Determinants of antigenicity and specificity in immune response for protein sequences

Design and validation of a novel multi-epitopes vaccine against hantavirus

Hantavirus is a member of the order Bunyavirales and an emerging global pathogen. Hantavirus infections have affected millions of people globally based on available epidemiological data and research studies. Hemorrhagic fever with renal syndrome (HFRS) and hantavirus pulmonary syndrome (HPS) are the two main human diseases associated with hantavirus infections. Hence, efforts are required to develop a potent vaccine against the pathogen. The only vaccine that is in use for hantavirus is an inactivated virus vaccine, "Hantavax", but it failed to produce neutralizing antibodies. Vaccine development is of much importance in dealing with the surge of hantavirus globally. In this study, hantavirus five proteins (N protein, G1 and G2, L protein, and non-structural proteins) were used in NetCTL 1.2 program to predict T-cell epitopes. To predict major histocompatibility complex (MHC) binding alleles, an immune epitope database (IEDB) was used. All predicted epitopes were then investigated for different immunoinformatics analyses such as antigenicity and toxicity analyses. The good water-soluble, non-toxic, probable antigenic, and DRB*0101 binder was selected. A multi-epitopes-based vaccine designing was then done where linkers were used to connect the shortlisted epitopes. In addition, an adjuvant molecule was supplementary to the multi-epitopes peptide to improve the vaccine's immunogenic potential. The final vaccine construct's three-dimensional structure was modeled by ab initio method. The vaccine molecule was then evaluated for its binding potential with TLR-3 immune receptor, which is key for its recognition and processing by the host immune system. Docking studies were performed using HADDOCK software. The best-docked complex was selected and visualized for intermolecular binding and interactions using UCSF Chimera 1.16 software. The findings revealed that the designed vaccine might be a potential vaccine against hantavirus and can be used in experimental animal model testings.Communicated by Ramaswamy H. Sarma.

Glycylglycine promotes the solubility and antigenic utility of recombinant HCV structural proteins in a point-of-care immunoassay for detection of active viremia

BACKGROUND

Although E. coli is generally a well-opted platform for the overproduction of recombinant antigens as heterologous proteins, the optimization of expression conditions to maximize the yield of functional proteins remains empirical. Herein, we developed an optimized E. coli (BL21)-based system for the overproduction of soluble immunoreactive HCV core/envelope proteins that were utilized to establish a novel immunoassay for discrimination of active HCV infection.

METHODS

The core/E1-E2 genes were amplified and expressed in E. coli BL21 (DE3) in the absence/presence of glycylglycine. The antigenic performance of soluble proteins was assessed against 63 HCV-seronegative (Ab-) sera that included normal and interferent sera (HBV and/or chronic renal failure), and 383 HCV-seropositive (Ab+) samples that included viremic (chronic/relapsers) and recovered patients' sera. The color intensity (OD450) and S/Co values were estimated.

RESULTS

The integration of 0.1-0.4M glycylglycine in the growth media significantly enhanced the solubility/yield of recombinant core and envelope proteins by ~ 225 and 242 fold, respectively. This was reflected in their immunoreactivity and antigenic performance in the developed immunoassay, where the soluble core/E1/E2 antigen mixture showed 100% accuracy in identifying HCV viremic sera with a viral RNA load as low as 3800 IU/mL, without cross-reactivity against normal/interferent HCV-Ab-sera. The ideal S/Co threshold predicting active viremia (> 2.75) showed an AUC value of 0.9362 (95% CI: 0.9132 to 0.9593), with 87.64, 91.23% sensitivity and specificity, and 94.14, 82.11% positive and negative predictive values, respectively. The different panels of samples assayed with our EIA showed a good concordance with the viral loads and also significant correlations with the golden standards of HCV diagnosis in viremic patients. The performance of the EIA was not affected by the immunocompromised conditions or HBV co-infection.

CONCLUSION

The applicability of the proposed platform would extend beyond the reported approach, where glycylglycine, low inducer concentration and post-induction temperature, combined with the moderately-strong constitutive promoter enables the stable production of soluble/active proteins, even those with reported toxicity. Also, the newly developed immunoassay provides a cost-effective point-of-care diagnostic tool for active HCV viremia that could be useful in resource-limited settings.

Multi-perspectives and challenges in identifying B-cell epitopes

The identification of B-cell epitopes (BCEs) in antigens is a crucial step in developing recombinant vaccines or immunotherapies for various diseases. Over the past four decades, numerous in silico methods have been developed for predicting BCEs. However, existing reviews have only covered specific aspects, such as the progress in predicting conformational or linear BCEs. Therefore, in this paper, we have undertaken a systematic approach to provide a comprehensive review covering all aspects associated with the identification of BCEs. First, we have covered the experimental techniques developed over the years for identifying linear and conformational epitopes, including the limitations and challenges associated with these techniques. Second, we have briefly described the historical perspectives and resources that maintain experimentally validated information on BCEs. Third, we have extensively reviewed the computational methods developed for predicting conformational BCEs from the structure of the antigen, as well as the methods for predicting conformational epitopes from the sequence. Fourth, we have systematically reviewed the in silico methods developed in the last four decades for predicting linear or continuous BCEs. Finally, we have discussed the overall challenge of identifying continuous or conformational BCEs. In this review, we only listed major computational resources; a complete list with the URL is available from the BCinfo website (https://webs.iiitd.edu.in/raghava/bcinfo/).

In silico formulation of a next-generation multiepitope vaccine for use as a prophylactic candidate against Crimean-Congo hemorrhagic fever

BACKGROUND

Crimean-Congo hemorrhagic fever (CCHF) is a widespread disease transmitted to humans and livestock animals through the bite of infected ticks or close contact with infected persons' blood, organs, or other bodily fluids. The virus is responsible for severe viral hemorrhagic fever outbreaks, with a case fatality rate of up to 40%. Despite having the highest fatality rate of the virus, a suitable treatment option or vaccination has not been developed yet. Therefore, this study aimed to formulate a multiepitope vaccine against CCHF through computational vaccine design approaches.

METHODS

The glycoprotein, nucleoprotein, and RNA-dependent RNA polymerase of CCHF were utilized to determine immunodominant T- and B-cell epitopes. Subsequently, an integrative computational vaccinology approach was used to formulate a multi-epitopes vaccine candidate against the virus.

RESULTS

After rigorous assessment, a multiepitope vaccine was constructed, which was antigenic, immunogenic, and non-allergenic with desired physicochemical properties. Molecular dynamics (MD) simulations of the vaccine-receptor complex show strong stability of the vaccine candidates to the targeted immune receptor. Additionally, the immune simulation of the vaccine candidates found that the vaccine could trigger real-life-like immune responses upon administration to humans.

CONCLUSIONS

Finally, we concluded that the formulated multiepitope vaccine candidates would provide excellent prophylactic properties against CCHF.

Proteome-Wide Screening of Potential Vaccine Targets against Brucella melitensis

The ongoing antibiotic-resistance crisis is becoming a global problem affecting public health. Urgent efforts are required to design novel therapeutics against pathogenic bacterial species. Brucella melitensis is an etiological agent of brucellosis, which mostly affects sheep and goats but several cases have also been reported in cattle, water buffalo, yaks and dogs. Infected animals also represent the major source of infection for humans. Development of safer and effective vaccines for brucellosis remains a priority to support disease control and eradication in animals and to prevent infection to humans. In this research study, we designed an in-silico multi-epitopes vaccine for B. melitensis using computational approaches. The pathogen core proteome was screened for good vaccine candidates using subtractive proteomics, reverse vaccinology and immunoinformatic tools. In total, 10 proteins: catalase; siderophore ABC transporter substrate-binding protein; pyridoxamine 5'-phosphate oxidase; superoxide dismutase; peptidylprolyl isomerase; superoxide dismutase family protein; septation protein A; hypothetical protein; binding-protein-dependent transport systems inner membrane component; and 4-hydroxy-2-oxoheptanedioate aldolase were selected for epitopes prediction. To induce cellular and antibody base immune responses, the vaccine must comprise both B and T-cells epitopes. The epitopes were next screened for antigenicity, allergic nature and water solubility and the probable antigenic, non-allergic, water-soluble and non-toxic nine epitopes were shortlisted for multi-epitopes vaccine construction. The designed vaccine construct comprises 274 amino acid long sequences having a molecular weight of 28.14 kDa and instability index of 27.62. The vaccine construct was further assessed for binding efficacy with immune cell receptors. Docking results revealed that the designed vaccine had good binding potency with selected immune cell receptors. Furthermore, vaccine-MHC-I, vaccine-MHC-II and vaccine-TLR-4 complexes were opted based on a least-binding energy score of -5.48 kcal/mol, 0.64 kcal/mol and -2.69 kcal/mol. Those selected were then energy refined and subjected to simulation studies to understand dynamic movements of the docked complexes. The docking results were further validated through MMPBSA and MMGBSA analyses. The MMPBSA calculated -235.18 kcal/mol, -206.79 kcal/mol, and -215.73 kcal/mol net binding free energy, while MMGBSA estimated -259.48 kcal/mol, -206.79 kcal/mol and -215.73 kcal/mol for TLR-4, MHC-I and MHC-II complexes, respectively. These findings were validated by water-swap and entropy calculations. Overall, the designed vaccine construct can evoke proper immune responses and the construct could be helpful for experimental researchers in formulation of a protective vaccine against the targeted pathogen for both animal and human use.

B-Cell Epitope Predictions Using Computational Methods

Identifying protein antigenic epitopes that are recognizable by antibodies is a key step in immunologic research. This type of research has broad medical applications, such as new immunodiagnostic reagent discovery, vaccine design, and antibody design. However, due to the countless possibilities of potential epitopes, the experimental search through trial and error would be too costly and time-consuming to be practical. To facilitate this process and improve its efficiency, computational methods were developed to predict both linear epitopes and discontinuous antigenic epitopes. For linear B-cell epitope prediction, many methods were developed, including PREDITOP, PEOPLE, BEPITOPE, BepiPred, COBEpro, ABCpred, AAP, BCPred, BayesB, BEOracle/BROracle, BEST, LBEEP, DRREP, iBCE-EL, SVMTriP, etc. For the more challenging yet important task of discontinuous epitope prediction, methods were also developed, including CEP, DiscoTope, PEPITO, ElliPro, SEPPA, EPITOPIA, PEASE, EpiPred, SEPIa, EPCES, EPSVR, etc. In this chapter, we will discuss computational methods for B-cell epitope predictions of both linear and discontinuous epitopes. SVMTriP and EPCES/EPCSVR, the most successful among the methods for each type of the predictions, will be used as model methods to detail the standard protocols. For linear epitope prediction, SVMTriP was reported to achieve a sensitivity of 80.1% and a precision of 55.2% with a fivefold cross-validation based on a large dataset, yielding an AUC of 0.702. For discontinuous or conformational B-cell epitope prediction, EPCES and EPCSVR were both benchmarked by a curated independent test dataset in which all antigens had no complex structures with the antibody. The identified epitopes by these methods were later independently validated by various biochemical experiments. For these three model methods, webservers and all datasets are publicly available at http://sysbio.unl.edu/SVMTriP , http://sysbio.unl.edu/EPCES/ , and http://sysbio.unl.edu/EPSVR/ .

High relatedness of bioinformatic data and realistic experimental works on the potentials of Fasciola hepatica and F. gigantica cathepsin L1 as a diagnostic and vaccine antigen

Introduction

Fascioliasis is a parasitic foodborne disease caused by the liver flukes, Fasciola hepatica and F. gigantica. Such parasites cause serious illness in numerous domestic animals and also in humans. Following infection, the parasite secretes a variety of molecules that immediately interact with the host immunity to establish successful infection. These molecules include cathepsin L peptidase 1 (CatL1); the highly investigated diagnostic and vaccine antigens using various animal models. However, a few studies have analyzed the potentials of FhCatL1 as a diagnostic or vaccine antigen using bioinformatic tools and much less for FgCatL1. The present study provides inclusive and exclusive information on the physico-chemical, antigenic and immunogenic properties of F. hepatica cathepsin L1 (FhCatL1) protein using multiple bioinformatic analysis tools and several online web servers. Also, the validation of our employed available online servers was conducted against a huge collection of previously published studies focusing on the properties of FhCatL1as a diagnostic and vaccine antigen.

Methods

For this purpose, the secondary, tertiary, and quaternary structure of FhCatL1 protein were also predicted and analyzed using the SWISS-MODEL server. Validation of the modeled structures was performed by Ramachandran plots. The antigenic epitopes of the protein were predicted by IEDB server.

Results and discussion

Our findings revealed the low similarity of FhCatL1 with mammalian CatL1, lacking signal peptides or transmembrane domain, and the presence of 33 phosphorylation sites. Also, the containment of FhCatL1 for many topological, physico-chemical, immunological properties that favored its function of solubility and interaction with the immune components were reported. In addition, the earlier worldwide reports documented the high efficacy of FhCatL1 as a diagnostic and vaccine antigen in different animals. Altogether, FhCatL1 is considered an excellent candidate for using in commercialized diagnostic assays or vaccine products against fascioliasis in different animal species. Our assessment also included FgCatL1 and reported very similar findings and outputs to those of FhCatL1.

Immunoinformatics Approach to Design Novel Subunit Vaccine against the Epstein-Barr Virus

Epstein-Barr virus (EBV) is a lymphotropic virus responsible for numerous epithelial and lymphoid cell malignancies, including gastric carcinoma, Hodgkin's lymphoma, nasopharyngeal carcinoma, and Burkitt lymphoma. Hundreds of thousands of people worldwide get infected with this virus, and in most cases, this viral infection leads to cancer. Although researchers are trying to develop potential vaccines and drug therapeutics, there is still no effective vaccine to combat this virus. In this study, the immunoinformatics approach was utilized to develop a potential multiepitope subunit vaccine against the two most common subtypes of EBV, targeting three of their virulent envelope glycoproteins. Eleven cytotoxic T lymphocyte (CTL) epitopes, 11 helper T lymphocyte (HTL) epitopes, and 10 B-cell lymphocyte (BCL) epitopes were predicted to be antigenic, nonallergenic, nontoxic, and fully conserved among the two subtypes, and nonhuman homologs were used for constructing the vaccine after much analysis. Later, further validation experiments, including molecular docking with different immune receptors (e.g., Toll-like receptors [TLRs]), molecular dynamics simulation analyses (including root means square deviation [RMSD], root mean square fluctuation [RMSF], radius of gyration [Rg], principal-component analysis [PCA], dynamic cross-correlation [DCC], definition of the secondary structure of proteins [DSSP], and Molecular Mechanics Poisson-Boltzmann Surface Area [MM-PBSA]), and immune simulation analyses generated promising results, ensuring the safe and stable response of the vaccine with specific immune receptors after potential administration within the human body. The vaccine's high binding affinity with TLRs was revealed in the docking study, and a very stable interaction throughout the simulation proved the potential high efficacy of the proposed vaccine. Further, in silico cloning was also conducted to design an efficient mass production strategy for future bulk industrial vaccine production. IMPORTANCE Epstein-Barr virus (EBV) vaccines have been developing for over 30 years, but polyphyletic and therapeutic vaccines have failed to get licensed. Our vaccine surpasses the limitations of many such vaccines and remains very promising, which is crucial because the infection rate is higher than most viral infections, affecting a whopping 90% of the adult population. One of the major identifications covers a holistic analysis of populations worldwide, giving us crucial information about its effectiveness for everyone's unique immunological system. We targeted three glycoproteins that enhance the virulence of the virus to design an epitope-based polyvalent vaccine against two different strains of EBV, type 1 and 2. Our methodology in this study is nonconventional yet swift to show effective results while designing vaccines.

A comparative analysis of amino acid encoding schemes for the prediction of flexible length linear B-cell epitopes

Linear B-cell epitopes have a prominent role in the development of peptide-based vaccines and disease diagnosis. High variability in the length of these epitopes is a major reason for low accuracy in their prediction. Most of the B-cell epitope prediction methods considered fixed length of epitope sequences and achieved good accuracy. Though a number of tools are available for the prediction of flexible length linear B-cell epitopes with reasonable accuracy, further improvement in the prediction performance is still expected. Thus, here we made an attempt to analyze the performance of machine learning approaches (MLA) with 18 different amino acid encoding schemes in the prediction of flexible length linear B-cell epitopes. We considered B-cell epitope sequences of variable lengths (11-56 amino acids) from well-established public resources. The performances of machine learning algorithms with the encoded epitope sequence datasets were evaluated. Besides, the feasible combinations of encoding schemes were also explored and analyzed. The results revealed that amino-acid composition (AC) and distribution component of composition-transition-distribution encoding schemes are suitable for heterogeneous epitope data, whereas amino-acid-anchoring-pair-composition (APC), dipeptide-composition and amino-acids-pair-propensity-scale (APP) are more appropriate for homogeneous data. Further, two combinations of peptide encoding schemes, i.e. APC + AC and APC + APP with random forest classifier were identified to have improved performance over the state-of-the-art tools for flexible length linear B-cell epitope prediction. The study also revealed better performance of random forest over other considered MLAs in the prediction of flexible length linear B-cell epitopes.

BepFAMN: A Method for Linear B-Cell Epitope Predictions Based on Fuzzy-ARTMAP Artificial Neural Network

The public health system is extremely dependent on the use of vaccines to immunize the population from a series of infectious and dangerous diseases, preventing the system from collapsing and millions of people dying every year. However, to develop these vaccines and effectively monitor these diseases, it is necessary to use accurate diagnostic methods capable of identifying highly immunogenic regions within a given pathogenic protein. Existing experimental methods are expensive, time-consuming, and require arduous laboratory work, as they require the screening of a large number of potential candidate epitopes, making the methods extremely laborious, especially for application to larger microorganisms. In the last decades, researchers have developed in silico prediction methods, based on machine learning, to identify these markers, to drastically reduce the list of potential candidate epitopes for experimental tests, and, consequently, to reduce the laborious task associated with their mapping. Despite these efforts, the tools and methods still have low accuracy, slow diagnosis, and offline training. Thus, we develop a method to predict B-cell linear epitopes which are based on a Fuzzy-ARTMAP neural network architecture, called BepFAMN (B Epitope Prediction Fuzzy ARTMAP Artificial Neural Network). This was trained using a linear averaging scheme on 15 properties that include an amino acid ratio scale and a set of 14 physicochemical scales. The database used was obtained from the IEDB website, from which the amino acid sequences with the annotations of their positive and negative epitopes were taken. To train and validate the knowledge models, five-fold cross-validation and competition techniques were used. The BepiPred-2.0 database, an independent database, was used for the tests. In our experiment, the validation dataset reached sensitivity = 91.50%, specificity = 91.49%, accuracy = 91.49%, MCC = 0.83, and an area under the curve (AUC) ROC of approximately 0.9289. The result in the testing dataset achieves a significant improvement, with sensitivity = 81.87%, specificity = 74.75%, accuracy = 78.27%, MCC = 0.56, and AOC = 0.7831. These achieved values demonstrate that BepFAMN outperforms all other linear B-cell epitope prediction tools currently used. In addition, the architecture provides mechanisms for online training, which allow the user to find a new B-cell linear epitope, and to improve the model without need to re-train itself with the whole dataset. This fact contributes to a considerable reduction in the number of potential linear epitopes to be experimentally validated, reducing laboratory time and accelerating the development of diagnostic tests, vaccines, and immunotherapeutic approaches.

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

Background

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

Methods

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

Results

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

Conclusion

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

Linear B-Cell Epitope Prediction for In Silico Vaccine Design: A Performance Review of Methods Available via Command-Line Interface

Linear B-cell epitope prediction research has received a steadily growing interest ever since the first method was developed in 1981. B-cell epitope identification with the help of an accurate prediction method can lead to an overall faster and cheaper vaccine design process, a crucial necessity in the COVID-19 era. Consequently, several B-cell epitope prediction methods have been developed over the past few decades, but without significant success. In this study, we review the current performance and methodology of some of the most widely used linear B-cell epitope predictors which are available via a command-line interface, namely, BcePred, BepiPred, ABCpred, COBEpro, SVMTriP, LBtope, and LBEEP. Additionally, we attempted to remedy performance issues of the individual methods by developing a consensus classifier, which combines the separate predictions of these methods into a single output, accelerating the epitope-based vaccine design. While the method comparison was performed with some necessary caveats and individual methods might perform much better for specialized datasets, we hope that this update in performance can aid researchers towards the choice of a predictor, for the development of biomedical applications such as designed vaccines, diagnostic kits, immunotherapeutics, immunodiagnostic tests, antibody production, and disease diagnosis and therapy.

Microbiota as Drivers and as Therapeutic Targets in Ocular and Tissue Specific Autoimmunity

Autoimmune uveitis is a major cause of blindness in humans. Activation of retina-specific autoreactive T cells by commensal microbiota has been shown to trigger uveitis in mice. Although a culprit microbe and/or its immunogenic antigen remains to be identified, studies from inducible and spontaneous mouse models suggest the potential of microbiota-modulating therapies for treating ocular autoimmune disease. In this review, we summarize recent findings on the contribution of microbiota to T cell-driven, tissue-specific autoimmunity, with an emphasis on autoimmune uveitis, and analyze microbiota-altering interventions, including antibiotics, probiotics, and microbiota-derived metabolites (e.g., short-chain fatty acids), which have been shown to be effective in other autoimmune diseases. We also discuss the need to explore more translational animal models as well as to integrate various datasets (microbiomic, transcriptomic, proteomic, metabolomic, and other cellular measurements) to gain a better understanding of how microbiota can directly or indirectly modulate the immune system and contribute to the onset of disease. It is hoped that deeper understanding of these interactions may lead to more effective treatment interventions.

Peptides-based vaccine against SARS-nCoV-2 antigenic fragmented synthetic epitopes recognized by T cell and β-cell initiation of specific antibodies to fight the infection

The World Health Organization has declared the rapidly spreading coronavirus to be a global pandemic. The FDA is yet to approve a vaccine for human novel coronavirus. Here, we developed a peptide-based vaccine and used high-throughput screening by molecular dynamics simulation to identify T-cell- and β-cell-recognized epitopes for producing specific antibodies against SARS-nCoV-2. We construct ~ 12 P' antigenic epitope peptides to develop a more effective vaccine and identify specific antibodies. These epitope peptides selectively presented the best antigen presentation scores for both human pMHC class I and II alleles to develop a strong binding affinity. All antigens identified of SARS-nCoV-2 different proteins by each attached specific ~ 1-7 L linker adaptor were used to construct a broad single peripheral peptide vaccine. It is expected to be highly antigenic with a minimum allergic effect. As a result of these exciting outcomes, expressing a vaccine using the intimated peptide was highly promising and positive to be highly proposed as epitope-based peptide vaccine of specific antibody against SARS-nCoV-2 by initiating T cells and β-cells. An in vitro study for the proposed peptide-based vaccine is mostly recommended. Further clinical trials are required to check the efficacy of this vaccine.

SVMTriP: A Method to Predict B-Cell Linear Antigenic Epitopes

Identifying protein antigenic epitopes recognizable by antibodies is the key step for new immuno-diagnostic reagent discovery and vaccine design. To facilitate this process and improve its efficiency, computational methods were developed to predict antigenic epitopes. For the linear B-cell epitope prediction, many methods were developed, including BepiPred, ABCPred, AAP, BCPred, BayesB, BEOracle/BROracle, BEST, and SVMTriP. Among these methods, SVMTriP, a frontrunner, utilized Support Vector Machine by combining the tri-peptide similarity and Propensity scores. Applied on non-redundant B-cell linear epitopes extracted from IEDB, SVMTriP achieved a sensitivity of 80.1% and a precision of 55.2% with a five-fold cross-validation. The AUC value was 0.702. The combination of similarity and propensity of tri-peptide subsequences can improve the prediction performance for linear B-cell epitopes. A webserver based on this method was constructed for public use. The server and all datasets used in the corresponding study are available at http://sysbio.unl.edu/SVMTriP . This chapter describes the webserver of SVMTriP.

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.

Narrow-Leafed Lupin Main Allergen β-Conglutin (Lup an 1) Detection and Quantification Assessment in Natural and Processed Foods

The increasing prevalence of lupin allergy as a consequence to the functional characteristics of a growing number of sweet lupin-derived foods consumption makes the imperious necessity to develop analytical tools for the detection of allergen proteins in foodstuffs. The current study developed a new highly specific, sensitive and accurate ELISA method to detect, identify and quantify the lupin main allergen β-conglutin (Lup an 1) protein in natural and processed food. The implementation of accurate standards made with recombinant conglutin β1, and an anti-Lup an 1 antibody made from a synthetic peptide commonly shared among β-conglutin isoforms from sweet lupin species was able to detect up to 8.1250 ± 0.1701 ng (0.0406 ± 0.0009 ppm) of Lup an 1. This identified even lupin traces present in food samples which might elicit allergic reactions in sensitized consumers, such as β-conglutin proteins detection and quantification in processed (roasted, fermented, boiled, cooked, pickled, toasted, pasteurized) food, while avoiding cross-reactivity (false positive) with other legumes as peanut, chickpea, lentils, faba bean, and cereals. This study demonstrated that this new ELISA method constitutes a highly sensitive and reliable molecular tool able to detect, identify and quantify Lup an 1. This contributes to a more efficient management of allergens by the food industry, the regulatory agencies and clinicians, thus helping to keep the health safety of the consumers.

Conserved B and T cell epitopes prediction of ebola virus glycoprotein for vaccine development: An immuno-informatics approach

Ebola virus (EBOV), a non-segmented single-stranded RNA virus, is often-most transmitted through body fluids like sweat, tears, saliva, and nasal secretions. Till date, there is no licensed vaccine of EBOV is available in the market; however, the world is increasingly vulnerable to this emerging threat. Hence, it is the need of time to develop a vaccine for EBOV to hinder its dissemination. The current study has been designed for identification and characterization of the potential B and T-cell epitopes using the Immuno-informatics tools, and it helped in finding the potent vaccine candidates against EBOV. Prediction, antigenicity and allergenicity testing of predicted B and T cells' epitopes was done as well to identify their potential as a vaccine candidate and to measure their safety level respectively. Among B-cell epitopes "WIPAGIGVTGVIIA" showed a high antigenicity score and it would play an important role in evoking the immune response. In T-cell epitopes, peptides "AIGLAWIPY" and "IRGFPRCRY" presented high antigenicity score, which binds to MHC class-I and MHC class-II alleles respectively. All predicted epitopes were analyzed and compared with already reported peptides carefully. Comparatively, Peptides predicted in the present study showed more immunogenicity score than already reported peptides, used as positive control, and are more immunogenic as compared to them. Peptides reported in the present study do not target only Zaire EBOV (ZEBOV), as in previous studies, but also other species, i.e. Tai Forest EBOV (TAFV), Sudan EBOV (SUDV), Bundibugyo EBOV (BDBV), and Reston EBOV (RESTV) and would bring the promising results as potent vaccine candidates.

Advances in In-silico B-cell Epitope Prediction

Identification of B-cell epitopes in target antigens is one of the most crucial steps for epitopebased vaccine development, immunodiagnostic tests, antibody production, and disease diagnosis and therapy. Experimental methods for B-cell epitope mapping are time consuming, costly and labor intensive; in the meantime, various in-silico methods are proposed to predict both linear and conformational B-cell epitopes. The accurate identification of B-cell epitopes presents major challenges for immunoinformaticians. In this paper, we have comprehensively reviewed in-silico methods for B-cell epitope identification. The aim of this review is to stimulate the development of better tools which could improve the identification of B-cell epitopes, and further for the development of therapeutic antibodies and diagnostic tools.

Finding an Optimal Corneal Xenograft Using Comparative Analysis of Corneal Matrix Proteins Across Species

Numerous animal species have been proposed as sources of corneal tissue for obtaining decellularized xenografts. The selection of an appropriate animal model must take into consideration the differences in the composition and structure of corneal proteins between humans and other animal species in order to minimize immune response and improve outcome of the xenotransplant. Here, we compared the amino-acid sequences of 16 proteins present in the corneal stromal matrix of 14 different animal species using Basic Local Alignment Search Tool, and calculated a similarity score compared to the respective human sequence. Primary amino acid structures, isoelectric point and grand average of hydropathy (GRAVY) values of the 7 most abundant proteins (i.e. collagen α-1 (I), α-1 (VI), α-2 (I) and α-3 (VI), as well as decorin, lumican, and keratocan) were also extracted and compared to those of human. The pig had the highest similarity score (91.8%). All species showed a lower proline content compared to human. Isoelectric point of pig (7.1) was the closest to the human. Most species have higher GRAVY values compared to human except horse. Our results suggest that porcine cornea has a higher relative suitability for corneal transplantation into humans compared to other studied species.

Optimized Whole-Mount In Situ Immunolocalization for Arabidopsis thaliana Root Meristems and Lateral Root Primordia

Immunolocalization is a valuable tool for cell biology research that allows to rapidly determine the localization and expression levels of endogenous proteins. In plants, whole-mount in situ immunolocalization remains a challenging method, especially in tissues protected by waxy layers and complex cell wall carbohydrates. Here, we present a robust method for whole-mount in situ immunolocalization in primary root meristems and lateral root primordia in Arabidopsis thaliana. For good epitope preservation, fixation is done in an alkaline paraformaldehyde/glutaraldehyde mixture. This fixative is suitable for detecting a wide range of proteins, including integral transmembrane proteins and proteins peripherally attached to the plasma membrane. From initiation until emergence from the primary root, lateral root primordia are surrounded by several layers of differentiated tissues with a complex cell wall composition that interferes with the efficient penetration of all buffers. Therefore, immunolocalization in early lateral root primordia requires a modified method, including a strong solvent treatment for removal of hydrophobic barriers and a specific cocktail of cell wall-degrading enzymes. The presented method allows for easy, reliable, and high-quality in situ detection of the subcellular localization of endogenous proteins in primary and lateral root meristems without the need of time-consuming crosses or making translational fusions to fluorescent proteins.

DRREP: deep ridge regressed epitope predictor

INTRODUCTION

The ability to predict epitopes plays an enormous role in vaccine development in terms of our ability to zero in on where to do a more thorough in-vivo analysis of the protein in question. Though for the past decade there have been numerous advancements and improvements in epitope prediction, on average the best benchmark prediction accuracies are still only around 60%. New machine learning algorithms have arisen within the domain of deep learning, text mining, and convolutional networks. This paper presents a novel analytically trained and string kernel using deep neural network, which is tailored for continuous epitope prediction, called: Deep Ridge Regressed Epitope Predictor (DRREP).

RESULTS

DRREP was tested on long protein sequences from the following datasets: SARS, Pellequer, HIV, AntiJen, and SEQ194. DRREP was compared to numerous state of the art epitope predictors, including the most recently published predictors called LBtope and DMNLBE. Using area under ROC curve (AUC), DRREP achieved a performance improvement over the best performing predictors on SARS (13.7%), HIV (8.9%), Pellequer (1.5%), and SEQ194 (3.1%), with its performance being matched only on the AntiJen dataset, by the LBtope predictor, where both DRREP and LBtope achieved an AUC of 0.702.

CONCLUSION

DRREP is an analytically trained deep neural network, thus capable of learning in a single step through regression. By combining the features of deep learning, string kernels, and convolutional networks, the system is able to perform residue-by-residue prediction of continues epitopes with higher accuracy than the current state of the art predictors.

Determining Zebrafish Epitope Reactivity to Commercially Available Antibodies

Antibodies raised against mammalian proteins may exhibit cross-reactivity with zebrafish proteins, making these antibodies useful for fish studies. However, zebrafish may express multiple paralogues of similar sequence and size, making them difficult to distinguish by traditional Western blot analysis. To identify the zebrafish proteins that are recognized by an antimammalian antibody, we developed a system to screen putative epitopes by cloning the sequences between the yeast SUMO protein and a C-terminal 6xHis tag. The recombinant fusion protein was expressed in Escherichia coli and analyzed by Western blot to conclusively identify epitopes that exhibit cross-reactivity with the antibodies of interest. This approach can be used to determine the species cross-reactivity and epitope specificity of a wide variety of peptide antigen-derived antibodies.

SEPIa, a knowledge-driven algorithm for predicting conformational B-cell epitopes from the amino acid sequence

BACKGROUND

The identification of immunogenic regions on the surface of antigens, which are able to be recognized by antibodies and to trigger an immune response, is a major challenge for the design of new and effective vaccines. The prediction of such regions through computational immunology techniques is a challenging goal, which will ultimately lead to a drastic limitation of the experimental tests required to validate their efficiency. However, current methods are far from being sufficiently reliable and/or applicable on a large scale.

RESULTS

We developed SEPIa, a B-cell epitope predictor from the protein sequence, which is sufficiently fast to be applicable on a large scale. The originality of SEPIa lies in the combination of two classifiers, a naïve Bayesian and a random forest classifier, through a voting algorithm that exploits the advantages of both. It is based on 13 sequence-based features, whose values in a 9-residue sequence window are compiled to predict the epitope/non-epitope state of the central residue. The features are related to the type of amino acid, its conservation in homologous proteins, and its tendency of being exposed to the solvent, soluble, flexible, and disordered. The highest signal is obtained from statistical amino acid preferences, but all 13 features contribute non-negligibly in the predictor. SEPIa's average prediction accuracy is limited, with an AUC score (area under the receiver operating characteristic curve) that reaches 0.65 both in 10-fold cross-validation and on an independent test set. It is nevertheless slightly higher than that of other methods evaluated on the same test set.

CONCLUSIONS

SEPIa was applied to a test protein whose epitopes are known, human β2 adrenergic G-protein-coupled receptor, with promising results. Although the actual AUC score is rather low, many of the predicted epitopes cluster together and overlap the experimental epitope region. The reasons underlying the limitations of SEPIa and of all other B-cell epitope predictors are discussed.

Improvement influenza HA2 DNA vaccine cellular and humoral immune responses with Mx bio adjuvant

Immunization with DNA vaccines as a novel alternative to conventional vaccination strategy requires adjuvant for improving vaccine efficacy. The conserved immunogenic HA2 subunit, which harbors neutralizing epitopes is a promising vaccine candidate against influenza viruses. In this study, for the first time we explore the idea of using host interferon inducible Mx protein to increase the immunogenicity of HA2 H9N2 influenza DNA vaccine. The potency and safety of the Mx adjuvanted-HA2 vaccine was evaluated in BALB/c mice by different prime-boost strategies. To assess the effect of the vaccination on the virus clearance rate, mice were challenged with homologous influenza virus. Administration of the adjuvanted vaccine and boosting with the same regimen could effectively enhance both humoral and cellular immune responses in treated mice. These data demonstrated that Mx as host defense peptide can be potentiated for improving influenza vaccine efficacy.

Multi-walled carbon nanotubes increase antibody-producing B cells in mice immunized with a tetravalent vaccine candidate for dengue virus

Background

In recent times, studies have demonstrated that carbon nanotubes are good candidates for use as vehicles for transfection of exogenous material into the cells. However, there are few studies evaluating the behavior of carbon nanotubes as DNA vectors and few of these studies have used multi-walled carbon nanotubes (MWCNTs) or carboxylated MWCNTs. Thus, this study aims to assess the MWCNTs’ (carboxylated or not) efficiency in the increase in expression of the tetravalent vaccine candidate (TVC) plasmid vector for dengue virus in vitro using Vero cells, and in vivo, through the intramuscular route, to evaluate the immunological response profile.

Results

Multi-walled carbon nanotubes internalized by Vero cells, have been found in the cytoplasm and nucleus associated with the plasmid. However, it was not efficient to increase the messenger ribonucleic acid (mRNA) compared to the pure vaccine candidate associated with Lipofectamine^® 2000. The in vivo experiments showed that the use of intramuscular injection of the TVC in combination with MWCNTs reduced the immune response compared to pure TVC, in a general way, although an increase was observed in the population of the antibody-producing B cells, as compared to pure TVC.

Conclusions

The results confirm the data found by other authors, which demonstrate the ability of nanotubes to penetrate target cells and reach both the cytoplasm and the cell nucleus. The cytotoxicity values are also in accordance with the literature, which range from 5 to 20 µg/mL. This has been found to be 10 µg/mL in this study. Although the expression levels are higher in cells that receive the pure TVC transfected using Lipofectamine^® 2000, the nanotubes show an increase in B-cells producing antibodies.

Electronic supplementary material

The online version of this article (doi:10.1186/s12951-016-0196-7) contains supplementary material, which is available to authorized users.

Sendai virus intra-host population dynamics and host immunocompetence influence viral virulence during in vivo passage

In vivo serial passage of non-pathogenic viruses has been shown to lead to increased viral virulence, and although the precise mechanism(s) are not clear, it is known that both host and viral factors are associated with increased pathogenicity. Under- or overnutrition leads to a decreased or dysregulated immune response and can increase viral mutant spectrum diversity and virulence. The objective of this study was to identify the role of viral mutant spectra dynamics and host immunocompetence in the development of pathogenicity during in vivo passage. Because the nutritional status of the host has been shown to affect the development of viral virulence, the diet of animal model reflected two extremes of diets which exist in the global population, malnutrition and obesity. Sendai virus was serially passaged in groups of mice with differing nutritional status followed by transmission of the passaged virus to a second host species, guinea pigs. Viral population dynamics were characterized using deep sequence analysis and computational modeling. Histopathology, viral titer and cytokine assays were used to characterize viral virulence. Viral virulence increased with passage and the virulent phenotype persisted upon passage to a second host species. Additionally, nutritional status of mice during passage influenced the phenotype. Sequencing revealed the presence of several non-synonymous changes in the consensus sequence associated with passage, a majority of which occurred in the hemagglutinin-neuraminidase and polymerase genes, as well as the presence of persistent high frequency variants in the viral population. In particular, an N1124D change in the consensus sequences of the polymerase gene was detected by passage 10 in a majority of the animals. In vivo comparison of an 1124D plaque isolate to a clone with 1124N genotype indicated that 1124D was associated with increased virulence.

A computational method for designing diverse linear epitopes including citrullinated peptides with desired binding affinities to intravenous immunoglobulin

BACKGROUND

Understanding the interactions between antibodies and the linear epitopes that they recognize is an important task in the study of immunological diseases. We present a novel computational method for the design of linear epitopes of specified binding affinity to Intravenous Immunoglobulin (IVIg).

RESULTS

We show that the method, called Pythia-design can accurately design peptides with both high-binding affinity and low binding affinity to IVIg. To show this, we experimentally constructed and tested the computationally constructed designs. We further show experimentally that these designed peptides are more accurate that those produced by a recent method for the same task. Pythia-design is based on combining random walks with an ensemble of probabilistic support vector machines (SVM) classifiers, and we show that it produces a diverse set of designed peptides, an important property to develop robust sets of candidates for construction. We show that by combining Pythia-design and the method of (PloS ONE 6(8):23616, 2011), we are able to produce an even more accurate collection of designed peptides. Analysis of the experimental validation of Pythia-design peptides indicates that binding of IVIg is favored by epitopes that contain trypthophan and cysteine.

CONCLUSIONS

Our method, Pythia-design, is able to generate a diverse set of binding and non-binding peptides, and its designs have been experimentally shown to be accurate.

In Silico Analysis of HA2/Mx Chimera Peptide for Developing an Adjuvanted Vaccine to Induce Immune Responses Against Influenza Viruses

PURPOSE

The direct transmission of avian influenza viruses to human and increasing drug resisted strains posing new threats for public health. Therefore, development of efficient vaccines is needed to generate protective and persistent immunity to the viruses.

METHODS

Three motifs of Mx protein sequence in human, mouse and poultry located in interferon induced (GTP ase) domain were candidate as biologic adjuvant for enhancing the immune responses against influenza virus. Chimera proteins composed with the conserved HA2 subunit of influenza virus and the Mx motifs named HA2/Mx were modeled and evaluated by in silico analysis includes bioinformatics algorithms in order to explore biological characteristics of these peptides.

RESULTS

Amongst the predicted models, HA2/Mx1 peptide showed the better results following protein structures prediction, antigenic epitopes determination and model quality evaluation. Comparative homology modeling was performed with Swiss Model and the model was validated using ProSA. Epitope predictions revealed the construct could induce both B and T cell epitopes that expect a high immune response.

CONCLUSION

Taken together, these data indicate that the HA2/Mx1 chimera peptide can be potentiated for developing an adjuvant-fused influenza vaccine capable of stimulating effective immune response.

New salivary anti-haemostatics containing protective epitopes from Ornithodoros moubata ticks: Assessment of their individual and combined vaccine efficacy

Ornithodoros moubata is the main vector of the pathogens causing African swine fever and human relapsing fever in Africa. The development of an efficient vaccine against this tick would facilitate its control and the prevention of the diseases it transmits to a considerable extent. Previous efforts to identify vaccine target candidates led us to the discovery of novel salivary proteins that probably act as anti-haemostatics at the host-tick interface, including a secreted phospholipase A2 (PLA2), a 7DB-like protein (7DB-like), a riboprotein 60S L10 (RP-60S), an apyrase (APY), and a new platelet aggregation inhibitor peptide, designated mougrin (MOU). In this work, the corresponding recombinant proteins were expressed in Escherichia coli and their individual vaccine efficacy was tested in rabbit vaccination trials. All of them, except the less immunogenic RP-60S, induced strong humoral responses that reduced tick feeding and survival, providing vaccine efficacies of 44.2%, 43.2% and 27.2%, 19.9% and 17.3% for PLA2, APY, MOU, RP-60S and 7DB-like, respectively. In the case of the more protective recombinant antigens (PLA2, APY and MOU), the immunodominant protective linear B-cell epitopes were identified and their combined vaccine efficacy was tested in a second vaccine trial using different adjuvants. In comparison with the best efficacy of individual antigens, the multicomponent vaccine increased vaccine efficacy by 13.6%, indicating additive protective effects rather than a synergistic effect. Tick saliva inoculated during natural tick-host contacts had a boosting effect on vaccinated animals, increasing specific antibody levels and protection.

Analysis of Conformational B-Cell Epitopes in the Antibody-Antigen Complex Using the Depth Function and the Convex Hull

The prediction of conformational b-cell epitopes plays an important role in immunoinformatics. Several computational methods are proposed on the basis of discrimination determined by the solvent-accessible surface between epitopes and non-epitopes, but the performance of existing methods is far from satisfying. In this paper, depth functions and the k-th surface convex hull are used to analyze epitopes and exposed non-epitopes. On each layer of the protein, we compute relative solvent accessibility and four different types of depth functions, i.e., Chakravarty depth, DPX, half-sphere exposure and half space depth, to analyze the location of epitopes on different layers of the proteins. We found that conformational b-cell epitopes are rich in charged residues Asp, Glu, Lys, Arg, His; aliphatic residues Gly, Pro; non-charged residues Asn, Gln; and aromatic residue Tyr. Conformational b-cell epitopes are rich in coils. Conservation of epitopes is not significantly lower than that of exposed non-epitopes. The average depths (obtained by four methods) for epitopes are significantly lower than that of non-epitopes on the surface using the Wilcoxon rank sum test. Epitopes are more likely to be located in the outer layer of the convex hull of a protein. On the benchmark dataset, the cumulate 10th convex hull covers 84.6% of exposed residues on the protein surface area, and nearly 95% of epitope sites. These findings may be helpful in building a predictor for epitopes.

EPMLR: sequence-based linear B-cell epitope prediction method using multiple linear regression

Background

B-cell epitopes have been studied extensively due to their immunological applications, such as peptide-based vaccine development, antibody production, and disease diagnosis and therapy. Despite several decades of research, the accurate prediction of linear B-cell epitopes has remained a challenging task.

Results

In this work, based on the antigen’s primary sequence information, a novel linear B-cell epitope prediction model was developed using the multiple linear regression (MLR). A 10-fold cross-validation test on a large non-redundant dataset was performed to evaluate the performance of our model. To alleviate the problem caused by the noise of negative dataset, 300 experiments utilizing 300 sub-datasets were performed. We achieved overall sensitivity of 81.8%, precision of 64.1% and area under the receiver operating characteristic curve (AUC) of 0.728.

Conclusions

We have presented a reliable method for the identification of linear B cell epitope using antigen’s primary sequence information. Moreover, a web server EPMLR has been developed for linear B-cell epitope prediction: http://www.bioinfo.tsinghua.edu.cn/epitope/EPMLR/.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-014-0414-y) contains supplementary material, which is available to authorized users.

Prediction of B-Cell Epitopes in Listeriolysin O, a Cholesterol Dependent Cytolysin Secreted by Listeria monocytogenes

Listeria monocytogenes is a gram-positive, foodborne bacterium responsible for disease in humans and animals. Listeriolysin O (LLO) is a required virulence factor for the pathogenic effects of L. monocytogenes. Bioinformatics revealed conserved putative epitopes of LLO that could be used to develop monoclonal antibodies against LLO. Continuous and discontinuous epitopes were located by using four different B-cell prediction algorithms. Three-dimensional molecular models were generated to more precisely characterize the predicted antigenicity of LLO. Domain 4 was predicted to contain five of eleven continuous epitopes. A large portion of domain 4 was also predicted to comprise discontinuous immunogenic epitopes. Domain 4 of LLO may serve as an immunogen for eliciting monoclonal antibodies that can be used to study the pathogenesis of L. monocytogenes as well as develop an inexpensive assay.

Computational prediction of B cell epitopes from antigen sequences

Computational identification of B-cell epitopes from antigen chains is a difficult and actively pursued research topic. Efforts towards the development of method for the prediction of linear epitopes span over the last three decades, while only recently several predictors of conformational epitopes were released. We review a comprehensive set of 13 recent approaches that predict linear and 4 methods that predict conformational B-cell epitopes from the antigen sequences. We introduce several databases of B-cell epitopes, since the availability of the corresponding data is at the heart of the development and validation of computational predictors. We also offer practical insights concerning the use and availability of these B-cell epitope predictors, and motivate and discuss feature research in this area.

SVMTriP: a method to predict antigenic epitopes using support vector machine to integrate tri-peptide similarity and propensity

Identifying protein surface regions preferentially recognizable by antibodies (antigenic epitopes) is at the heart of new immuno-diagnostic reagent discovery and vaccine design, and computational methods for antigenic epitope prediction provide crucial means to serve this purpose. Many linear B-cell epitope prediction methods were developed, such as BepiPred, ABCPred, AAP, BCPred, BayesB, BEOracle/BROracle, and BEST, towards this goal. However, effective immunological research demands more robust performance of the prediction method than what the current algorithms could provide. In this work, a new method to predict linear antigenic epitopes is developed; Support Vector Machine has been utilized by combining the Tri-peptide similarity and Propensity scores (SVMTriP). Applied to non-redundant B-cell linear epitopes extracted from IEDB, SVMTriP achieves a sensitivity of 80.1% and a precision of 55.2% with a five-fold cross-validation. The AUC value is 0.702. The combination of similarity and propensity of tri-peptide subsequences can improve the prediction performance for linear B-cell epitopes. Moreover, SVMTriP is capable of recognizing viral peptides from a human protein sequence background. A web server based on our method is constructed for public use. The server and all datasets used in the current study are available at http://sysbio.unl.edu/SVMTriP.

Immunogenicity to biologics: mechanisms, prediction and reduction

Currently, there is a significant rise in the development and clinical use of a unique class of pharmaceuticals termed as Biopharmaceuticals or Biologics, in the management of a range of disease conditions with, remarkable therapeutic benefits. However, there is an equally growing concern regarding development of adverse effects like immunogenicity in the form of anti-drug antibodies (ADA) production and hypersensitivity. Immunogenicity to biologics represents a significant hurdle in the continuing therapy of patients in a number of disease settings. Efforts focussed on the identification of factors that contribute towards the onset of immunogenic response to biologics have led to reductions in the incidence of immunogenicity. An in-depth understanding of the cellular and molecular mechanism underpinning immunogenic responses will likely improve the safety profile of biologics. This review addresses the mechanistic basis of ADA generation to biologics, with emphasis on the role of antigen processing and presentation in this process. The article also addresses the potential contribution of complement system in augmenting or modulating this response. Identifying specific factors that influences processing and presentation of biologic-derived antigens in different genotype and disease background may offer additional options for intervention in the immunogenic process and consequently, the management of immunogenicity to biologics.

Virtual interactomics of proteins from biochemical standpoint

Virtual interactomics represents a rapidly developing scientific area on the boundary line of bioinformatics and interactomics. Protein-related virtual interactomics then comprises instrumental tools for prediction, simulation, and networking of the majority of interactions important for structural and individual reproduction, differentiation, recognition, signaling, regulation, and metabolic pathways of cells and organisms. Here, we describe the main areas of virtual protein interactomics, that is, structurally based comparative analysis and prediction of functionally important interacting sites, mimotope-assisted and combined epitope prediction, molecular (protein) docking studies, and investigation of protein interaction networks. Detailed information about some interesting methodological approaches and online accessible programs or databases is displayed in our tables. Considerable part of the text deals with the searches for common conserved or functionally convergent protein regions and subgraphs of conserved interaction networks, new outstanding trends and clinically interesting results. In agreement with the presented data and relationships, virtual interactomic tools improve our scientific knowledge, help us to formulate working hypotheses, and they frequently also mediate variously important in silico simulations.

B-Pred, a structure based B-cell epitopes prediction server

The ability to predict immunogenic regions in selected proteins by in-silico methods has broad implications, such as allowing a quick selection of potential reagents to be used as diagnostics, vaccines, immunotherapeutics, or research tools in several branches of biological and biotechnological research. However, the prediction of antibody target sites in proteins using computational methodologies has proven to be a highly challenging task, which is likely due to the somewhat elusive nature of B-cell epitopes. This paper proposes a web-based platform for scoring potential immunological reagents based on the structures or 3D models of the proteins of interest. The method scores a protein's peptides set, which is derived from a sliding window, based on the average solvent exposure, with a filter on the average local model quality for each peptide. The platform was validated on a custom-assembled database of 1336 experimentally determined epitopes from 106 proteins for which a reliable 3D model could be obtained through standard modeling techniques. Despite showing poor sensitivity, this method can achieve a specificity of 0.70 and a positive predictive value of 0.29 by combining these two simple parameters. These values are slightly higher than those obtained with other established sequence-based or structure-based methods that have been evaluated using the same epitopes dataset. This method is implemented in a web server called B-Pred, which is accessible at http://immuno.bio.uniroma2.it/bpred. The server contains a number of original features that allow users to perform personalized reagent searches by manipulating the sliding window's width and sliding step, changing the exposure and model quality thresholds, and running sequential queries with different parameters. The B-Pred server should assist experimentalists in the rational selection of epitope antigens for a wide range of applications.

Production and Purification of Mycolyl Transferase B of Mycobacterium tuberculosis

BACKGROUND

Antigen 85 complex of Mycobacterium tuberculosis includes three immunogenic proteins which are TB vaccine candidates of great importance. As they are very hard to be achieved in natural form, recombinant production of them fuels immunological experiments. Production of such apolar mycobacterial proteins located in the cell wall faces substantial challenges mainly regarding their solubility. This study reports the production of soluble recombinant Ag85B with an efficient yield.

MATERIALS AND METHODS

Ag85B gene was cloned in pJET1.2 and subsequently in pET32a (+). Both recombinant plasmids were sequenced. Expression of the recombinant protein was induced with 1mM IPTG. Recombinant Ag85B was purified through dissolving inclusions in 8M urea buffer, absorbing to Ni-NTA resins, washing by buffers with decreasing urea concentrations and finally eluted in imidazole. Western blot analysis was performed using anti-6His tag antibody, rabbit anti- M. tuberculosis polyclonal antibody and serum of hospitalized TB patients.

RESULTS

Ag85B gene was successfully cloned in both plasmid vectors. The recombinant Ag85B was expressed in E. coli host and purified with significant yield.

CONCLUSION

Western blot results along with those of sequencing ensured accurate production of recombinant Ag85B and retaining of its antigenic structure.