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. 2020 Aug 14;10(9):391. doi: 10.1007/s13205-020-02388-x

Computational-based insights into the phylogeny, structure, and function of Rhodococcus alkane-1-monooxygenase

Siddhartha Pal 1, Kriti Sengupta 2,✉
PMCID: PMC7429611  PMID: 32832341

Abstract

Alkane-1-monooxygenase of alkanotrophic Rhodococcus species has been characterized using standard bioinformatics tools to investigate phylogenetic relationships, and three-dimensional structure and functions. Results revealed that activity of the Rhodococcus alkane-1-monooxygenase would be optimum in alkaline pH as their isoelectric points were in the range of 7.5 to 9. Higher aliphatic index (87 to 95) indicated that these enzymes are thermostable. Extinction coefficient of the enzyme varied from 68,793 to 1,25,820 M−1 cm−1 and average molecular weight was 45 kDa. Secondary structures predicted maximum alpha-helical content rather than the other conformations such as sheets or turns. The instability index (II) of most stable query protein was 39.7% which was lowest among all 76 proteins analysed in this study. Predicted 3D structure of query protein revealed that it contains homodimer polypeptides. The suitable template for query protein was Flavin-dependent luciferase-type alkane monooxygenase. The presence of 98.3% amino acid residues in Ramachandran plot was determined in 3-D protein model which confirmed the model feasibility. The predicted model contains 12% more α-helix than template protein which indicated towards membrane localization of the protein. The protein interactome partners of predicted model were determined as FMN-dependent oxidoreductase, molybdopterin, nuclear transport factor, and peroxiredoxin. The predicted tertiary model of R. rhodochrous alkane-1-monooxygenase (OOL33526.1) was deposited in Protein Model Database (Accession No.: PM0083166). The overall report is unique to best of our knowledge, and the importance of this study is to understand the theoretical aspects of structure and functions of alkane-1-monooxygenase of hydrocarbonoclastic strains of Rhodococcus.

Electronic supplementary material

The online version of this article (10.1007/s13205-020-02388-x) contains supplementary material, which is available to authorized users.

Keywords: Rhodococcus, Alkane-1-monooxygenase, Luciferase monooxygenase, Homology modelling, Protein interactome

Introduction

Use of excessive crude oil in the recent decades to meet the worldwide energy demand and its accidental spillage during transportation and storage has led to introduction of huge amount of petroleum hydrocarbons in the environment, especially alkanes (Singh et al. 2012). Alkanes constitute nearly 20–50% of crude oil which makes it the most abundant hydrocarbon fraction (Liu et al. 2014). Even the oily sludge produced after crude oil refinement has quite a good proportion of alkanes left in it (Jasmine and Mukherji 2015). Properties such as non-polarity, low water solubility, high degree of accumulation in cell membranes, high-energy requirement for activation, etc. make this molecule very inert and challenging for microbial metabolism (Rojo 2009; Liu et al. 2014). Being a major component of the oil contaminants, aliphatics are aerobically degraded by oxidation of terminal methylated carbon catalysed by alkane-1-monoxygenase (alkB) to 1-alkanol (Liang et al. 2011; Lu et al. 2012). Liang et al. (2011) found the abundance of alkane-1-monooxygenase genes derived from various Rhodococci and other members of Actinobacteria from various hydrocarbon contaminated soil samples. Because of their ability to degrade wide range of pollutants, Rhodococcus is considered as an environmentally important genera (Gao et al. 2015; Yang et al. 2016). Several studies on different strains of Rhodococcus are suggestive of their enormous potential to degrade crude oil and medium- to long-chain aliphatic hydrocarbons by virtue of their alkane degrading enzymatic machinery (Margesin et al. 2013; Táncsics et al. 2015; Pi et al. 2017).

The genus Rhodococcus are usually isolated from environments with contamination of petroleum hydrocarbon and frequently involved in the degradation of n-alkane which is an aliphatic hydrocarbon. However, the existence of catabolic gene of alkane degradation, the alkB gene, has been revealed only in the case of well-known members of the genus (e.g., R. jostii, R. opacus, R. rhodochrous, R. erythropolis, and R. ruber). The occurrence of the alkB gene among all Rhodococcus species is still unknown. Although the sequence information confines the investigation of the ecological variety of rhodococcal alkB genes but still researchers are actively involved in sequence-based evolutionary or phylogenetic analysis (Táncsics et al. 2015). Moreover, the alkane monooxygenase is popular for the production of value-added products from hydrocarbon waste which are abundant in natural environment, but the lack of structure–function-based biochemistry of enzyme challenged the industrial development process (Jan et al. 2005). The study regarding the overall biochemistry of promiscuous enzyme which has evolved through extensive evolutionary events of horizontal gene transfer makes it difficult to find out the conserved regions within multi-specific monooxygenases (Nie et al. 2014). Thus, various computational-based analyses of protein homology modelling for deducing the functional structure have been coming into the scenario of biochemical characterization of such proteins with industrial applications. Apart from isolating and characterizing numerous types of alkane-1-monooxygenase, widespread computational surveys of these enzymes have been successful in the initial stages to determine various unknown properties lying within the amino acid sequences which are often helpful prior to laboratory-based studies. The consequences of these investigations are biotechnologically beneficial to employ them in industrial and environmental bioremediation perspectives.

This study outlines the usage of several bioinformatics tools to accomplish the phylogenetic, structural, and functional analyses of the alkane-1-monooxygenase enzyme of Rhodococcus species to understand its unique properties indispensable for its industrial or bioremediation applications.

Materials and methods

The amino acid sequences of alkane-1-monooxygenase of different strains of Rhodococcus species and their respective cDNA sequences were retrieved in FASTA format from NCBI (National Center for Biotechnology Information) database (www.ncbi.nlm.nih.gov) for bioinformatics analysis.

Phylogeny of alkane-1-monooxygenase

Phylogenetic tree of alkane-1-monooxygenase was constructed using MEGA X and neighbor-joining model was used to calculate the distance between sequences (Kumar et al. 2018). Phylogeny of the enzyme was deduced for both amino acid and their respective cDNA sequences. Variation in gene sequences in terms of G + C content was determined for each cDNA sequence by online GC calculator (https://www.endmemo.com/bio/gc.php).

Primary sequence analysis

The amino acid sequence analyses included determination of amino acid composition and physiochemical properties such as isoelectric point, molecular weight, instability index, aliphatic index, extinction coefficient, grand average of hydropathicity (GRAVY), and positively charged and negatively charged residues. The physiochemical properties of alkane-1-monooxygenase from different strains of Rhodococcus species were analysed by Expasy Protparam online tool (https://web.expasy.org/protparam). Percent similarity index within the sequences was determined by Clustal Omega (https://www.ebi.ac.uk/Tools/msa/clustalo/). The pairwise alignment was performed by Expasy-SIM alignment tool (https://web.expasy.org/sim/).

Secondary structure prediction

Protein folding prediction was performed by determining the number of α-helix, β-sheet, and turns present in alkane-1-monooxygenase in different species of Rhodococcus. Secondary structure prediction was achieved by PSIPRED (https://bioinf.cs.ucl.ac.uk/psipred) and CFSSP server (https://www.biogem.org/tool/chou-fasman) (Paramanik et al. 2017).

Protein homology modelling

Alkane-1-monooxygenase of Rhodococcus rhodochrous strain 11Y (OOL33526.1) was further selected as query for computational analysis of protein structure. SWISS-Model workspace (https://swissmodel.expasy.org) was used to predict the 3-D model of the enzyme by selecting most suitable template (Waterhouse et al. 2018). Also, the predicted 3-D structure was further visualized for its hydrophobic regions into Swiss PDB Viewer (https://spdbv.vital-it.ch) (Johansson et al. 2012).

Evaluation of predicted protein 3-D model

Predicted protein model of alkane-1-monooxygenase of R. rhodochrous 11Y was evaluated and verified from both QMEAN and SAVES server (http:/nihserver.mbi.ucla.edu/SAVES). Ramachandran plot, Verify 3D, and PROCHECK were assessed from SAVES (Laskowski et al. 1993).

Ligand-binding site and protein–protein interaction

Ligand-binding site was predicted by COACH-Zhang lab online tool (Yang et al. 2013) (https://zhanglab.ccmb.med.umich.edu/COACH). Cofactor of the enzyme was predicted by Cofactory 1.0 (https://services.healthtech.dtu.dk/service.php/Cofactory-1.0) (Geertz-Hansen et al. 2014). Protein–protein interactome was predicted by STRING database version 11.0 (https://string-db.org) (Szklarczyk et al. 2019). Additionally, MOTIF search (https://www.genome.jp/tools/motif) (Bucher et al. 1996) was performed to identify the protein family of this enzyme and the protein subcellular localization was detected by PSORTb version 3.0 (https://www.psort.org/psortb) (Yu et al. 2010).

Results and discussion

Phylogenetic analysis

A total of 76 alkane-1-monooxygenase complete peptide sequences from different strains of hydrocarbon degrading Rhodococcus species were retrieved from database. The amino acid and respective cDNA sequence accession numbers are provided in supplementary table (Suppl. 1). Phylogenetic relationship was derived based on distance between amino acid sequences calculated by maximum-likelihood method (500 bootstrapping method). The phylogeny based on amino acid sequence homology in various species of Rhodococcus suggested that the enzyme is less diverse within species of R. erythropolis, R. opacus, and R. jostii which are clustered in major clades of homologues (Fig. 1a). However, the enzyme in R. rhodochrous are scattered in various clades of phylogenetic tree and showed intraspecies diversity of alkane-1-monooxygenase. The major clusters consist of R. opacus strains DSM 44186 (RKM77422.1), ATCC 51882 (TDU52305.1), 04-OD7 (PQP22743.1), M213 (EKT81020.1) and R. erythropolis strains X5 (QEX13571.1), KB1 (QIP42937.1), S-43 (KIM14790.1), DN1 (EQM34986.1), VSD3 (OHF28929.1), JCM 3201 (GCB58853.1), and BG43 (AKD99790.1). Among R. rhodochrous, the amino acid sequence of enzyme in strain KG-21 (KOS56955.1) and strain 11Y (OOL33526.1) is clustered together due to their maximum sequence similarity (Suppl. 2). They both form a separate cluster which indicated their uniqueness. Rest of the species such as R. qingshengii, R. aethenivorans, R. biphenylivorans, and R. fascians were in separate clade showing interrelationship between them. Hence, multiple sequence alignment was performed among amino acid sequences of all strains of R. rhodochrous which disclosed the conserved and semiconserved regions of alkane-1-monooxygenase (Suppl. 3). The linear chain of amino acid sequence is a translated message from its gene sequence but DNA sequences are much prone to recombination events by transposons or lateral gene transfer within relative species, especially in prokaryotes (Villa et al. 2019). The genetic diversity is often remarkable in comparison to protein sequence-level variety. Therefore, the cDNA sequence of each protein was also subjected to phylogenetic analysis to deduce the interspecies diversity based on nucleotide sequence of the enzyme (Fig. 1b). In this case, the gene-based phylogeny of the enzyme was similar to amino acid-based phylogeny where largest clade included the various strains of R. erythropolis. In the several strains of R. rhodochrous, the gene sequence compositions were not conserved like other species of Rhodococcus. Also, difference in G + C% of cDNA sequence of alkane-1-monooxygenase within Rhodococcus species represented the interspecies heterogenicity of cDNA sequence of the enzyme (Suppl. 4). The comparable phylogenetic analysis was also made by some previous researchers to decode the evolutionary consequences of alkane-1-monooxygenase of different taxa exclusively based on their gene and primary protein sequences (Yurui et al. 2013; Li et al. 2013; Nie et al. 2014).

Fig. 1.

Fig. 1

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Sequence homology-based phylogenetic analysis of 76 alkane-1-monooxygenase sequences from different strains of Rhodococcus spp.; a amino acid sequence-based phylogeny; b cDNA sequence-based phylogeny. Both the phylogenetic trees are constructed using MEGA X by neighbor-joining method with 500 bootstrap value

Physiochemical characterization

The theoretical information regarding physicochemical properties of the enzymes was obtained from protein linear amino acid sequence through several computational-based analysis to predict the enzymatic functions. The linear chain of amino acid sequence is a translated message from its gene sequence and thus carries some important information regarding the basic functional properties of the proteins. In this study, all the 76 alkane-1-monooxygenase protein sequences were characterized based on their physicochemical features by several bioinformatics tools (Table 1). The analysis suggested that isoelectric point (pI) is 7.5–9 for the enzyme which is above neutral and within alkaline range. The pI are those values where amphoteric amino acid molecules shows net zero charge and probably lose its ionic strength and consecutively affects the solubility of the protein in aqueous environment. Here, the average value of pI remains 8.25 ± 0.75 in all the strains of the genus, whereas the average pI value of the protein in R. rhodochrous is 8.2 ± 0.7 which is very similar with the average value among all Rhodococcus species. This information can be used to separate this enzyme from cellular extract by isoelectric focusing method of protein separation (Sengupta et al. 2019). Additionally, higher aliphatic index range (nearly 87–95) indicated that these enzymes are thermostable and can be exploited for research or industrial applications. It is globally believed that the poor thermostability of enzyme have limited application as biocatalysts in industrial process development, and thus, researchers are actively involved in searching highly thermostable enzymes for application purpose (Delgove et al. 2018). Moreover, versatile enzyme machinery in Rhodococcus species is already reported which includes thermostable monooxygenase and dioxygenase enzymes (Gakhar et al. 2005; Busch et al. 2019). Considering all pairs of cysteine residues form cystines, the molar extinction coefficients (EC) were calculated in aqueous condition at 280 nm. The EC ranges from 68,793 to 1,25,820 M−1 cm−1, and average molecular weight of the protein was 45 KDa. The GRAVY, calculated from ExPASy, showed very low range (− 0.16 to − 0.05), which infers that the proteins have better interactions with water molecules. Moreover, the instability index (II) of the enzyme in R. rhodochrous strain 11Y was 39.7% which was lowest among all 76 proteins. This indicated that it was the most stable protein among all alkane-1-monooxygenase considered for this study. A comparative illustration of differences in amino acid composition of alkane-1-monooxygenase present in several strains of Rhodococcus species is shown in Fig. 2. Two amino acids, alanine and arginine, varied significantly (P < 0.05) in contrast to rest of the amino acid composition which are more or less similar in all species. This result indicated the flexibility in alanine and arginine composition in alkane-1-monooxygenase which has not been addressed earlier in Rhodococcus species.

Table 1.

Amino acid sequence-based analysis of physiochemical features of alkane-1-monooxygenase of Rhodococcus spp

Rhodococcus spp. AA MW(Kda) PI II Al EC GRAVY TPR TNR Half-life (h)
R. erythropolis 392 ± 10 44.58 ± 1.2 9.00 ± 0.2 51.37 ± 4.6 93.70 ± 3.7 117304 ± 4774 − 0.05 ± 0.04 39 ± 1 34 ± 2 10
R. imtechensis 426 ± 0 48.67 ± 0 9.27 ± 0 43.54 ± 0 89.77 ± 0 125820 ± 0 − 0.06 ± 0 42 ± 0 34 ± 0 10
R. opacus 412 ± 6 47.02 ± 0.7 8.91 ± 0.3 43.76 ± 0.5 88.90 ± 1.6 121891 ± 2485 − 0.08 ± 0.01 38 ± 3 36 ± 2 10
R. qingshengii 388 ± 3 44.02 ± 0.5 9.01 ± 0.2 50.19 ± 6.9 95.40 ± 3.1 114934 ± 5241 − 0.03 ± 0.05 38 ± 1 33 ± 3 10
R. aetherivorans 405 ± 37 46.07 ± 5.2 9.05 ± 1.5 47.21 ± 0.8 91.94 ± 2 119987 ± 44,727 − 0.06 ± 0.02 39 ± 4 34 ± 2 10
R. biphenylivorans 390 ± 0 44.82 ± 0 9.54 ± 0 56.10 ± 0 89.08 ± 0 100270 ± 0 − 0.16 ± 0 42 ± 0 30 ± 0 10
R. fascians 368 ± 36 40.77 ± 4.6 7.49 ± 1.9 44.39 ± 2.5 92.93 ± 7.4 68793 ± 34,144 − 0.09 ± 0.13 36 ± 6 36 ± 5 10
R. jostii 410 ± 0 46.73 ± 0.1 9.09 ± 0.01 42.96 ± 0.8 87.66 ± 0.1 120320 ± 0 − 0.08 ± 0.01 39 ± 1 33 ± 1 10
R. pyridinivorans 392 ± 30 44.44 ± 4.1 7.89 ± 1.5 50.92 ± 3.7 94.97 ± 4.2 90318 ± 27,835 − 0.10 ± 0.05 41 ± 5 37 ± 6 10
R. rhodochrous 396 ± 19 45.14 ± 2.2 8.20 ± 0.7 48.42 ± 5.1 91.91 ± 10.2 97014 ± 20,523 − 0.11 ± 0.09 39 ± 5 42 ± 8 10
R. ruber 396 ± 17 45.19 ± 2 8.15 ± 1.6 41.00 ± 2.5 88.21 ± 7.4 110008 ± 25,854 − 0.15 ± 0.07 39 ± 1 39 ± 9 10
R. wratislaviensis 409 ± 3 46.69 ± 0.4 8.84 ± 0.1 44.23 ± 0.7 89.34 ± 0.8 121237 ± 2050 − 0.07 ± 0.01 39 ± 0 35 ± 1 10
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AA, amino acids; MW, Molecular weight; PI, Isoelectric point; II, Instability index; AI, Aliphatic index; EC, Extinction coefficient, GRAVY, Grand average of hydropathicity; TPR, Total positively charged residue; TNR, Total negatively charged residue

Fig. 2.

Fig. 2

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Comparative representation of differences in amino acid composition of alkane 1-monooxygenase of 76 strains of Rhodococcus spp.; encircled area represents the interspecies differences. Two amino acids, alanine and arginine, varied significantly (P < 0.05) among all species

Secondary structure prediction

The predicted secondary structural organization of different Rhodococcus spp. consists of mainly three types of secondary components, α-helices, β-sheets, and turns (Suppl. 5). The average helical content was highest in R. rhodochrous (71.12%) among which R. rhodochrous strain 11Y (OOL33526.1) and KG-21 (KOS56955.1) contain maximum helices (Suppl. 6). An X-ray crystallographic study of alkane monooxygenase suggested that this enzyme contains several transmembrane helices and has ability to anchor with cell membrane (Lieberman and Rosenzweig 2005). The presence of such high number of helices in strain 11Y was also supported by PSIPRED prediction of transmembrane regions with alpha helix structure (Suppl. 7). This showed the thermostable property of the enzyme as α-helical configurations are abundant in enzymes of thermophiles to resist high temperatures (Kumar et al. 1999). In 2008, first crystal structure of long-chain alkane-1-monooxygenase described it as a new member of the bacterial luciferase monooxygenase family which showed an astonishing structural similarity with luciferase (Li et al. 2008). Similar result was observed by motif finder which computationally predicted luciferase as subfamily of our query enzyme (Suppl. 8).

Protein modelling, its evaluation, and submission

Homology modelling was attempted for ten amino acid sequences of Rhodococcus rhodochrous by selecting their most suitable template protein (Table 2) and strain 11Y (OOL33526.1) was selected as a representative strain (Fig. 3). The evaluation of predicted structures of alkane-1-monooxygenase from R. rhodochrous strains was based on acceptable QMEAN score, an overall quality parameter from SAVES server, and maximum amino acid percentage in favoured region of Ramachandran plot. The predicted 3D model suggested that the protein is composed of alpha chain which has corroborated with the previous report that the enzyme is homodimer of two chains, alpha and beta monomeric subunit (Li et al. 2008). The representations of helix, sheets, and turns are shown in predicted 3D structure (Fig. 3a), whereas surface view to highlight the major and minor grooves for several molecular interactions is shown in Fig. 3b. A model visualizing tool swiss PDB viewer constructed the hydrophobic regions of the enzyme shown in yellow patches in Fig. 3c, whereas COACH prediction of ligand-binding site for FMN/FAD/NADP is shown in Fig. 3d and Suppl. 9, which was supported by Cofactory 1.0 prediction (Suppl. 10). Assessment and quality check of the built 3D (.pdb) model were executed, and Ramachandran plot was constructed to display the locations assigned for each amino acid residues (Table 2). An acceptable QMEAN score (− 3.57) and the presence of 98.3% amino acid residues in Ramachandran plot was determined in alkane-1-monooxygenase of R. rhodochrous strain 11Y. Existence of more than 90% amino acids in the favoured region of Ramachandran plot of a protein could be considered as the good-quality protein model (Berman et al. 2000; Yadav et al. 2013). The Z score of query protein sequence was within acceptable range, i.e., 1 < [Z score] < 2, in comparison with PDB non-redundant protein matches. The protein model evaluation by PROCHECK suggested that there are very less unfavourable conformations and it has been predicted with better resolution (> 2 Å) along with best fit planarity. The model also passed the verification by SAVES server where 87.96% residues have scored significantly, i.e., 3D-1D score ≥ 0.2 whereas only 80% residue is required to the same score to attain the best fit model structure (Laskowski et al. 1993). The suitable template for query protein was alkane monooxygenase of bacterial luciferase subfamily protein (PDB ID: 3fgc.2.A) which was selected on the basis of maximum sequence similarity (28.14%), maximum sequence coverage (95%), and acceptable QMEAN score (− 3.57). The alignment of template sequence with query protein was performed to identify the functionally similar regions (Suppl. 11). Although the sequence similarity percentage between template and model was less (28.14%), but the template was best match as per sequence coverage (95%) and QMEAN score was within acceptable range. Furthermore, sequence heterogenicity among these enzymes is justified by the previous reports which already suggested that huge phylogenetic diversity is responsible for the versatility of bacterial alkane monooxygenase (Táncsics et al. 2015; Cappelletti et al. 2019). The multi-evaluation of the query protein is summarized in Fig. 4. A similar type of model validation was also conducted for other family of enzymes (Paramanik et al. 2017; Paramanik et al. 2018). Among all ten protein models, the best predicted model of Rhodococcus rhodochrous strain 11Y (OOL33526.1) was deposited in Protein Model  Database (in.pdb format) and its accession number obtained was PM0083166, and the mentioned model is now available in public database for any additional investigations.

Table 2.

Comparison of secondary and tertiary protein structural properties among all strains of R. rhodochrous

graphic file with name 13205_2020_2388_Tab2_HTML.jpg

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Acceptable QMEAN score; Not acceptable QMEAN score; #a good-quality model would be expected to have over 90% in the most favoured regions *best predicted model as per QMEAN score and percent amino acid in favoured region of Ramachandran plot

Fig. 3.

Fig. 3

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Three-dimensional models of alkane-1-monooxygenase of R. rhodochrous 11Y; a showing helix and sheets; b surface view; c yellow patches showing the hydrophobic regions in amino acid backbone model of the protein obtained from SWISS MODEL and SWISS PDB viewer; d ligand-binding areas for FMN/FAD/NADP predicted by COACH

Fig. 4.

Fig. 4

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Evaluation of protein model of alkane-1-monooxygenase of R. rhodochrous 11Y from QMEAN and SAVES server. Local quality estimation, Ramachandran plot, QMEAN score, and the Z score of query protein as compared with non-redundant proteins available in PDB

Functional prediction and protein–protein interaction

Function of the query enzyme was determined primarily by identifying the conserved motif/domain search which suggested that the query protein belongs to luciferase-like monooxygenase (Pfam ID, PF200296). The first structure of long-chain alkane-1-monooxygenase was demonstrated by Li et al. (2008), which claimed that they are newly characterized under the subfamily of luciferase-like monooxygenase. It further explained that this enzyme utilized FMN as cofactor to catalyse the hydrolysis of long-chain alkane by mechanism of flavoprotein monooxygenase reactions. These reactions are favourable for alkane degradation due to the presence of hydrophobic cavity which provided a solvent-free environment and His-138 residue of this cavity helped in hydrogen-bond formation. Similar result was obtained for our query protein by ligand prediction tool where binding sites for FMN/FAD have been determined and hydrophobic patches were also visualized in the predicted model. The predicted protein model of alkane-1-monooxygenase is flavin-dependent heterodimeric protein, similar to other crystal structures available in protein structure database such as RCSB or PDB. However, the available structures contain isoalloxazine ring which is coordinated by cis-Ala–Ala peptide bond. Moreover, the available structures contained reactive sulfhydryl group of Cys106 in the flavin oxygenation site of catalytic subunit of alkane-1-monooxygenase (Campbell et al. 2009). There is no such evidence of isoalloxazine ring present in the predicted model. Also, a significant heterogenicity among the sequences was observed while multiple sequence alignment of query protein with other homologous protein, except two major conserved domains which included ‘NLQRHSDHHANP’ at 359–373 position and ‘RRYQTLR’ at 375–381 residual position of complete peptide. These regions were homologous among all strains of R. rhodochrous strains. However, the sequences of 11Y and KG-21 were closely related, possessing many homologous regions. Additionally, the predicted model contains more α-helix (80.5%) than template protein (68.1%) which shows its flexibility and membrane localization ability. The predicted model is thermostable with high aliphatic index which is an interesting finding of this study regarding alkane-1-monooxygenase in Rhodococcus species which is an important criterion for its industrial application. Moreover, the network of protein–protein interactions obtained through STRING server portrayed that the query protein directly interacts with four different proteins of oxidoreductase family (Suppl. 12). The protein interactomes of the query protein included enzymes majorly of FMN-dependent oxidoreductase, molybdopterin oxidoreductase with iron–sulphide cluster, nuclear transport factor protein, and peroxiredoxin (OsmC family protein). Computational-based protein interacting network analysis is very significant for molecular-level understanding of several cellular processes in a theoretical way. The protein interactome is helpful for clarifying and evaluating functional genomics data which deliver an automatic platform for functionally annotating protein features as well as functional protein structure characteristics. This type of predicted protein–protein interaction network could be helpful in visualizing the fundamental directions for future laboratory-based research, e.g., prediction of their putative role in several cellular pathways. Comparable protein–protein interaction analyses were earlier performed to deduce the functional role of a query protein from its amino acid sequence (Schwartz et al. 2008; Gao et al. 2012; Zhang et al. 2016).

Conclusion

In silico characterization of Rhodococcus species alkane-1-monooxygenase unravelled that an averagely 45 KDa protein was homodimers and thermostable. The enzyme was having alkaline isoelectric point and affiliated to the luciferase-like monooxygenase subfamily. This type of thermostable alkane-1-monooxygenase originated from Rhodococcus rhodochrous might be potential for biotechnological applications in the bioremediation of hydrocarbon contaminated sites. Moreover, these could be applicable in synthetic industries for converting petroleum/hydrocarbon waste into value-added by-products. Hence, this study would help to understand the important structural and functional properties of alkane-1-monooxygenase in Rhodococcus species which are well-known bacterial genera for degrading hydrocarbons.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary file1 (DOCX 2949 kb) (2.9MB, docx)

Acknowledgements

Both the authors thank their respective institutes. The authors are thankful to Mr. Hemant Sengupta, Prime Focus Technologies, Bangalore, India for his useful suggestions to design the computational-based analysis. No financial grants were used for the work.

Authors declare no conflict of interest. No humans and animals were used for conducting the study.

Author contributions

SP and KS conceived the study. KS and SP performed the computational analysis. KS wrote the manuscript and SP edited the manuscript. Both the authors revised the manuscript thoroughly and approved the final version.

Compliance with ethical standards

Conflict of interest

Authors declare no conflict of interest.

References

  1. Berman HM, Westbrook J, Feng Z, et al. The Protein Data Bank. Nucl Acids Res. 2000;28:235–242. doi: 10.1093/nar/28.1.235. [DOI] [PMC free article] [PubMed] [Google Scholar]
  2. Bucher P, Karplus K, Moeri N, Hofmann K. A flexible motif search technique based on generalized profiles. Comput Chem. 1996;20:3–24. doi: 10.1016/S0097-8485(96)80003-9. [DOI] [PubMed] [Google Scholar]
  3. Busch H, Hagedoorn PL, Hanefeld U. Rhodococcus as A Versatile Biocatalyst in Organic Synthesis. Int J Mol Sci. 2019;20:4787. doi: 10.3390/ijms20194787. [DOI] [PMC free article] [PubMed] [Google Scholar]
  4. Campbell ZT, Weichsel A, Montfort WR, Baldwin TO. Crystal structure of the bacterial luciferase/flavin complex provides insight into the function of the beta subunit. Biochemistry. 2009;48:6085–6094. doi: 10.1021/bi900003t. [DOI] [PMC free article] [PubMed] [Google Scholar]
  5. Cappelletti M, Fedi S, Zannoni D (2019) Degradation of Alkanes in Rhodococcus. In: Alvarez H. (eds) Biology of Rhodococcus. Microbiology Monographs, vol 16. Springer, Cham. 10.1007/978-3-030-11461-9_6
  6. Delgove MA, Elford MT, Bernaerts KV, De Wildeman SM. Application of a thermostable Baeyer-Villiger monooxygenase for the synthesis of branched polyester precursors. J Chem Technol Biotechnol. 2018;93:2131–2140. doi: 10.1002/jctb.5623. [DOI] [PMC free article] [PubMed] [Google Scholar]
  7. Gakhar L, Malik ZA, Allen CR, Lipscomb DA, Larkin MJ, Ramaswamy S. Structure and Increased Thermostability of Rhodococcus sp. Naphthalene 1,2-Dioxygenase. J Bacteriol. 2005;187:7222–7231. doi: 10.1128/JB.187.21.7222-7231.2005. [DOI] [PMC free article] [PubMed] [Google Scholar]
  8. Gao P, Wang Q-P, Chen L, et al. Prediction of human genes’ regulatory functions based on protein–protein interaction network. Protein Pept Lett. 2012;19:910–916. doi: 10.2174/092986612802084528. [DOI] [PubMed] [Google Scholar]
  9. Gao P, Tian H, Li G, Sun H, Ma T. Microbial diversity and abundance in the Xinjiang Luliang long-term water-flooding petroleum reservoir. Microbiol Open. 2015;42:332–342. doi: 10.1002/mbo3.241. [DOI] [PMC free article] [PubMed] [Google Scholar]
  10. Geertz-Hansen HM, Blom N, Feist AM, Brunak S, Petersen TN. Cofactory: Sequence-based prediction of cofactor specificity of Rossmann folds. Proteins. 2014;82:1819–1828. doi: 10.1002/prot.24536. [DOI] [PubMed] [Google Scholar]
  11. Jan VB, Rico F. Expanding the alkane oxygenase toolbox: New enzymes and applications. Curr Opin Biotechnol. 2005;16:308–314. doi: 10.1016/j.copbio.2005.04.005. [DOI] [PubMed] [Google Scholar]
  12. Jasmine J, Mukherji S. Characterization of oily sludge from a refinery and biodegradability assessment using various hydrocarbon degrading strains and reconstituted consortia. J Environ Manage. 2015;149:118–125. doi: 10.1016/j.jenvman.2014.10.007. [DOI] [PubMed] [Google Scholar]
  13. Johansson MU, Zoete V, Michielin O, Guex N. Defining and searching for structural motifs using deep view/Swiss-PdbViewer. BMC Bioinformatics. 2012;13:173. doi: 10.1186/1471-2105-13-173. [DOI] [PMC free article] [PubMed] [Google Scholar]
  14. Kumar S, Tsai C-J, Nussinov R. Factors enhancing protein thermostability. Protein Eng. 1999;13:179–191. doi: 10.1093/protein/13.3.179. [DOI] [PubMed] [Google Scholar]
  15. Kumar S, Stecher G, Li M, Knyaz C, Tamura K. MEGA X: molecular evolutionary genetics analysis across computing platforms. Mol Biol Evol. 2018;35:1547–1549. doi: 10.1093/molbev/msy096. [DOI] [PMC free article] [PubMed] [Google Scholar]
  16. Laskowski RA, MacArthur MW, Moss DS, Thornton JM. PROCHECK - a program to check the stereochemical quality of protein structures. J App Cryst. 1993;26:283–291. doi: 10.1107/S0021889892009944. [DOI] [Google Scholar]
  17. Li L, Liu X, Yang W, et al. Crystal structure of long-chain alkane monooxygenase (LadA) in complex with coenzyme FMN: unveiling the long-chain alkane hydroxylase. J Mol Biol. 2008;376:453–465. doi: 10.1016/j.jmb.2007.11.069. [DOI] [PubMed] [Google Scholar]
  18. Li P, Wang L, Feng L. Characterization of a novel Rieske-type alkane monooxygenase system in Pusillimonas sp. strain T7–7. J Bacteriol. 2013;195:1892–1901. doi: 10.1128/JB.02107-12. [DOI] [PMC free article] [PubMed] [Google Scholar]
  19. Liang Y, Nostrand JDV, Deng Y, He Z, Wu L, Zhang X, Li G, Zhou J. Functional gene diversity of soil microbial communities from five oil-contaminated fields in China. ISME J. 2011;5:403–413. doi: 10.1038/ismej.2010.142. [DOI] [PMC free article] [PubMed] [Google Scholar]
  20. Lieberman RL, Rosenzweig AC. Crystal structure of a membrane-bound metalloenzyme that catalyses the biological oxidation of methane. Nature. 2005;434:177–182. doi: 10.1038/nature03311. [DOI] [PubMed] [Google Scholar]
  21. Liu H, Xu J, Liang R, Liu J. Characterization of the medium- and long-chain n-alkanes degrading Pseudomonas aeruginosa strain SJTD-1 and its alkane hydroxylase genes. PLoS ONE. 2014;9:e105506. doi: 10.1371/journal.pone.0105506. [DOI] [PMC free article] [PubMed] [Google Scholar]
  22. Lu Z, Deng Y, Nostrand JDV, He Z, Voordeckers J, Zhou A, et al. Microbial gene functions enriched in the deep water horizon deep-sea oil plume. ISME J. 2012;6:451–460. doi: 10.1038/ismej.2011.91. [DOI] [PMC free article] [PubMed] [Google Scholar]
  23. Margesin R, Moertelmaier C, Mair J. Low-temperature biodegradation of petroleum hydrocarbons (n-alkanes, phenol, anthracene, pyrene) by four actinobacterial strains. Int Biodeter Biodegrad. 2013;84:185–191. doi: 10.1016/j.ibiod.2012.05.004. [DOI] [Google Scholar]
  24. Nie Y, Chi CQ, Fang H, Liang JL, Lu SL, Lai GL, Tang YQ, Wu XL. Diverse alkane hydroxylase genes in microorganisms and environments. Sci Rep. 2014;15:4968. doi: 10.1038/srep04968. [DOI] [PMC free article] [PubMed] [Google Scholar]
  25. Paramanik K, Kundu S, Banerjee S, Ghosh PK, Maiti TK. Computational-based structural, functional and phylogenetic analysis of Enterobacter phytases. 3 Biotech. 2018;8:262. doi: 10.1007/s13205-018-1287-y. [DOI] [PMC free article] [PubMed] [Google Scholar]
  26. Pi Y, Chen B, Bao M, Fan F, Cai Q, Ze L, et al. Microbial degradation of four crude oil by biosurfactant producing strain Rhodococcus sp. Bioresour Technol. 2017;232:263–269. doi: 10.1016/j.biortech.2017.02.007. [DOI] [PubMed] [Google Scholar]
  27. Pramanik K, Soren T, Mitra S, et al. In silico structural and functional analysis of Mesorhizobium ACC deaminase. Comp Biol Chem. 2017;68:12–21. doi: 10.1016/j.compbiolchem.2017.02.005. [DOI] [PubMed] [Google Scholar]
  28. Rojo F. Degradation of alkanes by bacteria. Environ Microbiol. 2009;11:2477–2490. doi: 10.1111/j.1462-2920.2009.01948.x. [DOI] [PubMed] [Google Scholar]
  29. Schwartz AS, Yu J, Gardenour KR, et al. Cost-effective strategies for completing the interactome. Nat Methods. 2008;6:55–61. doi: 10.1038/nmeth.1283. [DOI] [PMC free article] [PubMed] [Google Scholar]
  30. Sengupta K, Alam M, Pailan S, Saha P. Biodegradation of 4-nitrophenol by a Rhodococcus species and a preliminary insight into its toxicoproteome based on mass spectrometry analysis. J Environ Biol. 2019;40:356–362. doi: 10.22438/jeb/40/3/MRN-931. [DOI] [Google Scholar]
  31. Singh SN, Kumari B, Mishra S. Microbial degradation of alkanes. In: Singh S, editor. Microbial degradation of xenobiotics. Environmental science and engineering. Berlin Heidelberg: Springer; 2012. [Google Scholar]
  32. Szklarczyk D, Gable AL, Lyon D, Junge A, Wyder S, Huerta-Cepas J, Simonovic M, Doncheva NT, Morris JH, Bork P, Jensen LJ, von Mering C. STRING v11: protein-protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets. Nucleic Acids Res. 2019;47:607–613. doi: 10.1093/nar/gky1131. [DOI] [PMC free article] [PubMed] [Google Scholar]
  33. Táncsics A, Benedek T, Szoboszlay S, et al. The detection and phylogenetic analysis of the alkane 1-monooxygenase gene of members of the genus Rhodococcus. Syst Appl Microbiol. 2015;38:1–7. doi: 10.1016/j.syapm.2014.10.010. [DOI] [PubMed] [Google Scholar]
  34. Villa TG, Feijoo-Siota L, Sánchez-Pérez A, Rama JR, Sieiro C. Horizontal gene transfer in bacteria, an overview of the mechanisms involved. In: Viñas M, Villa T, editors. Horizontal gene transfer. Cham: Springer; 2019. [Google Scholar]
  35. Waterhouse A, Martino Bertoni M, Stefan Bienert S, Gabriel Studer G, et al. SWISS-MODEL: homology modelling of protein structures and complexes. Nucleic Acids Res. 2018;46:296–303. doi: 10.1093/nar/gky427. [DOI] [PMC free article] [PubMed] [Google Scholar]
  36. Yadav PK, Singh G, Gautam B, et al. Molecular modeling, dynamics studies and virtual screening of Fructose 1, 6-biphosphate aldolase-II in community acquired-methicillin resistant Staphylococcus aureus (CA-MRSA) Bioinformation. 2013;9:158–164. doi: 10.6026/97320630009158. [DOI] [PMC free article] [PubMed] [Google Scholar]
  37. Yang J, Roy A, Zhang Y. Protein-ligand binding site recognition using complementary binding-specific substructure comparison and sequence profile alignment. Bioinformatics. 2013;29:2588–2595. doi: 10.1093/bioinformatics/btt447. [DOI] [PMC free article] [PubMed] [Google Scholar]
  38. Yang S, Wen X, Shi Y, Liebner S, Jin H, Perfumo A. Hydrocarbon degraders establish at the costs of microbial richness, abundance and keystone taxa after crude oil contamination in permafrost environments. Sci Rep. 2016;6:37473. doi: 10.1038/srep3747. [DOI] [PMC free article] [PubMed] [Google Scholar]
  39. Yu NY, Wagner JR, Laird MR, Melli G, Rey S, Lo R, Dao P, et al. PSORTb 3.0: Improved protein subcellular localization prediction with refined localization subcategories and predictive capabilities for all prokaryotes. Bioinformatics. 2010;26:1608–1615. doi: 10.1093/bioinformatics/btq249. [DOI] [PMC free article] [PubMed] [Google Scholar]
  40. Yurui J, Guannan M, Yingying W, Mark B. Structural insights into diversity and n-alkane biodegradation mechanisms of alkane hydroxylases. Front Microbiol. 2013;4:58. doi: 10.3389/fmicb.2013.00058. [DOI] [PMC free article] [PubMed] [Google Scholar]
  41. Zhang Q, Zhang PW, Cai YD. The use of protein-protein interactions for the analysis of the associations between PM2.5 and some diseases. BioMed Res Int. 2016;2016:4895476. doi: 10.1155/2016/4895476. [DOI] [PMC free article] [PubMed] [Google Scholar]

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