the initial naïve peptide library. In a previous study, we implemented next generation sequencing to evaluate a naïve library and discovered severe deviations from randomness in UAG codon over-representation as well as in high G phosphoramidite abundance causing amino acid distribution biases. In this study, we demonstrate that the UAG over-representation can be attributed to the burden imposed on the phage upon the assembly of the recombinant Protein 8 subunits. This was corrected by constructing the libraries using supE44-containing bacteria which suppress the UAG driven abortive termination. We also demonstrate that the overabundance of G stems from variant synthesis-efficiency and can be corrected using compensating oligonucleotide-mixtures calibrated by mass spectroscopy. Construction of libraries implementing these correctives results in markedly improved libraries that display random distribution of amino acids, thus ensuring that enriched peptides obtained in biopanning represent a genuine selection event, a fundamental assumption for phage display applications.