Immunoinformatics and Similarity Analysis of House Dust Mite Tropomyosin - Ranjbar, Mohammad Mehdi; Nabian, Sedigheh; Ahmadi, Nayeb Ali; Ghorban, Khodayar; Sazmand, Alireza; Dadmanesh, Maryam; Hekmatimoghaddam, Seyed Hossein (Novelty in Biomedicine, 2015)
Background: Dermatophagoides farinae and Dermatophagoides pteronyssinus are house dust mites (HDM) that they cause severe asthma and allergic symptoms. Tropomyosin protein plays an important role in mentioned immune and allergic reactions to HDMs. Here, tropomyosin protein from Dermatophagoides spp. was comprehensively screened in silico for its allergenicity, antigenicity and similarity/conservation. Materials and Methods: The amino acid sequences of D. farinae tropomyosin, D. pteronyssinus and other mites were retrieved. We included alignments and evaluated conserved/ variable regions along sequences, constructed their phylogenetic tree and estimated overall mean distances. Then, followed by with prediction of linear B-cell epitope based on different approaches, and besides in-silico evaluation of IgE epitopes allergenicity (by SVMc, IgE epitope, ARPs BLAST, MAST and hybrid method). Finally, comparative analysis of results by different approaches was made. Results: Alignment results revealed near complete identity between D. farina and D. pteronyssinus members, and also there was close similarity among Dermatophagoides spp. Most of the variations among mites’ tropomyosin were approximately located at amino acids 23 to 80, 108 to 120, 142 to 153 and 220 to 230. Topology of tree showed close relationships among mites in tropomyosin protein sequence, although their sequences in D. farina, D. pteronyssinus and Psoroptes ovis are more similar to each other and clustered. Dermanyssus gallinae (AC: Q2WBI0) has less relationship to other mites, being located in a separate branch. Hydrophilicity and flexibility plots revealed that many parts of this protein have potential to be hydrophilic and flexible. Surface accessibility represented 7 different epitopes. Beta-turns in this protein are with high probability in the middle part and its two terminals. Kolaskar and Tongaonkar method analysis represented 11 immunogenic epitopes between amino acids 7-16. From comparative analysis of predicted probable consensus epitope regions by machine learning approaches these epitopes were gained: AA23-48, AA59-80, AA91-110, AA114-143, AA154-168, AA182-200, AA208-225, and AA254-272. Prediction of allergenic proteins by AlgPred server showed 10 matches for IgE epitope, and prediction by hybrid approach showed that IgE epitope is undoubtedly the major allergen. Conclusion: Immunoinformatic approaches in allergenic protein analysis are now reliable tools for explanation/interpretation of clinically observed complexities. Results of present study, would help in HDM immunotherapy against several species of parasites as a wide range epitopic desensitization or prevention (vaccine) regime.