https://orcid.org/0000-0001-9318-9364
Figures
Abstract
Wheat is a major staple food and has been extensively grown around the globe. Sessile nature of plants has exposed them to a lot of biotic and abiotic stresses including fungal pathogen attack. Puccinia graminis f.sp. tritici causes stem rust in the wheat crop and leads to 70% decrease in its production. Pathogenesis-related (PR) proteins provide plants with defense against different fungal pathogens as these proteins have antifungal activities. This study was designed to screen Pakistani wheat varieties for PR2 and PR3 proteins and their in silico characterization. PR2 and PR3 genes were screened and isolated by PCR amplification from wheat variety Chenab-70 and Frontana, respectively. The nucleotide sequences of PR2 and PR3 genes were deposited in GenBank with accession numbers MT303867 and MZ766118, respectively. Physicochemical properties, secondary and tertiary structure predictions, and molecular docking of protein sequences of PR2 and PR3 were performed using different bioinformatics tools and software. PR2 and PR3 genes were identified to encode β–1,3–glucanase and chitinase proteins, respectively. Molecular docking of both PR2 and PR3 proteins with beta-glucan and chitin (i.e. their respective ligands) showed crucial amino acid residues involved in molecular interactions. Conclusively, molecular docking analysis of β–1,3–glucanase and chitinase proteins revealed crucial amino acid residues which are involved in ligand binding and important interactions which might have important role in plant defense against fungal pathogens. Moreover, the active residues in the active sties of these proteins can be identified through mutational studies and resulting information might help understanding how these proteins are involved in plant defense mechanisms.
Citation: Numan M, Bukhari SA, Rehman M-u-, Mustafa G, Sadia B (2021) Phylogenetic analyses, protein modeling and active site prediction of two pathogenesis related (PR2 and PR3) genes from bread wheat. PLoS ONE 16(9): e0257392. https://doi.org/10.1371/journal.pone.0257392
Editor: Yanbin Yin, University of Nebraska-Lincoln, UNITED STATES
Received: January 26, 2021; Accepted: August 31, 2021; Published: September 10, 2021
Copyright: © 2021 Numan et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: All relevant data are within the manuscript and its Supporting Information files. The accession numbers of nucleotide sequences deposited in sequence repositories are given in the manuscript. The nucleotide sequences of PR2 and PR3 genes were deposited in GenBank with accession numbers MT303867 and MW269567, respectively. After careful analyses, the sequences of PR2 and PR3 genes were submitted to GenBank and allotted with accession numbers: MT303867 and MW269567 for PR2 and PR3, respectively. Phylogenetic analyses of amino acid sequence of PR2 protein has showed more than 98% similarity (GenBank accession number: AAY96422.1) while PR3 protein sequence has showed 87% similarity (GenBank accession number: AKQ09030.1)
Funding: We are highly thankful to Higher Education Commission (HEC), Islamabad, Government of Pakistan for financial assistance to this research work through a project No. TDF02-150.
Competing interests: The authors have declared that they have no conflict of interest.
Introduction
Wheat (Triticum aestivum L.) is the most important and a major staple food being grown in approximately 89 countries of the world. The world population, including humans and livestock, has been increasing continuously which has increased the food demand proportionally that has ultimately increased the production of high-yielding and stress-tolerant wheat varieties [1, 2]. According to the World Urbanization Prospect of United Nations, the current population of Pakistan is 220.9 M with the increased growth rate of 2.0% in 2020 [3]. The increased population has developed a huge gap between lower wheat production and higher consumer demand for food.
Wheat being sessile in nature is continuously exposed to various fungal pathogens [4]. Puccinia graminis f.sp. tritici is a major fungal pathogen which causes stem rust and decreases the wheat production up to 70% approximately [5]. Plants employ several genes to provide defense against fungal attacks by activating innate and systemic acquired resistance mechanisms [6]. Among those several defense-related genes, pathogenesis-related (PR) gene family provides plants with the best defense against fungal pathogens. PR gene family contains 17 different classes of PR genes [7, 8]. Among 17 gene classes, PR2 and PR3 genes are reported to encode β–1,3–glucanase and chitinase, respectively [7, 9, 10].
β–1,3–glucans are the polymers of glucose and make up to 60–90% of fungal cell wall. β–1,3–glucanase breaks down the fungal cell wall by degrading β–1,3 glycosidic linkage presented between glucose residues of β–1,3–glucans and provides defense to the plants [9, 11]. Chitin is also a polymer of glucose residues with β–1,4 glycosidic linkage and abundant in plant cell wall [12]. Chitinase enzyme hydrolyzes the β–1,4 glycosidic linkage present in chitin polymer and provides defense to plants against fungal pathogens [8, 13]. β–1,3–glucanase and chitinase proteins are reported as best soldiers against fungal pathogens. In addition to plants, β–1,3–glucanase and chitinase proteins produced by bacteria [14] and fungi [15] also show excellent antifungal activities.
In silico studies of both β–1,3–glucanase and chitinase proteins from bacteria, fungi and different plants have been reported [12, 15–17]. However, there is a lack of in silico characterization and molecular interactions of both β–1,3–glucanase and chitinase proteins from wheat and their respective ligands in literature. The aim of this study was therefore to screen and identify PR2 and PR3 genes from Pakistani wheat varieties and their respective sequences. Furthermore, in silico characterization and molecular docking of both β–1,3–glucanase and chitinase proteins with their ligands (i.e., beta glucan and chitin) were performed through different bioinformatics tools and software. We found important amino acid residues which are involved in the molecular interactions in active sites of both β–1,3–glucanase and chitinase proteins.
Materials and methods
Seed collection and plant growth
Seeds of nine different wheat varieties were collected from the Ayub Agricultural Research Institute, Faisalabad, Pakistan and sown in 4-inch pots in the Molecular and Medical Genetics Laboratory (MMGL), the Department of Biochemistry, Government College University, Faisalabad, Pakistan.
RNA extraction and screening of pathogenesis-related gene(s)
The total RNA was extracted using GeneJet RNA Purification Kit (ThermoScientific, USA; Catalog # K0731) from the fresh leaves of wheat plants [18]. Complimentary DNA (cDNA) template was synthesized by using RevertAid First strand cDNA synthesis kit (ThermoScientific, USA; Catalog # K1621) from all purified RNA samples. The PR2 and PR3 genes were amplified from cDNA template through polymerase chain reaction (PCR) and the amplicons were gel purified.
Phylogenetic tree construction
The purified PCR products were sequenced using commercial services of the Eurofins Genomics, USA. The contigs were made using DNA Dragon software version 1.6.0 (SequentiX–Digital DNA Processing, Germany). All the assembled contigs (nucleotide sequences) of PR2 and PR3 genes were translated into protein using online bioinformatics tool (i.e. ExPASy–Translate (https://web.expasy.org/translate/)). Amino acid sequences of PR2 and PR3 proteins were used to search homologs by basic local alignment search tool for proteins (BLASTp) [19] from reference sequence (Refseq) database [20]. Phylogenetic trees for PR2 and PR3 proteins were reconstructed with MEGA-X software (Version 10.2.0) using Neighbor-Joining method with 1000 bootstrap value and p-distance as substitution model as described previously with slight modifications [21].
In silico characterization
The physicochemical parameters such as amino acid composition, theoretical isoelectric point (pI), molecular weight, extinction coefficient, instability index, aliphatic index and total number of positively and negatively charged amino acids were obtained using ProtParam [22]. Secondary structures were predicted using SOPMA [23], GOR4 [24] and HNN [25] servers while domain analysis was performed using pfam database [26].
3D model prediction and evaluation
The homology modeling approach was used to predict 3D structures of PR proteins and SWISS-Model online server was used for this purpose [27]. Ramachandran plots were built through PROCHECK [28] to analyze correct stereochemistry of the predicted models. Furthermore, all the predicted models were structurally and energetically verified by different model evaluation tools such as protein structure analysis (ProSA) server, ERRAT [29] and Verify3D [30].
Evaluation of ligand interactions with receptor proteins
The interactions of ligands with their respective receptor proteins were explored by molecular docking approach using Molecular Operating Environment (MOE) software (v2014.0.1.9) [31]. The ligand structures i.e., β-glucan (PubChem ID: 71312131) and chitin (PubChem ID: 6857375) were retrieved in.sdf format from PubChem database [32, 33] and saved in the MOE database after energy minimization. The docking algorithm of MOE was used to dock prepared ligand database with the active site of the receptor protein. The siteFINDER tool of MOE was used to find the binding residues with default parameters such as rescoring 1: London dG, retain: 10, refinement: force field, rescoring 2: London dG, and retain: 10.
Results
Seed collection and plant growth
Seeds of nine different wheat varieties were sown in 4ʺ plastic pots and kept in the plant growth chamber up to three leaf stage. All the wheat varieties used in this study are shown in S1 Fig in S1 File.
RNA extraction and screening of PR proteins
RNA from the selected wheat varieties was extracted (S2 Fig in S1 File) and PCR was done to amplify PR2 and PR3 genes from cDNA template using previously designed primers (S1 Table in S1 File). The Fig 1 shows the clear bands and approximate lengths of PR2 and PR3 genes. PR2 gene was amplified only in the wheat variety Chenab-70 (S3a Fig in S1 File) and PR3 in the wheat variety Frontana (S3b Fig in S1 File).
M = 1 kb DNA ladder, 01 = PR2 gene from wheat variety Chenab-70 and 02 = PR3 gene from wheat variety Frontana.
Phylogenetic tree construction
The sequenced forward and reverse fragments of PR2 and PR3 genes were assembled into two separate contigs to obtain full-length gene sequences. The sequences of PR2 and PR3 genes were found to be 1076 and 1012 nucleotides long, respectively. After careful analyses, the mRNA sequences of PR2 and PR3 genes were submitted to GenBank and allotted with accession numbers: MT303867 and MZ766118, respectively. Phylogenetic trees of both genes were reconstructed using their respective protein sequences. The phylogenetic trees of both proteins were divided into two clusters i.e., cluster I and cluster II. Protein sequence of PR2 was clustered with T. aestivum spp. and more closely related to the accession number AAY96422 (Fig 2) and the protein sequence of PR3 was also clustered with T. aestivum spp. and showed a close evolutionary relationship with AKQ09030 that any other member of the family as shown in red rectangle in Fig 3.
Phylogenetic tree was reconstructed with bootstrap method using 1000 bootstrap replications and p-distance was employed as substitutional model with substitution type as amino acids and rates and patterns were kept uniform. For phylogenetic tree reconstruction, the PR2 protein sequences of different members of the Poaceae family were used. Our protein sequence was clustered with the Triticum aestivum spp. and showed a close relationship with AAY96422.1 than any other member of the family in the same lineage.
Phylogenetic tree was reconstructed with bootstrap method using 1000 bootstrap replications and p-distance was employed as substitutional model with substitution type as amino acids and rates and patterns were kept uniform. For phylogenetic tree reconstruction, the PR3 protein sequences of different members of the Poaceae family were used. Our protein sequence was clustered with the Triticum aestivum spp. and showed a close relationship with AKQ09030.1 than any other member of the family in the same lineage.
Physicochemical properties, secondary structure and protein domain prediction
Physicochemical properties of both proteins were predicted from online server ProtParam. Both proteins were predicted to be small with molecular weights of 35.35 kDa and 33.5 kDa, respectively. PR2 protein was predicted to be basic in nature with pI of 8.50 while PR3 was slightly acidic in nature with pI of 6.89 (Table 1).
The secondary structures of both proteins were predicted by SOPMA, GOR4 and HNN servers which showed that random coils were predominantly present in PR2 (39.22–50.60%) and PR3 (56.74–65.20%) proteins (Table 2).
Protein domain analysis was performed using Hidden Markov Model (HMM) through Pfam. The PR2 protein sequence showed a significant match to HMM with E-value of 8.8e-105 and found to be a member of the glycosyl hydrolases family (Glyco Hydro 17) (S4A Fig in S1 File). The protein sequence of PR3 showed a total of four matches (i.e., two of which were significant while other two were found to be non-significant). The HMM match with the lowest E-value of 3.0e-130 was selected and found to be the member of the chitinase class I family (Glyco hydro 19) (S4B Fig in S1 File).
Homology modeling
To study the structural arrangement of both proteins, 3D models were built by an online server SWISS-Model. SWISS-Model used BLAST [34] and HHblits [35] to align the target sequence with previously characterized sequences and searches for the best template(s). The best selected templates were 1ghs.1.A and 1cns.1.A for PR2 and PR3 proteins with maximum sequence identities (95.10% and 81.40%), coverage (92% and 76%) for PR2 and PR3, respectively (Fig 4A and 4B). The range of the predicted model of PR2 protein was from amino acid 29 to 334 and for PR3 the range was from amino acid 76 to 319. The values of root-mean-square deviation (RMSD) for PR2 and PR3 with their respective templates were found to be 0.068 and 0.081, respectively. The stereochemistry of both models was validated by building the Ramachandran plots for amino acids in core, additionally allowed, generously allowed and disallowed regions (Table 3).
3D structures of PR2 and PR3 proteins, respectively (a, b); Ramachandran plots of PR2 and PR3 showing amino acid placement in allowed and disallowed regions of plots (c, d); Z-score of both PR2 and PR3 proteins is shown which is the indicator of overall quality of predicted 3D model (e, f).
Ramachandran plot of PR2 protein had 90.8% of amino acids in the core region and no amino acid was appeared in the disallowed region while PR3 protein had 86.4% amino acids in the core region, one in generously allowed and one in disallowed region (Fig 4C and 4D). The overall protein structure quality was measured by z-plot for both predicted models using ProSA. The overall quality of both models is represented by the Z-scores of -9.67 and -6.99, respectively. The Z-score of predicted 3D model of PR2 was located within the space of X-ray, while the Z-score of 3D model of PR3 protein was present within the space of NMR protein structure (Fig 4E and 4F).
To investigate the correctly and incorrectly determined regions and overall correctness of the 3D models, the models were validated through online servers ERRAT and Verify3D. ERRAT calculated the overall quality factor of 97.65 and 93.62 for PR2 and PR3, respectively (Fig 5A and 5B). Verify3D calculated 98.04% and 100% of the amino acid residues for averaged 3D-1D score of ≥0.2 of PR2 and PR3 3D models, respectively (Fig 5C and 5D). From the Verify3D results, as the cut-off scores were ≥0.2 therefore, these imply the predicted models are valid. The results of both servers duly verified the predicted models of PR2 and PR3 proteins.
ERRAT graphs of both PR2 and PR3 protein models are showing the correctly and incorrectly determined regions of proteins structures. Two lines (error bars) showed the confidence with which incorrectly determined regions can be rejected. A good and high-resolution structure produces value around 95% or more (A, B). More than 80% of amino acids have shown overall structure correctness value above 0.2 which is an indicator of the correctness of 3D structures of both PR2 and PR3 protein models (C, D).
On the axis of error value, the two lines are indicating the confidence with which it is possible to reject the regions that exceed that error value. The overall quality factor is determined as the percentage of the protein for which the calculated error value falls below the 95% rejection limit. Good high resolution structures generally produce values around 95% or higher. For lower resolution (i.e., 2.5 to 3Å) the average overall quality factor is around 91%. More than 80% of amino acids have shown overall structure correctness value >0.2, which is an indicator of the correctness of 3D structures of both PR2 and PR3 proteins (c, d).
Molecular docking studies
To investigate the molecular interactions of β-d-glucan with PR2 protein and chitin with PR3 protein, the molecular docking was performed. The docking results of β-d-glucan with PR2 protein showed Lys310 as a crucial amino acid in the molecular interaction because it was acting as sidechain hydrogen bond donor (HBD). It interacted with three oxygen atoms (i.e., two oxygen atoms of hydroxyl groups attached to the two different rings of β-d-glucan and one oxygen joining the same two rings of β-d-glucan with ether linkage) and three glutamic acid residues (i.e., Glu259, Glu307 and Glu316) interacted as sidechain hydrogen bond acceptors (HBA) (Fig 6A). Furthermore, Tyr61, Phe62, Asn84, Asn121, Glu122, Asn194, Phe199 and Phe302 were present in the environmental space (Fig 6A). The strong binding mode of the β-d-glucan with PR2 protein is given in Fig 6B. Similarly, the interactions of chitin with PR3 as receptor protein are shown in Fig 6C and the binding mode of chitin with PR3 as target molecule is given in Fig 6D. The molecular docking results of chitin with PR3 protein showed two amino acids (i.e., Gln194 and Ile274) interacting as backbone HBAs and Lys241 acted as sidechain HBD. The Asn200 and Gln238 were acting as sidechain HBAs in the molecular interactions (Fig 6C). Furthermore, Glu143, Glu165, Tyr172, Ser196 and Asn275 were present in the environmental space (Fig 6C).
PR2 protein residues (Glu307, Glu259, Lys310 and Glu316) are showing interactions with beta-glucan. Lys310 residue was the most crucial and involved in most interactions with ligand (a); surface map of PR2 protein and ligand interaction (b). PR3 protein residues (Gln194, Asn200, Gln238, Lys241 and Ile274) are showing interactions with chitin (c); surface map of PR3 protein and ligand interactions (d). Green surface is representing the binding pocket of the receptor proteins while ligands are rendered as sticks.
Discussion
Crop protection and increased production is a major challenge in agricultural sciences worldwide as plants are continuously exposed to fungal pathogens. In developed countries, it is estimated that 25% of wheat crop is lost due to plant diseases while in developing countries more than 50% of the crop is lost. So, it is necessary to develop new tolerant varieties for more crop production [10, 36]. This study was focused to screen local Pakistani wheat varieties for pathogen-resistant genes and their In-silico analyses so that more and more information can be extracted for the development of tolerant and high-yielding wheat varieties.
In current study, two pathogenesis-related genes i.e., PR2 and PR3 were screened and identified from two Pakistani wheat varieties Chenab 70 and Frontana, respectively. Phylogenetic analyses of amino acid sequences have shown that PR2 protein from Triticum aestivum Chenab-70 has a strong evolutionary relationship with Triticum aestivum (Chitinase: AAY96422.1) (i.e., with a bootstrap value of 100) and PR3 protein from Tritucum aestivum Frontana was closely clustered with Triticum aestivum (Chitinase: AKQ09030.1) (i.e., with a bootstrap value of 87). Phylogenetic analysis has been extensively used for investigating the evolutionary relationships among related groups of taxa [10, 37].
A range of β–1,3–glucanase and chitinase proteins have been identified and their in silico characterization including physicochemical properties, and predictions of their secondary and tertiary structures have been done from different organisms ranging from bacteria, algae [38], fungi [39] and insects [40, 41] to plants [11, 17]. The molecular weight of β–1,3–glucanases ranges from 33–36 kDa and can be classified into three different classes (i.e., classes I, II and III). The molecular weight of class I β–1,3–glucanases is approximately 33 kDa and reported to be basic in nature while class II and III β–1,3–glucanases molecular weight ranges from 34–36 kDa and have been found to be acidic in nature [7, 42, 43]. Sinha et al. [7] has reported that chitinases also have different basic and acidic proteins with molecular weight ranges from 25–35 kDa. Furthermore, recent studies have revealed the molecular weight of chitinases ranged from 34.5 to 49.5 KDa with theoretical pI 4.81–7.94 showing acidic to slightly basic nature of chitinase genes [15, 44]. In our study, the molecular weight of β–1,3–glucanase and chitinase proteins were found to be 35.4 kDa and 33.5 kDa, respectively while theoretical pI for β–1,3–glucanase was slightly basic (i.e., 8.50) and pI for chitinase protein was slightly acidic (i.e., 6.89). On the basis of above discussion our findings are in line with previously reported results.
Present study described high proportions of random coils and α-helices in both β–1,3–glucanase and chitinase proteins. In previous studies secondary structure prediction has shown higher proportions of random coils (46.8%) and α-helices (30.4%) in Beauveria bassiana chitinase and β–1,3–glucanase proteins. The higher proportions of random coils and α-helices provide stability to the enzyme [15, 45, 46]. β–1,3–glucanase and chitinase proteins belong to hydrolase family which hydrolyzes the β–1,3 and β–1,4 glycosidic linkages present in β-glucans and chitin, respectively [11, 12]. In current study, the domain analysis identified β–1,3–glucanase as a member of glycosyl hydrolase family 17 and chitinase as a member of glycosyl hydrolase family 19 (S4 Fig in S1 File). The domain of β-amylase-like protein has been found to be a member of glyco hydro family 14 [47].
The 3D structure prediction contributes towards understanding of the functions of different protiens. Determination of actual crystal structure of any protein is very difficult as the process involves highly complex techniques such as nuclear magnatic resonanse and crystallography [48, 49]. Bioinformatics tools such as SWISS-Model, ProSA, PROCHECK, ERRAT, Verify3D are the best alternatives to predict and evaluate the protein 3D structures [50, 51]. In this study, 3D structures of PR2 and PR3 proteins were predicted using SWISS-Model. The Z-score serves as an indicator of overall quality of protein predicted 3D structures and determine the average energy deviation with regards to engergy distribution derived from random conformations. The Z-score is calculated by comparing the related proteins structures exerimentally resolved by X-rays or NMP present in the current protein databank [52, 53]. Furthermore, PROCHECK showes the amino acid distribution in allowed, additionally allowed and disallowed regions by means of Ramachanran plot. ERRAT and Verify3D estimate the values for overall quality factor and analyse correctly and incorrectly determined regions in the predicted 3D structures. The two lines (error bars) are showing the confidence with which incorrectly determined regions can be rejected. A good and high-resolution structure produces value around 95% or more (Fig 5A and 5B). On the axis of error value, the two lines are indicating the confidence with which it is possible to reject the regions that exceed that error value. The overall quality factor is determined as the percentage of the protein for which the calculated error value falls below the 95% rejection limit. Good high-resolution structures generally produce values around 95% or higher. For lower resolution (i.e., 2.5 to 3Å) the average overall quality factor is around 91%. In this study, ERRAT calculated the overall quality factor of 97.65% for PR2 which indicated high-solution structure and 93.62% for PR3, which indicated the structure of an average resolution, respectively (Fig 5A and 5B). The predicted 3D structures of proteins are considered good if a least 80% of the residues exhibited score ≥0.2 in the 3D-ID profile [39].
Furthermore, active site prediction and identification of active residues in protein binding pockets assist in designing new drugs or finding potential targets in proteins. Molecular docking analyses using MOE has been a great way to investigate ligand-protein and protein-protein interactions. Several studies have reported the molecular interactions of chitin with chitinase enzyme and other molecular interactions which are important for plant defense strategies [54–56]. Molecular docking of extracellular chitinase from Bacillus pumilus MCB-7 revealed that amino acid residues Ala75, Cys98, Gln99, Val113 and Met114 interacted with chitin [54]. Moreover, chitinase from Aspergillus fumigatus has Trp312, Ala124, Tyr125, Tyr232 and Asn233 while chitinase from Blastomyces dermatitidis has Ala97, Thr98, Tyr205, Asn206, and Trp288 active residues which are involved in the protein interactions [57, 58]. In current study, chitinase protein from wheat Frontana had Gln194, Asn200, Gln238, Lys241 and Ile274 residues present in the active site pocket which were found to be involved in the interactions. Similarly, β–1,3–glucanase had Glu259, Glu307, Glu316 and Lys310 residues were found to be involved in the molecular interactions with β-glucan.
Conclusion
Conclusively, molecular docking analysis of β–1,3–glucanase and chitinase proteins has revealed crucial amino acid residues which are involved in ligand binding and important interactions which might have important role in plant defense against various fungal pathogens. Moreover, active residues in the active sites of PR2 and PR3 proteins can also be determined through mutational studies and resulting information might help understanding how these proteins are involved in plant defense mechanisms. Lastly, the resultant information might also help to improve the plant genomic structure resulting in resistant crop and high production to meet consumer demand.
Acknowledgments
All authors of this article would like to thank the Department of Bioinformatics and Biotechnology, Government College University, Faisalabad and Centre of Agricultural Biochemistry and Biotechnology, University of Agriculture, Faisalabad for their academic support to conduct this research.
References
- 1.
FOOD O (2016) The state of food and agriculture.
- 2. Bukhari SA, Mustafa G, Bashir S, Akram NA, Sadia B, et al. (2020) Genetic transformation of Sr22 gene in a high yielding susceptible cultivar of commercial wheat (Triticum aestivum L.). 3 Biotech 10: 1–9. pmid:31815083
- 3.
Anonymous (2018) United Nations, Department of Economic and Social Affairs, Population Division. World Urbanization Prospects: The 2018 Revision.
- 4. Shahzad B, Bukhari SA, Irfan A, Ghulam M (2019) Molecular identification of stem rust resistance gene (s) from Pakistani wheat cultivars. International Journal of Agriculture and Biology 21: 1013–1018.
- 5. Schumann GL, Leonard KJ (2011) Stem rust of wheat. Stem rust of wheat.
- 6. Bekhit M (2018) Fighting Wheat Stem Rust: Pathogenesis-Related Genes, From Conventional To Modern Approaches. Annals of Agricultural Science, Moshtohor 56: 1031–1044.
- 7. Sinha M, Singh RP, Kushwaha GS, Iqbal N, Singh A, et al. (2014) Current overview of allergens of plant pathogenesis related protein families. The Scientific World Journal 2014. pmid:24696647
- 8.
Devi EL, Kumar S, Singh TB, Sharma SK, Beemrote A, et al. (2017) Adaptation strategies and defence mechanisms of plants during environmental stress. Medicinal Plants and Environmental Challenges: Springer. pp. 359–413.
- 9.
Leubner-Metzger G, Meins F Jr (1999) Functions and regulation of plant β-(PR-2). Pathogenesis-Related Proteins in Plants; CRC Press: Boca Raton, FL, USA. https://doi.org/10.1007/s004250050622 pmid:10436221
- 10. Singh A, Kirubakaran SI, Sakthivel N (2007) Heterologous expression of new antifungal chitinase from wheat. Protein expression and purification 56: 100–109. pmid:17697785
- 11. Dogra V, Sreenivasulu Y (2015) Cloning and functional characterization of β-1, 3-glucanase gene from Podophyllum hexandrum—A high altitude Himalayan plant. Gene 554: 25–31. pmid:25303872
- 12. Suma K, Podile AR (2013) Chitinase A from Stenotrophomonas maltophilia shows transglycosylation and antifungal activities. Bioresource technology 133: 213–220. pmid:23428818
- 13. Barboza-Corona J, Contreras J, Velázquez-Robledo R, Bautista-Justo M, Gómez-Ramírez M, et al. (1999) Selection of chitinolytic strains of Bacillus thuringiensis. Biotechnology Letters 21: 1125–1129.
- 14. Vaaje-Kolstad G, Westereng B, Horn SJ, Liu Z, Zhai H, et al. (2010) An oxidative enzyme boosting the enzymatic conversion of recalcitrant polysaccharides. Science 330: 219–222. pmid:20929773
- 15. Bhagwat P, Amobonye A, Singh S, Pillai S (2020) A comparative analysis of GH18 chitinases and their isoforms from Beauveria bassiana: An in-silico approach. Process Biochemistry 100: 207–216.
- 16. Akeed Y, Atrash F, Naffaa W (2020) Partial purification and characterization of chitinase produced by Bacillus licheniformis B307. Heliyon 6: e03858. pmid:32395650
- 17. Shin S, Mackintosh CA, Lewis J, Heinen SJ, Radmer L, et al. (2008) Transgenic wheat expressing a barley class II chitinase gene has enhanced resistance against Fusarium graminearum. Journal of experimental botany 59: 2371–2378. pmid:18467324
- 18. Simbolo M, Gottardi M, Corbo V, Fassan M, Mafficini A, et al. (2013) DNA qualification workflow for next generation sequencing of histopathological samples. PloS one 8: e62692. pmid:23762227
- 19. Altschul SF, Gish W, Miller W, Myers EW, Lipman DJ (1990) Basic local alignment search tool. J Mol Biol 215: 403–410. pmid:2231712
- 20. O’Leary NA, Wright MW, Brister JR, Ciufo S, Haddad D, et al. (2016) Reference sequence (RefSeq) database at NCBI: current status, taxonomic expansion, and functional annotation. 44: D733–D745.
- 21. Kumar S, Stecher G, Li M, Knyaz C, Tamura KJMb, et al. (2018) MEGA X: molecular evolutionary genetics analysis across computing platforms. 35: 1547.
- 22.
Gasteiger E, Hoogland C, Gattiker A, Wilkins MR, Appel RD, et al. (2005) Protein identification and analysis tools on the ExPASy server. The proteomics protocols handbook: Springer. pp. 571–607.
- 23.
Geourjon C, Deléage G (1995) SOPMA: significant improvements in protein secondary structure prediction by consensus prediction from multiple alignments. Computer applications in the biosciences: CABIOS 11: 681–684. https://doi.org/10.1093/bioinformatics/11.6.681 pmid:8808585
- 24. Kouza M, Faraggi E, Kolinski A, Kloczkowski A (2017) The GOR Method of Protein Secondary Structure Prediction and Its Application as a Protein Aggregation Prediction Tool. Methods in molecular biology (Clifton, NJ) 1484: 7–24. pmid:27787816
- 25. Combet C, Blanchet C, Geourjon C, Deleage GJTibs (2000) NPS@: network protein sequence analysis. 25: 147–150.
- 26. Mistry J, Chuguransky S, Williams L, Qureshi M, Salazar GA, et al. (2021) Pfam: The protein families database in 2021. 49: D412–D419.
- 27. Waterhouse A, Bertoni M, Bienert S, Studer G, Tauriello G, et al. (2018) SWISS-MODEL: homology modelling of protein structures and complexes. 46: W296–W303.
- 28. Laskowski RA, MacArthur MW, Moss DS, Thornton JMJJoac (1993) PROCHECK: a program to check the stereochemical quality of protein structures. 26: 283–291.
- 29. Colovos C, Yeates TOJPs (1993) Verification of protein structures: patterns of nonbonded atomic interactions. 2: 1511–1519. pmid:8401235
- 30.
Eisenberg D, Lüthy R, Bowie JU (1997) [20] VERIFY3D: Assessment of protein models with three-dimensional profiles. Methods in Enzymology: Academic Press. pp. 396–404. https://doi.org/10.1016/s0076-6879(97)77022-8 pmid:9379925
- 31.
Inc. CCG (2016) Molecular operating environment (MOE). Chemical Computing Group Inc 1010 Sherbooke St. West, Suite# 910, Montreal
- 32.
Information NCfB (2021) PubChem Compound Summary for CID 71312131, beta Glucan.
- 33.
Information NCfB (2021) PubChem Compound Summary for CID 6857375, Chitin from shrimp shells.
- 34. Camacho C, Coulouris G, Avagyan V, Ma N, Papadopoulos J, et al. (2009) BLAST+: architecture and applications. BMC Bioinformatics 10: 421. pmid:20003500
- 35. Steinegger M, Meier M, Mirdita M, Vöhringer H, Haunsberger SJ, et al. (2019) HH-suite3 for fast remote homology detection and deep protein annotation. BMC Bioinformatics 20: 473. pmid:31521110
- 36.
Bowyer P (1999) Plant disease caused by fungi: phytopathogenicity In: Molecular Fungal Biology,(RP Oliver, M Schweizer). Cambridge University Press, Cambridge.
- 37. Katiyar A, Lenka SK, Lakshmi K, Chinnusamy V, Bansal KC (2009) In silico characterization and homology modeling of thylakoid bound ascorbate peroxidase from a drought tolerant wheat cultivar. Genomics, proteomics & bioinformatics 7: 185–193. pmid:20172491
- 38. Bukhari S, Tahir M, Akhter N, Anjum F, Anwar H, et al. (2018) Phylogeny and comparative modeling of phytochelatin synthase from Chlorella sp. as an efficient bioagent for detoxification of heavy metals. Journal of biological regulators and homeostatic agents 32: 1191–1197. pmid:30334412
- 39. Mustafa G, Arif R, Bukhari SA, Ali M, Sharif S, et al. (2018) Structural and functional annotation of citrate synthase from Aspergillus niger ANJ-120. Pakistan journal of pharmaceutical sciences 31. pmid:29348081
- 40. Hughes AL (2012) Evolution of the βGRP/GNBP/β-1, 3-glucanase family of insects. Immunogenetics 64: 549–558. pmid:22410636
- 41. Arakane Y, Muthukrishnan S (2010) Insect chitinase and chitinase-like proteins. Cellular and molecular life sciences 67: 201–216. pmid:19816755
- 42. Simmons CR (1994) The physiology and molecular biology of plant 1, 3-β-D-glucanases and 1, 3; 1, 4-β-D-glucanases. Critical Reviews in Plant Sciences 13: 325–387.
- 43. Sticher L, Hinz U, Meyer AD, Meins F (1992) Intracellular transport and processing of a tobacco vacuolar β-1, 3-glucanase. Planta 188: 559–565. pmid:24178389
- 44. Fang W, Leng B, Xiao Y, Jin K, Ma J, et al. (2005) Cloning of Beauveria bassiana chitinase gene Bbchit1 and its application to improve fungal strain virulence. Applied and environmental microbiology 71: 363–370. pmid:15640210
- 45. Gouripur GC, Kaliwal RB, Kaliwal BB (2016) In silico characterization of beta-galactosidase using computational tools. Journal of Bioinformatics and Sequence Analysis 8: 1–11.
- 46. Mishra A, Pandey B, Tyagi C, Chakraborty O, Kumar A, et al. (2015) Structural and functional analysis of chitinase gene family in wheat (Triticum aestivum).
- 47. Filiz E, Koç İ (2014) In silico sequence analysis and homology modeling of predicted beta-amylase 7-like protein in Brachypodium distachyon. L Journal of BioScience and Biotechnology 3: 61–67.
- 48. Kopp J, Schwede T (2004) Automated protein structure homology modeling: a progress report. Pharmacogenomics 5: 405–416. pmid:15165176
- 49.
Jaroszewski L (2009) Protein structure prediction based on sequence similarity. Biomedical Informatics: Springer. pp. 129–156.
- 50. Mohan C, Santos Júnior CD, Chandra S (2020) In silico characterisation and homology modelling of a pathogenesis-related protein from Saccharum arundinaceum. Archives of Phytopathology and Plant Protection: 1–17.
- 51. Jabbir F, Irfan M, Mustafa G, Ahmad HI (2019) Bioinformatics approaches to explore the phylogeny and role of BRCA1 in breast cancer. Critical Reviews™ in Eukaryotic Gene Expression 29. pmid:32422010
- 52. Wiederstein M, Sippl MJ (2007) ProSA-web: interactive web service for the recognition of errors in three-dimensional structures of proteins. Nucleic acids research 35: W407–W410. pmid:17517781
- 53. Sippl MJ (1993) Recognition of errors in three‐dimensional structures of proteins. Proteins: Structure, Function, and Bioinformatics 17: 355–362.
- 54. Rishad K, Varghese S, Jisha M (2020) Sequence analysis and docking performance of extracellular chitinase from Bacillus pumilus MCB-7, a novel mangrove isolate. Enzyme and Microbial Technology 140: 109624. pmid:32912684
- 55. Khan FI, Govender A, Permaul K, Singh S, Bisetty K (2015) Thermostable chitinase II from Thermomyces lanuginosus SSBP: Cloning, structure prediction and molecular dynamics simulations. Journal of theoretical biology 374: 107–114. pmid:25861869
- 56. Pan M, Li J, Lv X, Du G, Liu L (2019) Molecular engineering of chitinase from Bacillus sp. DAU101 for enzymatic production of chitooligosaccharides. Enzyme and microbial technology 124: 54–62. pmid:30797479
- 57. Schüttelkopf AW, Gros L, Blair DE, Frearson JA, van Aalten DM, et al. (2010) Acetazolamide-based fungal chitinase inhibitors. Bioorganic & medicinal chemistry 18: 8334–8340. pmid:21044846
- 58. Searle AJ, Winston V, Scalarone GM (2012) Blastomyces dermatitidis: Chitinase Homology Model, in Silico Docking, and Inhibition Assay. Open Journal of Medical Microbiology 2: 1–7.