Allermatch, a webtool for the prediction of potential allergenicity according to current FAO/WHO Codex alimentarius guidelines

Discovery, Expression, and In Silico Safety Evaluation of Honey Truffle Sweetener, a Sweet Protein Derived from Mattirolomyces terfezioides and Produced by Heterologous Expression in Komagataella phaffii

Honey truffle sweetener (HTS), a 121 amino acid protein is identified as a high-intensity sweetener found naturally occurring in the Hungarian Sweet Truffle Mattirolomyces terfezioides, an edible mushroom used in regional diets. The protein is intensely sweet, but the truffle is difficult to cultivate; therefore, the protein was systematically characterized, and the gene coding for the protein was expressed in a commonly used host yeast Komagataella phaffii. The heterologously expressed protein maintained the structural characteristics and sweet taste of the truffle. Preliminary safety evaluations for use as a food ingredient were performed on the protein including digestibility and in silico approaches for predicting the allergenicity and toxicity of the protein. HTS is predicted to be nonallergenic, nontoxic, and readily digestible. This protein is readily produced by precision fermentation of the host yeast, making it a potential replacement for both added sugars and small molecule high-intensity sweeteners in food.

SEP-AlgPro: An efficient allergen prediction tool utilizing traditional machine learning and deep learning techniques with protein language model features

Allergy is a hypersensitive condition in which individuals develop objective symptoms when exposed to harmless substances at a dose that would cause no harm to a "normal" person. Most current computational methods for allergen identification rely on homology or conventional machine learning using limited set of feature descriptors or validation on specific datasets, making them inefficient and inaccurate. Here, we propose SEP-AlgPro for the accurate identification of allergen protein from sequence information. We analyzed 10 conventional protein-based features and 14 different features derived from protein language models to gauge their effectiveness in differentiating allergens from non-allergens using 15 different classifiers. However, the final optimized model employs top 10 feature descriptors with top seven machine learning classifiers. Results show that the features derived from protein language models exhibit superior discriminative capabilities compared to traditional feature sets. This enabled us to select the most discriminatory baseline models, whose predicted outputs were aggregated and used as input to a deep neural network for the final allergen prediction. Extensive case studies showed that SEP-AlgPro outperforms state-of-the-art predictors in accurately identifying allergens. A user-friendly web server was developed and made freely available at https://balalab-skku.org/SEP-AlgPro/, making it a powerful tool for identifying potential allergens.

Comprehensive amino acid composition analysis of seed storage proteins of cereals and legumes: identification and understanding of intrinsically disordered and allergenic peptides

The seed storage proteins of cereal and legumes are the primary source of amino acids which are required for sustaining the nitrogen and carbon demands during germination and growth. Humans derive most of their dietary proteins from storage proteins in form of a wide variety of foods, for consumption. The amino acid content of most of these proteins is biased and the need for this biasness is not understood. The high abundance of proline, glutamine, and cysteine in cereals makes the gluten fraction viscoelastic. The cereal proteins have less charge and legume proteins have more charge on them. Their non-polar amino acid distribution has large variations. These characteristics are strongly responsible for the partial and complete unfolding of several domains of the storage proteins. Many of the storage proteins share a highly conserved structural feature within the cupin superfamily spread across all kingdoms of life. The intrinsically disordered viscoelastic proteins help in making dough which is vital for the quality of bread. Unfolded regions harbor more immunogenic sequences and cause food-related allergies and intolerance. We have discussed these properties in terms of comparison of cereal and legume storage protein sequences and allergy. Our study supports the findings that large disordered regions contain allergen-representative peptides. Interestingly, a high number of allergen-representative peptides were cleavable by digestive enzymes. Furthermore, unfolded storage proteins mimic microbial immunogens to induce a memory immune response. Results findings can be used to guide the understanding of immunological characteristics of storage proteins and may assist in treatment decisions for food allergy.Communicated by Ramaswamy H. Sarma.

A bioinformatic analysis: Previous allergen exposure may support anti- SARS-CoV-2 immune response

COVID-19, caused by infection with the SARS-CoV-2 has become a global health problem due to significant mortality rates; the exact pathophysiological mechanism remains uncertain. Articles reporting patient data are quite heterogeneous and have several limitations. Surviving patients develop a CD4 and CD8 T-cell response to the virus SARS-CoV-2 during COVID-19. Interestingly, pre-existing virus-reactive T-cells have been found in patients that were not infected before, suggesting some form of cross-reactivity or immunological mimicry. To better understand this phenomenon, we performed a bioinformatic study, which was aimed to identify antigenic structures that may explain the presence of such "reactive" T-cells, which may support or modulate the immune response to SARS-CoV-2 infections. Seven different common environmental allergen epitopes identical to the SARS-CoV-2 S-protein were identified that share affinity to 8 MHCI-specific epitope regions. Pollen showed the greatest similarity with the S protein epitope. In the epitope similarity analysis between the S protein and MHC-II / T helper epitopes, the highest similarity was determined for mites. When S-protein that stimulates B cells and identical epitope antigens are examined, the most common allergens were hornbeam and wheat. The high epitope similarity observed for the allergens examined and S protein epitopes suggest that these allergens may be a reason for pre-existing SARS-CoV-2 - reactive T-cells in previously non-infected subjects and such a previous exposure may affect the course of the disease in COVID-19 infection. It remains to be determined whether such a previous existence of SARS-CoV-2 reactive cells can support the clearance of the virus or if they, in contrast, may even aggravate the disease course. (Table 4, Ref 54).

Utilizing proteomics to identify and optimize microalgae strains for high-quality dietary protein: a review

Algae-derived protein has immense potential to provide high-quality protein foods for the expanding human population. To meet its potential, a broad range of scientific tools are required to identify optimal algal strains from the hundreds of thousands available and identify ideal growing conditions for strains that produce high-quality protein with functional benefits. A research pipeline that includes proteomics can provide a deeper interpretation of microalgal composition and biochemistry in the pursuit of these goals. To date, proteomic investigations have largely focused on pathways that involve lipid production in selected microalgae species. Herein, we report the current state of microalgal proteome measurement and discuss promising approaches for the development of protein-containing food products derived from algae.

The updated Structural Database of Allergenic Proteins (SDAP 2.0) provides 3D models for allergens and incorporated bioinformatics tools

Background

Allergenic proteins can cause IgE-mediated adverse reactions in sensitized individuals. Although the sequences of many allergenic proteins have been identified, bioinformatics data analysis with advanced computational methods and modeling is needed to identify the basis for IgE binding and cross-reactivity.

Objective

We aim to present the features and use of the updated Structural Database of Allergenic Proteins 2.0 (SDAP 2.0) webserver, a unique, publicly available resource to compare allergens using specially designed computational tools and new high-quality 3-D models for most known allergens.

Methods

Previously developed and novel software tools for identifying cross-reactive allergens using sequence and structure similarity are implemented in SDAP 2.0. A comprehensive set of high-quality 3-D models of most allergens was generated with the state-of-the-art AlphaFold 2 software. A graphics tool enables the interactive visualization of IgE epitopes on experimentally determined and modeled 3-D structures.

Results

A user can search for allergens similar to a given input sequence with the FASTA algorithm or the window-based World Health Organization/International Union of Immunological Societies (WHO/IUIS) guidelines on safety concerns of novel food products. Peptides similar to known IgE epitopes can be identified with the property distance tool and conformational epitopes by the Cross-React method. The updated database contains 1657 manually curated sequences including all allergens from the IUIS database, 334 experimentally determined X-ray or NMR structures, and 1565 3-D models. Each allergen/isoallergen is classified according to its protein family.

Conclusions

SDAP provides access to the steadily increasing information on allergenic structures and epitopes with integrated bioinformatics tools to identify and analyze their similarities. In addition to serving the research and regulatory community, it provides clinicians with tools to identify potential coallergies in a sensitive patient and can help companies to design hypoallergenic foods and immunotherapies.

Computational prediction of allergenic proteins based on multi-feature fusion

Allergy is an autoimmune disorder described as an undesirable response of the immune system to typically innocuous substance in the environment. Studies have shown that the ability of proteins to trigger allergic reactions in susceptible individuals can be evaluated by bioinformatics tools. However, developing computational methods to accurately identify new allergenic proteins remains a vital challenge. This work aims to propose a machine learning model based on multi-feature fusion for predicting allergenic proteins efficiently. Firstly, we prepared a benchmark dataset of allergenic and non-allergenic protein sequences and pretested on it with a machine-learning platform. Then, three preferable feature extraction methods, including amino acid composition (AAC), dipeptide composition (DPC) and composition of k-spaced amino acid pairs (CKSAAP) were chosen to extract protein sequence features. Subsequently, these features were fused and optimized by Pearson correlation coefficient (PCC) and principal component analysis (PCA). Finally, the most representative features were picked out to build the optimal predictor based on random forest (RF) algorithm. Performance evaluation results via 5-fold cross-validation showed that the final model, called iAller (https://github.com/laihongyan/iAller), could precisely distinguish allergenic proteins from non-allergenic proteins. The prediction accuracy and AUC value for validation dataset achieved 91.4% and 0.97%, respectively. This model will provide guide for users to identify more allergenic proteins.

NetAllergen, a random forest model integrating MHC-II presentation propensity for improved allergenicity prediction

Motivation

Allergy is a pathological immune reaction towards innocuous protein antigens. Although only a narrow fraction of plant or animal proteins induce allergy, atopic disorders affect millions of children and adults and cost billions in healthcare systems worldwide. In silico predictors can aid in the development of more innocuous food sources. Previous allergenicity predictors used sequence similarity, common structural domains, and amino acid physicochemical features. However, these predictors strongly rely on sequence similarity to known allergens and fail to predict protein allergenicity accurately when similarity diminishes.

Results

To overcome these limitations, we collected allergens from AllergenOnline, a curated database of IgE-inducing allergens, carefully removed allergen redundancy with a novel protein partitioning pipeline, and developed a new allergen prediction method, introducing MHC presentation propensity as a novel feature. NetAllergen outperformed a sequence similarity-based BLAST baseline approach, and previous allergenicity predictor AlgPred 2 when similarity to known allergens is limited.

Availability and implementation

The web service NetAllergen and the datasets are available at https://services.healthtech.dtu.dk/services/NetAllergen-1.0/.

Immunoinformatics for Novel Multi-Epitope Vaccine Development in Canine Parvovirus Infections

Canine parvovirus (CPV-2) is one of the most important pathogens of dogs of all ages, causing pandemic infections that are characterized by fatal hemorrhagic enteritis. The CPV-2 vaccine is recommended as a core vaccine for pet animals. Despite the intensive practice of active immunization, CPV-2 remains a global threat. In this study, a multi-epitope vaccine against CPV-2 was designed, targeting the highly conserved capsid protein (VP2) via in silico approaches. Several immunoinformatics methods, such as epitope screening, molecular docking, and simulation were used to design a potential vaccine construct. The partial protein sequences of the VP2 gene of CPV-2 and protein sequences retrieved from the NCBI were screened to predict highly antigenic proteins through antigenicity, trans-membrane-topology screening, an allergenicity assessment, and a toxicity analysis. Homologous VP2 protein sequences typically linked to the disease were identified using NCBI BLAST, in which four conserved regions were preferred. Overall, 10 epitopes, DPIGGKTGI, KEFDTDLKP, GTDPDDVQ, GGTNFGYIG, GTFYFDCKP, NRALGLPP, SGTPTN, LGLPPFLNSL, IGGKTG, and VPPVYPN, were selected from the conserved regions to design the vaccine construct. The molecular docking demonstrated the higher binding affinity of these epitopes with dog leukocyte antigen (DLA) molecules. The selected epitopes were linked with Salmonella enterica flagellin FliC adjuvants, along with the PADRE sequence, by GGS linkers to construct a vaccine candidate with 272 nucleotides. The codon adaptation and in silico cloning showed that the generated vaccine can be expressed by the E. coli strain, K12, and the sequence of the vaccine construct showed no similarities with dog protein. Our results suggest that the vaccine construct might be useful in preventing canine parvoviral enteritis (CPE) in dogs. Further in vitro and in vivo experiments are needed for the validation of the vaccine candidate.

In silico tools to assess the potential allergenicity of shiitake mushrooms (Lentinula edodes)

BACKGROUND

Computational tools may have an edge over conventional methods for the preliminary evaluation of food allergenicity. In this study, the allergenic potential of Lentinula edodes was evaluated and validated using in silico tools.

RESULTS

The potential cross-reactivity of mushroom proteins with fungal allergens was determined using sequence alignment - the Fast Alignment (FASTA) and Basic Local Alignment Search Tool (BLAST) algorithm. Eight L. edodes proteins were cross-reactive with allergens from fungal origin, showing 52%-89% sequence identity using FASTA algorithm-based alignment. The BLAST data were corroborated by percentage identity and query coverage. Physico-chemical property-based allergenicity was deciphered by AlgPred, Allermatch, and AllergenFP software, which predicted six out of eight proteins as potential allergens. Sequence alignment showed 66%-86% conservancy between mushroom protein and known fungal allergens. Secondary structure and amino acid composition supported structural affinity between query and fungal proteins. Three-dimensional structures of five mushroom proteins were generated, quality assessed, and superimposed with fungal allergens, suggesting possible allergenicity of mushroom proteins. An enzyme-linked immunosorbent assay (ELISA) demonstrated immunoglobulin E (IgE) binding in 13 out of 21 food-hypersensitive patients' sera.

CONCLUSION

In silico tools provide preliminary indications about the potential allergenicity and cross-reactivity of mushroom proteins. This approach may be used for the prelusive allergenicity assessment of allergen sources. © 2022 Society of Chemical Industry.

Safety evaluation of oubli fruit sweet protein (brazzein) derived from Komagataella phaffii, intended for use as a sweetener in food and beverages

Naturally sweet proteins have no glycemic effect and offer a fundamentally new approach to sweetness and health for individuals seeking to reduce their added sugar intake. However, unlike many commercial sweeteners, little research has been performed on the potential safety implications of adding these uniquely sweet proteins to food and beverages. In this study, a naturally sweet protein found in the West African Oubli plant ( Pentadiplandra brazzeana), referred to as Oubli fruit sweet protein or brazzein, was expressed in Komagataella phaffii (formerly Pichia pastoris) and produced via precision fermentation, and a safety and risk assessment was undertaken for its use as a sweetener in food and beverages. Potential consumption levels of brazzein were estimated to be 3 mg/kg body weight/day based on the National Health and Nutrition Examination Survey. The safety of brazzein derived from K. phaffii was evaluated through in silico allergenicity, in vitro genotoxicity (reverse mutation and mammalian micronucleus assays), and a 90-day dietary oral toxicity study in rats. There was no indication of allergenicity in the in silico analyses. Brazzein was non-genotoxic in the in vitro assays and showed no adverse effects in the 90-day oral toxicity study up to the highest dose tested, where the no-observed-adverse-effect level (NOAEL) was 978 and 985 mg/kg body weight/day in males and females, respectively. The totality of evidence in the in silico allergenicity, in vitro genotoxicity, and 90-day dietary toxicity studies demonstrates that brazzein derived from K. phaffii is considered safe for use as a sweetener in food and beverages.

Insight into the first multi-epitope-based peptide subunit vaccine against avian influenza A virus (H5N6): An immunoinformatics approach

The rampant spread of highly pathogenic avian influenza A (H5N6) virus has drawn additional concerns along with ongoing Covid-19 pandemic. Due to its migration-related diffusion, the situation is deteriorating. Without an existing effective therapy and vaccines, it will be baffling to take control measures. In this regard, we propose a revers vaccinology approach for prediction and design of a multi-epitope peptide based vaccine. The induction of humoral and cell-mediated immunity seems to be the paramount concern for a peptide vaccine candidate; thus, antigenic B and T cell epitopes were screened from the surface, membrane and envelope proteins of the avian influenza A (H5N6) virus, and passed through several immunological filters to determine the best possible one. Following that, the selected antigenic with immunogenic epitopes and adjuvant were linked to finalize the multi-epitope-based peptide vaccine by appropriate linkers. For the prediction of an effective binding, molecular docking was carried out between the vaccine and immunological receptors (TLR8). Strong binding affinity and good docking scores clarified the stringency of the vaccines. Furthermore, molecular dynamics simulation was performed within the highest binding affinity complex to observe the stability, and minimize the designed vaccine's high mobility region to order to increase its stability. Then, Codon optimization and other physicochemical properties were performed to reveal that the vaccine would be suitable for a higher expression at cloning level and satisfactory thermostability condition. In conclusion, predicting the overall in silico assessment, we anticipated that our designed vaccine would be a plausible prevention against avian influenza A (H5N6) virus.

Proteome Exploration of Legionella pneumophila To Identify Novel Therapeutics: a Hierarchical Subtractive Genomics and Reverse Vaccinology Approach

Legionella pneumophila is a human pathogen distributed worldwide, causing Legionnaires’ disease (LD), a severe form of pneumonia and respiratory tract infection. L. pneumophila is emerging as an antibiotic-resistant strain, and controlling LD is now difficult. Hence, developing novel drugs and vaccines against L. pneumophila is a major research priority.

Identification of Allergens in White- and Red-Fleshed Pitaya (Selenicereus undatus and Selenicereus costaricensis) Seeds Using Bottom-Up Proteomics Coupled with Immunoinformatics

White-fleshed pitaya (Selenicereus undatus) and red-fleshed pitaya (Selenicereus costaricensis) are becoming increasingly popular because of their nutritional and medicinal benefits. However, in addition to their beneficial properties, allergy to pitaya fruits has occurred in daily life. In this study, we investigated the protein profile of pitaya fruit seeds and focused on the most reactive proteins against immunoglobulin E (IgE) in sera from allergic patients by immunoblotting. A protein band of approximately 20 kDa displayed a clear reaction with the serum IgE. The protein bands of interest were excised, in-gel digested, and analyzed using liquid chromatography–tandem mass spectrometry (LC–MS/MS), followed by data searching against a restricted database (Caryophyllales in UniProtKB) for protein identification. Immunoinformatic tools were used to predict protein allergenicity. The potential allergens included cupin_1 and heat shock protein 70 (HSP70) in white-fleshed pitaya seeds, and cupin_1, heat shock protein 70, and heat shock protein sti1-like in red-fleshed pitaya seeds are potential allergens. The expression of potential allergens was further verified at the transcriptional level in the species of S. undatus and S. costaricensis.

Peptide-Based Vaccines for Tuberculosis

Tuberculosis (TB) is an infectious disease caused by Mycobacterium tuberculosis. As a result of the coronavirus disease 2019 (COVID-19) pandemic, the global TB mortality rate in 2020 is rising, making TB prevention and control more challenging. Vaccination has been considered the best approach to reduce the TB burden. Unfortunately, BCG, the only TB vaccine currently approved for use, offers some protection against childhood TB but is less effective in adults. Therefore, it is urgent to develop new TB vaccines that are more effective than BCG. Accumulating data indicated that peptides or epitopes play essential roles in bridging innate and adaptive immunity and triggering adaptive immunity. Furthermore, innovations in bioinformatics, immunoinformatics, synthetic technologies, new materials, and transgenic animal models have put wings on the research of peptide-based vaccines for TB. Hence, this review seeks to give an overview of current tools that can be used to design a peptide-based vaccine, the research status of peptide-based vaccines for TB, protein-based bacterial vaccine delivery systems, and animal models for the peptide-based vaccines. These explorations will provide approaches and strategies for developing safer and more effective peptide-based vaccines and contribute to achieving the WHO's End TB Strategy.

Immunological Cross-Reactivity Involving Mollusc Species and Mite-Mollusc and Cross-Reactive Allergen PM Are Risk Factors of Mollusc Allergy

Marine molluscs are seafood consumed worldwide and could cause food allergies, while investigation on their sensitizing components and cross-reactivity seems to be rare. Furthermore, allergy to mites may result in anaphylaxis in mollusc-allergic individuals owing to their cross-reactivity. The aim of the study was to identify cross-reactive allergens and investigate the cross-reactivity between different mollusc groups and mite-mollusc. The extracted mollusc and dust mite proteins were separated by SDS-PAGE, and IgE-binding components were recognized by immunoblotting with sera from patients sensitized to mollusc and mite. Cross-reactivity of different mollusc groups and mite-mollusc was assessed using ELISA and inhibition ELISA. The results of the immune detection, ELISA, and inhibition ELISA indicated that different mollusc groups and mite-mollusc showed varying degrees of cross-reactivity. The most frequently recognized cross-reactive protein was paramyosin from different mollusc groups and dust mite, while cross-reactive allergen paramyosin in the mite extract was identified and evaluated by MS and Allermatch, respectively. Inhibition ELISA studies also revealed that paramyosin played an important role in molluscan and mite-molluscan cross-reactivity. These findings contribute to a better understanding of the cross-reactivity involving mollusc species and mite-mollusc, which can be used to assist in the diagnosis and treatment of mite- and mollusc-allergic disorders.

Isolation and proteomic characterization of tropomyosin extracted from edible insect protein

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The pan-allergen, tropomyosin, was extracted and isolated from edible crickets.

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Effect of heat and protease treatments on tropomyosin was evaluated.

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Proteomics show tropomyosin was lower in protease treated crickets.

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Bioinformatics predicted 31 potential tropomyosin epitope regions.

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Heat and proteolysis can effectively decrease cricket immunoreactivity.

Edible insects are considered promising sustainable protein sources. Thermal treatments and proteolysis are commonly used to improve their safety and quality. However, their allergenicity remains mostly unexplored. Tropomyosin, a major insect pan-allergen, can be used to study processing effects on its immunoreactivity. In this study, selective precipitation was used to extract tropomyosin from heated and protease-treated crickets. Immunoinformatics predicted 31 epitope regions, while proteomic analysis suggested decreased amounts of intact epitope regions in microwave-heated/protease-treated crickets. Tropomyosin peptide sequences were identified in higher abundance in convection-heated samples. Finally, tropomyosin immunoreactivity by immunoblotting and ELISA, revealed that protease treatments under microwave heating had lower (p < 0.05) IgE and IgG reactivity. Based on results, processing insects using proteolysis and microwave-heating could be effective for generating hypoallergenic cricket protein ingredients. The use of proteomics and bioinformatics proved to be useful tools in understanding the impact of processing on allergenic reactivity of insect proteins.

Thermal processing of insect allergens and IgE cross-recognition in Italian patients allergic to shrimp, house dust mite and mealworm

Edible insects are considered as a promising and sustainable alternative protein source for humans, although risk assessments, with particular reference to the allergic potential of insect proteins, are required. Considering that insects are likely to be consumed after processing, it is crucial to assess how processing can influence allergenicity. In our study, we investigated how boiling and frying affect the IgE cross-recognition of proteins from five edible insects (mealworm, buffalo worm, silkworm, cricket and grasshopper). We considered three groups of Italian patients allergic to shrimps and to house dust mites, who had never consumed insects before and two subjects with occupational allergy and food sensitization to mealworm. Our data suggest that thermal processing may change the solubility of proteins, thereby resulting in a protein shift from water-soluble fractions to water-insoluble fractions. Immunoblot and LC-MS/MS analyses have shown that tropomyosin may play an important role as a cross-allergen for house dust mite and shrimp allergic patients, while larval cuticle protein seems to play a major role in the cross-reactivity of patients primarily sensitized to mealworm. On the basis of our results, the effects of processing appear to be protein-, species- and treatment-specific. Therefore, house dust mite, shrimp and mealworm allergic patients should consume insects with caution, even after thermal processing.

Multi-epitope based subunit vaccine construction against Banna virus targeting on two outer proteins (VP4 and VP9): A computational approach

Recently, RNA viruses have gained a mammoth concern for causing various outbreaks, and due to pandemics, they are acquiring additional attention throughout the world. An emerging RNA as well as vector-borne Banna Virus (BAV) is a human pathogen resulting in encephalitis, fever, headache, muscle aches, and severe coma. Besides human, pathogenic BAV was also detected from pigs, cattle, ticks, midges, and mosquitoes in Indonesia, China, and Vietnam. Due to high mutation tendency and dearth of a species barrier, this virus will consider as a significant threat in the near future throughout the planet, particularly in Africa. Despite of severe human case fatalities in several countries, there are no specific therapeutics, available vaccines, and other preventive measures against BAV. Thus, to find out the effective therapeutics and preventive strategies are crying exigency. In the present study, a unique multi-epitope-based peptide vaccine candidate is constructed using bioinformatics' tools that efficiently instigate immune cells for generating BAV antibodies. The potential vaccine candidates were developed using both T and B -cell epitopes. UniprotKB database was used to retrieve of two outer proteins (VP9 and VP4), and homologous sequences of BAV taxid: 7763, 649,604, 77,763, and 8453 were searched by NCBI BLAST. These serotypes are the most closely associated with the disease. Then combining the best-selected epitopes in various combinations with different adjuvants, three distinct vaccine candidates were formed. The validity tests were performed for the screened vaccine candidate regarding stability, allergenicity, and antigenicity parameters. Moreover, molecular dynamic simulations of the selected vaccine with TLR-8 immune receptor confirmed the stability of the binding pose and showed a significant response to immune cells. Thus, the results established that the designed chimeric peptide vaccine could enhance the immune response against BAV.

The COMPARE Database: A Public Resource for Allergen Identification, Adapted for Continuous Improvement

Motivation: The availability of databases identifying allergenic proteins via a transparent and consensus-based scientific approach is of prime importance to support the safety review of genetically-modified foods and feeds, and public safety in general. Over recent years, screening for potential new allergens sequences has become more complex due to the exponential increase of genomic sequence information. To address these challenges, an international collaborative scientific group coordinated by the Health and Environmental Sciences Institute (HESI), was tasked to develop a contemporary, adaptable, high-throughput process to build the COMprehensive Protein Allergen REsource (COMPARE) database, a publicly accessible allergen sequence data resource along with bioinformatics analytical tools following guidelines of FAO/WHO and CODEX Alimentarius Commission.

Results: The COMPARE process is novel in that it involves the identification of candidate sequences via automated keyword-based sorting algorithm and manual curation of the annotated sequence entries retrieved from public protein sequence databases on a yearly basis; its process is meant for continuous improvement, with updates being transparently documented with each version; as a complementary approach, a yearly key-word based search of literature databases is added to identify new allergen sequences that were not (yet) submitted to protein databases; in addition, comments from the independent peer-review panel are posted on the website to increase transparency of decision making; finally, sequence comparison capabilities associated with the COMPARE database was developed to evaluate the potential allergenicity of proteins, based on internationally recognized guidelines, FAO/WHO and CODEX Alimentarius Commission

Insect Protein-Based Diet as Potential Risk of Allergy in Dogs

Before insects can be used widely as an alternative source of dietary protein, their allerginicity should be investigated. Therefore, the aim of our study was to assess the potential adverse reactions of the immune system of dogs against Tenebrio molitor proteins. Dogs sensitised to storage mites T. putrescentiae and A. siro were included. Clinically healthy and clinically allergic dogs were compared. Proteins were extracted from mealworm larvae and their digestibility determined by in vitro incubation with digestive proteases. Mealworm protein extracts and digests were analysed by SDS-PAGE. Canine sera tested for the presence of mite-specific IgEs were used for subsequent Western blotting. LC-MS/MS analysis was used to identify mealworm proteins and their allergenic potential was predicted with the AllermatchTM tool. The binding of canine sera IgEs to mealworm proteins was confirmed; however, the differences between the two groups of dogs were not significant. Moreover, no clear correlation was found between sensitisation to storage mites and clinical status of the dogs. Altogether, 17 different proteins were identified, including tropomyosin, α-amylase, and Tm-E1a cuticular protein that are known cross-reacting IgE-binding allergens. Our results suggest that dogs allergic to mites may clinically express also the cross-reactivity with mealworm proteins.

New applications of advanced instrumental techniques for the characterization of food allergenic proteins

Current approaches based on electrophoretic, chromatographic or immunochemical principles have allowed characterizing multiple allergens, mapping their epitopes, studying their mechanisms of action, developing detection and diagnostic methods and therapeutic strategies for the food and pharmaceutical industry. However, some of the common structural features related to the allergenic potential of food proteins remain unknown, or the pathological mechanism of food allergy is not yet fully understood. In addition, it is also necessary to evaluate new allergens from novel protein sources that may pose a new risk for consumers. Technological development has allowed the expansion of advanced technologies for which their whole potential has not been entirely exploited and could provide novel contributions to still unexplored molecular traits underlying both the structure of food allergens and the mechanisms through which they sensitize or elicit adverse responses in human subjects, as well as improving analytical techniques for their detection. This review presents cutting-edge instrumental techniques recently applied when studying structural and functional aspects of proteins, mechanism of action and interaction between biomolecules. We also exemplify their role in the food allergy research and discuss their new possible applications in several areas of the food allergy field.

Combination of highly antigenic nucleoproteins to inaugurate a cross-reactive next generation vaccine candidate against Arenaviridae family

Arenaviral infections often result lethal hemorrhagic fevers, affecting primarily in African and South American regions. To date, there is no FDA-approved licensed vaccine against arenaviruses and treatments have been limited to supportive therapy. Hence, the study was employed to design a highly immunogenic cross-reactive vaccine against Arenaviridae family using reverse vaccinology approach. The whole proteome of Lassa virus (LASV), Lymphocytic Choriomeningitis virus (LCMV), Lujo virus and Guanarito virus were retrieved and assessed to determine the most antigenic viral proteins. Both T-cell and B-cell epitopes were predicted and screened based on transmembrane topology, antigenicity, allergenicity, toxicity and molecular docking analysis. The final constructs were designed using different adjuvants, top epitopes, PADRE sequence and respective linkers and were assessed for the efficacy, safety, stability and molecular cloning purposes. The proposed epitopes were highly conserved (84%–100%) and showed greater cumulative population coverage. Moreover, T cell epitope GWPYIGSRS was conserved in Junin virus (Argentine mammarenavirus) and Sabia virus (Brazilian mammarenavirus), while B cell epitope NLLYKICLSG was conserved in Machupo virus (Bolivian mammarenavirus) and Sabia virus, indicating the possibility of final vaccine construct to confer a broad range immunity in the host. Docking analysis of the refined vaccine with different MHC molecules and human immune receptors were biologically significant. The vaccine-receptor (V1-TLR3) complex showed minimal deformability at molecular level and was compatible for cloning into pET28a(+) vector of E. coli strain K12. The study could be helpful in developing vaccine to combat arenaviral infections in the future. However, further in vitro and in vivo trials using model animals are highly recommended for the experimental validation of our findings.

D-Allulose 3-epimerase of Bacillus sp. origin manifests profuse heat-stability and noteworthy potential of D-fructose epimerization

BACKGROUND

D-Allulose is an ultra-low calorie sugar of multifarious health benefits, including anti-diabetic and anti-obesity potential. D-Allulose 3-epimerase family enzymes catalyze biosynthesis of D-allulose via epimerization of D-fructose.

RESULTS

A novel D-allulose 3-epimerase (DaeB) was cloned from a plant probiotic strain, Bacillus sp. KCTC 13219, and expressed in Bacillus subtilis cells. The purified protein exhibited substantial epimerization activity in a broad pH spectrum, 6.0-11.0. DaeB was able to catalyze D-fructose to D-allulose bioconversion at the temperature range of 35 °C to 70 °C, exhibiting at least 50 % activity. It displaced excessive heat stability, with the half-life of 25 days at 50 °C, and high turnover number (kcat 367 s- 1). The coupling of DaeB treatment and yeast fermentation of 700 g L- 1 D-fructose solution yielded approximately 200 g L- 1 D-allulose, and 214 g L- 1 ethanol.

CONCLUSIONS

The novel D-allulose 3-epimerase of Bacillus sp. origin discerned a high magnitude of heat stability along with exorbitant epimerization ability. This biocatalyst has enormous potential for the large-scale production of D-allulose.

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.

Computational approach for the identification of putative allergens from Cucurbitaceae family members

Certain substances referred to as allergens, induce hypersensitivity (allergic reactions) which normally are considered to be innocuous, are small in size and incite IgE response. This study was focused to predict the putative allergens from other Cucurbitaceae family members using computational approach by analyzing the already reported allergens of the same family. The four reported allergens Cuc m 1, Cuc m 2, Cuc m 3 and Citr I 2 of Cucurbitaceae family were obtained from International Union of Immunological Societies, in which three were from Cucumis melo (Muskmelon) and one from Citrullus lanatus (Watermelon) respectively. BlastP analysis reported 44 similar sequences to these allergens from other members of Cucurbitaceae family namely Cucurbita moschata, Cucurbita pepo and Cucurbita maxima. The allergenicity of these sequences was predicted using AlgPred tool in which it revealed 26 protein sequences as putative allergens. These selected sequences were further analyzed for their physicochemical properties using ProtParam tool in which 13 sequences were found to satisfy the required parameters, and therefore further analyzed by AllerMatch™ and AllergenOnline tools to check the Codex Alimentarius rules for allergens. Finally, 13 sequences that were selected were structurally analyzed for similarity using PROMALS3D tool and phylogenetic relationship was established with the reported allergens using MEGA-X software. It was concluded that 13 sequences from Cucurbitaceae family belonging to different species of Pumpkin showed potential allergenicity based on the computational analysis that possibly can play a role in allergies and cross reactivity.

Comprehensive genome based analysis of Vibrio parahaemolyticus for identifying novel drug and vaccine molecules: Subtractive proteomics and vaccinomics approach

Multidrug-resistant Vibrio parahaemolyticus has become a significant public health concern. The development of effective drugs and vaccines against Vibrio parahaemolyticus is the current research priority. Thus, we aimed to find out effective drug and vaccine targets using a comprehensive genome-based analysis. A total of 4822 proteins were screened from V. parahaemolyticus proteome. Among 16 novel cytoplasmic proteins, 'VIBPA Type II secretion system protein L' and 'VIBPA Putative fimbrial protein Z' were subjected to molecular docking with 350 human metabolites, which revealed that Eliglustat, Simvastatin and Hydroxocobalamin were the top drug molecules considering free binding energy. On the contrary, 'Sensor histidine protein kinase UhpB' and 'Flagellar hook-associated protein of 25 novel membrane proteins were subjected to T-cell and B-cell epitope prediction, antigenicity testing, transmembrane topology screening, allergenicity and toxicity assessment, population coverage analysis and molecular docking analysis to generate the most immunogenic epitopes. Three subunit vaccines were constructed by the combination of highly antigenic epitopes along with suitable adjuvant, PADRE sequence and linkers. The designed vaccine constructs (V1, V2, V3) were analyzed by their physiochemical properties and molecular docking with MHC molecules- results suggested that the V1 is superior. Besides, the binding affinity of human TLR-1/2 heterodimer and construct V1 could be biologically significant in the development of the vaccine repertoire. The vaccine-receptor complex exhibited deformability at a minimum level that also strengthened our prediction. The optimized codons of the designed construct was cloned into pET28a(+) vector of E. coli strain K12. However, the predicted drug molecules and vaccine constructs could be further studied using model animals to combat V. parahaemolyticus associated infections.

An overview of in silico vaccine design against different pathogens and cancer

INTRODUCTION

Due to overcome the hardness of the vaccine design, computational vaccinology is emerging widely. Prediction of T cell and B cell epitopes, antigen processing analysis, antigenicity analysis, population coverage, conservancy analysis, allergenicity assessment, toxicity prediction, and protein-peptide docking are important steps in the process of designing and developing potent vaccines against various viruses and cancers. In order to perform all of the analyses, several bioinformatics tools and online web servers have been developed. Scientists must take the decision to apply more suitable and precise servers for each part based on their accuracy.

AREAS COVERED

In this review, a wide-range list of different bioinformatics tools and online web servers has been provided. Moreover, some studies were proposed to show the importance of various bioinformatics tools for predicting and developing efficient vaccines against different pathogens including viruses, bacteria, parasites, and fungi as well as cancer.

EXPERT OPINION

Immunoinformatics is the best way to find potential vaccine candidates against different pathogens. Thus, the selection of the most accurate tools is necessary to predict and develop potent preventive and therapeutic vaccines. To further evaluation of the computational and in silico vaccine design, in vitro/in vivo analyses are required to develop vaccine candidates.

A Novel d-Allulose 3-Epimerase Gene from the Metagenome of a Thermal Aquatic Habitat and d-Allulose Production by Bacillus subtilis Whole-Cell Catalysis

A novel d-allulose 3-epimerase gene (daeM) has been identified from the metagenomic resource of a hot-water reservoir. The enzyme epimerizes d-fructose into d-allulose, a functional sugar of rare abundance in nature. The metagenomic DNA fragment was cloned and expressed in Escherichia coli The purified recombinant protein (DaeM) was found to be metal dependent (Co2+ or Mn2+). It displayed the maximal levels of catalytic activity in a pH range of 6 to 11 and a temperature range of 75°C to 80°C. The enzyme exhibited remarkably high thermal stability at 60°C and 70°C, with half-life values of 9,900 and 3,240 min, respectively. To the best of our knowledge, this is the highest thermal stability demonstrated by a d-allulose 3-epimerase that has been characterized to date. The enzymatic treatment of 700 mg·ml-1 d-fructose yielded about 217 mg·ml-1 d-allulose, under optimal condition. The catalytic product was purified, and its nuclear magnetic resonance (NMR) spectra were found to be indistinguishable from those of standard d-allulose. For biomolecule production, the whole-cell catalysis procedure avoids the tedious process of extraction and purification of enzyme and also offers better biocatalyst stability. Further, it is desirable to employ safe-grade microorganisms for the biosynthesis of a product. The daeM gene was expressed intracellularly in Bacillus subtilis A whole-cell catalysis reaction performed with a reaction volume of 1 liter at 60°C yielded approximately 196 g·liter-1 d-allulose from 700 g·liter-1 d-fructose. Further, the whole recombinant cells were able to biosynthesize d-allulose in apple juice, mixed fruit juice, and honey.IMPORTANCE d-Allulose is a noncaloric sugar substitute with antidiabetes and antiobesity potential. With several characteristics of physiological significance, d-allulose has wide-ranging applications in the food and pharmacology industries. The development of a thermostable biocatalyst is an objective of mainstream research aimed at achieving industrial acceptability of the enzyme. Aquatic habitats of extreme temperatures are considered a potential metagenomic resource of heat-tolerant biocatalysts of industrial importance. The present study explored the thermal-spring metagenome of the Tattapani geothermal region, Chhattisgarh, India, discovering a novel d-allulose 3-epimerase gene, daeM, encoding an enzyme of high-level heat stability. The daeM gene was expressed in the microbial cells of a nonpathogenic and safe-grade species, B. subtilis, which was found to be capable of performing d-fructose to d-allulose interconversion via a whole-cell catalysis reaction. The results indicate that DaeM is a potential biocatalyst for commercial production of the rare sugar d-allulose. The study established that extreme environmental niches represent a genomic resource of functional sugar-related biocatalysts.

Food allergomics based on high-throughput and bioinformatics technologies

Food allergy is a serious food safety problem worldwide, and the investigation of food allergens is the foundation of preventing and treating them, but relevant knowledge is far from sufficient. With the advent of the "big data era", it has been possible to investigate food allergens by high-throughput methods, proposing the concept of allergomics. Allergomics is the discipline studying the repertoire of allergens, which has relatively higher throughput and is faster and more sensitive than conventional methods. This review introduces the basis of allergomics and summarizes its major strategies and applications. Particularly, strategies based on immunoblotting, phage display, allergen microarray, and bioinformatics are reviewed in detail, and the advantages and limitations of each strategy are discussed. Finally, further development of allergomics is predicted. This provides basic theories and recent advances in food allergomics research, which could be insightful for both food allergy research and practical applications.

Immunoinformatics design of a novel multi-epitope peptide vaccine to combat multi-drug resistant infections caused by Vibrio vulnificus

Multi-drug resistant Vibrio vulnificus is a Gram-negative bacillus responsible for diseases, such as: sepsis, septicemia, gastroenteritis, and fatal necrotizing fasciitis in humans. The treatment and prevention of V. vulnificus infections are challenging because of resistance to antibiotics and the non-availability of a licensed vaccine. Considering this, an in-silico based approach comprising subtractive proteomics, immunoinformatics, molecular docking, and dynamics simulation studies is applied herein to identify potential epitope vaccine candidates for the mentioned pathogen. Two potential vaccine candidates: vibC and flgL are filtered based on essentiality, outer membrane localization, virulence, antigenic, pathway mapping, and cellular protein-protein network analysis. Using immunoinformatic tools, 9-mer B-cell derived T-cell antigenic epitopes are predicted for the said shortlisted two proteins that are demonstrating excellent affinity for predominant HLA allele (DRB1*0101) in human population. Screened peptides are used further in multi-epitope peptide designing and linked to an adjuvant to enhance the immunogenic properties of the designed construct. Furthermore, the construct was docked blindly to TLR4 immune receptor, and analyzed in conformational dynamics simulation to decipher the complex affinity and understand time dependent behavior, respectively. We expect this designed in silico construct to be useful for vaccinologists to evaluate its immune protective efficacy in in vivo animal models.

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.

Contriving a chimeric polyvalent vaccine to prevent infections caused by herpes simplex virus (type-1 and type-2): an exploratory immunoinformatic approach

Herpes simplex virus type 1 (HSV-1) and 2 (HSV-2) cause a variety of infections including oral-facial infections, genital herpes, herpes keratitis, cutaneous infection and so on. To date, FDA-approved licensed HSV vaccine is not available yet. Hence, the study was conducted to identify and characterize an effective epitope based polyvalent vaccine against both types of Herpes Simplex Virus. The selected proteins were retrieved from ViralZone and assessed to design highly antigenic epitopes by binding analyses of the peptides with MHC class-I and class-II molecules, antigenicity screening, transmembrane topology screening, allergenicity and toxicity assessment, population coverage analysis and molecular docking approach. The final vaccine was constructed by the combination of top CTL, HTL and BCL epitopes from each protein along with suitable adjuvant and linkers. Physicochemical and secondary structure analysis, disulfide engineering, molecular dynamic simulation and codon adaptation were further employed to develop a unique multi-epitope peptide vaccine. Docking analysis of the refined vaccine structure with different MHC molecules and human immune TLR-2 receptor demonstrated higher interaction. Complexed structure of the modeled vaccine and TLR-2 showed minimal deformability at molecular level. Moreover, translational potency and microbial expression of the modeled vaccine was analyzed with pET28a(+) vector for E. coli strain K12 and the vaccine constructs had no similarity with entire human proteome. The study enabled design of a novel chimeric polyvalent vaccine to confer broad range immunity against both HSV serotypes. However, further wet lab based research using model animals are highly recommended to experimentally validate our findings.Communicated by Ramaswamy H. Sarma.

Immunoinformatics approaches for designing a novel multi epitope peptide vaccine against human norovirus (Norwalk virus)

Norovirus is known as a major cause of several acute gastroenteritis (AGE) outbreaks each year. A study was conducted to develop a unique multi epitope subunit vaccine against human norovirus by adopting reverse vaccinology approach. The entire viral proteome of Norwalk virus was retrieved and allowed for further in silico study to predict highly antigenic epitopes through antigenicity, transmembrane topology screening, allergenicity assessment, toxicity analysis, population coverage analysis and molecular docking approach. Capsid protein VP1 and protein VP2 were identified as most antigenic viral proteins which generated a plethora of antigenic epitopes. Physicochemical properties and secondary structure of the designed vaccine were assessed to ensure its thermostability, hydrophilicity, theoretical PI and structural behavior. Molecular docking analysis of the refined vaccine with different MHCs and human immune TLR8 receptor demonstrated higher binding interaction as well. Complexed structure of the modeled vaccine and TLR8 showed minimal deformability at molecular level. The designed construct was reverse transcribed and adapted for E. coli strain K12 prior to insertion within pET28a(+) vector for its heterologous cloning and expression, and sequence of vaccine constructs showed no similarity with human proteins. However, the study could initiate in vitro and in vivo studies regarding effective vaccine development against human norovirus.

Reverse vaccinology approach to design a novel multi-epitope subunit vaccine against avian influenza A (H7N9) virus

H7N9, a novel strain of avian origin influenza was the first recorded incidence where a human was transited by a N9 type influenza virus. Effective vaccination against influenza A (H7N9) is a major concern, since it has emerged as a life threatening viral pathogen. Here, an in silico reverse vaccinology strategy was adopted to design a unique chimeric subunit vaccine against avian influenza A (H7N9). Induction of humoral and cell-mediated immunity is the prime concerned characteristics for a peptide vaccine candidate, hence both T cell and B cell immunity of viral proteins were screened. Antigenicity testing, transmembrane topology screening, allergenicity and toxicity assessment, population coverage analysis and molecular docking approach were adopted to generate the most antigenic epitopes of avian influenza A (H7N9) proteome. Further, a novel subunit vaccine was designed by the combination of highly immunogenic epitopes along with suitable adjuvant and linkers. Physicochemical properties and secondary structure of the designed vaccine were assessed to ensure its thermostability, h ydrophilicity, theoretical PI and structural behavior. Homology modeling, refinement and validation of the designed vaccine allowed to construct a three dimensional structure of the predicted vaccine, further employed to molecular docking analysis with different MHC molecules and human immune TLR8 receptor present on lymphocyte cells. Moreover, disulfide engineering was employed to lessen the high mobility region of the designed vaccine in order to extend its stability. Furthermore, we investigated the molecular dynamic simulation of the modeled subunit vaccine and TLR8 complexed molecule to strengthen our prediction. Finally, the suggested vaccine was reverse transcribed and adapted for E. coli strain K12 prior to insertion within pET28a(+) vector for checking translational potency and microbial expression.

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

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

Genetically engineered bananas resistant to Xanthomonas wilt disease and nematodes

Banana is an important staple food crop feeding more than 100 million Africans, but is subject to severe productivity constraints due to a range of pests and diseases. Banana Xanthomonas wilt caused by Xanthomonas campestris pv. musacearum is capable of entirely destroying a plantation while nematodes can cause losses up to 50% and increase susceptibility to other pests and diseases. Development of improved varieties of banana is fundamental in order to tackle these challenges. However, the sterile nature of the crop and the lack of resistance in Musa germplasm make improvement by traditional breeding techniques either impossible or extremely slow. Recent developments using genetic engineering have begun to address these problems. Transgenic banana expressing sweet pepper Hrap and Pflp genes have demonstrated complete resistance against X. campestris pv. musacearum in the field. Transgenic plantains expressing a cysteine proteinase inhibitors and/or synthetic peptide showed enhanced resistance to a mixed species population of nematodes in the field. Here, we review the genetic engineering technologies which have potential to improve agriculture and food security in Africa.

Immunoinformatics as a Tool for New Antifungal Vaccines

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

In silico allergenicity prediction of several lipid transfer proteins

Non-specific lipid transfer proteins (nsLTPs) are common allergens and they are particularly widespread within the plant kingdom. They have a highly conserved three-dimensional structure that generate a strong cross-reactivity among the members of this family. In the last years several web tools for the prediction of allergenicity of new molecules based on their homology with known allergens have been released, and guidelines to assess potential allergenicity of proteins through bioinformatics have been established. Even if such tools are only partially reliable yet, they can provide important indications when other kinds of molecular characterization are lacking. The potential allergenicity of 28 amino acid sequences of LTPs homologs, either retrieved from the UniProt database or in silico deduced from the corresponding EST coding sequence, was predicted using 7 publicly available web tools. Moreover, their similarity degree to their closest known LTP allergens was calculated, in order to evaluate their potential cross-reactivity. Finally, all sequences were studied for their identity degree with the peach allergen Pru p 3, considering the regions involved in the formation of its known conformational IgE-binding epitope. Most of the analyzed sequences displayed a high probability to be allergenic according to all the software employed. The analyzed LTPs from bell pepper, cassava, mango, mungbean and soybean showed high homology (>70%) with some known allergenic LTPs, suggesting a potential risk of cross-reactivity for sensitized individuals. Other LTPs, like for example those from canola, cassava, mango, mungbean, papaya or persimmon, displayed a high degree of identity with Pru p 3 within the consensus sequence responsible for the formation, at three-dimensional level, of its major conformational epitope. Since recent studies highlighted how in patients mono-sensitized to peach LTP the levels of IgE seem directly proportional to the chance of developing cross-reactivity to LTPs from non-Rosaceae foods, and these chances increase the more similar the protein is to Pru p 3, these proteins should be taken into special account for future studies aimed at evaluating the risk of cross-allergenicity in highly sensitized individuals.

Influence of processing and in vitro digestion on the allergic cross-reactivity of three mealworm species

Edible insects are currently being evaluated as an alternative and more sustainable protein source for humans. The introduction of new food sources can lead to development of novel allergies. Because in the Western world, insects are unlikely to be consumed raw, it is important to know how processing and in vitro digestion might influence their allergenicity. Three edible mealworm species (Tenebrio molitor, Zophobas atratus and Alphitobius diaperinus) subjected to processing and in vitro digestion were analysed for IgE cross-reactivity. Immunoblot and MALDI-MS/MS analyses revealed that IgE from crustaceans or House dust mite (HDM) allergic patients showed cross-reactivity to mealworm tropomyosin or α-amylase, hexamerin 1B precursor and muscle myosin, respectively. Heat processing as well as in vitro digestion did diminish, but not eliminate, HDM or tropomyosin IgE cross-reactivity. Results show that individuals allergic to HDM or crustaceans might be at risk when consuming mealworms, even after heat processing.

Allergenic characterization of a novel allergen, homologous to chymotrypsin, from german cockroach

Purpose

Cockroach feces are known to be rich in IgE-reactive components. Various protease allergens were identified by proteomic analysis of German cockroach fecal extract in a previous study. In this study, we characterized a novel allergen, a chymotrypsin-like serine protease.

Methods

A cDNA sequence homologous to chymotrypsin was obtained by analysis of German cockroach expressed sequence tag (EST) clones. The recombinant chymotrypsins from the German cockroach and house dust mite (Der f 6) were expressed in Escherichia coli using the pEXP5NT/TOPO vector system, and their allergenicity was investigated by ELISA.

Results

The deduced amino acid sequence of German cockroach chymotrypsin showed 32.7 to 43.1% identity with mite group 3 (trypsin) and group 6 (chymotrypsin) allergens. Sera from 8 of 28 German cockroach allergy subjects (28.6%) showed IgE binding to the recombinant protein. IgE binding to the recombinant cockroach chymotrypsin was inhibited by house dust mite chymotrypsin Der f 6, while it minimally inhibited the German cockroach whole body extract.

Conclusions

A novel allergen homologous to chymotrypsin was identified from the German cockroach and was cross-reactive with Der f 6.

Computational vaccinology and epitope vaccine design by immunoinformatics

Human immune system includes variety of different cells and molecules correlating with other body systems. These instances complicate the analysis of the system; particularly in postgenomic era by introducing more amount of data, the complexity is increased and necessity of using computational approaches to process and interpret them is more tangible.Immunoinformatics as a subset of bioinformatics is a new approach with variety of tools and databases that facilitate analysis of enormous amount of immunologic data obtained from experimental researches. In addition to directing the insight regarding experiment selections, it helps new thesis design which was not feasible with conventional methods due to the complexity of data. Considering this features immunoinformatics appears to be one of the fields that accelerate the immunological research progression.In this study we discuss advances in genomics and vaccine design and their relevance to the development of effective vaccines furthermore several division of this field and available tools in each item are introduced.

Integrative immunoinformatics for Mycobacterial diseases in R platform

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