- Open Access
In silico characterization of bacterial chitinase: illuminating its relationship with archaeal and eukaryotic cousins
Journal of Genetic Engineering and Biotechnology volume 19, Article number: 19 (2021)
Chitin is one of the most abundant biopolymers on Earth, only trailing second after cellulose. The enzyme chitinase is responsible for the degradation of chitin. Chitinases are found to be produced by wide range of organisms ranging from archaea to higher plants. Though chitin is a major component of fungal cell walls and invertebrate exoskeletons, bacterial chitinase can be industrially generated at low cost, in facile downstream processes at high production rate. Microbial chitinases are more stable, active, and economically practicable compared to the plant- and animal-derived enzymes.
In the present study, computationally obtained results showed functional characteristics of chitinase with particular emphasis on bacterial chitinase which is fulfilling all the required qualities needed for commercial production. Sixty-two chitinase sequences from four different groups of organisms were collected from the RCSB Protein Data Bank. Considering one suitable exemplary sequence from each group is being compared with others. Primary, secondary, and tertiary structures are determined by in silico models. Different physical parameters, viz., pI, molecular weight, instability index, aliphatic index, GRAVY, and presence of functional motifs, are determined, and a phylogenetic tree has been constructed to elucidate relationships with other groups of organisms.
This study provides novel insights into distribution of chitinase among four groups and their characterization. The results represent valuable information toward bacterial chitinase in terms of the catalytic properties and structural features, can be exploited to produce a range of chitin-derived products.
Chitinases (EC 184.108.40.206) are glycosyl hydrolases that catalyze the hydrolytic degradation of chitin. Chitin represents the second most abundant carbohydrate after cellulose, occurring as natural insoluble biopolymer featuring linear β-1,4-linkages between N-acetyl d-glucosamine (GlcNAc). It is extensively distributed in nature in different forms and constitutes, among other things, an essential component of the shells of crustaceans and exoskeletons of insects, as well as the key component in cell walls of a variety of fungi.
Aspergillus, Trichoderma, etc. are common fungal strains known to produce chitinase [1, 2]. Besides the fungal sources, many bacterial genera including Serratia sp., Streptomyces sp., and Bacillus cereus produce high levels of chitinolytic enzymes . Also, some archaea like Pyrococcus furiosus  or Thermococcus kodakarensis  can produce chitinase under hyperthermophilic conditions. Furthermore, just recently, a new family of chitinase from Thermococcus chitonophagus has been reported by Horiuchi et al.  which contains two additional chitin-binding domains along with catalytic domains.
Those chitinases are found to be active in a broad temperature range with a good pH tolerance. Whereas endochitinase from mesophilic actinomycetes Streptomyces violaceusniger can tolerate temperatures up to 28 °C , Streptomyces thermoviolaceus produces thermostable chitinase withstanding 80 °C at pH 8–10 , and the mesophilic bacterium Aeromonas hydrophila provides chitinolytic proteins performing at 37 °C and pH 8.0 . On the opposite side of extremophilic behavior, the psychrophilic bacterium, Arthrobacter sp. from the Antarctic sea sediment, is reported by Lonhienne et al. to secrete chitinase at 4 °C , while Stenotrophomonas maltophilia  shows an acidic pH optimum range of 4.5 to 5.0 for production of chitinase enzymes.
The diverse role of plant chitinase varies from the evolutionary oldest moss to highly evolved monocot plants. They are not only involved in the defense response but also implicated in symbiotic associations, organ development, and resistance to abiotic stresses. Bryum coronatum is a moss that produces chitinase-A as safeguard from fungal pathogen . Class III chitinase from Vitis vinifera  including fern genera Pteris ryukyuensis  features a specific two lysine motif for enhancing resistance against fungi. Moreover, chitinase-A from Cycas revoluta expresses additional transglycosylation activity which is unique in the plant kingdom . The saffron chitinase from Crocus sativus  promotes the jasmonic acid signal pathway as defense activation mechanism.
Chitinase enzymes have a broad range of applications such as preparation of pharmaceutical products, production of single-cell proteins [17, 18], isolation of protoplasts from fungi , biomarker for diagnosis of cancer  as well as controlling malaria transmission.
According to Oerke et al. , around 35% of crop yields are lost due to diseases in the field and 15% covers the post-harvest loss. In this preventive aspect, microbial chitinases offers the opportunity for cell wall degradation of many pests and pathogens, thereby unveiling antibacterial, antifungal, insecticidal, and nematocidal activity .
A multidisciplinary approach is necessary for the successful integration of chitinase in industrial scale applications as well as multi-fold product yield. An efficient enzyme production depends on desired physical, chemical, and mechanical properties of the biocatalysts. Herein, computational design plays an appreciable role to understand the overall properties of bacterial chitinase in comparison with archaeal, fungal, and plant-derived analogs. Therefore, a co-ordinated study following the recent trends of genetic engineering would be helpful to use chitinolytic microorganisms in rendering plant defense.
Retrieval of the sequences from RCSB PDB
A total of 62 different chitinase sequences of archaeal, bacterial, fungal, and plant origin have been retrieved from the RCSB protein data bank (http://www.rcsb.org) on 4 October 2018.
Determination of physical parameters
Different physiochemical characteristics of chitinase enzymes were analyzed by means of using the ExPASy–ProtParam tool (http://web.expasy.org/protparam) . Computational approaches have been employed covering parameters such as molecular weight, theoretical pI value, instability index, aliphatic index, and grand average of hydrophobicity (GRAVY).
Analysis of primary structure
The core amino acids that form the primary structures of the proteins are extracted and listed by using the ExPASy–ProtParam tool.
MEME Suite 5.0.2 was utilized to detect any signature sequence of chitinase enzyme present in all the evaluated organisms (http://www.meme-suite.org) . MEME-ChIP was performed using classic mode of motif discovery. Default width of MEME motif was set to 3 and 20 as minimum and maximum motif respectively.
Analysis of secondary structure
Analysis of the secondary structure by SOPMA has been performed (https://npsa-prabi.ibcp.fr), according to Geourjon and Deleage .The PDBsum tool provides the pictorial overviews of the macromolecular structures (http://www.ebi.ac.uk/) showing the possible arrangements of long α-helices and large β-sheets in this resolution. Ramachandran plots were generated by the PROCHECK tool (http://servicesn.mbi.ucla.edu/PROCHECK/) [26,27,28].
Analysis of tertiary structure
The overall quality of the constructed 2D model has been evaluated with ERRAT value (services.mbi.ucla.edu/ERRAT) . ProSA-web was exploited to assess the Z score and energy plots (https://prosa.services.came.sbg.ac.at/prosa.php) . Validation of the homology modeling of protein 3D structures was performed on a SWISS MODEL web-server (https://swissmodel.expasy.org/interactive/6Dk7as/models/) [31,32,33]. QMEAN comprised three different models (QMEAN local quality, DisCo, Brane) of assessment to understand the geometrical features of the protein model (https://swissmodel.expasy.org/qmean/). Finally, QMEAN 4 was used to fit cumulative QMEAN value in global scale at a range of 0 to 1 (https://swissmodel.expasy.org/qmean/).
The protein family shares common evolutionary pathways among other groups of organisms. Contrarily, GO (gene ontology) defines the classes used to describe the gene function, from molecular activities of gene product to large biological pathways. To understand both these purposes, the InterProScan server (www.ebi.ac.uk/interpro/search/sequence-search) was used.
The SBASE tool (http://pongor.itk.ppke.hu/protein/sbase.html) facilitated the detection of protein domain sequences representing various structural and functional segments of proteins.
The knowledge of protein membrane topography is very crucial to assess how they react with outer side biomolecules. The TMHMM tool was used (http://www.cbs.dtu.dk/services/TMHMM/) to predict whether the protein is membrane-spanning or extracellular in nature.
Subcellular localization of the protein was confirmed by the mGOASVM tool (http://bioinfo.eie.polyu.edu.hk/mGoaSvmServer/mGOASVM.html).
In proteomics studies, the cutting of proteins and polypeptides to smaller peptides is essential for mass spectrometric analysis. The Peptide Cutter tool predicts computational substrate cleavage site of a protein by endopeptidase or chemical treatment (web.expasy.org/peptide_cutter/).
Construction of phylogenetic tree
Phylogenetic analysis was performed using the MEGA 10.0.5 software  with all of the 62 sequences. Alignment of amino acid sequences was achieved by CLUSTAL W. Maximum likelihood method was used for generating tree with a Poisson correction model for multiple amino acid substitution with 1000 random bootstrap replicates. The trees were optimized with heuristic nearest neighbor interchange (NNI) method.
Sequence retrieval from RCSB PDB
Sixty-two different chitinase enzyme sequences from four different groups of organisms have been retrieved. Both FASTA and PDB formats were recorded as input to perform computational studies. Here, we were considering one organism as candidate(s) from each four groups based on the specificity.
Comparison of physiochemical parameters
A comparison of physiochemical properties with the four groups of organisms is presented in Table 1. Stability of all sequences has been studied by analyzing the values for the instability index, the aliphatic index, and the grand average of hydropathicity (GRAVY) index.
Proteins whose instability index are lower than 40 are predicted by Guruprasad et al.  to be stable while levels above 40 indicate unstable sequences. The evaluated bacterial chitinase here (with an instability index of 15.39) appears as stable protein sequence in this analysis. Fungi and higher plants are predicted to produce unstable chitinase with relatively high instability indices compared to bacteria.
The aliphatic index measures the relative volume occupied by aliphatic side chains of a given protein. An increase in the aliphatic index increases the thermostability of globular proteins, and consequently, a high aliphatic index of 74.31 accounts for a good temperature stability of the bacterial protein [37, 38].
The isoelectric point (pI) expresses the pH at which an amino acid, peptide, or protein does not migrate in an electric field. The ProtParam analysis displayed the theoretical pI value for the bacterial chitinase as 5.67 indicating as negative charge of the protein, predicting it to be acidic in nature. In contrast, plant chitinases are found to be preferably basic in nature (pI value 8.84).
Primary structure determination
Proteins differ from one another by their structures, primarily in their sequences of amino acids. The abundance of certain amino acids determines different structural organizational elements of proteins. Eight prevalent amino acids are found in the structure of bacterial chitinases. The average percentage is displayed as follows: alanine (9.3%), glycine (11.1%), asparagine (6.1%), arginine (2.0%), proline (4.3%), threonine (6.1%), aspartic acid (6.9%), phenylalanine (4.6%). Figure 1a shows the comparative percentage of amino acids present in archaea, fungi, and plants.
The MEME suite tool found significant consensus motifs (E value ≤ 0.05). Five functional motifs are identified from all the sequences represented in Fig. 1b.
Secondary structure determination
The understanding of the secondary structures is of paramount importance as this configuration elucidates the reactive nature of proteins.
The SOPMA tool revealed that chitinase enzymes are dominated by random coils which pointed toward evolutionary conserveness of the proteins. In addition, random coils are often described as regions where the folded chain acts more flexibly and dynamically than other secondary conformational structures . According to Shortle , no sidechain-sidechain interaction occurs along the polypeptide chain, and the connecting bridges between beta strand and alpha helix is supposed to be the most conserved region of random coils. Figure 2a shows comparative percentage of random coils, α helices, extended strands, and β turns within all four groups of organisms.
PDBSum shows that the bacterial chitinase contained three domains. Domain 1 mainly features β-sandwich, domain 2 α-β-barrel, and domain 3 consists of an α-β-roll.
Figure 2b shows a β-hairpin in between residues 45 and 55, as well as small hairpins between 115 and 120, 450 and 465, 495 and 500, and between 504 and 510. Residues 195/200 as well as 204/215 are connected with disulfide bridges.
The PROCHECK tool was exploited to generate the Ramachandran plot (Phi/Psi) of proteins. Figure 2c shows the 90.8% residues falling in most favored region denoted as core by the red color, indicating a good quality of model. The brown-colored region is covering 8.8% of residues in additionally allowed region. The dark yellow-colored region contains 0.4% residues in generously allowed regions. Although four groups of sequences showed approximately 90% of residues falling in favored regions in Table 2, there are no residues under the white-colored disallowed region in bacterial protein. Residues tend to fall in disallowed regions because of steric hindrance or clashes between atoms. Active amino acids in disallowed region indicate the declination of stability of protein .
Tertiary structure determination
The protein molecule bends and twists in such a fashion to achieve maximum stability on lowest energy state resulting in a defined overall three-dimensional shape. The selected bacterial chitinase model was evaluated using the ERRAT server. The error values that fall below 95% are measured in rejection limit . In Table 3, ERRAT showed an overall quality factor of 95.085, a result expected for crystallographic models with resolutions > 2.5 Å. Although the overall ERRAT quality factor value lies above 95% (except archaea) in bacteria, fungi, and higher plants, further model validation tools suggest that these are not as reliable with high confidence as bacteria.
ERRAT value was further normalized with the protein size and quantitatively assessed by highresolution X-ray crystallography. ProSA generated Z score for bacterial sequence (PDB ID:1EDQ) is −10.23. The score fits well within the range indicating a highly stable structure. A plot(Fig. 3a) showing normalized Z score on the Y axis versus number of residues on the X axis found to be well in tolerable limits. SWISS MODEL Workspace visualizes the final 3D model. A total of 26 templates were found for the bacterial chitinase (PDB ID: 1EDQ). The best fit model generated by model-template alignment in Fig. 3b was based on the top five templates. The templates were selected for model building based upon the highest quality of sequence identity, best E value, and maximum number of query sequence covered. Four N-acetyl d-glucosamine residues bind as ligand molecules with protein. The generated model was estimated by assessing cumulative QMEAN (QMEAN local quality, DisCo, Brane) score and is displayed in Fig. 3c. Larger QMEAN scores indicate better models whereas negative scores refer to unstable models . Figure 3c shows that QMEAN scored 0.65 which provides an estimation of the degree of nativeness. 0.65 is very well fitted within the expected range as the standard deviation is less than 1 from the mean score, very similar to related results by Benkert et al. . Furthermore, QMEAN4 is a reliability score based on four linear combinations—local geometry, distance-dependent interaction, agreement of the predicted secondary structure and solvent accessibility, solvation potential calculation. The global model reliability estimation (GMQE) for QMEAN4 ranged between 0 and 1 . Comparing with other groups of organisms from Table 4, only bacterial proteins fit under the GMQE scale.
Analysis of protein function
InterPro Scan provides information regarding the functional protein families. In all the four organisms, chitinase belongs to the glycoside hydrolase superfamily. Figure S(A) shows bacterial chitinase belonging more specifically to glycoside family 18 featuring both a catalytic domain and chitin-binding domain. For the overall knowledge about protein functions, it is necessary to understand protein-protein interaction. STRING database infer the protein association with other proteins regulated by co-operative binding . An illustration from Fig. S(B) interlinks ten interacting chitin-binding domains containing proteins within the glycoside hydrolase family. Spro_2725 is our query protein and shows interplay with eight Spro proteins and one nagK and nagZ proteins.
Two proteins, N-acetyl d-glucosamine (nagK) and β-hexosaminidase (nagZ), are made up of 306 amino acids and 339 amino acid residues respectively. nagK is involved in phosphorylation of GlcNAc to produce GlcNAc-6-P supporting the glycosylation signaling pathways. Muropeptides are hydrolyzed to 1,6-anhydromurNAc by the nagZ protein in the cytoplasm. Both of the proteins are associated with amino sugar and peptide recycling. Furthermore, MurNAc-6-phosphate was converted to GlcNAc-6-P by an etherase enzyme. GlcNAc-6-P is then deacetylated and either enters glycolysis or remains as precursor of chitin-like tertiary compounds for murein biosynthesis.
The domain included in SBASE represents bacterial chitinase composed of 540 amino acids. Figure S(C) showed one core domain and one ligand-binding domain.
The TMHMM result depicts that there is no transmembrane portion in either of the four sequences. It clearly indicates the extracellular production nature of chitinase enzymes .
The mGOASVM results confirmed the subcellular localization of chitinase in the cytoplasm.
Proteolytic cutting of enzymes has utmost importance. A comparative study among the four groups of organisms with five different types of endopeptidases, viz., chymotrypsin, pepsin, proteinase K, thermolysin, and Asp-N terminal endopeptidase, having highest cutting sites have been given in Fig. 4.
Phylogeny study of chitinase
Sixty-two sequences were found to be distributed in all fourteen clades. Figure 5 shows that each clade was divided into branches, and the branches are subdivided further into subbranches. The fungal sequence 2Y8V, from Aspergillus fumigates, was placed in a separate branch line. In the second clade, the archaeal sequence 2DSK from Pyrococcus furiosus (PDB ID 2DSK) got its position at same branch length with bacteria. 1EDQ from Serratia marcescens situated in a different clade with other bacteria. The plant sequence 3AQU from Arabidopsis thaliana shared the position with fungi, shedding some light that they are evolutionary closely related.
Fossil relics tell an evolutionary story of chitinase. In fossil-calibrated dating studies, molecular clock-measured time ensured that multiple clades of bacteria gathered chitinase by horizontal gene transfer during late Neoproterozoic-Cambrian transition to the early Paleozoic .
The phylogenetic study characterizes GH chitinase family 18 clusters A and B that is widely distributed in three domains of life, whereas family 19 is predominant in plants and some Actinobacteria like Streptomyces sp. . Extra copies of genes are inserted by duplication and are supposed to diverge in plants. The GH18 cluster C includes bacterial and Archaean representatives.
The evolutionary lineage depicts that fungi colonized land during Cambrian, earlier than arthropods in Ordovician. Later, micro fungus-feeding insects, beetles, depend on two-way interaction between the two chitin-driven organisms.
From the current study, a definable similarity has been observed among four groups of organisms as well as structural and functional alterations obtained from the analysis. In our work, the average results of a given parameter are being used as taking all the sequences would have made the analysis too exhaustive. One well-studied exemplary member from each group, i.e., the archaeal model Pyrococcus furiosus (2DSK), the bacterial model Serratia marcescens (1EDQ), the fungal model Aspergillus fumigates (2Y8V), and the plant model Arabidopsis thaliana (3AQU), suffices to characterize different empirical results. The features include that they are able to perform all the computational tools being used in this study and the model connectivity correlated with the rest of the member in that group. Very high value of aliphatic indices was found in archaea (84.63) and fungi (87.72) whereas higher plants feature significantly lower aliphatic values (70.22). Thus, the order of great thermal stability plants < bacteria < archaea < fungi suggests that thermophilic fungi are close cousin to archaea having a high aliphatic value. The moderate aliphatic range in bacteria suggests the ancestry of chitinase in higher plants and fungi originated form bacteria. This hypothesis is based on the earlier studies by Gruen et al. that showed chitinase gene from bacteria radiated toward plant and fungal lineage . Onaga and Taira  identified that the LysM domains of plant class IIIb chitinase shared moderate level of homology to fungal (Aspergillus fumigatus) LysM domain proteins. The resemblance shown by plant and fungal chitinases revealed that the catalytic domains are similar in all of these.
Very high pI values in plants are relatable to the modified five kingdom system carried out by Margulis and Schwartz , where different entities belong to the prokaryote and four eukaryotic kingdoms—the Protocista, the Fungi, the Plantae, and the Animalia. This is regarded as primarily acidic chitinases arise in bacteria, and gradually basic nature of chitinases are found in plants.
On the other hand, the three domain classifications, a category above Kingdom, proposed by Woese , nucleotide sequences of ribosomal rRNA act as an evolutionary chronometer. It became obvious from our computed physiochemical analysis that there are several differences between archaea and bacteria although having prokaryotic origin. With all the characteristics studied, it has been realized that these bacterial chitinase are more closely related to the eukaryotes, especially plants.
A negative GRAVY value of − 0.322 suggests an overall non-polar and hydrophobic nature of the evaluated bacterial chitinase. Similar observations of Edbeib et al.  suggested that negative GRAVY values convey low hydrophobicity of proteins; the more negative value tends to improve interaction with water molecules. The GRAVY value indicates that chitinases evolved first in bacteria, and then diverged to fungi and plants. The amino acid composition depicted in Fig. 1a shows that alanine, glycine, and proline as hydrophobic amino acids tend to be in side chains, buried within the hydrophobic core of the protein, or within the lipid portion of the membrane. Arginine, threonine, and asparagine being the hydrophilic amino acids have a tendency to interact in the aqueous environment due to polarity and found on the exterior surface. Glycine with good percentage in the sequences gives high flexibility to the polypeptide chain and provides rigidity to the structure. Its presence in the surface provides a particular shape at these locations. By performing motif search, it has been observed that chitinase-specific domains are present in all organisms but in varied position. Secondary structural features were predicted by the SOPMA tool and indicate a high amount of random coil. Proline which is prominent in chitinases has the property to provide conformational rigidity and might be responsible for high content of random coiled structures. Rose  showed that coils contain more repetitive structure. The Z score signifies that the predicted tertiary structure is stable. Annotation with InterPro entries with GO (gene ontology) terms enabled to interpret biosynthetic pathway of chitinase derived from carbohydrate metabolism. Funkhouser and Aronson  addressed that the GH 18 multiprotein family of chitinases evolutionarily radiated from archaea to eukaryotes through gene duplication, loss, and selection process .The phylogenetic tree revealed that all the chitinase sequences were originated from archaea and diverged into subgroups during time and course of evolution. Above all, the presence of clusters for archaea and bacteria along with fungi and plants confirms the molecular level changes among the species during evolution.
The present study findings strongly affirmed that the bacterial chitinase is thermostable, hydrophobic in nature with less occurrence of thermolabile residues. Due to non-reliability of some scores for other groups except bacterial enzyme, structural modeling was performed to predict the 3D structure which is stable and kinetically accessible. The chitinase sequences from archaea, bacteria, fungi, and plants have fundamental functional relationship, as they have motif identity. Plant model sequence shared same branch length with fungal sequence in the phylogenetic tree. Also, extreme high and low aliphatic indices of fungi and plants respectively throw some light on their close relationship. Bacteria having moderate aliphatic value and sharing the branch length with other bacteria provides a rational framework of chitinase among these four groups. In the future, robust computational methods combined with advance strategies will provide highly efficient microbe-based de novo industrial chitinase. Here, chitinolytic microorganisms have the potential as promising replacements for the more harmful practices of applying insecticidal and antifungal chemicals. From the present in silico study of chitinase, it is assured that bacteria could be used as a potential source of chitinase production posing an attractive alternative to commonly exploited fungal sources.
Availability of data and materials
Supplementary data associated with this article are available in the online version.
Protein Data Bank
Research Collaboratory for Structural Bioinformatics
Grand average of hydrophobicity
Qualitative Model Energy Analysis
Global Model Quality Estimation
Molecular Evolutionary Genetics Analysis
Multiple Em for Motif Elicitation
Hartl L, Zach S, Seidl-Seiboth V (2012) Fungal chitinases: diversity, mechanistic properties and biotechnological potential. Appl Microbiol Biotechnol 93:533–543. https://doi.org/10.1007/s00253-011-3723-3
Asran-Amal A, Moustafa-Mahmoud SM, Sabet KK, El Banna OH (2010) In vitro antagonism of cotton seedlings fungi and characterization of chitinase isozyme activities in Trichoderma harzianum. Saudi J Biol Sci 17(2):153–157. https://doi.org/10.1016/j.sjbs.2010.02.009
Bhattacharya D, Nagpure A, Gupta RK (2007) Bacterial chitinases: properties and potential. Crit Rev Biotechnol 27:21–28. https://doi.org/10.1080/07388550601168223
Gao J, Bauer MW, Shockley KR, Pysz MA, Kelly RM (2003) Growth of hyperthermophilic archaeon Pyrococcus furiosus on chitin involves two family 18 chitinases. Appl Environ Microbiol 69:3119–3128. https://doi.org/10.1128/AEM.69.6.3119-3128.2003
Imanaka T, Fukui T, Fujiwara S (2001) Chitinase from Thermococcus kodakaraensis KOD1. Methods Enzymol 330:319–329. https://doi.org/10.1016/S0076-6879(01)30385-3
Horiuchi A, Aslam M, Kanai T, Atomi H (2016) A structurally novel chitinase from the chitin-degrading hyperthermophilic archaeon, Thermococcus chitonophagus. Appl Environ Microbiol. https://doi.org/10.1128/AEM.00319-16
Shekhar N, Bhattacharya D, Kumar D, Gupta RK (2006) Biocontrol of wood-rotting fungi with Streptomyces violaceusniger XL-2. Can J Microbiol 52:805–808. https://doi.org/10.1139/w06-035
Tsujibo HI, Minoura KA, Miyamoto KA, Endo HI, Moriwaki MA, Inamori YO (1993) Purification and properties of a thermostable chitinase from Streptomyces thermoviolaceus OPC-520. Appl Environ Microbiol 59:620–622
Kuddus M, Ahmad IZ (2013) Isolation of novel chitinolytic bacteria and production optimization of extracellular chitinase. J Genet Eng Biotechnol 11:39–46. https://doi.org/10.1016/j.jgeb.2013.03.001
Lonhienne T, Mavromatis K, Vorgias CE, Buchon L, Gerday C, Bouriotis V (2001) Cloning, sequences, and characterization of two chitinase genes from the Antarctic Arthrobacter sp. strain TAD20: isolation and partial characterization of the enzymes. J Bacteriol 183:1773–1779. https://doi.org/10.1128/JB.183.5.1773-1779.2001
Zhang Z, Yuen GY, Sarath G, Penheiter AR (2001) Chitinases from the plant disease biocontrol agent, Stenotrophomonas maltophilia C3. Phytopathology 91:204–211. https://doi.org/10.1094/PHYTO.2001.91.2.204
Taira T, Mahoe Y, Kawamoto N, Onaga S, Iwasaki H, Ohnuma T, Fukamizo T (2011) Cloning and characterization of a small family 19 chitinase from moss (Bryum coronatum). Glycobiology. 21:644–654. https://doi.org/10.1093/glycob/cwq212
Ano A, Takayanagi T, Uchibori T, Okuda T, Yokotsuka K (2003) Characterization of a class III chitinase from Vitis vinifera cv. Koshu J Biosci Bioeng 95(6):645–647. https://doi.org/10.1016/S1389-1723(03)80179-2
Onaga S, Taira T (2008) A new type of plant chitinase containing LysM domains from a fern (Pteris ryukyuensis): roles of LysM domains in chitin binding and antifungal activity. Glycobiology. 18:414–423. https://doi.org/10.1093/glycob/cwn018
Taira T, Hayashi H, Tajiri Y, Onaga S, Uechi GI, Iwasaki H, Ohnuma T, Fukamizo T (2009) A plant class V chitinase from a cycad (Cycas revoluta): biochemical characterization, cDNA isolation, and posttranslational modification. Glycobiology. 19:1452–1461. https://doi.org/10.1093/glycob/cwp119
López RC, Gómez-Gómez L (2009) Isolation of a new fungi and wound-induced chitinase class in corms of Crocus sativus. Plant Physiol Biochem 47:426–434. https://doi.org/10.1016/j.plaphy.2009.01.007
Revah-Moiseev S, Carroad PA (1981) Conversion of the enzymatic hydrolysate of shellfish waste chitin to single-cell protein. Biotechnol Bioeng 23:1067–1078. https://doi.org/10.1002/bit.260230514
Wang J, Zhang J, Song F, Gui T, Xiang J (2015) Purification and characterization of chitinases from ridgetail white prawn Exopalaemon carinicauda. Molecules 20:1955–1967. https://doi.org/10.3390/molecules20021955
Dahiya N, Tewari R, Hoondal GS (2006) Biotechnological aspects of chitinolytic enzymes: a review. Appl Microbiol Biotechnol 71:773–782. https://doi.org/10.1007/s00253-005-0183-7
Adrangi S, Faramarzi MA (2013) From bacteria to human: a journey into the world of chitinases. Biotechnol Adv 31:1786–1795. https://doi.org/10.1016/j.biotechadv.2013.09.012
Oerke EC, Dehne HW, Schonbeck F, Weber A (1994) Estimated crop losses in wheat. Crop production and crop protection: estimated losses in major food and cash crops
Edreva A (2005) Pathogenesis-related proteins: research progress in the last 15 years. Gen Appl Plant Physiol 31:105–124 pp.179-296. Elsevier Science Publishers
Gasteiger E, Hoogland C, Gattiker A, Duvaud S, Wilkins MR, Appel RD, Bairoch A (2005) Protein identification and analysis tools on the ExPASy server. In: The Proteomics Protocols Handbook, pp 571–607. https://doi.org/10.1385/1592598900
Bailey TL, Boden M, Buske FA, Frith M, Grant CE, Clementi L, Ren J, Li WW, Noble WS (2009) MEME SUITE: tools for motif discovery and searching. Nucleic Acids Res 37:W202–W208. https://doi.org/10.1093/nar/gkp335
Geourjon C, Deleage G (1995) SOPMA: significant improvements in protein secondary structure prediction by consensus prediction from multiple alignments. Comput Appl Biosci 11:681–684. https://doi.org/10.1093/bioinformatics/11.6.681
Ramachandran GN, Ramakrishnan C, Sasisekharan V (1963) Stereochemistry of polypeptide chain configurations. J Mol Biol 7:95–99. https://doi.org/10.1016/S0022-2836(63)80023-6
Laskowski RA, MacArthur MW, Moss DS, Thornton JM (1993) PROCHECK - a program to check the stereochemical quality of protein structures. J Appl Cryst 26:283–291. https://doi.org/10.1107/S0021889892009944
Raza M, Khan Z, Ahmad A, Raza S, Khan A, Mohammadzai IU, Zada S (2017) In silico 3-D structure prediction and molecular docking studies of inosine monophosphate dehydrogenase from Plasmodium falciparum. Comput Biol Chem 71:10–19. https://doi.org/10.1016/j.compbiolchem.2017.09.002
Colovos C, Yeates TO (1993) Verification of protein structures: patterns of nonbonded atomic interactions. Protein Sci 2:1511–1519. https://doi.org/10.1002/pro.5560020916
Wiederstein M, Sippl MJ (2007) ProSA-web: interactive web service for the recognition of errors in three-dimensional structures of proteins. Nucleic Acids Res 35:W407–W410. https://doi.org/10.1093/nar/gkm290
Arnold K, Bordoli L, Kopp J, Schwede T (2006) The SWISS-MODEL workspace: a web-based environment for protein structure homology modelling. Bioinformatics. 22:195–201. https://doi.org/10.1093/bioinformatics/bti770
Benkert P, Biasini M, Schwede T (2011) Toward the estimation of the absolute quality of individual protein structure models. Bioinformatics. 27:343–350. https://doi.org/10.1093/bioinformatics/btq662
Batumalaie K, Edbeib MF, Mahat NA, Huyop F, Wahab RA (2017) In silico and empirical approaches toward understanding the structural adaptation of the alkaline-stable lipase KV1 from Acinetobacter haemolyticus. J Biomol Struct Dyn 36(12):3077–3093. https://doi.org/10.1080/07391102.2017.1377635
Szklarczyk D, Morris JH, Cook H, Kuhn M, Wyder S, Simonovic M, Santos A, Doncheva NT, Roth A, Bork P, Jensen LJ (2017) The STRING database in 2017: quality-controlled protein–protein association networks, made broadly accessible. Nucleic Acids Res 45:D362–D368. https://doi.org/10.1093/nar/gkw937
Kumar S, Stecher G, Li M, Knyaz C, Tamura K (2018) MEGA X: molecular evolutionary genetics analysis across computing platforms. MolBiolEvol. 35:1547–1549. https://doi.org/10.1093/molbev/msy096
Guruprasad K, Reddy BB, Pandit MW (1990) Correlation between stability of a protein and its dipeptide composition: a novel approach for predicting in vivo stability of a protein from its primary sequence. Protein Eng Des Sel 4:155–161. https://doi.org/10.1093/protein/4.2.155
Ikai A (1980) Thermostability and aliphatic index of globular proteins. J Biochem 88(6):1895–1898. https://doi.org/10.1093/oxfordjournals.jbchem.a133168
Dutta B, Banerjee A, Chakraborty P, Bandopadhyay R (2018) In silico studies on bacterial xylanase enzyme: structural and functional insight. J Genet Eng Biotechnol 16(2):749–756. https://doi.org/10.1016/j.jgeb.2018.05.003
Cooper A (1999) Thermodynamics of protein folding and stability. In: Protein: a comprehensive treatise, vol 2. JAI Press.Inc., Stamford, pp 217–270
Shortle D (1996) The denatured state (the other half of the folding equation) and its role in protein stability. FASEB J 10(1):27–34
Hovmöller S, Zhou T, Ohlson T (2002) Conformations of amino acids in proteins. Acta Crystallogr D Biol Crystallogr 58(5):768–776. https://doi.org/10.1107/s0907444902003359
Benkert P, Künzli M, Schwede T (2009) QMEAN server for protein model quality estimation. Nucleic Acids Res 37(suppl_2):W510–W514
Ahmad K, Baig MH, Gupta GK, Kamal MA, Pathak N, Choi I (2016) Identification of common therapeutic targets for selected neurodegenerative disorders: an in silico approach. J Comput Sci 17:292–306. https://doi.org/10.1016/j.jocs.2016.03.007
Rauta PR, Ashe S, Nayak D, Nayak B (2016) In silico identification of outer membrane protein (Omp) and subunit vaccine design against pathogenic Vibrio cholerae. Comput Biol Chem 65:61–68. https://doi.org/10.1016/j.compbiolchem.2016.10.004
Gruen DS, Wolfe JM, Fournier GP (2019) Paleozoic diversification of terrestrial chitin-degrading bacterial lineages. BMC Evol Biol 19(1):1–9. https://doi.org/10.1186/s12862-019-1357-8
Kawase T, Saito A, Sato T, Kanai R, Fujii T, Nikaidou N, Miyashita K, Watanabe T (2004) Distribution and phylogenetic analysis of family 19 chitinases in Actinobacteria. Appl Environ Microbiol 70(2):1135–1144. https://doi.org/10.1128/aem.70.2.1135-1144.2004
Margulis L, Schwartz KV (1982) Five Kingdoms. An illustrated guide to the phyla of life on earth. Freeman, San Francisco xiv + 338
Woese CR, Kandler O, Wheelis ML (1990) Towards a natural system of organisms: proposal for the domains Archaea, Bacteria, and Eucarya. Proc Natl AcadSci 87(12):4576–4579
Edbeib MF, Aksoy HM, Kaya Y, Wahab RA, Huyop F (2019) Haloadaptation: insights from comparative modelling studies between halotolerant and non-halotolerant dehalogenases. J Biomol Struct Dyn:1–10. https://doi.org/10.1080/07391102.2019.1657498
Rose GD (2004) Secondary structure in protein analysis. In: Encyclopedia of Biological Chemistry, pp 1–6. https://doi.org/10.1016/B0-12-443710-9/00613-X
Funkhouser JD, Aronson NN (2007) Chitinase family GH18: evolutionary insights from the genomic history of a diverse protein family. BMC Evol Biol 7:96. https://doi.org/10.1186/1471-2148-7-96
First author is grateful to Juva Truffle Center (JTC), Finland, for perusing Research Internship Program and also acknowledge DST PURSE Phase II for providing fellowship at the Department of Botany, The University of Burdwan, West Bengal, India. We are thankful to Mr. Antti Kinnunen for administrative service and Mrs. Heli Valtonen for her technical assistance during this work at JTC.
This work was financially supported by the Regional Council of Southern Savo, Finland.
Ethics approval and consent to participate
Consent for publication
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Evaluation of functional annotations of chitinase. Fig. S(A). InterPro Scan server showing (PDB ID 1EDQ) predicted protein family relationship, domains and sites of chitinase. Fig. S(B). Protein-protein interacting partners of chitinase found through STRING database (PDB ID 4NZC). Fig. S(C). Structural domains of bacterial chitinase (PDB ID 1EDQ) predicted by SBASE tool.
About this article
Cite this article
Dutta, B., Deska, J., Bandopadhyay, R. et al. In silico characterization of bacterial chitinase: illuminating its relationship with archaeal and eukaryotic cousins. J Genet Eng Biotechnol 19, 19 (2021). https://doi.org/10.1186/s43141-021-00121-6
- Phylogenetic relationships
- Physical parameters
- Structural and functional analysis