Allergenicity prediction of novel and modified proteins: Not a mission impossible! Development of a Random Forest allergenicity prediction model

Westerhout, Joost; Krone, Tanja; Snippe, Almar; Babé, Lilia M.; McClain, Scott; Ladics, Gregory S.; Houben, Geert F.; Verhoeckx, Kitty

doi:10.1016/j.yrtph.2019.104422

Cited by 35 publications

(18 citation statements)

References 29 publications

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“…Allergenicity prediction is a critical step in therapeutics and bio-pharmaceuticals due to their involvement in foods and/or food products. Sequence identity of known allergens is important for predicting the cross-reactive potential of novel proteins and their resistance to pepsin digestion and glycosylation status is used to evaluate denovo allergenicity potential 96 . AllerTOP v2.0 is a bioinformatics tool and web-based server used for protein allergenicity prediction in this study.…”

Section: Methodsmentioning

confidence: 99%

Immunoinformatics design of a novel epitope-based vaccine candidate against dengue virus

Fadaka

Sibuyi

Martin

et al. 2021

Sci Rep

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Dengue poses a global health threat, which will persist without therapeutic intervention. Immunity induced by exposure to one serotype does not confer long-term protection against secondary infection with other serotypes and is potentially capable of enhancing this infection. Although vaccination is believed to induce durable and protective responses against all the dengue virus (DENV) serotypes in order to reduce the burden posed by this virus, the development of a safe and efficacious vaccine remains a challenge. Immunoinformatics and computational vaccinology have been utilized in studies of infectious diseases to provide insight into the host–pathogen interactions thus justifying their use in vaccine development. Since vaccination is the best bet to reduce the burden posed by DENV, this study is aimed at developing a multi-epitope based vaccines for dengue control. Combined approaches of reverse vaccinology and immunoinformatics were utilized to design multi-epitope based vaccine from the sequence of DENV. Specifically, BCPreds and IEDB servers were used to predict the B-cell and T-cell epitopes, respectively. Molecular docking was carried out using Schrödinger, PATCHDOCK and FIREDOCK. Codon optimization and in silico cloning were done using JCAT and SnapGene respectively. Finally, the efficiency and stability of the designed vaccines were assessed by an in silico immune simulation and molecular dynamic simulation, respectively. The predicted epitopes were prioritized using in-house criteria. Four candidate vaccines (DV-1–4) were designed using suitable adjuvant and linkers in addition to the shortlisted epitopes. The binding interactions of these vaccines against the receptors TLR-2, TLR-4, MHC-1 and MHC-2 show that these candidate vaccines perfectly fit into the binding domains of the receptors. In addition, DV-1 has a better binding energies of − 60.07, − 63.40, − 69.89 kcal/mol against MHC-1, TLR-2, and TLR-4, with respect to the other vaccines. All the designed vaccines were highly antigenic, soluble, non-allergenic, non-toxic, flexible, and topologically assessable. The immune simulation analysis showed that DV-1 may elicit specific immune response against dengue virus. Moreover, codon optimization and in silico cloning validated the expressions of all the designed vaccines in E. coli. Finally, the molecular dynamic study shows that DV-1 is stable with minimum RMSF against TLR4. Immunoinformatics tools are now applied to screen genomes of interest for possible vaccine target. The designed vaccine candidates may be further experimentally investigated as potential vaccines capable of providing definitive preventive measure against dengue virus infection.

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Section: Methodsmentioning

confidence: 99%

Immunoinformatics design of a novel epitope-based vaccine candidate against dengue virus

Fadaka

Sibuyi

Martin

et al. 2021

Sci Rep

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“…resistance to enzymatic digestion) treatments on the allergenic properties of a novel food. (Mazzucchelli et al, 2018;Verhoeckx et al, 2016;Westerhout et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Edible insects and food safety: allergy

Ribeiro

Sousa‐Pinto

Fonseca

et al. 2021

JIFF

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Edible insects are a unique food source, requiring extensive allergenic risk assessment before its safe introduction in the food market. In a recent systematic review, crustacean allergic subjects were identified as a risk group due to cross-reactivity mainly mediated by tropomyosin and arginine kinase. Immunologic co-sensitisation to house dust mites (HDM) was also demonstrated, but its clinical significance and molecular mechanisms were unclear. Furthermore, case reports of food allergy to insects were also analysed but lack of contextual information hindered the analysis. The main goal of this review is to provide an update of new information regarding food allergy caused by insects, covering relevant topics considering the guidelines for allergic risk assessment in novel foods. Newly published studies have further confirmed the role of tropomyosin as a cross-reactive allergen between edible insects and crustaceans, although there are some questions regarding the immunoglobulin E (IgE)-reactivity of this allergen in mealworm species. Furthermore, only specific treatments (enzymatic hydrolysis combined with thermal treatments) were able to eliminate IgE-reactivity of edible insects. Primary sensitisation (e.g. to Tenebrio molitor) has also been shown to be an important pathway for the development of food allergies, with responsible allergens being dependent on the route of sensitisation. However, more studies are necessary to better understand the potential of primary sensitisation causing cross-reactivity with other insect species, crustaceans or HDM. The clinical significance and molecular mechanisms involved in cross-reactivity between edible insects and HDM are still unclear, and a major focus should be given to better understand which allergens cause co-sensitisations between HDM and edible insects and what is the risk of HDM-only allergic subjects consuming edible insects. Contextual information about the reported cases of allergic reactions to insects have further demonstrated that insect-rearing workers and subjects with allergic diseases (in particular, food allergy to crustaceans) are the major risk groups.

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“…They achieved the accuracy of 91.19% via the support-vector machines (SVM) algorithm, which was a significant success. In addition, very recently, Westerhout et al [55] published a paper on predicting the allergenicity of novel and modified proteins using random forest algorithm based on protein physicochemical and biochemical properties. Their results showed robust model performance with an accuracy of ≥85%.…”

Section: Discussionmentioning

confidence: 99%

A Comparative Analysis of Allergen Proteins between Plants and Animals Using Several Computational Tools and Chou’s PseAAC Concept

Behbahani¹,

Rabiei²,

Mohabatkar³

2020

Int Arch Allergy Immunol

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Background: A large number of allergens are derived from plant and animal proteins. A major challenge for researchers is to study the possible allergenic properties of proteins. The aim of this study was in silico analysis and comparison of several physiochemical and structural features of plant- and animal-derived allergen proteins, as well as classifying these proteins based on Chou’s pseudo-amino acid composition (PseAAC) concept combined with bioinformatics algorithms. Methods: The physiochemical properties and secondary structure of plant and animal allergens were studied. The classification of the sequences was done using the PseAAC concept incorporated with the deep learning algorithm. Conserved motifs of plant and animal proteins were discovered using the MEME tool. B-cell and T-cell epitopes of the proteins were predicted in conserved motifs. Allergenicity and amino acid composition of epitopes were also analyzed via bioinformatics servers. Results: In comparison of physiochemical features of animal and plant allergens, extinction coefficient was different significantly. Secondary structure prediction showed more random coiled structure in plant allergen proteins compared with animal proteins. Classification of proteins based on PseAAC achieved 88.24% accuracy. The amino acid composition study of predicted B- and T-cell epitopes revealed more aliphatic index in plant-derived epitopes. Conclusions: The results indicated that bioinformatics-based studies could be useful in comparing plant and animal allergens.

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Allergenicity prediction of novel and modified proteins: Not a mission impossible! Development of a Random Forest allergenicity prediction model

Cited by 35 publications

References 29 publications

Immunoinformatics design of a novel epitope-based vaccine candidate against dengue virus

Immunoinformatics design of a novel epitope-based vaccine candidate against dengue virus

Edible insects and food safety: allergy

A Comparative Analysis of Allergen Proteins between Plants and Animals Using Several Computational Tools and Chou’s PseAAC Concept

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