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Article

In Silico Comparative Exploration of Allergens of Periplaneta americana, Blattella germanica and Phoenix dactylifera for the Diagnosis of Patients Suffering from IgE-Mediated Allergic Respiratory Diseases

by
Mohd Adnan Kausar
1,*,
Tulika Bhardwaj
2,
Sadaf Anwar
1,
Fahaad Alenazi
3,
Abrar Ali
4,
Khalid Farhan Alshammari
5,
Shimaa Mohammed Hasnin AboElnaga
6,
Rajeev Singh
7,8 and
Mohammad Zeeshan Najm
9
1
Department of Biochemistry, College of Medicine, University of Ha’il, Ha’il 2440, Saudi Arabia
2
Department Agricultural Food and Nutritional Sciences, University of Alberta, Edmonton, AB T6G 2P5, Canada
3
Department of Pharmacology, College of Medicine, University of Ha’il, Ha’il 2440, Saudi Arabia
4
Department of Ophthalmology, College of Medicine, University of Ha’il, Ha’il 2440, Saudi Arabia
5
Department of Internal Medicine, College of Medicine at University of Ha’il, Ha’il 2440, Saudi Arabia
6
Department of Basic Science, Deanship of Preparatory Year, University of Hail, Ha’il 2440, Saudi Arabia
7
Department of Environmental Studies, Satyawati College, University of Delhi, Ashok Vihar III, Delhi 110052, India
8
Department of Environmental Science, Jamia Millia Islamia (Central University), New Delhi 110025, India
9
School of Biosciences, Apeejay Stya University, Sohna, Gurugram 122003, India
*
Author to whom correspondence should be addressed.
Molecules 2022, 27(24), 8740; https://doi.org/10.3390/molecules27248740
Submission received: 14 October 2022 / Revised: 3 December 2022 / Accepted: 5 December 2022 / Published: 9 December 2022
(This article belongs to the Section Computational and Theoretical Chemistry)

Abstract

:
The burden of allergic illnesses is continuously rising, and patient diagnosis is a significant problem because of how intricately hereditary and environmental variables interact. The past three to four decades have seen an outbreak of allergies in high-income countries. According to reports on the illness, asthma affects around 300 million individuals worldwide. Identifying clinically important allergens for the accurate classification of IgE-mediated allergy respiratory disease diagnosis would be beneficial for implementing standardized allergen-associated therapy. Therefore, the current study includes an in silico analysis to identify potential IgE-mediated allergens in date palms and cockroaches. Such an immunoinformatic approach aids the prioritization of allergens with probable involvement in IgE-mediated allergic respiratory diseases. Immunoglobulin E (IgE) was used for molecular dynamic simulations, antigen–antibody docking analyses, epitope identifications, and characterizations. The potential of these allergens (Per a7, Per a 1.0102, and Bla g 1.0101) in IgE-mediated allergic respiratory diseases was explored through the evaluation of physicochemical characteristics, interaction observations, docking, and molecular dynamics simulations for drug and vaccine development.

1. Introduction

Asthma and allergic rhinitis are two of the most prevalent respiratory allergies worldwide, and their prevalence is steadily rising. Traditional ways of life and the environments in which people live have an impact on the prevalence of asthma [1,2,3]. According to respiratory allergy data from the Kingdom of Saudi Arabia, allergic rhinitis and bronchial asthma are present in 13.5% and 11.2% of the population, respectively [4]. German cockroaches are some of the most common indoor allergens in countries that traditionally feed on floors, such as Saudi Arabia and other Gulf countries [5,6,7,8]. As a result of improved illness characterization, particularly through the application of cutting-edge technologies, including “omics,” it has become clear that many subgroups exist within various disease entities, such as asthma, allergic rhinitis, atopic dermatitis (AD), and angioedema (AE) [9,10]. In the current work, we attempted to explore several significant cockroach (CR) allergenic elements. Allergens are non-parasite proteins or molecules linked to proteins that cause atopic individuals to produce large amounts of IgE [11]. In nonatopic patients, these substances cause the development of alternative immunoglobulin isotypes, such as IgM and/or IgG, with little to no IgE [12,13].
CR allergens are glycosylated antigenic proteins with molecular weights of 6 to 120 kDa that exist in multimeric forms [14] and disintegrate in the human body. CR allergens can be found in several stages of insect life cycles and development, including in the cuticle, dead body debris, eggs, and egg casts [15]. Hemolymph, regurgitating fluid, urine, faeces, and body washes are all fluids that can contain parasites [16]. CR allergenic particles (>10 microns) are frequently present on surfaces such as floors, lamps, and tables, but the wind quickly disturbs and disperses them. American cockroach (Periplaneta americana) and German cockroach (Blattella germanica) allergens are the most common in Saudi households.
Bla g 1, Bla g 2, Bla g 4, Bla g 5, Bla g 6, and Bla g 7 (Per g = genus species; numerical value = the number of allergens from that species) are allergens from Blattella germanica. The allergens from Periplaneta americana are Per 1, Per 2, Per 3, Per 4, Per 6, Per 7, Per 9, and Per 10 [17]. With the help of a benzamidine sepharose column and immunological and biochemical analysis, Per a 10 was isolated from American CR extract, a serine proteinase, the molecular weight of which is thought to be around 28 kDa. Skin tests and immunoblots showed IgE reactivity in >80% of cockroach-sensitized patients, identifying Per a 10 as a significant allergen. According to the latest study, Per a 10 can increase the phenotype of dendritic cell type 2 by upregulating CD86, increasing high interleukin-6 (IL-6) secretion, and decreasing IL-12 secretion, in addition to changing the expression of CD40 on the dendritic cell surface via the nuclear factor-B (NF-B) pathway [18,19].
The in-silico method was used in this study to identify potential IgE-mediated allergens in date palms and cockroaches [20]. Therefore, epitope identification and characterization were performed to give future researchers a platform to develop potential vaccines against allergies caused by the allergens under investigation. In addition, antigen–antibody docking and molecular dynamic simulations with IgE were performed to assess the allergenic potential for future research.

2. Methodology

2.1. Retrieval of the Protein Sequences of Date Palm and Cockroach Allergens

The sequences of allergen proteins were obtained from the UniProt online database (https://www.uniprot.org/help/uniprotkb, accessed on 24 May 2022), which is the major archive for different types of repositories used to carry out comprehensive genomic and proteomic analyses.

2.2. Physiochemical Parameter Evaluations of the above Allergens

The proteins identified as possessing physical and chemical characteristics were investigated for physiochemical properties, and the ProtParam server [21] was used to determine theoretical parameters, such as molecular weights, amino acids, pI (isoelectric point) values, instability indices, etc.

2.3. Functional Classifications

The primary identification mechanism for understanding pathogenesis in organisms is the distinction between virulent and non-virulent proteins. The VICMpred online prediction server [22] was used for the functional classification of bacteria using a bi-layer cascade SVM approach, which applies sequence information for the prediction of different virulence factors. The VICMpred webserver uses amino acid sequences in pattern-based approaches that show extremely important values of functional classification, i.e., median values >1.0.

2.4. Subcellular Localization

Protein localization is a significant aspect in the development of new drug targets for drug sightings, as cytoplasmic and membrane proteins have been recognized as pharmacological targets. Since no information regarding the subcellular localization of these protein sequences was available at the time, Plant-mSubP, a two-level support vector machine tool [23] for the prediction of subcellular localizations of single and multiple protein sequences, was utilized for Phoenix dactylifera. In the case of American cockroach (Periplaneta americana) and German cockroach (Blattella germanica) allergens, WoLF-PSORT [24] enabled subcellular prediction based on sorting signals, amino acid compositions, and functional motifs, such as DNA-binding motifs. More than one software package was utilized for accurate computational identifications of subcellular localizations.

2.5. Prediction of IgE Epitopes and Allergenic Site Prediction

The AlgPred server [25] was used to ensure the allergenicity potential of the protein sequences. It includes the integrated method of combining SVM amino acid composition or dipeptide-based methods, IgE epitope mapping, BLAST searching against allergen representative peptides (ARPs), and MAST (Motif Alignment and Search Tool)–MEME (Multiple EM for Motif Elicitation) suites to measure putative allergenicity for default parameters [26]. Further, AllerCatPro 2.0 [27] predicts the allergenic potential of protein sequences based on the structural similarities of their three-dimensional structures and their amino acid compositions when compared with protein allergens derived from public repositories. IgE sensitization towards proteins is frequently recognized upon exposure to aeroallergens, food allergens, and personal care products [28].

2.6. Secondary Structure Prediction

The SOPMA web server [29] was used to predict the 2D structures of target protein sequences. This online server enables simple and accurate predictions for the identification of different forms of a characteristic in secondary structures, such as alpha helices, beta turns, extended strands, and random coil regions, which contain the primary elements of 2D structure prediction.

2.7. Tertiary Structure Prediction (3D Model) and Validation

Homology modeling was performed using MODELLER to generate the 3D molecular structures of identified stable protein sequences of date palm and cockroach allergens that contain experimentally proven IgE epitopes [30,31]. MODELLER is a computational platform for comparative protein structure modeling which can be used to generate tertiary protein models [32].

2.8. Antigen–Antibody Docking Studies

Molecular docking analysis of all the prioritized antigenic sites and IgE was performed to estimate binding affinities. The human IgE three-dimensional structure was retrieved from RCSPDB (PDB ID: 4J4P and UniProt ID: P01854). The structure includes a complex of Human IgE-Fc with two bound Fab fragments. UCSF Chimera (https://www.cgl.ucsf.edu/chimera/, accessed on on 24 May 2022) enables manual preprocessing of these peptides, further homodimers were reduced to a single chain to reduce docking time, and non-amino acid molecules (ligands, ions, and solvent water) were removed to prevent hindrances during docking. Rigid-body molecular docking of the engineered vaccine with the processed receptors was performed based on shape-complementarity principles, utilizing PatchDock server [33]. This server differentiates a protein’s surface into small patches (convex, concave, and flat) using a segmentation algorithm that is superposed using a shape-matching algorithm. The top conformations obtained with PatchDock were subjected to docking score refinement using FireDock [34]. FireDock refined the docked poses by optimizing side-chain conformations and rigid body orientations via Monte Carlo simulation.

2.9. MD Simulation

To analyze conformational stability, molecular dynamics and simulation studies were performed using GROMACS v5.1.5 and the OPLS-AA/L all-atom force field (2001 amino acid dihedrals). To study the interfacial atoms’ physical movements and the complexes’ stabilities in explicit water boxes (dodecahedrons), docked complexes were subjected to 309 K for 25 ns [35,36]. We used both NVT and NPT ensembles to mimic real experimental conditions. A six-step procedure was followed to analyze the trajectories of energy minimization in the MD simulations: (a) energy minimization of solvent molecules prior to the entire system using the Broyden–Fletcher–Goldfarb–Shanno (LBFGS) algorithm; (b) non-hydrogen solute atoms were restricted to 300 K temperature and 1 bar for 40 ns to attain equilibrium states; (c) control of temperature and pressures during initial simulations using Berendsen thermostats and the barostat algorithm; (d) initial trajectories were obtained to assist in the analysis of RMSDs; (e) RMSF hydrogen bond analysis was performed; and (f) determination of the radii of gyration for the antigen–antibody docked complexes [37,38].

3. Results and Discussion

3.1. Retrieval of the Protein Sequences for Date Palm and Cockroach Allergens

UniProt, a freely available database of protein sequences and their functional annotations from several genome sequencing projects, was utilized to mine the primary dataset protein sequences of the date palm and cockroach allergens. The primary dataset was then subjected to physiochemical characterization to evaluate pathogenic and allergenicity potential.

3.2. Physiochemical Parameter Evaluation of the above Allergens

ProtParam enabled the theoretical computation of instability indices, molecular weights, and GRAVYs of the identified allergens of date palms and cockroaches (Table 1). An instability index estimates the stability of the protein in a test tube. The sum of hydropathy values for all amino acids, when divided by the number of residues in a sequence, predicts the GRAVY (grand average of hydropathicity). The relative volume occupied by aliphatic side chains (alanine, valine, isoleucine, and leucine) in a protein sequence is termed an aliphatic index. The amino acid residue contents of the screened allergens ranged between 151 and 823. The instability index can be used to infer the stability of a measured protein in a test tube. Therefore, in this study, only stable amino acids with instability indices <40 were selected for further allergenicity and functional predictions.

3.3. Functional Characterization

VICMpred enables the functional characterization of iterated protein sequences into major functional modules (Table 2). This machine-learning-based tool identified the active participation of primary protein sequences in metabolism molecules (32.35%), virulence factors (5.88%), cellular processes (44.12%), and information and storage (17.65%). Functional characterization is important in proteomics studies to understand biological functions at the system level.

3.4. Subcellular Localization

Plant-mSubP and WoLF-PSORT both assist in the selective distribution of primary protein sequences within cellular compartments. The sorting of protein sequences was mainly in the cell membranes, extracellular matrices, mitochondria, and cytoplasmic domains (Supplementary Table S1). The prioritized subcellular localizations after analysis were in the cytoplasm, mitochondria, plastids, and vacuoles.

3.5. Prediction of IgE Epitopes and Allergenic Site Prediction

Prediction of allergenic proteins and mapping of IgE epitopes by AlgPred identified allergenic peptides by utilizing five approaches (IgE epitope + ARPs BLAST + MAST + SVM) to attain an overall accuracy of 85%. The protein sequences were predicted to be non-allergenic if the score was estimated in the -ve sign (Table 3). The results highlighted that the majority of inputted protein sequences did not contain an experimentally proven IgE epitope. A total of nine protein sequences were screened for allergenicity potential. Further, AllerCatPro 2.0 also validated the allergenicity potential of inputted protein sequences by estimating strong evidence (Table 4), which is a result of higher similarity search values against allergens present in public repositories.

3.6. Secondary Structure Prediction

In the absence of a three-dimensional structure and template sequence, secondary structure analysis is necessary to identify the percentages of alpha helices, extended strands, beta turns, and random coils in protein sequences (Table 5). Such analysis helps in the generation of the 3D structures of proteins. This analysis also renders information about protein activities, relationships, and functions. This study identified that alpha helices accounted for the majority coverage of the screened allergens, followed by random coils, beta turns, and extended strands. This implies that hydrogens bonds are mainly responsible for protein–protein interactions in further molecular docking procedures.

3.7. Tertiary Structure Prediction (3D model) and Validation

Three-dimensional structures of protein sequences (Bla g 1.0101, accession number: AF072219.2; Per a 7, accession number: ACS14052.1; and Per a 7.0102, accession number: AF106961.1) were mined from the UniProt database. MODELLER was then utilized to generate the three-dimensional structures of the protein sequences. These aided in the identification of template sequences with PDB codes 4JRB, 7KO4, and 6X5Z. Further, validation was performed using the SAVES server. We used a high-resolution structure refinement method, i.e., ModRefine (https://zhanglab.ccmb.med.umich.edu/ModRefiner/, accessed on 15 June 2022), which improves poor rotamers by simulating both protein backbones and side chains. The tertiary models generated were subjected to molecular docking analysis.

3.8. Antigen–Antibody Docking Studies

Protein–protein docking analysis was performed after protein backbone stabilization to determine the binding affinities of the resulting plausible antigenic protein sequences. Visualization of ions, ligands, and other non-amino acid molecules is possible by utilizing the Chimera Visualization tool (Figure 1). Rigid docking of antigenic sequences and antibodies was then carried out using PatchDock, and the poses so generated were sorted with respect to the binding energy functions. The top 10 docking outcomes were pulled and furthered for pose refinement using FireDock. The binding energies of the complexes and those with the lowest binding energies were screened (Blag 1.0101-IgE with −19.08 kcal/mol, Bla g 7-IgE with 11.2 kcal/mol, Per a 1.0101-IgE with −9.22 kcal/mol, Per a 1.0103-IgE with −7.12 kcal/mol, Per a 1.0104-IgE with −8.22 kcal/mol, Per a 1.0201-IgE with −7.76 kcal/mol, Per a 1.0102-IgE with −21.33 kcal/mol, and Per a 7-IgE with −19.71 kcal/mol). The visualization of protein–protein docking validated the major role of hydrogen bonding among protein sequences. The docked complexes with optimal minimum binding energies were considered for the MD simulation platform. In the case of Per a 1.0102-IgE, hydrogen bonds were formed among K90, Y82, N103, and Y105 of Per a 1.0102 with antigen recognition sites of IgE. A155, K160, and Q147 of Per a 7-IgE formed hydrogen bonds with variable regions of IgE. For Bla g 1.0101-IgE, the major interacting partners were Q229, L220, and K223 (Figure 1). Such prioritization and inclusion of a molecular docking approach enabled the identification of potential allergens to be considered for vaccine and drug discovery. For the onset and persistence of most immediate-type allergies and several asthma phenotypes, immunoglobulin E (IgE) is essential. As a result, IgE is a key target for both diagnostic and therapeutic objectives [39]. There are two categories of IgE-binding epitopes: linear (sequential) and conformational (discontinuous). While conformational epitopes are generated by spatially nearby AAs that are far apart in the protein’s AA primary sequence, linear epitopes are continuous AA sequences [40]. Bla g 1.0101 is secreted in the cockroach digestive tract, and sensitization occurs through inhalation of allergen-carrying faecal particles that are released into the environment [41]. Tropomyosins, for example, Per a 7, play a role in muscle contraction [42]. Tropomyosin is a pan-allergen found in the muscles of many animals [43,44]. Initially identified as a major shrimp allergen, it has since been found in a variety of insects and causes IgE cross-reactivity [45]. A study using RNA interference-mediated knockdown of this allergen in Periplaneta americana confirmed that Per a 1 is involved in digestion and nutrient absorption [43].

3.9. MD Simulation

MD simulations of the selected complexes for 25 ns using GROMACS v5.1.2 were performed and analyzed. The complexes were solvated in dodecahedron water boxes using a four-point TIP4P rigid water model with at least 1 nm of solvation on all sides, and neutralization was achieved by adding Na+ ions. The particle mesh Ewald (PME) summation method was used for the treatment of long-range interactions with all bonds constrained using the LINCS algorithm. Further, the energy minimization of the system was carried out using a steepest descent method at a temperature of 310 K and one atmospheric bar pressure via a V-rescale thermostat and Parrinello–Rahman barostat implementation. The conformations were obtained at intervals of 10 ps throughout the 25 ns trajectory. Post-simulation, energy minimization and trajectory analysis showed that the complex initially showed 2 Å deviations but achieved stability later for the top three selected complexes (Figure 2). Residue-based root mean square fluctuation (RMSF) analysis of antigen–antibody docked complexes was performed to understand the flexibility of each residue, as depicted in Figure 2a. RMSF values for all docked complexes showed large fluctuations (0.33–1.67 nm) for the initial 20 residues in each case due to the unavailability of structural information for the target proteins (Figure 2d). Further, lesser fluctuations at the binding and active sites indicated the intactness and rigidity of the binding cavities. gmx_gyrate was used to calculate Rg values indicating the compactness and structural changes of the docked complexes. Rg is a measure of the mass of atoms with respect to the center of mass of complexes (Figure 2b). Average Rg values for Bla g 1.0101, Per a 1.0102, and Per a 7 ranged between 2.36 and 2.66 nm, 2.29 and 2.89 nm, and 2.44 and 2.56 nm, respectively, with no fluctuations after 25,000 ps. Further, Rg values correlated with RMSD values for backbone Cα atoms, validating the stability of the prioritized antigen–antibody complexes. These results indicate the suitability of the prioritized allergenic protein sequences among all the proteomes for further investigation in bench-top experiments.

4. Conclusions

Blattella germanica allergens appear to be about equally concentrated in homes in Saudi Arabia, even though the prevalence of the Bla g 2 allergen was found to be slightly greater in patients’ homes. As there is little information available regarding cockroach-related allergens, it is essential to investigate this issue in detail to empower ourselves with a remedy in advance. The current study intended to find immunodominant peptides that may be employed in the future to develop a universal peptide vaccine to treat cockroach-related illnesses. This will aid in eradicating future possibilities of asthma and allergenic rhinitis. In this study, an immunoinformatics approach was applied to evaluate the immunogenicity of prioritized proteins. Research incorporating experimental confirmation of these predicted epitopes is necessary to ensure the capabilities of B-cell and T-cell stimulations for their efficient use as vaccine candidates and as diagnostic agents against cockroaches.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/molecules27248740/s1. Table S1. The sorting of protein sequences.

Author Contributions

Conceptualization, M.A.K., S.A.; writing—original draft preparation, T.B., M.Z.N. writing—review and editing, F.A., K.F.A., S.M.H.A. and R.S.; visualization F.A. and K.F.A.; supervision, M.A.K.; Data curation, A.A. All authors have read and agreed to the published version of the manuscript.

Funding

This research has been funded by Scientific Research Deanship at University of Ha’il- Saudi Arabia, through project number RG-21 047.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The authors confirm that the data supporting the findings of this study are available within the article, including the supplementary file. Raw data that support the findings of this study are available from the corresponding author upon request.

Acknowledgments

This research has been funded by Scientific Research Deanship at University of Ha’il- Saudi Arabia, through project number RG-21 047.

Conflicts of Interest

The authors declare no conflict of interest.

Sample Availability

Not applicable.

References

  1. Tungtrongchitr, A.; Sookrung, N.; Munkong, N.; Mahakittikun, V.; Chinabut, P.; Chaicumpa, W.; Bunnag, C.; Vichyanond, P. The levels of cockroach allergen in relation to cockroach species and allergic diseases in Thai patients. Asian Pac. J. Allergy Immunol. 2004, 22, 115–121. [Google Scholar] [PubMed]
  2. Rosenstreich, D.L.; Eggleston, P.; Kattan, M.; Baker, D.; Slavin, R.G.; Gergen, P.; Mitchell, H.; McNiff-Mortimer, K.; Lynn, H.; Ownby, D.; et al. The role of cockroach allergy and exposure to cockroach allergen in causing morbidity among inner-city children with asthma. N. Engl. J. Med. 1997, 336, 1356–1363. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  3. Alp, H.; Yu, B.H.; Grant, E.N.; Rao, V.; Moy, J.N. Cockroach allergy appears early in life in inner-city children with recurrent wheezing. Ann. Allergy Asthma Immunol. 2001, 86, 51–54. [Google Scholar] [CrossRef] [PubMed]
  4. Almatroudi, A.; Mousa, A.M.; Vinnakota, D.; Abalkhail, A.; Alwashmi, A.S.S.; Almatroodi, S.A.; Alhumaydhi, F.A.; Kabir, R.; Mahmud, I. Prevalence and associated factors of respiratory allergies in the Kingdom of Saudi Arabia: A cross-sectional investigation, September-December 2020. PLoS ONE. 2021, 16, e0253558. [Google Scholar] [CrossRef] [PubMed]
  5. Arruda, L.K.; Vailes, L.D.; Ferriani, V.P.; Santos, A.B.; Pomés, A.; Chapman, M.D. Cockroach allergens and asthma. J. Allergy Clin. Immunol. 2001, 107, 419–428. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  6. Liccardi, G.; Cazzola, M.; D’Amato, M.; D’Amato, G. Pets and cockroaches: Two increasing causes of respiratory allergy in indoor environments. Characteristics of airways sensitization and prevention strategies. Respir. Med. 2000, 94, 1109–1118. [Google Scholar] [CrossRef] [Green Version]
  7. Üzela, A.; Çapana, N.; Canbakana, S.; Yurdakul, A.S.; Dursun, B. Evaluation of the relationship between cockroach sensitivity and house-dust-mite sensitivity in Turkish asthmatic patients. Respir. Med. 2005, 99, 1032–1037. [Google Scholar] [CrossRef] [Green Version]
  8. AlKhater, S.A. Sensitization to Common Aeroallergens in Asthmatic Children in the Eastern Region of Saudi Arabia. Saudi J. Med. Med. Sci. 2017, 5, 136–141. [Google Scholar]
  9. Arruda, L.K.; Vailes, L.D.; Benjamin, D.C.; Chapman, M.D. Molecular cloning of German cockroach (Blattella germanica) allergens. Int. Arch. Allergy Immunol. 1995, 107, 295–297. [Google Scholar] [CrossRef]
  10. Sen, A.; Bansal, R.; Mohagaonkar, S.; Bhardwaj, T.; Rathi, B.; Almalki, A.H.; Janahi, E.M.; Alsulimani, A.; Tewari, B.N.; Somvanshi, P.; et al. In-silico analysis of multiepitope based vaccine targeting respiratory viruses SARS, MERS and SARS-CoV-2. Minerva Biotechnol. Biomol. Res. 2022, 34, 97–113. [Google Scholar] [CrossRef]
  11. Bernton, H.S.; Brown, H. Insect allergy preliminary studies of the cockroach. Allergy 1964, 35, 506–513. [Google Scholar] [CrossRef] [PubMed]
  12. Bernton, H.S.; McMahon, T.F.; Brown, H. Cockroach asthma. Br. J. Dis. Chest. 1972, 66, 61–66. [Google Scholar] [CrossRef] [PubMed]
  13. Kang, B.; Vellody, D.; Homburger, H.; Yunginger, J.W. Cockroach cause of allergic asthma. Its specificity and immunologic profile. J. Allergy Clin. Immunol. 1979, 63, 80–86. [Google Scholar] [CrossRef] [PubMed]
  14. Lluch, M.; Sastre, J.; Fernandez-Nieto, M.; Fernandez-Caldas, E.; Quirce, S. Eosinophilic and neutrophilic sputum response to bronchial challenge with cockroach. J. Allergy Clin. Immunol. 2003, 112, 802–803. [Google Scholar] [CrossRef]
  15. Kind, T.J.; Goldsby, R.A.; Osborne, B.A. Hypersensitivity reactions. In Kuby Immunology, 6th ed.; Kind, T.J., Goldsby, R.A., Osborne, B.A., Eds.; WH Freeman and Company: New York, NY, USA, 2007; pp. 371–388. [Google Scholar]
  16. Schou, C.; Lind, P.; Fernandez-Caldas, E.; Lockey, R.F.; Løwenstein, H. Identification and purification of an important cross-reactive allergen from American (Periplaneta americana) and German (Blattella germanica) cockroach. J. Allergy Clin. Immunol. 1990, 86, 935–946. [Google Scholar] [CrossRef]
  17. Fang, Y.; Long, C.; Bai, X.; Liu, W.; Rong, M.; Lai, R.; An, S. Two new types of allergens from the cockroach. Periplaneta Am. Allergy 2015, 70, 1674–1678. [Google Scholar] [CrossRef]
  18. Goel, C.; Govindaraj, D.; Singh, B.P.; Farooque, A.; Kalra, N.; Arora, N. Serine protease Per a 10 from Periplaneta americana bias dendritic cells towards type 2 by upregulating CD86 and low IL-12 secretions. Clin. Exp. Allergy 2012, 42, 412–422. [Google Scholar] [CrossRef]
  19. Goel, C.; Kalra, N.; Dwarakanath, B.S.; Gaur, S.N.; Arora, N. Per a 10 protease activity modulates CD40 expression on dendritic cell surface by nuclear factor-kappaB pathway. Clin. Exp. Immunol. 2015, 180, 341–351. [Google Scholar] [CrossRef] [Green Version]
  20. Satyam, R.; Janahi, E.M.; Bhardwaj, T.; Somvanshi, P.; Haque, S.; Najm, M.Z. In silico identification of immunodominant B-cell and T-cell epitopes of non-structural proteins of Usutu Virus. MicrobPathog 2018, 125, 129–143. [Google Scholar] [CrossRef]
  21. Gasteiger, E.; Hoogland, C.; Gattiker, A.; Duvaud, S.; Wilkins, M.R.; Appel, R.D.; Bairoch, A. Protein Identification and Analysis Tools on the ExPASy Server. In John M. Walker (ed): The Proteomics Protocols Handbook; Humana Press: Totowa, NJ, USA, 2005; pp. 571–607. [Google Scholar]
  22. Saha, S.; Raghava, G.P. VICMpred: An SVM-based method for the prediction of functional proteins of Gram-negative bacteria using amino acid patterns and composition. Genom. Proteom. Bioinform. 2006, 4, 42–47. [Google Scholar] [CrossRef] [Green Version]
  23. Sahu, S.S.; Loaiza, C.D.; Kaundal, R. Plant-mSubP: A computational framework for the prediction of single- and multi-target protein subcellular localization using integrated machine-learning approaches. AoB Plants 2019, 12, plz068. [Google Scholar] [CrossRef] [PubMed]
  24. Horton, P.; Park, K.J.; Obayashi, T.; Fujita, N.; Harada, H.; Adams-Collier, C.J.; Nakai, K. WoLF PSORT: Protein localization predictor. Nucleic Acids Res. 2007, 35, W585–W587. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  25. Saha, S.; Raghava, G.P. AlgPred: Prediction of allergenic proteins and mapping of IgE epitopes. Nucleic Acids Res. 2006, 34, W202–W209. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  26. Satyam, R.; Bhardwaj, T.; Jha, N.K.; Jha, S.K.; Nand, P. Toward a chimeric vaccine against multiple isolates of Mycobacteroides—An integrative approach. Life Sci. 2020, 250, 117541. [Google Scholar] [CrossRef] [PubMed]
  27. Maurer-Stroh, S.; Krutz, N.L.; Kern, P.S.; Gunalan, V.; Nguyen, M.N.; Limviphuvadh, V.; Eisenhaber, F.; Gerberick, G.F. AllerCatPro-prediction of protein allergenicity potential from the protein sequence. Bioinformatics 2019, 35, 3020–3027. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  28. Popescu, F.D. Cross-reactivity between aeroallergens and food allergens. World J. Methodol. 2015, 5, 31–50. [Google Scholar] [CrossRef]
  29. Geourjon, C.; Deléage, G. SOPMA: Significant improvements in protein secondary structure prediction by consensus prediction from multiple alignments. Comput. Appl. Biosci. 1995, 11, 681–684. [Google Scholar] [CrossRef]
  30. Khan, S.; Somvanshi, P.; Bhardwaj, T.; Mandal, R.K.; Dar, S.A.; Wahid, M.; Jawed, A.; Lohani, M.; Khan, M.; Areeshi, M.Y.; et al. Aspartate-β-semialdehyde dehydrogenase as a potential therapeutic target of Mycobacterium tuberculosis H37Rv: Evidence from in silico elementary mode analysis of biological network model. J. Cell Biochem. 2018, 119, 2832–2842. [Google Scholar] [CrossRef]
  31. Bhardwaj, T.; Haque, S.; Somvanshi, P. Comparative Assessment of the Therapeutic Drug Targets of C. botulinum ATCC 3502 and C. difficile str. 630 Using In silico Subtractive Proteomics Approach. J. Cell. Biochem. 2019, 120, 16160–16184. [Google Scholar] [CrossRef]
  32. Eswar, N.; Webb, B.; Marti-Renom, M.A.; Madhusudhan, M.S.; Eramian, D.; Shen, M.Y.; Pieper, U.; Sali, A. Comparative protein structure modeling using Modeller. Curr. Protoc. Bioinform. 2006, 54, 5.6.1–5.6.37, Chapter 5: Unit-5.6. [Google Scholar] [CrossRef] [Green Version]
  33. Schneidman-Duhovny, D.; Inbar, Y.; Nussinov, R.; Wolfson, H.J. PatchDock and SymmDock: Servers for rigid and symmetric docking. Nucleic Acids Res. 2005, 33, W363–W367. [Google Scholar] [CrossRef] [PubMed]
  34. Mashiach, E.; Schneidman-Duhovny, D.; Andrusier, N.; Nussinov, R.; Wolfson, H.J. FireDock: A web server for fast interaction refinement in molecular docking. Nucleic Acids Res. 2008, 36, W229–W232. [Google Scholar] [CrossRef] [PubMed]
  35. Khan, S.; Bhardwaj, T.; Somvanshi, P.; Mandal, R.; Dar, S.A.; Jawed, A.; Wahid, M.; Akhter, N.; Lohani, M.; Alouffi, S.; et al. Inhibition of C298S mutant of human aldose reductase for antidiabetic applications: Evidence from In-silico elementary mode analysis of biological network model. J. Cell. Biochem. 2018, 119, 6961–6973. [Google Scholar] [CrossRef]
  36. Kushwaha, P.; Singh, V.; Somvanshi, P.; Bhardwaj, T.; Barreto, G.E.; Ashraf, G.; Mishra, B.N.; Chundawat, R.S.; Haque, S. Identification of new BACE1 inhibitors for treating Alzheimer’s disease. J. Mol. Model. 2021, 27, 58. [Google Scholar] [CrossRef] [PubMed]
  37. Alsulimani, A.; Bhardwaj, T.; Janahi, E.M.; Almalki, A.H.; Tewari, B.N.; Wahid, M.; Mustfa, F.; Somvanshi, P.; Haque, S. Systematic structure guided clustering of chemical lead compounds targeting RdRp of SARS-CoV-2. Minerva Biotechnol. Biomol. Res. 2022, 34, 114–121. [Google Scholar] [CrossRef]
  38. Anwer, R.; AlQumaizi, K.I.; Haque, S.; Somvanshi, P.; Ahmad, N.; AlOsaimi, S.M.; Fatma, T. Unravelling the interaction of glipizide with human serum albumin using various spectroscopic techniques and molecular dynamics studies. J. Biomol. Struct. Dyn. 2021, 39, 336–347. [Google Scholar] [CrossRef]
  39. Krause, T.; Röckendorf, N.; Meckelein, B.; Sinnecker, H.; Schwager, C.; Möckel, S.; Jappe, U.; Frey, A. IgE Epitope Profiling for Allergy Diagnosis and Therapy—Parallel Analysis of a Multitude of Potential Linear Epitopes Using a High Throughput Screening Platform. Front Immunol. 2020, 11, 565243. [Google Scholar] [CrossRef]
  40. Matsuo, H.; Yokooji, T.; Taogoshi, T. Common food allergens and their IgE-binding epitopes. Allergol. Int. 2015, 64, 332–343. [Google Scholar] [CrossRef] [Green Version]
  41. Mueller, G.A.; Pedersen, L.C.; Lih, F.B.; Glesner, J.; Moon, A.F.; Chapman, M.D.; Tomer, K.B.; London, R.E.; Pomés, A. The novel structure of the cockroach allergen Bla g 1 has implications for allergenicity and exposure assessment. J. Allergy Clin. Immunol. 2013, 132, 1420–1426. [Google Scholar] [CrossRef] [Green Version]
  42. Sudha, V.T.; Arora, N.; Gaur, S.N.; Pasha, S.; Singh, B.P. Identification of a serine protease as a major allergen (Per a 10) of Periplaneta americana. Allergy 2008, 63, 768–776. [Google Scholar] [CrossRef]
  43. Pomés, A.; Arruda, L.K. Investigating cockroach allergens: Aiming to improve diagnosis and treatment of cockroach allergic patients. Methods 2014, 66, 75–85. [Google Scholar] [CrossRef] [PubMed]
  44. Pomés, A.; Mueller, G.A.; Randall, T.A.; Chapman, M.D.; Arruda, L.K. New Insights into Cockroach Allergens. Curr. Allergy Asthma Rep. 2017, 17, 25. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  45. Wang, L.; Xiong, Q.; Saelim, N.; Wang, L.; Nong, W.; Wan, A.T.; Shi, M.; Liu, X.; Cao, Q.; Hui, J.H.L.; et al. Genome assembly and annotation of Periplaneta americana reveal a comprehensive cockroach allergen profile. Allergy 2022, in press. [Google Scholar] [CrossRef] [PubMed]
Figure 1. Visualization of docked complexes of (a) Blag 1.0101-IgE (−19.08 kcal/mol), (b) Per a 1.0102-IgE (−21.33 kcal/mol), and (c) Per a 7-IgE (−19.71 kcal/mol). IgE is represented as a complex molecule, and selected protein allergens are visualized as blue polypeptide chains.
Figure 1. Visualization of docked complexes of (a) Blag 1.0101-IgE (−19.08 kcal/mol), (b) Per a 1.0102-IgE (−21.33 kcal/mol), and (c) Per a 7-IgE (−19.71 kcal/mol). IgE is represented as a complex molecule, and selected protein allergens are visualized as blue polypeptide chains.
Molecules 27 08740 g001
Figure 2. (a) RMSD, (b) Rg, (c) hydrogen bond, and (d) RMSF analyses of Blag 1.0101-IgE, Per a 1.0102-IgE, and Per a 7-IgE.
Figure 2. (a) RMSD, (b) Rg, (c) hydrogen bond, and (d) RMSF analyses of Blag 1.0101-IgE, Per a 1.0102-IgE, and Per a 7-IgE.
Molecules 27 08740 g002
Table 1. Physiochemical characterization of the protein sequences from date palms and cockroaches.
Table 1. Physiochemical characterization of the protein sequences from date palms and cockroaches.
S. No.Protein Seq.Uniprot
/NCBI
No. of Amino AcidsMolecular Wt.pIExt. Cof.Instability IndexAliphatic IndexGRAVY
Blattella germanica Allergens
1Bla g 1.0101Q9UAM541245817.744.491490038.52129.540.176
2Bla g 1.0201O9652249255507.764.701937046.59120.510.046
3Bla g 2P5495835238557.885.283500526.7291.850.076
4Bla g 3D0VNY765778737.116.4412311044.6565.21−0.665
5Bla g 4P5496218220927.456.482764028.0272.42−0.567
6Bla g 5O1859820423333.716.324538031.5480.39−0.490
7Bla g 6.0101Q1A7B315117216.223.96298035.4486.56−0.388
8Bla g 6.0201ABB8929715117095.143.96310531.4887.22−0.269
9Bla g 6.0301ABB8929815417749.864.10298053.8581.10−0.496
10Bla g 7Q9NG5628432837.844.72608544.6081.94−0.973
11Bla g 8A0ERA819521180.404.44699038.8867.28−0.567
Periplaneta americana Allergens
1Per a 1.0101Q9TZR623126222.884.461043057.52114.46−0.014
2Per a 1.0102O1853522825791.394.441043056.32115.960.004
3Per a 1.0103O1853039544610.994.552487052.06112.100.017
4Per a 1.0104O1852827431142.544.451891055.73114.64−0.020
5Per a 1.0201O1852744650547.545.311639056.57108.05−0.179
6Per a 3.0101Q2564168581175.356.2513238541.5667.11−0.612
7Per a 3.0201Q9464363175511.846.6112059044.0364.07−0.737
8Per a 3.0202Q25640470561888.057.028562542.0364.26−0.782
9Per a 3.0203Q2563939346746.526.526522546.0666.92−0.749
10Per a 6Q1M0Y315117130.993.84298024.9787.22−0.377
11Per a 7Q9UB8328432776.814.69608545.0481.58−0.920
12Per a 7.0102P0DSM728432793.874.72459545.0183.31−0.924
13Per a 9B9VAT135639735.095.583612030.9380.56−0.413
14Per a 10Q1M0X925626652.144.893442025.7784.490.273
Phoenix dactylifera Allergens
1XP_008803750.1P3305017618902.395.931184534.3973.75−0.109
2XP_008780644.1A0A0S3B0K018220044.546.251891027.8574.01−0.352
3XP_008782456.1-20122566.846.122329551.4870.75−0.286
4AGE46030.1-36141027.225.666846533.4681.05−0.247
5XP_008796227.1-52959084.125.939435035.7277.75−0.364
6XP_010911620.1O8135530933749.626.001742030.1298.45−0.013
7YP_005090378.1-50955292.256.022558036.2095.07−0.129
8XP_008781205.1-39643329.145.713041020.5681.46−0.315
9XP_008811417.1Q2UMD582392522.515.8918884031.8971.56−0.448
Table 2. Listing of primary protein sequences in functional modules.
Table 2. Listing of primary protein sequences in functional modules.
S. No.Protein AllergenUniprot
/NCBI
Functional ClassFunctionality
1Bla g 1.0101Q9UAM5Metabolism molecule-
2Bla g 1.0201O96522Virulence factors-
3Bla g 2P54958Cellular processAcid proteases (SSF50630)
4Bla g 3D0VNY7Cellular processE set domains (SSF81296); Hemocyanin, N-terminal domain (SSF48050); Di-copper centre-containing domain (SSF48056)
5Bla g 4P54962Cellular processLipocalins (SSF50814)
6Bla g 5O18598Metabolism moleculeThioredoxin-like (SSF52833); GST C-terminal domain-like (SSF47616)
7Bla g 6.0101Q1A7B3Information and storageEF-hand (SSF47473)
8Bla g 6.0201ABB89297Cellular process-
9Bla g 6.0301ABB89298Information and storage-
10Bla g 7Q9NG56Information and storage-
11Bla g 8A0ERA8Cellular processEF-hand (SSF47473)
12Per a 1.0101Q9TZR6Metabolism molecule-
13Per a 1.0102O18535Metabolism molecule-
14Per a 1.0103O18530Metabolism molecule-
15Per a 1.0104O18528Metabolism molecule-
16Per a 1.0201O18527Metabolism molecule-
17Per a 3.0101Q25641Metabolism moleculeE set domains (SSF81296); Hemocyanin, N-terminal domain (SSF48050); Di-copper centre-containing domain (SSF48056)
18Per a 3.0201Q94643Information and storageE set domains (SSF81296); Hemocyanin, N-terminal domain (SSF48050); Di-copper centre-containing domain (SS8F480596)
19Per a 3.0202Q25640Cellular processE set domains (SSF81296); Di-copper centre-containing domain (SSF48056)
20Per a 3.0203Q25639Cellular processE set domains (SSF81296); Di-copper centre-containing domain (SSF48056)
21Per a 6Q1M0Y3Cellular processEF-hand (SSF47473)
22Per a 7.0101Q9UB83Information and storage-
23Per a 7.0102P0DSM7Information and storage-
24Per a 9B9VAT1Cellular processGuanido kinase N-terminal domain (SSF48034); Glutamine synthetase/guanido kinase (SSF55931)
25Per a 10Q1M0X9Cellular processTrypsin-like serine proteases (SSF50494)
26XP_008803750.1P33050Metabolism moleculeEnolase C-terminal domain-like (SSF51604); Enolase N-terminal domain-like (SSF54826)
27XP_008780644.1A0A0S3B0K0Metabolism moleculeRmlC-like cupins (SSF51182)
28XP_008782456.1-Cellular process-
29AGE46030.1-Metabolism molecule-
30XP_008796227.1-Virulence factors-
31XP_010911620.1O81355Cellular processNAD(P)-binding Rossmann-fold domains (SSF51735)
32YP_005090378.1-Cellular process-
33XP_008781205.1-Cellular process-
34XP_008811417.1Q2UMD5Cellular process(Trans)glycosidases (SSF51445); Beta-galactosidase LacA, domain 3 (SSF117100); Galactose-binding domain-like (SSF49785)
Table 3. AlgPred: Prediction of allergenic proteins and mapping of IgE epitopes.
Table 3. AlgPred: Prediction of allergenic proteins and mapping of IgE epitopes.
S. No.Protein Seq.IgE EpitopeSequence MatchedPos.PIDsARPs
1Bla g 1.0101* LIRALFGL
* LIRALFGL
* LIRALFGL
*LIRALFGL
*LIRALFGL
*LIRALFGL
19
211
403
100
100
100
VDHFIQLIRALFGLS---RAARNLQDD
2Bla g 1.0201The protein sequence does not contain an experimentally proven IgE epitopeIHSIIGLPPFVPPSRRHARRGVGI
3Bla g 2The protein sequence does not contain an experimentally proven IgE epitopeIEDSLTISNLTTSQQDIVLADELS
4Bla g 3The protein sequence does not contain an experimentally proven IgE epitopeLYTYFEHFEHSLGNAMYIGKLEDL
5Bla g 4The protein sequence does not contain an experimentally proven IgE epitopeDALVSKYTDSQGKNRTTIRGRTKF
6Bla g 5The protein sequence does not contain an experimentally proven IgE epitopeYHYDADENSKQKKWDPLKKETIPY
7Bla g 6.0101The protein sequence does not contain an experimentally proven IgE epitopeNot found
8Bla g 6.0201The protein sequence does not contain an experimentally proven IgE epitopeNot found
9Bla g 6.0301The protein sequence does not contain an experimentally proven IgE epitopeNot found
10Bla g 7* AQLLAEEADRKYD
* EKYKSITDELDQTFS
* ELVNEKEKYKSITDE
* ESKIVELEEELRVVG
* MQQLENDLDQVQESLLK
* QKLQKEVDRLEDELV
* RIQLLEEDLERSEER
* RSLSDEERMDALENQ
* VAALNRRIQLLEEDL
* VDRLEDELVNEKEKY
* ARFMAEEADKKYD
* EKYKYICDDLDMTFT
* ELVHEKEKYKYICDD
* ESKIVELEEELRVVG
* IQQIENDLDQTMEQLMQ
* QKLQKEVDRLEDELV
* RIQLLEEDLERSEER
* KGLADEERMDALENQ
* VAALNRRIQLLEEDL
* VDRLEDELVHEKEKY
151
265
259
187
50
247
91
133
85
253
69.23
66.66
73.33
100
58.82
100
100
80
100
93.33
GESKIVELEEELRVVGNNLKSLEV
11Bla g 8The protein sequence does not contain an experimentally proven IgE epitopeNot found
12Per a 1.0101* LIRALFGL
* LIRALFGL
* LIRALFGL
* LIRSLFGL
35
223
100
87.5
FKNFLNFLQTNGLNAIEFLNNIH
13Per a 1.0102* LIRALFGL
* LIRALFGL
* LIRALFGL
* LIRSLFGL
32
220
100
87.5
FKNFLNFLQTNGLNAIEFLNNIH
14Per a 1.0103* LIRALFGL
* LIRALFGL
* LIRALFGL
* LIRSLFGL
199
387
100
87.5
AYLHADDFHKIITTIEA
15Per a 1.0104* LIRALFGL* LIRALFGL78100LPEDLQDFLALIPIDQILAIAAD
16Per a 1.0201* LIRALFGL* LIRALFGL101100FKNFLNFLQTNGLNAIEFLNNIH
17Per a 3.0101The protein sequence does not contain an experimentally proven IgE epitopeSVFHFYRLLVGHVVDPYHKNGLAP
18Per a 3.0201The protein sequence does not contain an experimentally proven IgE epitopeRLNHKPFTYNIEV
19Per a 3.0202The protein sequence does not contain an experimentally proven IgE epitopeRLNHKPFTYNIEV
20Per a 3.0203The protein sequence does not contain an experimentally proven IgE epitopeRLNHKPFTYNIEV
21Per a 6The protein sequence does not contain an experimentally proven IgE epitopeNot found
22Per a 7* AQLLAEEADRKYD
* EKYKSITDELDQTFS
* ELVNEKEKYKSITDE
* ESKIVELEEELRVVG
* MQQLENDLDQVQESLLK
* QKLQKEVDRLEDELV
* RIQLLEEDLERSEER
* RSLSDEERMDALENQ
* VAALNRRIQLLEEDL
* VDRLEDELVNEKEKY
* ARFMAEEADKKYD
* EKYKYICDDLDMTFT
* ELVHEKEKYKYICDD
* ESKIVELEEELRVVG
* IQQIENDLDQTMEQLMQ
* QKLQKEVDRLEDELV
* RIQLLEEDLERSEER
* KGLADEERMDALENQ
* VAALNRRIQLLEEDL
* VDRLEDELVHEKEKY
151
265
259
187
50
247
91
133
85
253
69.23
66.66
73.33
100
58.82
100
100
80
100
93.33
GESKIVELEEELRVVGNNLKSLEV
24Per a 7.0102* AQLLAEEADRKYD
* EKYKSITDELDQTFS
* ELVNEKEKYKSITDE
* ESKIVELEEELRVVG
* MQQLENDLDQVQESLLK
* QKLQKEVDRLEDELV
* RIQLLEEDLERSEER
* RSLSDEERMDALENQ
* VAALNRRIQLLEEDL
* VDRLEDELVNEKEKY
* ARFMAEEADKKYD
* EKYKYICDDLDMTFT
* ELVHEKEKYKYICDD
* ESKIVELEEELRVVG
* IQQIENDLDQTMEQLMQ
* QKLQKEVDRLEDELV
* RIQLLEEDLERSEER
* KGLADEERMDALENQ
* VAALNRRIQLLEEDL
* VDRLEDELVHEKEKY
151
265
259
187
50
247
91
133
85
253
69.23
66.66
73.33
100
58.82
100
100
80
100
93.33
GESKIVELEEELRVVGNNLKSLEV
23Per a 9The protein sequence does not contain an experimentally proven IgE epitopeKLPKLAANREKLEEVAAKFSLQVR
24Per a 10The protein sequence does not contain an experimentally proven IgE epitopeCNGDSGGPLVSANRKLTGIVSWG
25XP_008803750.1The protein sequence does not contain an experimentally proven IgE epitopeIQGQVYCDTCRAGFITELSEFI
26XP_008780644.1The protein sequence does not contain an experimentally proven IgE epitopeNot found
27XP_008782456.1The protein sequence does not contain an experimentally proven IgE epitopeNot found
28AGE46030.1The protein sequence does not contain an experimentally proven IgE epitopeNot found
29XP_008796227.1The protein sequence does not contain an experimentally proven IgE epitopeNot found
30XP_010911620.1The protein sequence does not contain an experimentally proven IgE epitopeMVSIFHTIYVKGDQTNFQIGP
31YP_005090378.1The protein sequence does not contain an experimentally proven IgE epitopeNot found
32XP_008781205.1The protein sequence does not contain an experimentally proven IgE epitopeNot found
33XP_008811417.1The protein sequence does not contain an experimentally proven IgE epitopeNot found
34XP_008803750.1The protein sequence does not contain an experimentally proven IgE epitopeNot found
* PID: Percent of identity, ARPs: allergen representative proteins, Pos: Protein composition.
Table 4. Protein allergenicity potential predictions (AllerCatPro 2.0 Results).
Table 4. Protein allergenicity potential predictions (AllerCatPro 2.0 Results).
S. No.Protein AllergenIgE PrevalenceSimilarity to Allergen and Resulting Predicted Evidence for Allergenicity
Uniprot
/NCBI
PfamInterPro
1Bla g 1.0101Q9UAM5PF06757IPR0106291052Strong evidence
2Bla g 7Q9NG56PF00261IPR000533-Strong evidence
3Per a 1.0101Q9TZR6PF06757IPR01062915Strong evidence
4Per a 1.0102O18535PF06757IPR010629211Strong evidence
5Per a 1.0103O18530PF06757IPR010629211Strong evidence
6Per a 1.0104O18528PF06757IPR01062915Strong evidence
7Per a 1.0201O18527PF06757IPR010629211Strong evidence
8Per a 7Q9UB83PF00261IPR00053317010Strong evidence
9Per a 7.0102P0DSM7---Strong evidence
Table 5. Secondary structure analysis of nine screened protein sequences.
Table 5. Secondary structure analysis of nine screened protein sequences.
S. No.Protein Seq.Alpha HelixExtended StrandBeta TurnRandom Coil
1Bla g 1.0101305 is 74.03%2 is 0.49%17 is 4.13%88 is 21.36%
2Bla g 7280 is 98.59%0 is 0.00%1 is 0.35%3 is 1.06%
3Per a 1.0101181 is 78.35%0 is 0.00%7 is 3.03%43 is 18.61%
4Per a 1.0102176 is 77.19%0 is 0.00%8 is 3.51%44 is 19.30%
5Per a 1.0103279 is 70.63%9 is 2.28%17 is 4.30%90 is 22.78%
6Per a 1.0104208 is 75.91%4 is 1.46%9 is 3.28%53 is 19.34%
7Per a 1.0201319 is 71.52%6 is 1.35%18 is 4.04%103 is 23.09%
8Per a 7278 is 97.89%0 is 0.00%1 is 0.35%5 is 1.76%
9Per a 7.0102281 is 98.94%0 is 0.00%0 is 0.00%3 is 1.06%
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Kausar, M.A.; Bhardwaj, T.; Anwar, S.; Alenazi, F.; Ali, A.; Alshammari, K.F.; AboElnaga, S.M.H.; Singh, R.; Najm, M.Z. In Silico Comparative Exploration of Allergens of Periplaneta americana, Blattella germanica and Phoenix dactylifera for the Diagnosis of Patients Suffering from IgE-Mediated Allergic Respiratory Diseases. Molecules 2022, 27, 8740. https://doi.org/10.3390/molecules27248740

AMA Style

Kausar MA, Bhardwaj T, Anwar S, Alenazi F, Ali A, Alshammari KF, AboElnaga SMH, Singh R, Najm MZ. In Silico Comparative Exploration of Allergens of Periplaneta americana, Blattella germanica and Phoenix dactylifera for the Diagnosis of Patients Suffering from IgE-Mediated Allergic Respiratory Diseases. Molecules. 2022; 27(24):8740. https://doi.org/10.3390/molecules27248740

Chicago/Turabian Style

Kausar, Mohd Adnan, Tulika Bhardwaj, Sadaf Anwar, Fahaad Alenazi, Abrar Ali, Khalid Farhan Alshammari, Shimaa Mohammed Hasnin AboElnaga, Rajeev Singh, and Mohammad Zeeshan Najm. 2022. "In Silico Comparative Exploration of Allergens of Periplaneta americana, Blattella germanica and Phoenix dactylifera for the Diagnosis of Patients Suffering from IgE-Mediated Allergic Respiratory Diseases" Molecules 27, no. 24: 8740. https://doi.org/10.3390/molecules27248740

APA Style

Kausar, M. A., Bhardwaj, T., Anwar, S., Alenazi, F., Ali, A., Alshammari, K. F., AboElnaga, S. M. H., Singh, R., & Najm, M. Z. (2022). In Silico Comparative Exploration of Allergens of Periplaneta americana, Blattella germanica and Phoenix dactylifera for the Diagnosis of Patients Suffering from IgE-Mediated Allergic Respiratory Diseases. Molecules, 27(24), 8740. https://doi.org/10.3390/molecules27248740

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