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Article

Sustainable Remediation Using Hydrocarbonoclastic Bacteria for Diesel-Range Hydrocarbon Contamination in Soil: Experimental and In Silico Evaluation

by
Fernanda Espinosa-López
1,
Karen Pelcastre-Guzmán
1,
Anabelle Cerón-Nava
1,
Alicia Rivera-Noriega
1,
Marco A. Loza-Mejía
2 and
Alejandro Islas-García
3,*
1
Chemical Sciences School, Universidad La Salle-México, Benjamín Franklin 45, Mexico City 06140, Mexico
2
Design, Isolation, and Synthesis of Bioactive Molecules Research Group, Universidad La Salle-México, Benjamín Franklin 45, Mexico City 06140, Mexico
3
Development and Innovation in Applied Environmental Science and Technology Research Group, Universidad La Salle-México, Benjamín Franklin 45, Mexico City 06140, Mexico
*
Author to whom correspondence should be addressed.
Sustainability 2025, 17(12), 5535; https://doi.org/10.3390/su17125535
Submission received: 5 April 2025 / Revised: 8 May 2025 / Accepted: 16 May 2025 / Published: 16 June 2025
(This article belongs to the Special Issue Green Microbiology: Advancing Sustainability through Microbial Innovations)
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Abstract

:
The increasing global oil consumption has led to significant soil contamination by hydrocarbons, notably diesel-range hydrocarbons. Soil bioremediation through bacterial bioaugmentation is an alternative to increase the degradation of organic pollutants such as petroleum products. Bioremediation is a sustainable practice that contributes to the Sustainable Development Goals (SDGs) because it is environmentally friendly, reduces the impact of human activities, and avoids the use of invasive and destructive methods in soil restoration. This study examines the bioremediation potential of hydrocarbonoclastic bacteria isolated from soil close to areas with a risk of spills due to pipelines carrying hydrocarbons. Among the isolated strains, Arthrobacter globiformis, Pantoea agglomerans, and Nitratireductor soli exhibited hydrocarbonoclast activity, achieving diesel removal of up to 90% in short-chain alkanes and up to 60% in long-chain hydrocarbons. The results from in silico studies, which included molecular docking and molecular dynamics simulations, suggest that the diesel removal activity can be explained by the bioavailability of the linear alkanes and their affinity for alkane monooxygenase AlkB present in the studied microorganisms, since long-chain hydrocarbons had lower enzyme affinity and lower aqueous solubility. The correlation of the experimental results with the computational analysis allows for greater insight into the processes involved in the microbial degradation of hydrocarbons with varying chain lengths. Furthermore, this methodology establishes a cost-effective approximation tool for the evaluation of the feasibility of using different microorganisms in bioremediation processes.

Graphical Abstract

1. Introduction

With 88,477 thousand barrels of oil consumed globally daily, it is no surprise that hydrocarbon contamination is a pressing issue. The Asia Pacific region and North America are the top consumers, with 33,615 and 20,772 thousand barrels, respectively [1]. This widespread usage and transportation of oil increases the risk of soil contamination, primarily by alkanes and their derivatives, during exploration, refining, storage, and transportation processes, often due to spills, leaks, damaged pipelines, and fuel theft [2,3].
Soil contamination as a result of the presence of petroleum and its derivatives represents a serious problem because these contaminants can remain for long periods, affect the physicochemical properties of the soil (pH, soil organic matter, and C/N ratio) and microbiological properties, interfere with the growth and germination of plants in crops, decrease soil fertility, affect soil biota, and contaminate groundwater, causing adverse effects on the environment and human health [4,5]. Furthermore, in agricultural soils contaminated with hydrocarbons, which have not been treated, crop productivity decreases, impacting the socioeconomic levels of farmers [6,7,8].
Diesel is one of the most widely used petroleum products for transportation and industry. It combines alkanes, cycloalkanes, and aromatics with 10 to 28 carbon atoms [9,10]. Diesel is a permanent source of soil contamination and a major global environmental problem because it has high hydrophobicity and low volatility in its longer carbon chain components and is poorly biodegradable, which increases its impact on ecosystems [11,12].
Several environmentally friendly technologies have been used to mitigate the impacts of organic pollutants in soil, like phytoremediation [13], advanced oxidation processes (AOP) [14], biochar filtration [15], and other green adsorbents [16]. Among them, environmental bioremediation is one of the most widely used. Although it has some limitations, it has several advantages over other methods, like its cost-effectiveness, low environmental impact, relatively simple operation, and less manpower needed [17]. This technology uses microorganisms to reduce, eliminate, contain, or transform harmful contaminants present in the soil [18]. Typically, when microorganisms are isolated from polluted sites, they display higher efficiency in contaminant remotion, since they are already adapted to the presence of the pollutant [19]. For bioremediation to be efficient, it is necessary to regulate the supply of oxygen, the temperature, and the soil nutrient ratio (C:N:P) to promote the growth of microorganisms capable of degrading the hydrocarbon [20]. For the treatment of hydrocarbons by bioremediation, biostimulation is used, which is the addition of nutrients, organic amendments, or biosurfactants to activate the enzymatic processes of microorganisms. Additionally, bioaugmentation is used, which is the addition of exogenous microbial cultures, autochthonous microbial communities, or genetically engineered microbes with a high hydrocarbonoclastic capacity to increase the degradation of hydrocarbons [21,22,23].
Hydrocarbon biodegradation in bacteria is catalyzed by oxidoreductase and hydrolase enzymes such as monooxygenases, dioxygenases, and hydroxylases [24]. The enzyme alkane monooxygenase (AlkB, EC 1.14.15.3), belonging to the alkane hydroxylase class, is an integral-membrane non-heme diiron monooxygenase that uses n-alkanes from C10 to C32 as substrates to hydroxylate them to alcohols [25,26,27]. The AlkB gene has been studied to evaluate the bacterial alkane degradation potential [28].
Computational tools have helped to predict the results that can be generated during an experiment. The field of environmental sciences has not been an exception and has become a crucial component of environmental research [29]. Some examples include the use of machine learning methods for biochar design [30], the understanding of the mechanisms of reactions in some methods used for the chemical remotion of pollutants [31], the interaction of contaminants with biological components [32], and the estimation of the toxicity of pollutants and the prediction of their degradation routes [33,34,35]. Strategies such as molecular docking, molecular dynamics simulation, and QSAR-based modeling have been applied for these purposes. Recently, these in silico strategies have been used to predict or explain the results obtained in bioremediation studies, helping to reduce the number of required experiments [36,37]. The molecular docking–molecular dynamics combination has become a standard in computational chemistry studies regarding interactions of small molecules with proteins [38], including some of environmental interest [39,40]. Therefore, it could be employed to simulate and understand the potential binding of pollutants to enzymes in some microorganisms that could participate in the degradation of noxious compounds.
In this work, we investigated the efficiency of diesel removal in soil with hydrocarbonoclastic bacteria with different metabolic characteristics isolated from Mexican soils near sites that transport petroleum products. We conducted a series of controlled experiments to measure the degradation capacity of these bacteria. Additionally, we performed a molecular docking–molecular dynamics study to evaluate the affinity of the alkanes present in diesel for the AlkB active site, seeking to evaluate its impact as one of the aspects correlated with a microorganism’s degradation capacity. This study could pave the way for the development of more efficient bioremediation strategies.

2. Materials and Methods

2.1. Soil Samples

The agricultural soil used for the biodegradation tests was obtained from the Juchitepec area, State of Mexico, located at latitude 19.1043 and longitude −98.867936, at an average height of 2543 masl. This area has a risk of spills due to pipelines with hydrocarbons. The average annual rainfall is 771.8 mm, and the average annual temperature is 14.4 °C. In this area, crop rotation of corn, oats, tomato, lettuce, beans, and chamomile is carried out. Five random points were excavated at a 30 cm depth to obtain soil samples on-site, and these were mixed to obtain a composite sample, which was collected, labeled, and stored at 4 °C until later use in the laboratory.

2.2. Soil Characterization Methods

Analyses were performed to define the physicochemical characteristics of the sampled soil, determining the texture (Bouyoucos hydrometer: Robsan, Mexico City, Mexico), pH (potentiometer: Conductronic PH10, Mexico City, Mexico) in water, organic matter (Walkley–Black), humidity (thermobalance), total nitrogen (micro-Kjeldahl), total phosphorus, and field capacity [41]. This characterization is important in adopting the appropriate conditions for the bioremediation process.

2.3. Soil Contamination

The soil was artificially contaminated with commercial diesel (PEMEX Company; density (g/m3): 0.87–0.95; flash point (°C): 45; solubility in water at 20 °C (g/100 mL): 0.0005) to obtain an initial concentration of 12,000 ppm. The diesel was diluted in hexane and added until it was thoroughly incorporated to ensure homogeneous soil contamination. The solvent was then allowed to evaporate, and, after a few days, it was used for microcosm tests.

2.4. Hydrocarbonoclastic Bacteria

The bacteria used for bioremediation were obtained from the Faculty of Chemical Sciences, Universidad La Salle, Mexico Strain Collection. These strains were isolated from different agricultural soils collected from three locations in Central Mexico: Juchitepec, Tlapala, and Santa María. Hydrocarbonoclastic bacteria (HB) were grown in Bushnell Haas medium, nitrogen-fixing bacteria (NFB) were grown in Ashby’s mannitol medium, and phosphorus-solubilizing bacteria (PSB) were grown in Pikovskaya medium. All media used noble agar and diesel as the only carbon sources and were incubated for 15 days at 37 °C. The isolated hydrocarbonoclastic bacterial strain was labeled H1, the two phosphorus-solubilizing strains were labeled P1 and P2; and one nitrogen-fixing strain was coded N2.

2.5. Bacterial Identification

For the selected strains, the colonial and microscopic morphologies were observed, and identification was carried out using molecular techniques. DNA extraction was performed using the DNeasy PowerLyzer PowerSoil kit (Qiagen: Hilden, Germany). Then, 16S rDNA amplification was performed using PCR with the universal primers 27F: 5′ AGAGTTTGATCMTGGCTCAG 3′) and 1492R: 5′ TACGGYTACCTTGTTACGACTT 3′ [42], provided by the Institute of Biotechnology, UNAM. The PCR product was purified using the Wizard SV Gel and PCR Clean-Up System kit (Promega: Madison, WI, USA) and was sent for sequencing to the Institute of Cellular Physiology, UNAM. Later, the DNA sequence was reviewed using the Chromas 2.6.6 program to identify the bacterial species by comparing sequences with known entries using the BLAST (version 2.16.0) bioinformatics program.

2.6. Biodegradation Experiment in Microcosms

The preparation of microcosms for the development of diesel-range hydrocarbon bioremediation experiments consisted of placing 30 g of contaminated soil, perfectly sifted (sieve ASTM no. 40, 425 µm: Lewis Center, OH, USA) and homogenized, in 125 mL glass serological flasks with a headspace of 90.8 mL. The microcosms were biostimulated by adding (NH4)2SO4 and KH2PO4 to maintain the 100:10:1 C:N:P ratio recommended to promote microbial growth in treating contaminated soil [43]. The humidity was adjusted to 20% to activate the system, and, for bioaugmentation, 1 mL of a bacterial suspension (0.8 × 108 bacteria/mL) of each strain was inoculated in different treatments (H1, P1, P2, and N2). The flasks were sealed and aerated and the humidity adjusted every two days, and they were kept at room temperature (22–25 °C). The bioremediation treatment time for the biostimulation and bioaugmentation of the contaminated soil was 20 days.

2.7. Chromatographic Analysis

For the extraction of diesel from the different soil treatments, the U.S. Environmental Protection Agency (EPA) method 3546 [44] was used with a CEM MARS-Xpress microwave oven, using dichloromethane–acetone (2:1) as solvents. Each sample was concentrated using a rotary evaporator and reconstituted up to 2 mL with dichloromethane in vials. Chromatographic analysis was performed according to EPA method 8015C [45] using a Hewlett Packard HP 5890 Series II gas chromatograph with a flame ionization detector (GC/IFD) (Hewlett Packard: Palo Alto, CA, USA) and an Agilent HP-5 capillary column (30 m × 0.53 mm, 5.00 µm), with a nitrogen carrier gas flow rate of 1 mL/min, a temperature program of 50–270 °C (17.67 min), an injector temperature of 250 °C (splitless), a detector temperature of 270 °C, a detection limit (LD) of 0.01 mg/kg, and an injection volume of 1 µL. The standard used was a mixture of n-alkane hydrocarbons (C10–C28) and UST-modified diesel-range organics (DRO) (Supelco:Bellefonte, USA). The areas under the curve were obtained using an integrator to determine the total concentration of the contaminant in the samples, and the areas of each n-alkane component of diesel from C10 to C28 (decane to octacosane) were also obtained.

2.8. Homology Modeling

Since experimental results have suggested the greater degradation of medium-chain than long-chain alkanes, AlkB was selected as the enzyme for in silico studies. The amino acid sequences of AlkB of the three bacterial species were obtained from the Universal Protein Resource (UniProt, http://www.uniprot.org/, accessed 10 December 2024 ) with the following accession numbers: Pantoea agglomerans KJH59911.1, Arthrobacter globiformis RAM39010.1, Nitratireductor estuarii GGA53670.1. The tridimensional structure predictions were carried out using the homology modeling module implemented as part of Yasara Structure (Yasara Biosciences GmbH, Vienna, Austria), which uses a CASP-approved protocol for structure prediction [46]. The validation of the generated models as part of the protocol implemented in Yasara Structure is based on Z-scores calculated from molecular dynamics force field energies. However, further validation was carried out using PROSA (https://prosa.services.came.sbg.ac.at/prosa.php, accessed 10 January 2025) [47] and PDBsum (http://www.ebi.ac.uk/thornton-srv/databases/pdbsum/Generate.html, accessed 10 January 2025). In all models, more than 90% of the residues were in favored regions in a Ramachandran plot (90.1–91.5%), with G-factor values higher than −0.5 (0.02–0.17) and Z-scores between −8.5 and −8.75, which are within the ranges expected for predicted structures for proteins with 350 residues.

2.9. Molecular Docking

The structures of the studied alkanes (C10–C28) were constructed in Spartan’10 (Wavefunction, Inc., Irvine, CA, USA) using standard fragments, and their geometry was optimized using MMFF//HF 6-31 G*. The docking studies were performed using the predicted structures from homology modeling. For the selection of the search site, we used 3DLigandSite (http://www.sbg.bio.ic.ac.uk/3dligandsite/, accessed 17 January 2025) [48] and DeepSite (https://playmolecule.com/deepsite/, accessed 17 January 2025 [49]). In the first site, ligands bound to structures similar to the query are superimposed onto the model and used to predict the binding site, while the latter uses machine learning-based methods. Both algorithms predicted that the ligand-binding site would be centered around the regions delimited by residues 43–49 and 77–79 for all enzymes. Docking studies were carried out in Molegro Virtual Docker v.6.0.1 (Qiagen Bioinformatics, Aarhus, Denmark) [50] using a previously reported methodology [51]. The search sites were defined as spheres of 15 Å diameter centered on residues 77 and 78. The charges on each enzyme and the analyzed ligands were based on standard templates implemented in the program. MolDock Optimizer was used as a searching algorithm with 50 runs and the rerank score as the scoring scheme. Each compound’s poses with the lowest score (higher theoretical affinity) were selected for further analysis [52].

2.10. Molecular Dynamics Simulations

The predicted docking poses of C10, C16, and C22 with the three enzymes were studied using molecular dynamics (MD). The MD simulations were performed using Yasara Structure v.18.4.24 [46] using the AMBER 14 force field [53]. The poses with the lowest docking scores for each complex were selected as the initial structures. Each complex was positioned in a cubic cell box extended 10 Å larger on each side of the complex with periodic boundary conditions. The cell box was filled with TIP3 water molecules. The temperature was set at 298 K, the water density to 0.997 g/cm3, and the pH to 7.4. The cutoff for van der Waals interactions was set to 8 Å, and the Particle Mesh Ewald algorithm was applied to evaluate long-range electrostatic interactions. A multiple step of 2.5 fs was set, and data were recorded each 100 ps up to a final simulation time of 100 ns. The results were analyzed with macros included as part of Yasara Structure, including the root mean square deviation (RMSD), root mean square fluctuations (RMSF), and ligand-binding energy variations (MM-PBSA), using the last 25 ns of the simulation period.

3. Results and Discussion

3.1. Soil Characterization

The results showed that the soil used for the remediation tests had a sandy loam texture, moderately rich in organic matter and with a slightly acidic pH (Table 1). A soil with a sandy texture and the presence of organic matter exhibits increased porosity. It allows for better oxygen circulation, which contributes to optimizing the activity of microorganisms, strengthening the stability and structure of the soil, and favoring water absorption and gas flow [54]. Therefore, this soil texture allows hydrocarbon biodegradation to develop appropriately, mainly through aerobic processes. The soil pH is a parameter that must be considered in the remediation of contaminated soils since it determines the communities of microorganisms that will participate in the biodegradation and transformation of the hydrocarbon. Biodegradation can occur at various pH levels; however, a range of 6.5 to 8.5 is usually the most suitable for this process, since these values can significantly increase the rates of hydrocarbon bioremediation [55,56].
The initial concentrations of nitrogen and phosphorus in the soil were very low (Table 1), but knowing these values allows for the adjustment of these nutrients to the C:N:P ratio of 100:10:1. This determination is important because the use of fertilizers in bioremediation allows for an increase in the activity of native microorganisms and induces the mineralization of contaminants. However, adverse effects have also been identified when the concentration of nutrients is too high or over-fertilization occurs, mainly because this can be toxic to microbial species [57,58]. Therefore, knowing the necessary nutrients or fertilizers in the contaminated soil will allow for a better bioremediation process using biostimulation.
Another important parameter for the development and activity of microorganisms is soil moisture, because it intervenes in various cellular metabolic reactions and functions as a solvent and transport medium for nutrients in the soil. Low moisture levels can decrease cellular activity, but high levels are undesirable because excess water reduces soil aeration. Thus, maintaining an optimal moisture range is essential [54,59]. In bioremediation, the moisture content is generally adjusted to a fraction of the soil’s water-holding capacity. The optimum moisture value, however, is a function of the soil type, pore size distribution, and soil texture. For the present study, water was added to establish moisture content of 20% during the treatment and to maintain the remediation process.
Observations of bacterial and fungal growth suggest the presence of native microbiota with the metabolic tools to utilize diesel as a carbon source. This fact is important because, under appropriate conditions, the native microbiota and the inoculated microorganisms will engage in a competitive microbiological interaction that favors or decreases hydrocarbon degradation [60].
Although, in this work, the initial soil characterization was used to optimize the remediation conditions, it is recommended to carry out a final characterization once the remediation is completed. This enables one to identify possible changes in its physicochemical and ecological properties to determine the use of the restored soil [61], considering the scaling up of this technology at the field level.

3.2. Microorganism Isolation and Characterization

The hydrocarbonoclastic bacteria in the soils used for diesel degradation were partially identified through biochemical tests and colonial morphological and microscopic observations. Subsequently, three different bacterial species were identified using molecular tests and bioinformatics tools: Arthrobacter globiformis, Pantoea agglomerans, and Nitratireductor soli. The P. agglomerans species was found at two sampling sites (Table 2). Furthermore, selective media showed that these three species can degrade hydrocarbons, and some also showed the ability to fix nitrogen or solubilize phosphorus. Therefore, these bacteria could not only degrade hydrocarbons but also benefit nutrient availability in the soil [62].
The bacterium A. globiformis has been recorded as a dominant microbial community from 7 to 90 days in hydrocarbon-contaminated soil at different concentrations, indicating its high adaptability and important role in enhancing hydrocarbon biodegradation in soil [63]. A. globiformis is a species that produces biosurfactants in the presence of hydrocarbons, demonstrating strong potential for the degradation of organic contaminants [64,65]. We recognize that the adaptability of A. globiformis to diesel environments does not necessarily mean that it can degrade hydrocarbons. However, it is plausible that it is a species with high degradation potential. This assertion is supported by the fact that A. globiformis expresses alkane 1-monooxygenase. Therefore, using computational tools, which is the approach that we propose in this work, and further laboratory experiments would provide more data to confirm this as a species that participates in the remediation process.
P. agglomerans is a bacterium isolated from hydrocarbon-laden soil samples and sludge from refinery treatment plants, exhibiting remarkably rapid reaction rates for hydrocarbon degradation [66,67]. The genus Pantoea is recognized for its role as a functional member of the microbial consortia involved in the biodegradation and bioremediation of hydrocarbon contaminants. More specifically, it has shown a strong ability to consume diesel [67].
N. soli has limited records of involvement in hydrocarbon degradation processes in soils but has been isolated from phenol-contaminated soils [68]. However, other species of the genus Nitratireductor have been isolated from oil-contaminated saline soil, arid mangrove sediments, and hydrothermal vent sediments, revealing cultivable bacteria that can be used for hydrocarbon degradation [69,70].
The efficiency of hydrocarbon removal through bioaugmentation with exogenous bacteria will depend on many physicochemical factors, the contaminant characteristics, and the metabolic capacity of the microorganisms. Although bioaugmentation can generate competitive interactions with native microbial populations and modify their metabolic pathways in contaminated environments, this methodology with exogenous strains can also improve the degradation processes of specific contaminants and enhance the remediation efficiency [71].

3.3. Biodegradation of Diesel-Range Hydrocarbons in Soil

The results regarding the diesel removal percentage show that, in all bacteria, there is a tendency for greater removal in the short carbon chains C10 and C12, with average values of 93.68 and 79.28%, respectively, and lower removal in long carbon chains between C14 and C28, with average values of 56.30 to 64.08% (Figure 1). In the control (no bioaugmentation), it was observed that the removal of diesel-range hydrocarbons was lower than in the treatments with added bacteria, demonstrating that bioaugmentation favors the bioremediation process, in conjunction with the native species of the contaminated soil. In the statistical analysis, it was observed that there was no significant difference between the removal of each alkane (C10 to C28) and the different species of hydrocarburoclastic bacteria (p ≥ 0.05), indicating that the degradation behavior of hydrocarbons with different carbon numbers is repeated in these bacteria. On the other hand, for each bacterial species, we examined whether there was a significant difference between the removal of diesel and the number of carbons (C10 to C28). It was obtained that Pantoea agglomerans (Tla), Pantoea agglomerans (SMT), and Nitratireductor soli showed significant differences (p ≤ 0.05). Arthrobacter globiformis did not present a significant difference, with a slightly higher value (p ≥ 0.05). Therefore, the results of the present work indicate that the bacteria added to the soil have an enzymatic metabolic capacity that enables them to act more rapidly in the degradation of short hydrocarbon chains and, to a lesser extent, longer chains.
There are reports that certain microorganisms, such as Rhodococcus, prefer the metabolism of short-chain alkanes, and other species, such as Pseudomonas and Acinetobacter, are inclined towards the degradation of long-chain alkanes [72,73]. Similarly, it has been reported that Marinobacter exhibits a high degradation capacity for short-chain alkanes of C8–C10, and no degradation was detected for long-chain alkanes of C15–C23 [74]. In contrast, in another diesel degradation experiment with different hydrocarbonoclastic bacteria, the degradation rates of C21–C30, C31–C35, and C10–C20 ranged from 68.8 to 84.7%, from 28.4 to 67.7%, and from 44.4 to 55.1%, respectively, observing higher removal in long alkane chains [75]. This behavior may be related to the enzymes involved and their specificity in the biodegradation of the different hydrocarbon chains. In this sense, it is known that the enzyme AlkB catalyzes the initial hydroxylation of n-alkanes with several carbon atoms between 5 and 12 (C5–C12). In contrast, the enzyme LadA (long-chain alkane monooxygenase) participates in the terminal oxidation of long-chain n-alkanes [76]. However, on the other hand, the degradation of n-alkanes may be conditioned by their toxicity, solubility, and bioavailability in the environment [77]. These factors were also observed during the in silico studies.
In this study, we assumed that all C10–C28 carbon chains in diesel fuel in soil were biodegraded by the added microorganisms because they produced the enzyme alkane monooxygenase. However, the decrease in the concentrations of C10 to C14 hydrocarbons can also be attributed to the volatilization of these compounds [78]. Diesel volatilization in soil is influenced by various factors, such as the types of soil particles, amount of organic matter, water content, temperature, and wind speed [79,80], but it can also be influenced by the characteristics of the diesel itself and its additives [81]. In this study, the treated soil contained water, organic matter, and fine particles, which can prevent complete volatilization and promote the biodegradation of short-chain hydrocarbons. However, further research is needed to determine the percentages of volatile organic compounds that are biodegraded and those that are volatilized in soils contaminated with diesel.

3.4. In Silico Studies

The low aqueous solubility of linear alkanes decreases as the size of the chain increases, so a possible explanation for the observed biodegradation profile is based on the decrease in the uptake efficiency of the alkanes present in diesel due to bacteria having less access to them [25,82]. However, different mechanisms by which microorganisms incorporate highly hydrophobic substances have been described, including biosurfactants [83]. The analysis of the correlation between the values of the aqueous solubility or its calculated partition coefficient (clogP) for the studied alkanes and the results of their experimental remotion revealed that alkanes with lower clogP values had a higher percentage of remotion and that the removal–clogP correlation was adapted to a quadratic model, as seen in Figure 2. This behavior suggests that the aqueous solubility and alkane uptake by the microorganisms are critical factors for biodegradation by these specific species.
We conducted in silico studies to explain the alkane degradation profile observed in the experimental results. First, selecting a probable enzyme implicated in the biodegradation of the alkanes present in the diesel samples was necessary. Since the results suggested the higher degradation of the medium-chain alkanes, alkane 1-monooxygenase appeared to be the best candidate. The AlkB amino acid sequences of A. globiforms, P. agglomerans, and N. soli are available in UniProt, but their tridimensional structures are still to be resolved. Therefore, homology modeling was used to predict the structures of the three AlkB enzymes; Figure 3 shows the structure predicted for AlkB from the three microorganisms. The results of structural validation indicated that the predicted structures were suitable for in silico experimentation. The overlap of the three enzymes structures shows their high similarity. However, the AlkB from A. globiformis is more similar to the enzyme from N. soli (RMSD value of 6.07 A) than that of P. agglomerans (RMSD value of 19.36 Å). The main differences are among the loops around residues 140–160.
Before performing molecular docking studies, a search site should be defined. The 3DLigandSite and DeepSite platforms were used to identify the potential binding sites of the alkane molecules. Both platforms predicted the same possible binding sites despite the different approaches. For AlkB of A. globiformis, the expected binding site is delimited by Ala 44, Glu 45, Gly 76, Gly 77, and Met 79; that for AlkB of P. agglomerans is represented by Ala 43, Glu 44, Gly 75, Gly 76, and Met 78. For AlkB of N. reductor, the predicted binding site is enclosed by the Arg 48, Val 49, Gly 78, and Val 79 residues. In all cases, these residues conform to a channel with histidine residues, which are important for the coordination of iron ions necessary for catalytic activity [84]. The location of these potential binding sites is also illustrated in Figure 3. As expected, the residues are primarily hydrophobic, with the exception of the polar residues of Glu and Arg. These cavities were also described using CAVER 2.0 [85] and are depicted in Figure 3.
The results of the molecular docking experiments using Molegro Virtual Docker are shown in Table 3. These results suggest that long-chain alkanes have a higher affinity than medium-chain molecules, contrasting with the experimental results. Since the scores from docking results should be used cautiously before making conclusions [86,87,88], a deeper analysis of the predicted poses was required. The analysis of these poses revealed that the plausible binding site around residues 44–45 and 76–77 was not occupied by any of the alkanes, especially those of longer hydrocarbon chains. A deeper analysis revealed that the cavity volume was only 214 Å3, as calculated with CAVER 2.0, which is somewhat restricted even for the binding of decane, which has a molecular volume of 230.03 Å3 in its most stable conformation. In this scenario, we decided to perform molecular dynamics simulations to better predict the complex formed by the alkanes and AlkB.
The predicted complexes from the molecular docking studies were taken as the initial structures for molecular dynamics simulation. RMSD analysis helps to study the dynamic stability of a protein–ligand complex as it is a global measure of structural fluctuations. Figure 4 shows the plot with the variations in the RMSD over the simulation time for the complexes of C10, C16, and C22 with AlkB from the three microorganisms. It can be observed that, after 30 ns of simulation, the variation in the RMSD for all complexes is less than 2 Å, suggesting that the protein–ligand complex is stable. Thus, the last 50 ns of the simulation was taken for subsequent analyses.
During the molecular dynamics simulations, a notable change was the increase in the size of the active site cavity, potentially improving the alkane accommodation. Although not critical, the accommodation of the alkane, mainly the terminal carbon, within the active site is relevant for its degradation [89]. Figure 5 depicts the complexes of C10, C16, and C22 alkanes in the active site of AlkB and the distance of the terminal carbon to the critical histidine residues. These figures show that the terminal carbons of C16 and C22 are farther (around 11 Å) from the active site than C10 (around 8 Å), potentially explaining the lower removal rates of these longer alkanes. Moreover, the ligand-binding energy values (Table 4) suggest that the three alkanes can bind to enzymes regardless of their chain length. However, C10 and C16 had higher ligand-binding energies (i.e., better affinity) than C22, which correlates with the experimental results. The better ligand-binding energy appears to be related to better accommodation within the active site, as the longer the hydrocarbon chain, the higher the conformational strain, as seen in Figure 5.
Using computational strategies to explain the experimental results has allowed the development of new hypotheses that could potentially lead to new findings. In the case of bioremediation-related assays, this approach has been recently exploited; however, the benefits that can be obtained could support the implementation of new methodologies that substantially improve contaminant removal [90,91]. Figure 6 shows the correlation between the ligand-binding energy towards the AlkB enzyme and the percentage of removal of the three alkanes analyzed in the molecular dynamics studies. In this figure, the better the binding of the alkane to the enzyme, the higher the percentage of its removal, which is partially independent of the microbial species. There is slightly higher removal in the case of A. globiformis, without being statistically significant; this seems to be related to the higher affinity of alkanes for the AlkB of this microorganism. Interestingly, the highest diesel removal capacity was observed with A. globiformis and N. soli, whose AlkB enzymes are more similar to those of P. agglomerans, which had the lowest removal capacity and the lowest affinity for longer-chain alkanes. Overall, our findings suggest that the microbial removal of linear hydrocarbons depends on the alkane’s bioavailability and the hydrocarbons’ affinity for the AlkB enzyme. Therefore, effective diesel bioremediation strategies should consider these aspects.

4. Conclusions

In conclusion, we have described the diesel remotion properties of Arthrobacter globiformis, Pantoea agglomerans, and Nitratireductor soli that were isolated from soils near pipeline infrastructure. All strains displayed significant hydrocarbon degradation activity, with the diesel removal efficiencies reaching up to 90% for short-chain alkanes (C10–C14) and up to 60% for long-chain hydrocarbons (≥C16). In silico studies, which included molecular docking and molecular dynamics simulations, suggested that the efficiency of diesel degradation was largely influenced by the bioavailability of the linear alkanes and their binding affinities to the alkane monooxygenase (AlkB) produced by the investigated strains. It was found that reduced enzymatic affinity and lower aqueous solubility were limiting factors for the biotransformation of long-chain hydrocarbons.
The integrated experimental and computational approach outlined in this study provides a predictive framework for the evaluation of the suitability of specific microbial strains in hydrocarbon bioremediation strategies.

Author Contributions

Conceptualization, A.I.-G.; methodology, A.C.-N., A.R.-N., A.I.-G. and M.A.L.-M.; software, K.P.-G. and M.A.L.-M.; formal analysis, A.I.-G. and M.A.L.-M.; investigation, F.E.-L., K.P.-G. and A.C.-N.; resources, A.I.-G. and M.A.L.-M.; writing—original draft preparation, all authors; writing—review and editing, all authors; project administration, A.I.-G. and M.A.L.-M. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by Universidad La Salle, grant numbers NEC-09/18 and NEC-14/20.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Data will be made available on direct request to authors.

Conflicts of Interest

The authors declare no conflicts of interest. The funders had no role in the design of the study; the collection, analysis, or interpretation of the data; the writing of the manuscript; or the decision to publish the results.

References

  1. British Petroleum. British Petroleum Full Report—Statistical Review of World Energy; British Petroleum: London, UK, 2021. [Google Scholar]
  2. Shahi, A.; Aydin, S.; Ince, B.; Ince, O. Evaluation of microbial population and functional genes during the bioremediation of petroleum-contaminated soil as an effective monitoring approach. Ecotoxicol. Environ. Saf. 2016, 125, 153–160. [Google Scholar] [CrossRef] [PubMed]
  3. Varjani, S.J. Microbial degradation of petroleum hydrocarbons. Bioresour. Technol. 2017, 223, 277–286. [Google Scholar] [CrossRef] [PubMed]
  4. Khudur, L.S.; Shahsavari, E.; Miranda, A.F.; Morrison, P.D.; Nugegoda, D.; Ball, A.S. Evaluating the efficacy of bioremediating a diesel-contaminated soil using ecotoxicological and bacterial community indices. Environ. Sci. Pollut. Res. 2015, 22, 14809–14819. [Google Scholar] [CrossRef]
  5. Ikuesan, F.A. Evaluation of Crude Oil Biodegradation Potentials of Some Indigenous Soil Microorganisms. J. Sci. Res. Rep. 2017, 13, 1–9. [Google Scholar] [CrossRef]
  6. Sutormin, O.S.; Goncharov, A.S.; Kratasyuk, V.A.; Petrova, Y.Y.; Bajbulatov, R.Y.; Yartsov, A.E.; Shpedt, A.A. Effects of Oil Contamination on Range of Soil Types in Middle Taiga of Western Siberia. Sustainability 2024, 16, 11204. [Google Scholar] [CrossRef]
  7. Skrypnik, L.; Maslennikov, P.; Novikova, A.; Kozhikin, M. Effect of Crude Oil on Growth, Oxidative Stress and Response of Antioxidative System of Two Rye (Secale cereale L.) Varieties. Plants 2021, 10, 157. [Google Scholar] [CrossRef]
  8. Chorom, M.; Sharifi, H.S.; Motamedi, H. Bioremediation of a crude oil-polluted soil by application of fertilizers. J. Environ. Health Sci. Eng 2010, 7, 319–326. [Google Scholar]
  9. Rocha, I.M.V.; Silva, K.N.O.; Silva, D.R.; Martínez-Huitle, C.A.; Santos, E.V. Coupling electrokinetic remediation with phytoremediation for depolluting soil with petroleum and the use of electrochemical technologies for treating the effluent generated. Sep. Purif. Technol. 2019, 208, 194–200. [Google Scholar] [CrossRef]
  10. Eremeeva, A.M.; Kondrasheva, N.K.; Khasanov, A.F.; Oleynik, I.L. Environmentally Friendly Diesel Fuel Obtained from Vegetable Raw Materials and Hydrocarbon Crude. Energies 2023, 16, 2121. [Google Scholar] [CrossRef]
  11. Maceiras, R.; Alfonsin, V.; Martinez, J.; Martinez Vara de Rey, C. Remediation of Diesel-Contaminated Soil by Ultrasonic Solvent Extraction. Int. J. Environ. Res. 2018, 12, 651–659. [Google Scholar] [CrossRef]
  12. Huang, Z.; Chen, Q.; Yao, Y.; Chen, Z.; Zhou, J. Micro-bubbles enhanced removal of diesel oil from the contaminated soil in washing/flushing with surfactant and additives. J. Environ. Manage. 2021, 290, 112570. [Google Scholar] [CrossRef]
  13. Nemati, B.; Baneshi, M.M.; Akbari, H.; Dehghani, R.; Mostafaii, G. Phytoremediation of pollutants in oil-contaminated soils by Alhagi camelorum: Evaluation and modeling. Sci. Rep. 2024, 14, 5502. [Google Scholar] [CrossRef]
  14. Zhou, Z.; Liu, X.; Sun, K.; Lin, C.; Ma, J.; He, M.; Ouyang, W. Persulfate-based advanced oxidation processes (AOPs) for organic-contaminated soil remediation: A review. Chem. Eng. J. 2019, 372, 836–851. [Google Scholar] [CrossRef]
  15. Nguyen, T.-B.; Sherpa, K.; Bui, X.-T.; Nguyen, V.-T.; Vo, T.-D.-H.; Ho, H.-T.-T.; Chen, C.-W.; Dong, C.-D. Biochar for soil remediation: A comprehensive review of current research on pollutant removal. Environ. Pollut. 2023, 337, 122571. [Google Scholar] [CrossRef]
  16. Liu, Y.; Biswas, B.; Hassan, M.; Naidu, R. Green Adsorbents for Environmental Remediation: Synthesis Methods, Ecotoxicity, and Reusability Prospects. Processes 2024, 12, 1195. [Google Scholar] [CrossRef]
  17. Michael-Igolima, U.; Abbey, S.J.; Ifelebuegu, A.O. A systematic review on the effectiveness of remediation methods for oil contaminated soils. Environ. Adv. 2022, 9, 100319. [Google Scholar] [CrossRef]
  18. Adams, G.O.; Fufeyin, P.T.; Samson, E.O.; Ehinomen, I. Bioremediation, Biostimulation and Bioaugmention:A Review. Int. J. Environ. Bioremediat. Biodegrad. 2015, 3, 28–39. [Google Scholar]
  19. Rusănescu, C.O.; Istrate, I.A.; Rusănescu, A.M.; Constantin, G.A. Bioremediation of Soil Contamination with Polycyclic Aromatic Hydrocarbons—A Review. Land 2025, 14, 10. [Google Scholar] [CrossRef]
  20. Safdari, M.S.; Kariminia, H.R.; Rahmati, M.; Fazlollahi, F.; Polasko, A.; Mahendra, S.; Wilding, W.V.; Fletcher, T.H. Development of bioreactors for comparative study of natural attenuation, biostimulation, and bioaugmentation of petroleum-hydrocarbon contaminated soil. J. Hazard. Mater. 2018, 342, 270–278. [Google Scholar] [CrossRef]
  21. Wu, M.; Dick, W.A.; Li, W.; Wang, X.; Yang, Q.; Wang, T.; Xu, L.; Zhang, M.; Chen, L. Bioaugmentation and biostimulation of hydrocarbon degradation and the microbial community in a petroleum-contaminated soil. Int. Biodeterior. Biodegradat. 2016, 107, 158–164. [Google Scholar] [CrossRef]
  22. Nwankwegu, A.S.; Onwosi, C.O. Bioremediation of gasoline contaminated agricultural soil by bioaugmentation. Environ. Technol. Innov. 2017, 7, 1–11. [Google Scholar] [CrossRef]
  23. Brzeszcz, J.; Kapusta, P.; Steliga, T.; Turkiewicz, A. Hydrocarbon Removal by Two Differently Developed Microbial Inoculants and Comparing Their Actions with Biostimulation Treatment. Molecules 2020, 25, 661. [Google Scholar] [CrossRef] [PubMed]
  24. Das, S.; Dash, H.R. Microbial Bioremediation: A Potential Tool for Restoration of Contaminated Areas. In Microbial Biodegradation and Bioremediation; Elsevier Inc.: Amsterdam, The Netherlands, 2014; pp. 2–21. ISBN 9780128004821. [Google Scholar]
  25. Rojo, F. Degradation of alkanes by bacteria. Environ. Microbiol. 2009, 11, 2477–2490. [Google Scholar] [CrossRef]
  26. Lima, S.D.; Oliveira, A.F.; Golin, R.; Lopes, V.C.P.; Caixeta, D.S.; Lima, Z.M.; Morais, E.B. Isolation and characterization of hydrocarbon-degrading bacteria from gas station leaking-contaminated groundwater in the Southern Amazon, Brazil. Brazilian J. Biol. 2020, 80, 354–361. [Google Scholar] [CrossRef]
  27. Nie, Y.; Chi, C.-Q.; Fang, H.; Liang, J.-L.; Lu, S.-L.; Lai, G.-L.; Tang, Y.-Q.; Wu, X.-L. Diverse alkane hydroxylase genes in microorganisms and environments. Sci. Rep. 2014, 4, 4968. [Google Scholar] [CrossRef]
  28. Groves, J.T.; Feng, L.; Austin, R.N. Structure and Function of Alkane Monooxygenase (AlkB). Acc. Chem. Res. 2023, 56, 3665–3675. [Google Scholar] [CrossRef]
  29. Sandoval-Pauker, C.; Yin, S.; Castillo, A.; Ocuane, N.; Puerto-Diaz, D.; Villagrán, D. Computational Chemistry as Applied in Environmental Research: Opportunities and Challenges. ACS ES&T Eng. 2024, 4, 66–95. [Google Scholar]
  30. Wang, R.; Chen, H.; Guo, S.; He, Z.; Ren, N.; Ho, S.-H. Machine Learning toward Realizing End-to-End Biochar Design for Environmental Remediation. ACS ES&T Eng. 2024, 4, 2332–2345. [Google Scholar]
  31. Mohebbi, E.; Minnelli, C.; Pavoni, E.; Sisti, L.; Laudadio, E.; Stipa, P. Layered Double Hydroxides as Systems for Capturing Small-Molecule Air Pollutants: A Density Functional Theory Study. Molecules 2024, 29, 4996. [Google Scholar] [CrossRef]
  32. Yadav, A.; Vuković, L.; Narayan, M. An Atomic and Molecular Insight into How PFOA Reduces α-Helicity, Compromises Substrate Binding, and Creates Binding Pockets in a Model Globular Protein. J. Am. Chem. Soc. 2024, 146, 12766–12777. [Google Scholar] [CrossRef]
  33. Aloui, S.; Raboudi, F.; Ghazouani, T.; Salghi, R.; Hamdaoui, M.H.; Fattouch, S. Use of molecular and in silico bioinformatic tools to investigate pesticide binding to insect (Lepidoptera) phenoloxidases (PO): Insights to toxicological aspects. J. Environ. Sci. Health B. 2014, 49, 654–660. [Google Scholar] [CrossRef] [PubMed]
  34. Librando, V.; Pappalardo, M. In silico bioremediation of polycyclic aromatic hydrocarbon: A frontier in environmental chemistry. J. Mol. Graph. Model. 2013, 44, 1–8. [Google Scholar] [CrossRef] [PubMed]
  35. Tratnyek, P.G.; Bylaska, E.J.; Weber, E.J. In silico environmental chemical science: Properties and processes from statistical and computational modelling. Environ. Sci. Process. Impacts 2017, 19, 188–202. [Google Scholar] [CrossRef] [PubMed]
  36. Singh, A.K.; Chowdhary, P.; Raj, A. In silico bioremediation strategies for removal of environmental pollutants released from paper mills using bacterial ligninolytic enzymes. In Microorganisms for Sustainable Environment and Health; Elsevier: Amsterdam, The Netherlands, 2020; pp. 249–285. [Google Scholar]
  37. Singh, P.K.; Imam, J.; Shukla, P. In Silico Approach in Bioremediation. In Microbial Biodegradation and Bioremediation; Elsevier: Amsterdam, The Netherlands, 2014; pp. 422–433. [Google Scholar]
  38. Vidal-Limon, A.; Aguilar-Toalá, J.E.; Liceaga, A.M. Integration of Molecular Docking Analysis and Molecular Dynamics Simulations for Studying Food Proteins and Bioactive Peptides. J. Agric. Food Chem. 2022, 70, 934–943. [Google Scholar] [CrossRef] [PubMed]
  39. Scott, S.E.; Fernandez, J.P.; Hadad, C.M.; MacKay, A.A. Molecular Docking as a Tool to Examine Organic Cation Sorption to Organic Matter. Environ. Sci. Technol. 2022, 56, 951–961. [Google Scholar] [CrossRef]
  40. Pan, Y.; Yu, S.-S.; Xiao, Z.-C.; Min, Y.; Tian, T.; Zheng, Y.-M.; Zhao, Q.-B.; Yuan, Z.-H.; Yu, H.-Q. Re-evaluation and modification of dehydrogenase activity tests in assessing microbial activity for wastewater treatment plant operation. Water Res. 2023, 246, 120737. [Google Scholar] [CrossRef]
  41. Fernández, L.; Rojas, N.; Roldán, T.; Ramírez, M.; Zegarra, H.; Hernández, R.; Reyes, R.; Flores, D.; Arce, J. Manual de Técnicas de Análisis de Suelos Aplicadas a la Remediación de Sitios Contaminados; Instituto Mexicano del Petróleo, Secretaria de Medio Ambiente y Recursos Naturales, Instituto Nacional de Ecología: Mexico City, Mexico, 2006. [Google Scholar]
  42. Heuer, H.; Krsek, M.; Baker, P.; Smalla, K.; Wellington, E.M. Analysis of actinomycete communities by specific amplification of genes encoding 16S rRNA and gel-electrophoretic separation in denaturing gradients. Appl. Environ. Microbiol. 1997, 63, 3233–3241. [Google Scholar] [CrossRef]
  43. US Environmental Protection Agency. Application, Performance, and Costs of Biotreatment Technologies for Contaminated Soils; US Environmental Protection Agency: Washington, DC, USA, 2002. [Google Scholar]
  44. US Environmental Protection Agency. Method 3546. Microwave Extraction; US Environmental Protection Agency: Washington, DC, USA, 2007. [Google Scholar]
  45. US Environmental Protection Agency. Method 8015C. Nonhalogenated organics by Gas Chromatography; US Environmental Protection Agency: Washington, DC, USA, 2007. [Google Scholar]
  46. Krieger, E.; Vriend, G. New ways to boost molecular dynamics simulations. J. Comput. Chem. 2015, 36, 996–1007. [Google Scholar] [CrossRef] [PubMed]
  47. Wiederstein, M.; Sippl, M.J. ProSA-web: Interactive web service for the recognition of errors in three-dimensional structures of proteins. Nucleic Acids Res. 2007, 35, W407–W410. [Google Scholar] [CrossRef]
  48. McGreig, J.E.; Uri, H.; Antczak, M.; Sternberg, M.J.E.; Michaelis, M.; Wass, M.N. 3DLigandSite: Structure-based prediction of protein–ligand binding sites. Nucleic Acids Res. 2022, 50, W13–W20. [Google Scholar] [CrossRef]
  49. Jiménez, J.; Doerr, S.; Martínez-Rosell, G.; Rose, A.S.; De Fabritiis, G. DeepSite: Protein-binding site predictor using 3D-convolutional neural networks. Bioinformatics 2017, 33, 3036–3042. [Google Scholar] [CrossRef] [PubMed]
  50. Thomsen, R.; Christensen, M.H. MolDock: A new technique for high-accuracy molecular docking. J. Med. Chem. 2006, 49, 3315–3321. [Google Scholar] [CrossRef]
  51. Vivar-Sierra, A.; Araiza-Macías, M.J.; Hernández-Contreras, J.P.; Vergara-Castañeda, A.; Ramírez-Vélez, G.; Pinto-Almazán, R.; Salazar, J.R.; Loza-Mejía, M.A. In Silico Study of Polyunsaturated Fatty Acids as Potential SARS-CoV-2 Spike Protein Closed Conformation Stabilizers: Epidemiological and Computational Approaches. Molecules 2021, 26, 711. [Google Scholar] [CrossRef] [PubMed]
  52. Passos, W.A.; Jesus, M.; Mata, F.; Menezes, M.S.; dos Santos, P.O.L.; Santos, B.L.P.; Santana, H.E.P.; Ruzene, D.S.; Silva, D.P. Bioremediation Potential of Sunflower-Derived Biosurfactants: A Bibliometric Description. Sustainability 2025, 17, 330. [Google Scholar] [CrossRef]
  53. Maier, J.A.; Martinez, C.; Kasavajhala, K.; Wickstrom, L.; Hauser, K.E.; Simmerling, C. Ff14sb: Improving the Accuracy of Protein Side Chain and Backbone Parameters from ff99SB. J. Chem. Theory Comput. 2015, 11, 3696–3713. [Google Scholar] [CrossRef] [PubMed]
  54. Haghollahi, A.; Fazaelipoor, M.H.; Schaffie, M. The effect of soil type on the bioremediation of petroleum contaminated soils. J. Environ. Manage. 2016, 180, 197–201. [Google Scholar] [CrossRef]
  55. Abatenh, E.; Gizaw, B.; Tsegaye, Z.; Wassie, M. The Role of Microorganisms in Bioremediation—A Review. Open J. Environ. Biol. 2017, 2, 038–046. [Google Scholar] [CrossRef]
  56. Ayilara, M.S.; Babalola, O.O. Bioremediation of environmental wastes: The role of microorganisms. Front. Agron. 2023, 5, 1183691. [Google Scholar] [CrossRef]
  57. Chaillan, F.; Chaîneau, C.H.; Point, V.; Saliot, A.; Oudot, J. Factors inhibiting bioremediation of soil contaminated with weathered oils and drill cuttings. Environ. Pollut. 2006, 144, 255–265. [Google Scholar] [CrossRef]
  58. Chaîneau, C.H.; Rougeux, G.; Yéprémian, C.; Oudot, J. Effects of nutrient concentration on the biodegradation of crude oil and associated microbial populations in the soil. Soil Biol. Biochem. 2005, 37, 1490–1497. [Google Scholar] [CrossRef]
  59. Schjønning, P.; Thomsen, I.K.; Petersen, S.O.; Kristensen, K.; Christensen, B.T. Relating soil microbial activity to water content and tillage-induced differences in soil structure. Geoderma 2011, 163, 256–264. [Google Scholar] [CrossRef]
  60. Cycoń, M.; Mrozik, A.; Piotrowska-Seget, Z. Bioaugmentation as a strategy for the remediation of pesticide-polluted soil: A review. Chemosphere 2017, 172, 52–71. [Google Scholar] [CrossRef] [PubMed]
  61. Lee, S.H.; Lee, J.H.; Jung, W.C.; Park, M.; Kim, M.S.; Lee, S.J.; Park, H. Changes in Soil Health with Remediation of Petroleum Hydrocarbon Contaminated Soils Using Two Different Remediation Technologies. Sustainability 2020, 12, 10078. [Google Scholar] [CrossRef]
  62. Girigiri, B.; Ariole, C.N.; Stanley, H.O. Bioremediation of Crude Oil Polluted Soil Using Biofertilizer from Nitrogen-fixing and Phosphate-solubilizing Bacteria. Am. J. Nanosci. 2019, 5, 27–38. [Google Scholar] [CrossRef]
  63. Marinescu, M.; Lacatusu, A.; Gament, E.; Plopeanu, G.; Carabulea, V. Bioremediation Potential of Native Hydrocarbons Degrading Bacteria in Crude Oil Polluted Soil. Bull. Univ. Agric. Sci. Vet. Med. Cluj Napoca. Agric. 2017, 74, 19. [Google Scholar] [CrossRef]
  64. Wang, X.; Lina, S.; Hui, W.; Hao, W.; Su, C.; Zheng, X. Surfactant-enhanced bioremediation of DDTs and PAHs in contaminated farmland soil. Environ. Technol. 2018, 39, 1733–1744. [Google Scholar] [CrossRef] [PubMed]
  65. Soltanighias, T.; Singh, A.E.A.; Satpute, S.K.; Banpurkar, A.G.; Koolivand, A.; Rahi, P. Assessment of biosurfactant-producing bacteria from oil contaminated soils and their hydrocarbon degradation potential. Environ. Sustain. 2019, 2, 285–296. [Google Scholar] [CrossRef]
  66. Bahobail, A.; El-Rab, S.M.F.G.; Amin, G.A.; Bahobail, A.; El-Rab, S.M.F.G.; Amin, G.A. Locally Isolated Bacterial Strains with Multiple Degradation Potential Capabilities on Petroleum Hydrocarbon Pollutants. Adv. Microbiol. 2016, 6, 852–866. [Google Scholar] [CrossRef]
  67. Hosseini, S.; Sharifi, R.; Habibi, A. Simultaneous removal of aliphatic and aromatic crude oil hydrocarbons by Pantoea agglomerans isolated from petroleum-contaminated soil in the west of Iran. Arch. Microbiol. 2024, 206, 98. [Google Scholar] [CrossRef]
  68. Chen, Q.; Ni, H.-Y.; Zhuang, W.; Sun, Z.-G.; Yang, Z.-Z.; Wang, H.-M.; He, Q.; He, J. Nitratireductor soli sp. nov., isolated from phenol-contaminated soil. Antonie Van Leeuwenhoek 2015, 108, 1139–1146. [Google Scholar] [CrossRef]
  69. Pan, X.-C.; Geng, S.; Mei, R.; Wang, Y.-N.; Cai, H.; Liu, X.-Y.; Tang, Y.-Q.; Nie, Y.; Ye, S.-Y.; Wu, X.-L. Nitratireductor shengliensis sp. nov., Isolated from an Oil-Polluted Saline Soil. Curr. Microbiol. 2014, 69, 561–566. [Google Scholar] [CrossRef] [PubMed]
  70. Marasco, R.; Michoud, G.; Sefrji, F.O.; Fusi, M.; Antony, C.P.; Seferji, K.A.; Barozzi, A.; Merlino, G.; Daffonchio, D. The identification of the new species Nitratireductor thuwali sp. nov. reveals the untapped diversity of hydrocarbon-degrading culturable bacteria from the arid mangrove sediments of the Red Sea. Front. Microbiol. 2023, 14, 1155381. [Google Scholar] [CrossRef] [PubMed]
  71. Oro, C.E.D.; Saorin Puton, B.M.; Venquiaruto, L.D.; Dallago, R.M.; Tres, M. V Effective Microbial Strategies to Remediate Contaminated Agricultural Soils and Conserve Functions. Agronomy 2024, 14, 2637. [Google Scholar] [CrossRef]
  72. Ji, Y.; Mao, G.; Wang, Y.; Bartlam, M. Structural insights into diversity and n-alkane biodegradation mechanisms of alkane hydroxylases. Front. Microbiol. 2013, 4, 58. [Google Scholar] [CrossRef]
  73. Garrido-Sanz, D.; Sansegundo-Lobato, P.; Redondo-Nieto, M.; Suman, J.; Cajthaml, T.; Blanco-Romero, E.; Martin, M.; Uhlik, O.; Rivilla, R. Analysis of the biodegradative and adaptive potential of the novel polychlorinated biphenyl degrader Rhodococcus sp. Way2 revealed by its complete genome sequence. Microb. Genomics 2020, 6, e000363. [Google Scholar] [CrossRef]
  74. Striebich, R.C.; Smart, C.E.; Gunasekera, T.S.; Mueller, S.S.; Strobel, E.M.; McNichols, B.W.; Ruiz, O.N. Characterization of the F-76 diesel and Jet-A aviation fuel hydrocarbon degradation profiles of Pseudomonas aeruginosa and Marinobacter hydrocarbonoclasticus. Int. Biodeterior. Biodegradat. 2014, 93, 33–43. [Google Scholar] [CrossRef]
  75. Lyu, L.; Li, J.; Chen, Y.; Mai, Z.; Wang, L.; Li, Q.; Zhang, S. Degradation potential of alkanes by diverse oil-degrading bacteria from deep-sea sediments of Haima cold seep areas, South China Sea. Front. Microbiol. 2022, 13, 920067. [Google Scholar] [CrossRef]
  76. Pandolfo, E.; Barra Caracciolo, A.; Rolando, L. Recent Advances in Bacterial Degradation of Hydrocarbons. Water 2023, 15, 375. [Google Scholar] [CrossRef]
  77. Vandera, E.; Koukkou, A.I. Bacterial Community Response to Hydrocarbon Contamination in Soils and Marine Sediments: A Critical Review of Case Studies. In Microbial Ecotoxicology; Cravo-Laureau, C., Cagnon, C., Lauga, B., Duran, R., Eds.; Springer International Publishing: Cham, Switzerland, 2017; pp. 185–226. ISBN 978-3-319-61795-4. [Google Scholar]
  78. Ma, Y.; Zheng, X.; Anderson, S.H.; Lu, J.; Feng, X. Diesel oil volatilization processes affected by selected porous media. Chemosphere 2014, 99, 192–198. [Google Scholar] [CrossRef]
  79. Ausma, S.; Grant, C.E.; Colleen, R.F.-H.; Laurie, H.-M.; Terry, J.G.; Mortimer, W.P. Volatile Hydrocarbon Emissions from a Diesel Fuel-Contaminated Soil Bioremediation Facility. J. Air Waste Manage. Assoc. 2002, 52, 769–780. [Google Scholar] [CrossRef]
  80. Chen, Y.-A.; Grace Liu, P.-W.; Whang, L.-M.; Wu, Y.-J.; Cheng, S.-S. Effect of soil organic matter on petroleum hydrocarbon degradation in diesel/fuel oil-contaminated soil. J. Biosci. Bioeng. 2020, 129, 603–612. [Google Scholar] [CrossRef] [PubMed]
  81. Shekhawat, V.S.; Padwa, R.S. Role of Additives and their Influence on Performance of Engine for Petroleum Diesel Fuel, Oxygenated-Diesel Blend: A Review. Int. J. Eng. Res. Technol. 2015, 4, 1–9. [Google Scholar]
  82. Rakowska, J. Remediation of diesel-contaminated soil enhanced with firefighting foam application. Sci. Rep. 2020, 10, 8824. [Google Scholar] [CrossRef] [PubMed]
  83. Wentzel, A.; Ellingsen, T.E.; Kotlar, H.K.; Zotchev, S.B.; Throne-Holst, M. Bacterial metabolism of long-chain n-alkanes. Appl. Microbiol. Biotechnol. 2007, 76, 1209–1221. [Google Scholar] [CrossRef]
  84. Guo, X.; Zhang, J.; Han, L.; Lee, J.; Williams, S.C.; Forsberg, A.; Xu, Y.; Austin, R.N.; Feng, L. Structure and mechanism of the alkane-oxidizing enzyme AlkB. Nat. Commun. 2023, 14, 2180. [Google Scholar] [CrossRef]
  85. Jurcik, A.; Bednar, D.; Byska, J.; Marques, S.M.; Furmanova, K.; Daniel, L.; Kokkonen, P.; Brezovsky, J.; Strnad, O.; Stourac, J.; et al. CAVER Analyst 2.0: Analysis and visualization of channels and tunnels in protein structures and molecular dynamics trajectories. Bioinformatics 2018, 34, 3586–3588. [Google Scholar] [CrossRef]
  86. Ramírez, D.; Caballero, J. Is It Reliable to Take the Molecular Docking Top Scoring Position as the Best Solution without Considering Available Structural Data? Molecules 2018, 23, 1038. [Google Scholar] [CrossRef]
  87. Tran-Nguyen, V.K.; Bret, G.; Rognan, D. True Accuracy of Fast Scoring Functions to Predict High-Throughput Screening Data from Docking Poses: The Simpler the Better. J. Chem. Inf. Model. 2021, 61, 2788–2797. [Google Scholar] [CrossRef]
  88. Wang, Z.; Sun, H.; Yao, X.; Li, D.; Xu, L.; Li, Y.; Tian, S.; Hou, T. Comprehensive evaluation of ten docking programs on a diverse set of protein–ligand complexes: The prediction accuracy of sampling power and scoring power. Phys. Chem. Chem. Phys. 2016, 18, 12964–12975. [Google Scholar] [CrossRef]
  89. Kim, D.; Noh, M.H.; Park, M.; Kim, I.; Ahn, H.; Ye, D.-Y.; Jung, G.Y.; Kim, S. Enzyme activity engineering based on sequence co-evolution analysis. Metab. Eng. 2022, 74, 49–60. [Google Scholar] [CrossRef]
  90. Amin, A.; Naveed, M.; Sarwar, A.; Rasheed, S.; Saleem, H.G.M.; Latif, Z.; Bechthold, A. In vitro and in silico Studies Reveal Bacillus cereus AA-18 as a Potential Candidate for Bioremediation of Mercury-Contaminated Wastewater. Front. Microbiol. 2022, 13, 847806. [Google Scholar] [CrossRef] [PubMed]
  91. Tasleem, M.; Hussein, W.M.; El-Sayed, A.-A.A.A.; Alrehaily, A. An In Silico Bioremediation Study to Identify Essential Residues of Metallothionein Enhancing the Bioaccumulation of Heavy Metals in Pseudomonas aeruginosa. Microorganisms 2023, 11, 2262. [Google Scholar] [CrossRef] [PubMed]
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Figure 1. Percentage of alkanes removed by each analyzed strain. Tla = Tlapala Site, SMT = Santa Maria Site. Error bars indicate standard deviations (n = 2).
Figure 1. Percentage of alkanes removed by each analyzed strain. Tla = Tlapala Site, SMT = Santa Maria Site. Error bars indicate standard deviations (n = 2).
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Figure 2. Correlation between clogP and percentage of diesel removed.
Figure 2. Correlation between clogP and percentage of diesel removed.
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Figure 3. Overlap of the structures generated by the homology modeling of AlkB from hydrocarbonoclastic bacteria. Potential active site cavities are shown in orange.
Figure 3. Overlap of the structures generated by the homology modeling of AlkB from hydrocarbonoclastic bacteria. Potential active site cavities are shown in orange.
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Figure 4. RMSD variations throughout simulation time for AlkB–alkane complexes.
Figure 4. RMSD variations throughout simulation time for AlkB–alkane complexes.
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Figure 5. Complexes of some alkanes (in blue) within the active site of AlkB of A. globiformis and the distance of the terminal carbon of the alkane to key histidine residues (in pink). (a) Complexes of C10-AlkB, (b) C16-AlkB, and (c) C22-AlkB.
Figure 5. Complexes of some alkanes (in blue) within the active site of AlkB of A. globiformis and the distance of the terminal carbon of the alkane to key histidine residues (in pink). (a) Complexes of C10-AlkB, (b) C16-AlkB, and (c) C22-AlkB.
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Figure 6. Correlation of diesel removal percentage and enzyme affinity expressed as ligand-binding energy. The higher the ligand-binding energy, the higher the affinity to the enzyme.
Figure 6. Correlation of diesel removal percentage and enzyme affinity expressed as ligand-binding energy. The higher the ligand-binding energy, the higher the affinity to the enzyme.
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Table 1. Physicochemical and microbiological characterization of the soil of Juchitepec, State of Mexico.
Table 1. Physicochemical and microbiological characterization of the soil of Juchitepec, State of Mexico.
Organic Matter4.8%
TextureSand 70%
Silt 27%, sandy loam
clay 3%
Field capacity29.04%
pH5.9
Humidity18.9%
Total nitrogen0.04%
Soluble phosphorus2.66 ppm
Hydrocarbonoclastic bacteria2.4 × 106 CFU/g d.s.
Hydrocarbonoclastic fungi3.76 × 104 CFU/g d.s.
Table 2. Identification of soil hydrocarbonoclastic bacteria used for bioaugmentation.
Table 2. Identification of soil hydrocarbonoclastic bacteria used for bioaugmentation.
Isolated Strain (Code)Species IdentificationSequence Identity
(%)		SiteMetabolic Characteristic:
Hydrocarbonoclastic (H),
Phosphorus-Solubilizing (P),
or Nitrogen-Fixing (N)
ForwardReverse
H1Arthrobacter globiformis95.7298.64JuchitepecH
F1Pantoea agglomerans96.6096.33Santa MaríaH + P
F2Pantoea agglomerans95.8498.45TlapalaH + P
N2Nitratireductor soli92.4794.10JuchitepecH + N
Table 3. Docking scores for the complexes of C10–C28 lineal alkanes with the studied AlkB enzymes.
Table 3. Docking scores for the complexes of C10–C28 lineal alkanes with the studied AlkB enzymes.
AlkaneA. globiformisP. agglomeransN. soli
C10−55.6−56.1−61.6
C12−64.2−62.6−73.3
C14−72.5−66.7−76.7
C16−71.5−72.8−80.4
C18−83.1−81.1−86.4
C20−82.1−81.9−88.7
C22−85.6−86.1−86.9
C24−91.2−85.8−99.4
C26−77.9−83.2−94.2
C28−76.3−81.5−91.7
Table 4. Ligand-binding energy (LBE, kcal/mol) for the complexes of C10–C28 lineal alkanes with the studied AlkB enzymes. Higher numbers indicate better affinity.
Table 4. Ligand-binding energy (LBE, kcal/mol) for the complexes of C10–C28 lineal alkanes with the studied AlkB enzymes. Higher numbers indicate better affinity.
AlkaneA. globiformisP. agglomeransN. soli
C1031.0035.4036.10
C1629.5423.2426.41
C2225.3710.3617.77
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Espinosa-López, F.; Pelcastre-Guzmán, K.; Cerón-Nava, A.; Rivera-Noriega, A.; Loza-Mejía, M.A.; Islas-García, A. Sustainable Remediation Using Hydrocarbonoclastic Bacteria for Diesel-Range Hydrocarbon Contamination in Soil: Experimental and In Silico Evaluation. Sustainability 2025, 17, 5535. https://doi.org/10.3390/su17125535

AMA Style

Espinosa-López F, Pelcastre-Guzmán K, Cerón-Nava A, Rivera-Noriega A, Loza-Mejía MA, Islas-García A. Sustainable Remediation Using Hydrocarbonoclastic Bacteria for Diesel-Range Hydrocarbon Contamination in Soil: Experimental and In Silico Evaluation. Sustainability. 2025; 17(12):5535. https://doi.org/10.3390/su17125535

Chicago/Turabian Style

Espinosa-López, Fernanda, Karen Pelcastre-Guzmán, Anabelle Cerón-Nava, Alicia Rivera-Noriega, Marco A. Loza-Mejía, and Alejandro Islas-García. 2025. "Sustainable Remediation Using Hydrocarbonoclastic Bacteria for Diesel-Range Hydrocarbon Contamination in Soil: Experimental and In Silico Evaluation" Sustainability 17, no. 12: 5535. https://doi.org/10.3390/su17125535

APA Style

Espinosa-López, F., Pelcastre-Guzmán, K., Cerón-Nava, A., Rivera-Noriega, A., Loza-Mejía, M. A., & Islas-García, A. (2025). Sustainable Remediation Using Hydrocarbonoclastic Bacteria for Diesel-Range Hydrocarbon Contamination in Soil: Experimental and In Silico Evaluation. Sustainability, 17(12), 5535. https://doi.org/10.3390/su17125535

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