Computer-aided Degradation Susceptibility Study of Crude Oil Compounds at Bacillus subtilis Protein Target

Article information

Environmental Engineering Research. 2023;28(3)
Publication date (electronic) : 2022 May 20
doi : https://doi.org/10.4491/eer.2021.565
1Department of Chemistry, Federal University of Technology Owerri, PMB 1526, Imo State Nigeria
2Surface Chemistry and Environmental Technology (SCENT) Research Unit, Department of Chemistry, Imo State University, Owerri, PMB 2000, Imo State, Nigeria
3Department of Chemistry, Imo State University, Owerri, PMB 2000, Imo State, Nigeria
4Department of Biochemistry, Federal University of Technology Akure, PMB 704, Ondo State, Nigeria
Corresponding author: E-mail: chidiedbertduru@gmail.com, Tel: +2348037131739, Fax:, ORCID: 0000-0002-3038-1970
Received 2021 November 12; Revised 2022 May 16; Accepted 2022 May 17.

Abstract

Certain bacteria and fungi have been associated with the oxidative degradation of hydrocarbons and hydrocarbon derivatives in soil and water contaminated by crude oil spilling in these ecosystems. The binding affinity of hydrocarbons and hydrocarbon derivatives in a crude oil sample on the cysteine dioxygenase of Bacillus subtilis was studied using computational methods. The study was aimed to validate the claim of the efficient use of this organism in crude oil remediation and to determine the selectivity of the compounds in the crude by this bacterium enzyme. The compounds in the studied crude oil sample were identified by gas chromatography-mass spectrometry. Straight chain hydrocarbons were the least selected class of compounds with binding free energy ranging between −2.9 kcal/mol to −3.1 kcal/mol. The straight-chain hydrocarbon derivatives containing carbonyl and hydroxyl groups formed the middle class of compounds selected by the enzyme with binding free energy ranging between − 3.3 kcal/mol to −3.7 kcal/mol. The best binding free energies (−3.8 kcal/mol to −5.1 kcal/mol) were obtained with the poly-branched hydrocarbons and the cyclic compounds. The amounts of the poly-branched and cyclic compounds in the crude oil sample suggested that cysteine dioxygenase of B. subtilis would show about 28 % efficiency in the bioremediation of environments polluted by crude oil of this composition.

Abstract

Graphical Abstract

1. Introduction

Spills are uncontrolled releases of any product, including crude oil, chemicals, or waste, into the environment. It is usually caused by equipment failure, operation mishaps, human error, or intentional damage to facilities [1]. The extent of damage depends on what, where, and how much has been spilled and how long it remains in the immediate and impacted environment [2]. An oil spill is the release of liquid petroleum hydrocarbons into the environment, especially the marine ecosystem [3]. Upon the release of oil, less volatile and heavier fractions are left behind while light fractions vaporize. Oil spill usually results in the death of aquatic and terrestrial animals and deprive the native human population of their food source and occupation [4, 5].

Cleanup of oil spill environments is laborious and may take months or even years to achieve. Different oil cleanup methods like hot water and high-pressure washing, the use of dispersants, sorbents, skimmers, oil booms, and bioremediation are currently in use [6]. Bioremediation which involves the utilization of native or introduced oil-degrading microorganisms or other forms of life to break down various components of the spilled crude in the marine environment, has become a promising innovation due to its minimal effort and eco-friendly nature [7,8]. Using this method, oil spills can be cleaned up safely, cheaply, and more efficiently than the other physical or chemical methods. Bioremediation functions basically on biodegradation, which involves the complete mineralization of organic contaminants into carbon dioxide, water, inorganic compounds, and cell protein [9].

Numerous investigations have uncovered an enormous number of hydrocarbon-degrading microorganisms in oil-rich conditions such as oil spill areas and oil reservoirs whose abundance is determined by the type of petroleum hydrocarbons and the encompassing environmental factors [1015]. Among these organisms are Pseudomonas fluorescens, P. aeruginosa, Bacillus subtilis, Bacillus sp., Alcaligenes sp., Acinetobacter lwoffi, Flavobacterium sp., Micrococcus roseus, and Corynebacterium sp. [16]. The genus Bacillus has been reported to be an outstanding hydrocarbon degrader, and their ability to form spores when nutrients are limited makes them self-sustainable bioremediation organisms [17]. The degradation pathways of various petroleum hydrocarbons have been shown to employ oxidizing reactions; however, these pathways differ significantly because of the specific oxygenases found in different bacterial species. For instance, some bacteria can metabolize specific alkanes, while others like Proteus vulgaris and Proteus cibarius break down aromatic or resin fractions of hydrocarbons [18].

Computer-aided learning uses computer hardware, software, and networking tools to study specific subjects. The application of computational methods in the bioremediation of crude oil-polluted environments would give first-hand information about the organisms best suited for the remediation of a given crude oil pollution site since microbial enzymes degrade crude oil hydrocarbons selectively. An initial determination of the right microbes for the degradation of crude oil of a given hydrocarbon composition using in silico methods would reduce the time and cost involved in direct trials of these microbes on polluted sites. A computer-guided degradation susceptibility study of crude oil compounds on the enzyme of Bacillus subtilis protein target was studied in this paper. The findings would validate the reports on the efficiency of this organism in crude oil degradation and identify hydrocarbons most susceptible to degradation by its enzyme.

2. Materials and Methods

2.1. Collection of Crude Oil Sample

The crude oil samples were collected from Agip Oil Company Ltd, Clough Creek well No.5 ST located in the OML 63 swamp area in Bayelsa State, Niger Delta region of Nigeria. Crude oil samples were collected from the wellheads with the assistance of the field personnel of the company. Amber glass bottles with Teflon-lined lids were used for the sample collection. The collected samples were sealed and labeled and then stored in a dark insulated cooler to ensure that evaporative changes and microbial degradation did not occur in the crude oil while being taken to the laboratory. Gas chromatography analysis of the samples was done on the same day of collection.

2.2. Analysis of Crude Oil Sample

The compounds in the crude oil sample were determined using Gas Chromatography-Mass Spectrometry (GCMS-QP2010 PLUS, Shimadzu, Japan) with internal standard solution containing from n-C3 to n-C44 (prepared from aliquots of pure compounds and diluted with dichloromethane to a final concentration of 0.5 mg/mL). The chromatogram of the standard calibration solutions is presented in Fig. S1. The instrument was validated by determining the calibration curve, the limit of detection (LOD) and quantification (LOQ). By diluting certified solutions containing n-C3 to n-C44, five concentrations of calibration solutions ranging from 0.02 mg/mL to 1 mg/mL are created and used to generate the calibration curve (Fig. S1). The LOD and LOQ were determined by performing a quadruplicate blank analysis. The LOD measures the smallest quantity of the analyte, and it shows when the signal is greater than three times the noise and could be expressed as LOD=3σs and LOQ=10σs meaning that the signal is ten times greater than the noise and it is the smallest amount that can be quantified. The standard deviation of the blank analysis is given as σ, and S is slope of calibration curve. The LOD and LOQ were 1.42 and 4.73 mg/mL, respectively. Following calibration, the crude oil samples were analyzed. The sample syringe was rinsed four times with the pre-solvent, four times with post-solvent, and three times with the sample. The operation conditions of the GC were as follows: carrier gas, helium (1.4 mL/min), and injector and detector temperature, 300 °C. The temperature program was 0 min at 90 °C, which was ramped to 270 °C at 6 °C/min, and held at this temperature for 30 min. The injected quantity was 1 μL of 2 % volume solution in tetrachloromethane [19].

2.3. Identification and Preparation of Ligands

The 3D structure-data files (SDF) of the compounds in the crude oil sample were identified and downloaded from the PubChem database. They were minimized in PyRx virtual screening tool, using Universal Force Field at 200 steps. They were then converted to AutoDock ligands (pdbqt) and used for the docking analysis.

2.4. Receptor Preparation

The protein of Bacillus subtilis, a putative cysteine dioxygenase (protein data band [PDB] ID: 4QM9) with resolution 2.30 Å was identified from literature [20] and used as a target in this study. Chain A of the protein was used for the docking analysis to improve the ligand-binding accuracy [21]. The interfering crystallographic water molecules and co-crystallized ligand were removed, and minimization of the energy of the protein was then done using UCSF Chimera 1.14 [22, 23]. The protein was minimized at 300 steepest descent steps at 0.02 Å. The conjugate gradient steps were 10 at 0.02 Å and 10 update intervals. Gasteiger charges were added using Dock Prep to get a good structure conformation. The active sites on the minimized protein (Fig. 1) were identified using Biovia Discovery studio 4.5, developed and distributed by Dassault Systemes BIOVIA [24].

Fig. 1

Cysteine dioxygenase of Bacillus subtilis (Chain A) showing binding sites.

2.5. Docking Studies

The multiple ligands docking of the crude oil compounds on the protein target was done with Autodock Vina in PyRx software version 0.8 [25,26]. Blind docking of the compounds at the protein cavities was performed to give the ligands unhindered access to interact with sites where they had minimum energy. The center grid box was set to the dimension center x: − 40.037, center y: − 18.620, center z: 142.089, and size x: 50.411, size y: 44.123, size z: 42.859. The results in terms of binding free energy (ΔG) for each compound were obtained.

2.6. Analysis of Protein-Ligand Interactions

Hydrogen bonding and other hydrophobic interactions between the protein-ligand complex of the compounds was visualized using Biovia Discovery studio 4.5.

3. Results and Discussion

The chemical compounds eluted in the gas-chromatography analysis of the crude oil sample are shown in Fig. 2. The identified compounds and their percentage composition in the crude oil sample are summarized in Table 1. The sample was composed of 47.48 % hydrocarbons and 52.52 % hydrocarbon derivatives. The hydrocarbons comprised 29.44 % straight-chain, 13.79 % branch-chain, and 4.25 % cyclic compounds.

Fig. 2

Gas chromatogram of crude oil sample.

Hydrocarbon and Hydrocarbon Derivatives in Petroleum Sample

The hydrocarbon derivatives had 22.83 % ketones, 1.72 % alcohol, and 27.97 % carboxylic acids.

The binding positions of the hydrocarbon compounds on the protein target are shown in Fig. 3.

Fig. 3

Cysteine dioxygenase of B. subtilis (Chain A) showing binding positions of compounds.

The binding of all the compounds occurred outside the active sites of the protein. Most of the compounds interacted at some specific positions on the protein target. The values of the binding free energies of the compounds on the protein are summarized in Table 2. The protein-ligand interactions of the docked hydrocarbon compounds are shown in Table 3. Hydrogen, alkyl, van der waals, pi-alkyl, and pi-sigma were the most common modes of interactions between the compounds and the protein. Most of the oxygen containing hydrocarbons as well as the control interacted with the proteins by hydrogen bonding. Alkyl interactions were found between the protein and the linear hydrocarbons, most of which had higher binding free energies, while pi-alkyl interactions were prevalent among compounds with lower free energies and the protein.

Binding Free Energy Values of Crude Oil Compounds on B. subtilis

Protein-Ligand Interaction of the Crude Oil Compounds and B. subtilis

The binding free energy gives the sum of all the intermolecular interactions present between a ligand and the target. The docking score is the scoring function that predicts the binding affinity of the ligand and target after docking. The binding free energy of the co-crystallized ligand cysteine (−3.6 kcal/mol) was used as a reference standard for comparing the susceptibility of the hydrocarbons to oxidation by the protein [27, 28].The binding free energy of decane, dodecane, and eicosane was the same (−2.9 kcal/mol) and the highest for all the studied compounds. The high binding free energy values for these compounds is an indication that their binding to the protein is relatively poor. As such, their oxidation by the protein enzyme would not be feasible. The docking of the compounds also occurred at similar positions on the protein where all the compounds interacted with LYS27, ALA32, ALA33, and MET85.Tetradecane and nonadecane-2-methyl-had the same binding free energy value (−3.1 kcal/mol), the next highest set obtained. Their dock score showed that the protein enzyme would poorly oxidize them. Their binding occurred at a similar site, and both compounds interacted with LYS34.

The binding free energy values of 2-pentanone 3-methyl- and 1,9-tetradecadiene were the same (−3.3 kcal/mol) and their binding occurred at different sites on the protein. The binding affinities of octane 2,4,6-trimethyl-tridecane, hexadecane, n-hexadecanoic acid, octadecane, 9-octadecenoic acid (Z)-, and n-octadecanoic acid (−3.4 kcal/mol) were also the same and their binding on the protein occurred at different sites. The oxidation of these compounds by the protein would also be relatively poor, as could be seen from their binding affinities.

The binding free energies of 2-heptanone 4-methyl-, 4-heptanol 3-methyl-, tetracosane, and 2-pentanone 4-hydroxy-4-methyl- were close and gave −3.5 kcal/mol, −3.6 kcal/mol, −3.6 kcal/mol, and −3.7 kcal/mol, respectively. These values were the median dock scores indicating that their oxidation by the protein is very likely. The binding of 4-heptanol 3-methyl- and 2-pentanone 4-hydroxy-4-methyl-occurred at the same site, and they interacted with ALA32, ALA33, and MET85. The binding of 2-heptanone-4-methyl- and tetracosane occurred at different sites on the protein. These compounds constituted 12.52 % of the analyzed crude oil.

The binding free energy of naphthalene decahydro-2,6-dimethyl-, methylene cyclododecanone, decane 2,3,5,8-tetramethyl-, 1H-indene octahydro 2,2,4,4,7,7-hexamethyl-trans-, pentadecane 2,6,10-trimethyl-, and pentadecane 2,6,10,14-tetramethyl-were −4.8 kcal/mol, −4.8 kcal/mol, −3.8 kcal/mol, −5.1 kcal/mol, −3.8 kcal/mol, and −4.0 kcal/mol respectively and were relatively very good. The binding of naphthalene decahydro 2,6-dimethyl-and 1H-indene octahydro-2,2,4,4,7,7-hexamethyl-trans-occurred at the same site, and they interacted with TYR4. Methylene cyclododecanone, decane 2,3,5,8-tetramethyl-, and pentadecane 2,6,10-trimethyl-binding occurred at the same site and interacted with TYR109. Binding of pentadecane 2,6,10,14-tetramethyl-occurred in a pocket different from all the other compounds in this classification, and it interacted with the residues TYR46 and ALA47. These compounds constituted 15.39 % of the analyzed crude oil and are either cyclic or highly branched. These observations suggested that compounds of this structural nature are more susceptible to oxidation by B. subtilis and would be efficiently remediated in environments polluted by crude oil.

The data from this study affirms earlier reports that crude oil hydrocarbons have different affinities on enzyme pockets. With advancements in the genetic engineering of different microbes to enhance their crude oil remediation potentials, in silico techniques would become a veritable tool for costless validation studies of the efficiency of the modified microbial enzymes in degrading specific hydrocarbons. Genetically engineered microbes are used in bioremediation to design novel strains that can degrade high molecular weight polyaromatic compounds [29]. Data from molecular docking of hydrocarbons on enzyme surfaces could be used to identify particular sites on the enzyme where degradation occurs, the mechanism of the degradation process, and possible positions on binding sites where modifications are required to increase the binding affinity of specific crude oil hydrocarbons at those sites before their actual implementation.

4. Conclusions

The selectivity of hydrocarbons in crude oil by the cysteine dioxygenase enzyme of Bacillus subtilis was studied in silico. The crude oil sample used for the study contained 47.48 % hydrocarbons and 52.52 % hydrocarbon derivatives. The binding free energy values of the compounds on the protein target indicated that most of the alkanes would not be easily oxidized by the bacterial enzyme as shown by their high binding energy (−2.9 kcal/mol), while poly-branched and cyclic hydrocarbons with binding energies in the range −3.8 kcal/mol to −5.1 kcal/mol would undergo this process more rapidly. These findings indicate that B. subtilis would not achieve complete oxidation of all hydrocarbons and hydrocarbon derivatives in crude oil polluted environments independently.

Supplementary Information

Acknowledgement

The authors are grateful to ChemSolvers Research and Computational Laboratory Studio, Owerri, Nigeria, for assisting in the in-silico studies.

Notes

Competing interests

The authors declare that they have no competing interests.

Funding

No funds, grants, or other support was received.

Authors’ contributions

CED (Ph.D.) conceived, designed, and wrote the research. IAD (Ph.D.) and HIU (HND) carried out the docking studies. CEE (Ph.D. student) prepared the final proof and corrections. All authors have approved the manuscript in the present form, and gave the permission to submit the manuscript for publication.

List of Abbreviations

PAHs

Polycyclic aromatic hydrocarbons

SDF

Structure-data files

PDB

Protein Data Bank

TYR

Tyrosine

MET

Methionine

TRP

Tryptophan

ILE

Isoleusine

SER

Serine

ALA

Alanine

PHE

Phenylalanine

THR

Threonine

VAL

Valine

ARG

Arginine

PRO

Proline

GLN

Glutamine

LEU

Leucine

HIS

Histidine

ASN

Asparagine

GLY

Glycine

LYS

Lysine

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Article information Continued

Fig. 1

Cysteine dioxygenase of Bacillus subtilis (Chain A) showing binding sites.

Fig. 2

Gas chromatogram of crude oil sample.

Fig. 3

Cysteine dioxygenase of B. subtilis (Chain A) showing binding positions of compounds.

Table 1

Hydrocarbon and Hydrocarbon Derivatives in Petroleum Sample

Peak RT Compound PubChem ID Formula %
1 3.63 4-heptanol, 3-methyl- 102700 C8H18O 1.72
2 4.83 2-pentanone, 4-hydroxy-4-methyl- 31256 C6H12O2 0.43
3 5.83 2-heptanone, 4-methyl- 94317 C8H16O 9.22
4 6.07 2-pentanone, 3-methyl- 11262 C6H12O 8.99
5 8.28 Decane 15600 C10H22 0.64
6 9.72 Naphthalene, decahydro-2,6-dimethyl- 15379 C12H22 0.82
7 10.23 Dodecane 8182 C12H26 1.40
8 12.31 Tridecane 12388 C13H28 4.21
9 13.82 1H-indene, octahydro-2,2,4,4,7,7-hexamethyl-, trans- 41207 C15H28 3.43
10 14.70 1,9-Tetradecadiene 5362699 C14H26 3.45
11 15.26 Methylene cyclododecanone 534631 C13H22O 4.19
12 15.62 Decane, 2,3,5,8-tetramethyl- 545611 C14H30 5.06
13 16.99 Hexadecane 11006 C16H34 9.14
14 17.61 Pentadecane, 2,6,10-trimethyl- 19775 C18H38 1.89
15 18.23 Tetradecane 12389 C14H30 2.08
16 18.31 Pentadecane, 2,6,10,14-tetramethyl- 15979 C19H40 3.23
17 19.38 Octadecane 11635 C18H38 3.53
18 20.47 Eicosane 8222 C20H42 3.84
19 21.37 n-hexadecanoic acid 985 C16H32O2 8.66
20 23.31 Nonadecane, 2-methyl- 137081 C20H42 2.65
21 24.07 9-octadecenoic acid (Z)- 445639 C18H32O2 14.52
22 24.33 n-octadecanoic acid 5281 C18H36O2 4.79
23 24.66 Tetracosane 12592 C24H50 1.15
24 25.71 Octane, 2,4,6-trimethyl- 545612 C11H24 0.96

Table 2

Binding Free Energy Values of Crude Oil Compounds on B. subtilis

Compound Molecular weight Type of bond interactions ΔG Energy (kcal/mol)
2-pentanone, 4-hydroxy-4-methyl- 116 Hydrogen; Alkyl −3.7
2-pentanone, 3-methyl- 100 Acceptor-acceptor; Alkyl −3.3
4-heptanol, 3-methyl- 130 Hydrogen; Alkyl −3.6
2-heptanone, 4-methyl- 128 Hydrogen; Alkyl −3.5
Decane 142 van der waal; Alkyl −2.9
Octane, 2,4,6-trimethyl- 156 Pi-sigma; Pi-alkyl −3.4
Naphthalene, decahydro-2,6-dimethyl- 166 Pi-sigma; Pi-alkyl; Pi-pi stacked −4.8
Dodecane 170 van der waal; Alkyl −2.9
Tridecane 184 van der waals; Alkyl −3.4
Methylene cyclododecanone 194 Pi-alkyl −4.8
1,9-tetradecadiene 194 van der waals; Alkyl; Pi-alkyl −3.3
Decane, 2,3,5,8-tetramethyl- 138 Alkyl; Pi-alkyl −3.8
Tetradecane 198 Alkyl −3.1
1H-indene, octahydro-2,2,4,4,7,7-hexamethyl-, trans- 208 van der waals; Pi-sigma −5.1
Hexadecane 226 Alkyl; Pi-alkyl −3.4
n-hexadecanoic acid 256 Hydrogen; Alkyl −3.4
Pentadecane, 2,6,10-trimethyl- 254 Alkyl; Pi-alkyl −3.8
Octadecane 254 Alkyl; Pi-alkyl −3.4
9-octadecenoic acid (Z)- 282 Hydrogen; Alkyl −3.4
n-octadecanoic acid 284 Hydrogen; Alkyl −3.4
Pentadecane, 2,6,10,14-tetramethyl- 268 Alkyl; Pi-alkyl −4.0
Eicosane 282 Alkyl −2.9
Nonadecane, 2-methyl- 282 Alkyl −3.1
Tetracosane 338 Alkyl; Pi-alkyl −3.6
Cysteine (Reference) 121 Hydrogen; Carbon-hydrogen −3.6

Key:

Table 3

Protein-Ligand Interaction of the Crude Oil Compounds and B. subtilis

Compound Structure Protein – ligand interaction Binding site
2-pentanone, 4-hydroxy-4-methyl Residues: ASN31
ALA32
ALA33
MET85
2-pentanone, 3-methyl Residues: SER142
PRO143
4-heptanol, 3-methyl Residues: LYS27
ILE29
ALA32
ALA33
MET85
LEU87
2-heptanone, 4-methyl Residues: ILE95
THR120
Decane Residues: ILE29
ASN31
ALA32
ALA33
MET85
VAL86
LEU87
GLU113
GLY114
Octane, 2,4,6-trimethyl Residues: TYR4
TYR54
Naphthalene, decahydro-2,6-dimethyl Residues: TYR4
Dodecane Residues: ILE29
ASN31
ALA32
ALA33
LYS34
MET85
VAL86
LEU87
GLU113
GLY114
Tridecane Residues: ASN16
PRO17
SER18
VAL19
ILE81
THR120
LYS121
SER142
PRO143
Octane, 2,4,6-trimethyl Residues: TYR4
TYR54
Naphthalene, decahydro-2,6-dimethyl Residues: TYR4
Dodecane Residues: ILE29
ASN31
ALA32
ALA33
LYS34
MET85
VAL86
LEU87
GLU113
GLY114
Tridecane Residues: ASN16
PRO17
SER18
VAL19
ILE81
THR120
LYS121
SER142
PRO143
Methylene cyclododecanone Residues: TYR109
1,9-tetradecadiene Residues: ILE95
ARG97
SER106
ASN107
TYR109
ILE118
THR120
LEU123
Decane, 2,3,5,8-tetramethyl Residues: ILE95
TYR109
ILE118
Tetradecane Residues: LYS34
LEU35
PRO38
1H-indene, octahydro-2,2,4,4,7,7-hexamethyl-, trans Residues: TYR4
GLU5
GLN8
TYR54
Hexadecane Residues: TYR46
ALA47
TYR48
n-hexadecanoic acid Residues: LYS27
ALA32
ALA33
LYS34
MET85
LEU87
Pentadecane, 2,6,10-trimethyl Residues: ILE95
TYR109
ILE118
LEU123
Octadecane Residues: ILE95
TYR109
ILE118
LEU123
9-octadecenoic acid (Z) Residues: ILE95
SER106
LEU123
n-octadecanoic acid Residues: ALA47
VAL74
GLU153
Pentadecane, 2,6,10,14-tetramethyl Residues: TYR46
ALA47
Eicosane Residues: LYS27
ALA32
ALA33
MET85
Nonadecane, 2-methyl Residues: LYS27
ALA32
ALA33
LYS34
Tetracosane Residues: ILE95
TYR109
VAL111
ILE118
LEU123
Cysteine (Reference) ASN16
GLU58

Key: Tyrosine (TYR), Methionine (MET), Tryptophan (TRP), Isoleucine (ILE), Serine (SER), Alanine (ALA), Phenylalanine (PHE), Threonine (THR), Valine (VAL), Arginine (ARG), Proline (PRO), Glutamine (GLN), Leucine (LEU), Histidine (HIS), Asparagine (ASN), Glycine (GLY), Lysine (LYS).