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Cyanobacteria in water bodies cause harmful cyanobacterial blooms, releasing toxins that degrade water quality and cause health issues. Common Harmful Algal Bloom-related disorders (HABs) include neurotoxic shellfish poisoning, ciguatera poisoning, paralytic shellfish poisoning, diarrhetic shellfish poisoning, and amnesic shellfish poisoning. Adapting to high temperatures and humidity, cyanobacteria also colonize historical sites, causing staining erosion, and reducing their aesthetic value. In this context, we have chosen carbonic anhydrase which plays an essential role in the interconversion of water and carbon dioxide to bicarbonates and makes it available to RuBisCo that regulates the photosynthetic pathways leading to cyanobacterial biomass generation. This study employed the molecular modeling approach to identify the potential natural inhibitors to carbonic anhydrase from the COCONUT database. Strategically we explore the Structural-Activity Relationship (SAR) of natural compounds to the reported sulphonamide inhibitors. Further, prediction-based online web servers such as pkCSM and SwissADME were used to determine the ADMET properties of the SAR molecules. An FDA-approved compound, Ethoxzolamide was chosen for comparative analysis. Next, the In-silico methodologies such as molecular docking and molecular dynamic simulations, free energy landscape analysis, hydrogen bond analysis, and binding free energy calculations were performed using various algorithms under virtual physiological conditions to identify potential SAR molecules against carbonic anhydrase. Further, we perform molecular dynamic simulation for a time period of 100 ns. It was evidenced from the molecular dynamic simulations and MM-PBSA calculations that some natural compounds outperform the Ethoxzolamide compound not only in stability but also in binding affinity. This study delves into the intricate interactions of natural compounds with the cyanobacteria carbonic anhydrase which plays a pivotal role in growth and development. Hence, we believe that our models could show extreme effectivity and might act as potent bio-algaecides.
Article Highlights
The utilization of the COCONUT database, an open-source repository of natural products, provides a vast and diverse chemical space to explore potential drug candidates.
The integration of in-silico techniques, such as molecular docking and molecular dynamics simulations, offers a rapid and cost-effective approach to identify promising compounds.
By targeting carbonic anhydrase, a key enzyme in cyanobacterial CO2 fixation, this research aims to develop novel inhibitors that can effectively reduce biomass generation.
Introduction
Cyanobacteria are primitive photosynthetic prokaryotes found in a wide range of environments, from hot springs to frigid arctic locations. They are one of the most valued organisms and globally fix the atmospheric carbon dioxide via the metallo-enzyme, carbonic anhydrase (CA) along with Ci (inorganic carbon) transporters [1, 2–3]. CAs are universally distributed among plants, microbes, and animals [4, 5–6]. CAs are encoded by three evolutionary unrelated gene families α CAs, β CAs, and γ CAs [6]. β-CAs are widely present in microorganisms, non-photosynthetic bacteria, cyanobacteria, plants, and invertebrates except vertebrates [7, 8]. β-CAs are the only class of CAs that exhibit unique allosteric regulation [7]. The active site of β-CAs is a dimer formed by a β-sheet core of ten β strands, where the N-terminal region of one monomer wraps around the other [9, 10]. This active site contains zinc ions coordinated by two Cys and His residues, along with a water molecule as the fourth coordination [9, 10]. This type of co-ordination sphere is known as “open type” and considered as type-I β CAs whereas in type-II β CAs the co-ordination sphere is “closed type”, where the fourth co-ordination by Zn ion is with Asp residue instead of water molecule [11, 12]. Zinc (Zn) is the metal ion present in the active site of almost all CAs coordinated by three amino acid residues and one water molecule which is ionized into OH− ion. α-CAs and Γ CAs have HIS as the conserved amino acid, whereas ß CAs has CYS2 HIS(X) residues arranged in a tetrahedral arrangement where X represents an Asp residue or any substitutable ligand [13, 14]. The abundant growth of various algae and cyanobacteria in fresh and marine water bodies leads to cyanobacterial blooms often associated with harmful toxins that degrade water quality, causing health issues and affecting daily life [15, 16–17]. Harmful algal blooms (HABs) are a significant environmental problem caused by the excessive growth of algae, often dominated by cyanobacteria. The occurrence and severity of these blooms are influenced by several environmental factors such as excessive nutrient availability of nitrogen and phosphorus from sources like agricultural runoff, wastewater treatment plants, and atmospheric deposition [18, 19]. Cyanobacteria have specific temperature ranges for optimal growth i.e., warmer water temperatures can accelerate their growth rates and metabolic processes [20]. These photosynthetic organisms require light for energy production, and increased light intensity can stimulate their growth [21]. Calm, stagnant waters are more prone to algal blooms as nutrients accumulate and algae can form dense mats [22]. Thus, nutrient-rich, warm, stagnant waters with high light intensity can lead to explosive growth of cyanobacteria.
The five most prevalent Harmful Algal Bloom-(HAB) related disorders are neurotoxic shellfish poisoning (NSP), ciguatera poisoning, paralytic shellfish poisoning, diarrhetic shellfish poisoning, and amnesic shellfish poisoning [23, 24]. Cyanobacteria colonize historical monuments, caves, and pre-historic sites, adapting to high temperatures and humidity [25, 26]. They secrete biogenic pigments, staining, and deteriorating stone surfaces [27]. Microbial toxins reduce the aesthetic value of pre-historic monuments due to erosion [28]. Beneficial actions should be implemented to combat the harmful cyanobacteria and develop effective strategies to prevent and mitigate harmful algal blooms. To overcome the challenges posed by synthetic drug molecules, natural compounds are a viable choice as beta-CA inhibitors. Majorly In-silico studies were widely used in academics and industrial sectors to identify lead molecules against the template target. In silico techniques can rapidly reconnoiter a vast parameter space, aiding faster identification of drug candidates with desired properties, and significantly reducing the time and cost of drug discovery [29]. Computational models can provide a holistic view of complex biological systems, such as protein–protein interactions, metabolic pathways, and cellular signaling networks. The simulations of these molecular interactions and dynamic processes provide a deeper insight into the underlying mechanisms of disease and drug action [30].
In this study, Collection of Open NatUral producTs’ (COCONUT) database has been explored using in silico approaches, and online search engines were used to find the best leads that can constrain the cellular functioning of carbonic anhydrase inhibiting their biomass generation. The SAR molecules were obtained from the (COCONUT) database and the potential hits from SAR molecules were achieved thoroughly by a molecular docking simulations approach. Molecules with the highest binding affinity to the catalytical site of carbonic anhydrase were further progressed by the molecular dynamic simulation approach. The molecular dynamics simulation tool is generally used to validate the docking study, which is primarily used to observe the motions and flexibility of the ligand–protein complex [31]. The less flexibility of the results the stability of the complex. Finally, this study offers a significant contribution to the field of drug discovery by leveraging computational approaches to identify potential inhibitors of carbonic anhydrase, a key enzyme involved in biomass generation. The perspective for utilization of the COCONUT database provides a vast and diverse chemical space to explore. This study demonstrates the power of computational tools in accelerating the drug discovery process and the insights gained from this current research can be applied to other target proteins and drug discovery efforts. The focus on natural products as potential drug candidates aligns with the growing interest in sustainable and environmentally friendly drug discovery. Overall, this study presents a promising approach to address the challenges associated with biomass generation and contributes to the development of innovative solutions for a variety of applications.
Materials and methods
Preparation of receptor protein and active site prediction
The Three-dimensional structure of beta-carbonic anhydrase of Synechocystis sp. PCC 6803 was retrieved from online protein database (https://www.rcbs.org) with PDB-ID 5SWC at resolution 1.45 Å. The refinement of this receptor protein was carried out; the water molecules were removed. The 3D protonation module was used to add non-polar hydrogen atoms. Further, the minimization was conducted by MMFF94x force field and the gradient was set at 0.05 using MOE 2024.06 software [32]. The receptor protein is a hexameric protein arranged in dimers of trimers. All chains are structurally similar in terms of amino acid residues and secondary structure elements such as loops, helices, turns, and sheets [33]. Thus, to reduce the complexity of the receptor protein, docking simulation was limited to a single dimer chain AB. The site finder module of MOE 2024.06 was used to identify the binding pocket.
Retrieval of the ligand and ADMET prediction
The natural compounds from the COCONUT database (https://coconut.naturalproducts.net/) have been considered for this study [34]. The Single molecular input line entry system (SMILES) format for each molecule was obtained and submitted to pkCSM (http://biosig.unimelb.edu.au/pkcsm/), and SwissADME (http://www.swissadme.ch/), webserver to forecast the ADMET properties as it is necessary to understand their pharmacokinetic behaviour [35]. Next, the bioavailability radar estimated the pharmacokinetics of the compounds based on various physicochemical parameters like herG-I/II, cardiac muscle toxicities, AMES toxicity, hepatotoxicity, and skin sensitization to detect toxicity. The Lipinski filter, the pioneer rule-of-five, was used in this tool to predict drug-likeness [36], and compounds that showed minimum toxicity were used for further in-silico investigations.
DFT optimization and molecular docking simulations
The non-toxic leads obtained from the ADMET filter were optimized using ORCA program 4.1.1 [37, 38]. The Density functional theory (DFT) was performed using functional Becke, 3-Parameter, Lee–Yang–Parr/Gaussian set (B3YLP/G*) level of theory. The HOMO and LUMO frontal orbitals (FO) were calculated. In this context the parameters such as ionization potential (I) = − EHOMO (eV), hardness (η) = 1 − A/2, softness (σ) = 1/η, electronegativity (χ) = 1 + A/2, chemical potential (μ) = −1 + A/2, electron affinity (A) = −ELUMO (eV) and global electrophilicity (ω) = μ2/2 η was calculated [39] to predict the nature of the molecule.
The ligands were optimized by adding partial charges using a protonated 3D module and were minimized by using the molecular operating environment MOE 2024.06 software [32] by sorting one by one in a molecular database (mdb) file using MOE software and each molecule was minimized by the default force field. The receptor protein is a hexameric protein arranged in dimers of trimers. All chains are structurally similar regarding amino acid residues and secondary structure elements such as loops, helices, turns, and sheets. Thus, to reduce the complexity of the receptor protein, docking simulation was limited to a single dimer chain AB. The site finder module of MOE 2024.06 [32] was used to identify the binding pocket. The triangle matcher algorithm was set for molecular docking simulations and the London dG scoring function was applied to generate the binding poses in the binding site, The most relevant interaction of the ligand molecules with the target were chosen based on their minimum binding scores. The LigX module of MOE was used to determine the interactions between protein and ligand.
Molecular dynamics simulations
The molecular dynamics MD simulations were carried out using the 22.04 version of GROMACS software (www.gromacs.org) [40]. First, water molecules and docked ligand were removed from the x-crystallographic structure. The topology of the carbonic anhydrase was generated by using the gromas54a7 force field [41], and the topology was generated by an automated forcefield topology builder (ATB) server [42]. The input files of both protein and ligand were merged manually. Then the complex was placed in a cubic box which is 1 nm apart from the periodic boundaries. The TIP3P (transferable intermolecular potential with 3 points) model was used and was solvated by 24,595 solvents. Further, the system was neutralized by 22 Na+ ions [43]. The prepared model system was minimized at increasing temperature 1–300 K using the steepest descent method and lasted < 1000 steps. Next, the system was equilibrated using NPT and NVT methods for 1 ns [44]. The LINCS algorithm was used to adjust the constrain of hydrogen bonds [45]. Particle-mesh Ewald (PME) summation was used to calculate electrostatic interactions and the coulomb interaction was set to be 1.2 nm [46]. Finally, simulations were conducted for 100 ns, and analysis was carried out using RMSD, RMSF, PCA, FEL, RoG, H-bond, and SASA modules [47, 48]. We also employed the distance modules of Gromacs to determine the distance from the co-factor Zn2+ with adjacent amino acids such as (CYS 39, HIS 98, CYS 101) present in the core of the binding site identified previously. Furthermore, we delve into intricate interactions of the ligand moiety that showed contacts with the co-factor over simulated time. For more insights into the conformational changes among the simulated systems we perform the cluster analysis. The cluster analysis was performed by optimization of the cutoff values, higher cutoff values might reflect only one cluster, in this study the cluster analysis was performed at the optimized cutoff was set to be 0.2 nm, and different clusters were generated. The generated data points were plotted using Xmgrace software https://plasma-gate.weizmann.ac.il/Grace/ and visualizations of complexes were done by Visual Molecular Dynamics (VMD) software www.ks.uiuc.edu/Research/vmd [49].
Assessment of free energy binding by MM-PBSA method
The MM-PBSA method is used to determine the difference between two solvated molecules’ bound and unbound states, as well as to compare the free energy of two distinct conformations of the same molecule [50]. The free energy bindings were calculated by GROMACS 5.01v. using g_mmpbsa method. For the free energy binding calculation, the g_mmpbsa package was given by Kumari and Kumar, 2014 [51]. The free energy was estimated using linearized classical PBE methods using the trajectory files as inputs obtained from the MD simulations. The entropy was ignored in the present case because they have no impact on the energy calculated by experimental methods [47] the following given by the equation was used to calculate the Free Energy Binding (FEB) of the natural compounds complexed with carbonic anhydrase:
1
The above Eq. (1) requires the calculation of numerous energy components, including van der Waals energy, electrostatic energy, internal energy derived from molecular mechanics, and polar contribution to solvation energy [52].
Results
Retrieval of the ligand and ADMET prediction
The COCONUT database contained 4,11,621 natural products [34]. Manually the database has been screened to obtain the sulphate-based molecules. We have obtained 628 natural products tethered with sulphate moiety from the COCONUT database that was further processed for toxicity predictions. This objective aims to identify the non-toxic molecules that could be a potential inhibitor of carbonic anhydrase (Fig. 1A). Several studies showed that sulphanomide compounds are potential CAs inhibitors [53, 54] and their mechanism involves binding to the zinc active site, dislocating catalytically essential water molecules either by the formation of the tetrahedral or bi-pyramidal [55, 56–57]. The non-toxic leads were obtained by ADMET profiling using online web servers such as pkCSM and SwissADME since it is required to choose non-carcinogenic and non-hepatotoxic lead compounds [58]. The pkCSM software provides 83.8% mutagenic test accuracy. Also, toxicology tests were conducted using the following parameters: herG-I/II cardiac muscle toxicity, AMES toxicity, hepatotoxicity, and skin sensitization. The chemicals with herG-I/II positive might cause rapid cardiac arrest and death [59]. The AMES test determines the mutagenic or carcinogenic properties of the compound [60]. The hepatotoxic chemicals can cause severe acute or chronic liver damage, and skin sensitization tests determine a compound’s allergenic properties [61, 62]. On the other hand, an eligible drug candidate must also follow Lipinski’s rule of five [36]. Based on the conditions given above, 40 chemicals met the non-toxic standards.
Fig. 1 [Images not available. See PDF.]
A Screening of coconut database to finally obtain the lead molecules, B Structures of selected zinc interacted with non-toxic natural compounds along with the reference ethoxzolamide
Frontier molecular orbitals
The frontier molecular orbitals (FMO), Higher Occupied Molecular Orbital (HOMO), and Lower Unoccupied Molecular Orbital (LUMO), as well as the molecular electrostatic potential of the chemical species, are particularly significant in defining their reactivity and determining the electrophilic and nucleophilic sites [63]. The FMO of Ethoxzolamide showed atoms surrounding the SO2-NH2 group along with the benzo-thiazole ring in LUMO and HOMO distribution was only around the 6-ethoxybenzo-thiazole moiety. In CNP0248071, HOMO was assigned to the hydroxy-sulfamoyloxy-phosphoryl-amino moiety and LUMO was assigned at the -COOH terminus. In CNP0246073, HOMO was found over the bromophenol ring and LUMO spreads over the hydroxyamino-butanamide moiety of CNP0248071 natural compound. From the global electrophilicity indices, the Egap of selected natural products CNP0248071 (3.73 eV) showed approximately similar results with the reference, ethoxzolamide (3.76 eV) and CNP0246073 showed a higher energy gap of 4.55 eV. (Fig. 2, Table 1). A molecule with a high Egap has low chemical reactivity, good kinetic stability, and is chemically harder than the compounds with low Egap [64] since it is energetically unfavorable to add an electron to the high-lying LUMO while removing electrons from the low-lying HOMO [65].
Fig. 2 [Images not available. See PDF.]
The higher occupied molecular orbital (HOMO) and lower unoccupied molecular orbital (LUMO) were calculated for Ethoxzolamide, CNP0248071 and CNP0246073
Table 1. Global electrophilicity indices and their associated parameters
Electrophilicity indices | Ethoxzolamide | CNP0248071 | CNP0246073 |
|---|---|---|---|
HOMO | − 6.45 | − 6.73 | − 6.21 |
LUMO | − 2.69 | − 2.99 | − 1.65 |
Δ Egap | 3.76 | 3.73 | 4.55 |
Ionizing Potential (I) | 6.45 | 6.73 | 6.21 |
Electron Affinity (A) | 2.69 | 2.99 | 1.65 |
Chemical potential (μ) | − 4.57 | − 4.86 | − 3.93 |
Electron negativity (χ) | 4.57 | 4.86 | 3.93 |
Hardness (η) | 1.88 | 1.86 | 2.27 |
Softness (σ) | 0.53 | 0.53 | 0.43 |
Electrophilicity (ω) | 5.55 | 6.32 | 3.39 |
Chemical hardness (η) can be expressed as η = (ELUMO—EHOMO)/2 is related to the stability and reactivity of a chemical system. It evaluates a compound’s resistance to changes in electron density distribution or electron charge transfer. The hardness increases in order CNP0248071 (1.86) < Ethoxzolamide (1.88) < CNP0246073 (2.87). The higher the HOMO–LUMO energy gap, the harder and more stable (less reactive) the compound [66].
The chemical potential (μ) represents the possibility of a chemical reaction [39]. It was calculated by using the formula μ = −1/2 (ELUMO + EHOMO). A lower electronic chemical potential (μ) indicates a more stable molecule. The CNP0248071 showed a lower chemical potential of (− 4.86), which suggests this compound is more stable followed by Ethoxzolamide (− 4.57) and CNP0246073 (− 3.93).
Electrophilicity indices (ω) quantify the ability to receive electrons, and energy stabilization would be attained after receiving the additional electronic charge. It is calculated considering both the chemical potential (μ) and chemical hardness (η) using the formula; ω = μ2/2η [39]. The stable compounds are associated with lower electrophilicity values as shown by CNP0246073 (3.39) > Ethoxzolamide (5.55) > CNP0248071 (6.32). This suggests CNP0248071 is an electrophile and CNP0246073 is a nucleophile. The parameters demonstrated the lead natural compounds were more stable than the ethoxzolamide FDA-approved drug molecules.
Receptor’s binding site prediction and molecular docking simulations
The receptor protein (β-CA) is a hexameric protein arranged in dimers of trimers [33]. All chains are structurally similar regarding amino acid residues and secondary structure elements such as loops, helices, turns, and sheets trimers [33]. Thus, to reduce the complexity of the receptor protein, docking simulation was limited to a single dimer chain AB. The catalytically active site consists of Zn301 coordinated by Cys 101, Cys 39, His 98 and adjacent amino acids 103 Ala, 41 Asp, 39 Cys, 84 Ala, 102 Gly, 98 His. A molecular docking simulation of the 40 non-toxic leads was carried out to the binding site of β-CA. The sulphate moiety of the natural compounds that showed interaction with the Zn ion along with its co-ordinate amino acids is CNP0051564, CNP0161908, CNP0175301, CNP0229928, CNP0246073, CNP0248071, CNP0268333, CNP0410679, CNP0411903, CNP0462016 (Table 2 and Fig. 1B). The reference Ethoxzolamide, an FDA-approved sulphonamide drug exhibited a binding score of −10.94 kcal/mol. Among these ligands that exhibited high binding affinity with the zinc co-factor and the adjacent amino acids present in the binding site of the receptor protein were CNP0248071 and CNP0246073 having binding scores of −13.2351 kcal/mol and −12.7894 kcal/mol respectively. Both of these compounds, along with the reference, interacted with the polar amino acids (Gln 30, Gly 102, Lys 105, Lys 109, Asp 41) via hydrogen bonds and formed ionic interactions with the Zn2+ and its coordinates with a score of 100% for zinc and 67.6%, 78.6%, 71.4% for Cys 39, His 98 and Cys 101 respectively. In CNP0246073, CNP0248071, and ethoxzolamide the distance for zinc interaction was found to be 1.99, 1.86, and 2.02 Å respectively (Fig. 3 A–D and Table S1).
Fig. 3 [Images not available. See PDF.]
Natural compounds exhibiting strong interactions between their sulphate moiety and the zinc co-factor of beta-CA. A represents the conformations of all three molecules in the binding site. B Represents the binding conformations of Ethoxzolamide in the binding site and interactions with amino acids present in the binding site. The natural product CNP0248071 and CNP0411903 and their interactions as well as conformations are represented in figure C and D
Molecular dynamics simulation
Molecular dynamics simulation is a necessary tool that helps to predict the dynamic behavior and stability of protein–ligand complexes using various algorithms over a simulation period [67]. The best protein–ligand complexes obtained after molecular docking along with the reference molecules were considered for further dynamics at a given physiological condition for a simulation time period of 100 ns.
RMSD evaluation of Cα backbone of receptor protein and its conformations
The root mean square deviation (RMSD) was used to calculate the difference between a protein’s backbones from its initial structural conformation to its final conformation. The variations generated during the simulation can be used to estimate the protein’s stability relative to its conformation [68]. The average RMSD of CNP0248071 was 0.28 nm, while CNP0246073 and Ethoxzolamide had 0.27 nm. The slight fluctuations were observed for CNP0248071 between 30–60 ns which could be attributed to the flexibility of the amino acid residues present in this region. The average RMSD achieved by apo-enzyme was 0.3 nm till the end of the simulation period (Fig. 5A). Ideally, the RMSD should be zero, but statistical errors induce variability [69]. The lesser the deviation, the more comparable the references. According to previous reports [70], an RMSD value of less than 0.25 nm is regarded very similar to the reference structure. The RMSDs of both compounds are within an acceptable range, indicating their stability.
Furthermore, cluster analysis was performed to categorize data into groups or clusters with comparable characteristics. Ideally, the similarity between two objects in the same cluster should be greater than the similarity between two objects in separate clusters. Cluster analysis is thus used to identify structure within a given data collection, but it does not explicitly explain why a specific pattern in the data occurs [71]. The RMS cluster analysis forecasts the changes that occurred in the overall structure throughout the simulation period. The RMSD cutoff of 0.2 nm was used. The apo-enzyme formed 30 clusters with 15.61 matrix energy and an average RMSD of 0.306 nm. The beta-CA complex with reference ethoxzolamide formed 25 clusters with 19.24 matrix energy and an average RMSD of 0.283 nm. The CNP0248071 had 29 clusters with an energy matrix of 19.43 and RMSD of 0.279 nm, while the CNP0246073 showed an energy matrix of 18.58 and RMSD of 0.270 nm. Cluster analysis identified multiple clusters, energy matrices, and average RMSDs that account for the protein–ligand complexes’ flexibility and conformational rigidity. The protein’s flexibility decreases with the number of clusters; however, the complex’s maximum stability is indicated by a decreased energy matrix and RMSD. The natural compounds and reference showed fewer clusters than the apo-enzyme, indicating a high flexibility of the protein, which is consistent with the RMSF study. The average RMSD values ranged between 0.2 and 0.3 nm, which is within a reasonable acceptable range. The stability of the natural compounds was investigated using the MD trajectories obtained from several clusters (Fig. 4).
Fig. 4 [Images not available. See PDF.]
Root means square distribution of Ethoxzolamide, CNP0248071, CNP0246073 and Apo enzyme
Root mean square fluctuations (RMSF), radius of gyration (Rg), and solvent accessible surface area (SASA) of β-Carbonic anhydrase
Root-mean-square fluctuation (RMSF) was used to evaluate the effect of lead natural compounds on the flexible region of the targeted protein [72]. Regions with high RMSF values are often more flexible, whereas regions with low RMSF values are typically stiffer [73]. In the C terminus of both the chains showed higher fluctuations for all the complexes. Although similar average fluctuations were observed for all the complexes both at chain A and B at 0.3 nm (Fig. 5B). These higher fluctuations do not have any impact on the binding of natural compounds including reference molecule. The rest of the amino acids showed fluctuations under permissible limits. The natural compounds were found to be more flexible exhibiting stable active site interaction as compared to the reference.
Fig. 5 [Images not available. See PDF.]
A Cα -RMSD plots B Root Mean Square Fluctuations (RMSF) of protein–ligand complex C radius of gyration and D SASA plot of Apo-protein, Ethoxzolamide, CNP0248071, CNP0246073 with beta-CA bound beta-CA simulated for 100 ns
The compactness of the docked complexes was determined from the radius of gyration (Rg). It is the distance from a body’s centre of mass at which the entire mass can be concentrated without changing its moment of rotational inertia about an axis through the centre of mass [74]. It quantifies the total size and shape of a protein structure, protein structures with a lower Rg value are typically more compact and globular, whereas those with a higher Rg value are more extended and flexible [73]. The Rg values of CNP0248071, CNP0246073, apo-enzyme as well as ethoxzolamide did not change significantly and the values kept fluctuating between 2.10 and 2.17 nm throughout the simulation period (Fig. 5C). This suggests that both the compounds remained strongly bound to the active site, contributing to the protein structure’s stability and compactness better than the reference molecules. The binding region has little influence over their structures. Additionally, Solvent accessible surface area (SASA) was estimated to determine the solvent behaviour of the entire complex [73]. It was calculated using gmx_sasa from Gromacs. The SASA values depicted the natural compounds, including ethoxzolamide showed overlapped results ranging between 195 and 225 nm2 (Fig. 5D). The SASA graph depicts both the natural compounds and the reference showed compactness with the protein structure. The SASA results are consistent with those of Rg, confirming the accuracy of MD simulation.
Distance scores and hydrogen bond analysis
The precise distance scores for zinc and its coordinate with the apo-enzyme, reference, and natural compounds have been plotted (Fig. 6). The average distance in apoenzyme for Cys39, His 98, Cys101, and zinc is 0.055 nm 0.407 nm and 0.764 nm respectively. The scores for ethoxzolamide are 0.590 nm, 0.406 nm, and 0.736 nm for Cys39, His98, Cys101, and zinc, respectively. Similarly, for CNP0248071, the average distances for Cys39, His 98, Cys101, and zinc are 0.660 nm, 0.379 nm, and 0.555 nm, respectively, whereas for CNP0246073, the distances are 0.421 nm, 0.401 nm, and 0.561 nm for Cys39, His 98, Cys101 and zinc respectively. Cys 39 had the shortest distance interaction with the apoenzyme and the longest with CNP0248071, while His 98 had the shortest distance score with CNP0248071 and a nearly equal distance with the apoenzyme, CNP0246073, and the reference. Cys 101 had the shortest distance interaction with CNP0248071 and the highest distance scores with both the apoenzyme and ethoxzolamide.
Fig. 6 [Images not available. See PDF.]
The distance scores obtained for apoenzyme, ethoxzolamide and the natural compounds with the zinc cofactor along with its coordinates (A) Cys 39, (B) His 98 and (C) Cys 101
Additionally, the hydrogen bond interaction of the compounds with the CAs was evaluated using the gmx h_bond module of the Gromacs. This analysis provides insight into the stability and dynamics of protein structures, as well as their interactions. It was observed the reference, CNP0248071, and CNP0246073 form 5, 8, and 7 hydrogen bond contacts respectively. Also, it was observed that the reference, CNP0248071, and CNP0246073 showed 8, 17, and 15 ligand contacts during simulations (Fig. 7A–F). This analysis demonstrates, CNP0248071, and CNP0246073 have higher binding strength than the reference ligand [75]. This data find support from the RMSD trajectory and more stability was conferred by CNP0248071, and CNP0246073 with beta-CA (Fig. 5A).
Fig. 7 [Images not available. See PDF.]
The monographs A, B, and C represent protein–ligand hydrogen bond contacts of Ethoxzolamide, CNP0248071, and CNP0246073 respectively. The D, E, and F represent their respective solvent-ligand contacts
Principal component analysis and Gibbs free energy landscape Analysis
PCA was calculated which reveals atomic position motions in MD trajectories using covariance matrices and eigenvectors restricted to c-alpha atoms [76]. The sum of eigenvalues describes how much variance the structure retains in relation to the total space [77]. PCA analysis was performed to investigate the Cα motions of the complexes during simulations. The PC covariance matrix was diagonalized and the eigenvalues were determined by the gmx_covar module and the PC analysis was calculated by gmx_aneig as plotted in the graph (Fig. 8A). These frames are sufficient to determine the Gibbs energy landscape analysis. The free energy landscape values of CNP0248071 were calculated to be 19.8 kJ/mol, CNP0246073 to be 19.4 kJ/mol, reference ethoxzolamide to be 20 kJ/mol and highest was shown by the apo-enzyme i.e. 20.7 kJ/mol (Fig. 8B–E). The data points obtained from PCA analysis and the images were generated by the gmx_xpm2ps module. The higher negative Gibbs energy landscape of the complex was known as the most stable complex, this study demonstrated Apo-enzyme achieved maximum stability followed by reference bound beta-CA complex, CNP0248071 and CNP0246073. The stability of the complexes significantly co-relates with each other.
Fig. 8 [Images not available. See PDF.]
A Principal Component analysis (PCA) graph plotted considering two principal component eigenvalues PC1 and PC2. Gibbs free energy landscape energy (FEL) for B Apo-enzyme C Ethoxzolamide D CNP0248071 and E CNP0246073. The 2D structures were plotted considering the PC1 and PC2 eigenvalues determined by red (energy minima), and blue (energy maxima) in kJ/mol
Binding free energy
The binding energy of the complexes was determined using the Molecular Mechanic/Poisson-Boltzmann Surface Area (MM-PBSA) technique from g_mmpbsa package [51, 78]. The binding affinity of the ligand and reference-bound beta-CA was calculated using MmPbSaStat.py. The energy components that contribute to binding free energy of selected compounds have been stated in Table 2. The free energy binding (Δ Gbinding) is an important factor that illustrates the strength of the bound molecule-receptor [79]. The free energy binding (Δ Gbinding) of beta-CAs with the natural compounds CNP0248071 and CNP0246073 were − 161.860 ± 3.044 kJ/mol and − 145.884 ± 3.499 (kJ/mol) respectively, whereas Ethoxzolamide showed free energy binding of − 133.459 ± 3.387 kJ/mol (Table 2). This elucidates both the natural compounds showed stable and better binding than the reference molecule.
Table 2. Binding free energy and other parameters calculated from MM-PBSA in kJ/mol for CNP0248071, CNP0246073, and reference ethoxzolamide
Compounds | ΔEvdw (kJ/mol) | ΔEElec (kJ/mol) | ΔGpolar (kJ/mol) | SASA (kJ/mol) | ΔGbinding (kJ/mol) |
|---|---|---|---|---|---|
Ethoxzolamide | − 89. 578 ± 1.030 | − 167.770 ± 3.662 | 134.043 ± 2.530 | − 10.131 ± 0.089 | − 133.459 ± 3.387 |
CNP0248071 | − 39.718 ± 1.242 | − 520.895 ± 3.111 | 489.868 ± 3.919 | − 11.100 ± 0.107 | − 161.860 ± 3.044 |
CNP0246073 | − 111.773 ± 1.203 | − 318.620 ± 4.581 | 299.972 ± 3.641 | − 15.479 ± 0.084 | − 145.884 ± 3.499 |
Discussion
The Carbonic anhydrase is present in many cyanobacteria, but not all, cyanobacteria species. This enzyme is crucial for many cyanobacteria, particularly those that rely on a CO2 concentrating mechanism (CCM) to enhance photosynthesis, but some species, especially those living in CO2-rich environments, may not require carbonic anhydrase [3]. Many cyanobacteria have specialized mechanisms to concentrate CO2 around the enzyme Rubisco, through carbonic anhydrase. Therefore, this enzyme is useful for inter-conversion of bicarbonate (HCO3−) into CO2, which is used by Rubisco to fix carbon dioxide and utilize it in photosynthesis and finally generate the biomass [5, 8]. It is worth to mention that some cyanobacteria do not have a carbon concentrating mechanism (CCM) but may still possess carbonic anhydrase for other cellular functions, such as pH regulation [5]. The presence or absence of carbonic anhydrase in a particular cyanobacteria species depends on its specific ecological niche and physiological requirements [80]. Cyanotoxins produced in aquatic bodies are a primary source of acute poisoning for human and aquatic animals, sometimes resulting in human fatalities and more often animal deaths [81, 82]. Cytotoxin release can only be reduced by inhibiting the biomass of the hazardous strain cyanobacteria. Cyanobacteria’s growth can be limited by targeting their key metalloenzyme beta-CA, which is responsible for the CCM and regulates the photosynthetic pathways that lead to growth [2, 3]. The sulphate compounds have been reported to be potent CAs inhibitors [53, 54] their mechanism involves binding to the zinc active site, dislocating catalytically essential water molecules either by the formation of the tetrahedral or bi-pyramidal [55, 56–57]. The inhibition of beta-CA from Archaea, Mycobacterium tuberculosis, and Helicobacter pylori has been reported earlier [83, 84–85]. In this study, we have taken the natural COCONUT database, where we have primarily focused on selecting natural compounds that can act as an inhibitor of cyanobacterial carbonic anhydrase by using computational methods of molecular docking, molecular dynamic simulations, and binding free energy calculation. An FDA-approved sulphonamide compound ethoxzolamide has been taken as a reference [86]. The non-toxic compounds were docked to the active site of CA’s and best hits were further optimized by DFT calculations. The electrophilicity indices were calculated and molecular dynamics simulations were carried out. The crystallized structure of cyanobacterial beta-CAs was obtained from the RCSB Protein Data Bank (PDB ID: 5SWC) [33]. The COCONUT database consisting of more than 4 K compounds was searched manually for sulphate-based molecules, and 628 sulphate molecules were retrieved. These molecules were virtually screened for their toxicity through online web servers such as pkCSM and SwissADME since it is necessary to choose non-toxic leads [58]. These virtually screened non-toxic compounds were docked in MOE.2024.06 [32]. The docking scores of the lead natural compounds CNP0248071 (− 13.2351 kcal/mol); and CNP0246073 (− 12.7894 kcal/mol) showed better binding affinity as compared to ethoxzolamide (− 10.94 kcal/mol). The parameters like chemical hardness, chemical potential, electrophilicity indices, and energy gap showed that both the leads natural compounds were stable [64, 65]. To check the stability of the protein–ligand complexes, a molecular dynamics simulation was carried out. The RMSD of ligand–protein and Cα is calculated to analyse the divergence of a structure from the reference over time [87]. Higher Cα variability during simulations leads to protein instability resulting in protein deformation and poor structural arrangement [88]. The RMSD of CNP0248071 was 0.28 nm, while CNP0246073 and Ethoxzolamide had 0.27 nm which is lesser than the threshold value of < 0.3 nm, which predicts ligand–protein complexes of both the molecules are stable [89]. The Rg and SASA values showed both the leads remained stable at the binding site throughout the simulation period of 100 ns. A lower SASA value depicts lower protein folding whereas a higher value depicts high protein folding [67]. The RMSF is a measure of the extent to which a protein’s residue fluctuates over time, with high RMSF values indicating increased mobility of the average structure [90, 91–92]. The natural compounds, along with the reference, displayed changes at the protein’s terminal end, whereas the remaining amino acids exhibited fluctuations within an acceptable range. To determine the exact distance between the Zn2+ ion and its adjacent amino acids (Cys39, His 98, Cys101) is also calculated. The average H-bond for CNP0248071 was 8 and CNP0246073 was 7. Both the molecules had high hydrogen bond contacts as compared to the reference i.e., 5, this suggests higher binding strength and stability of the selected leads [75]. The free energy landscape values of CNP0248071, CNP0246073, and reference ethoxzolamide were calculated to be 19.8 kJ/mol, 19.4 kJ/mol, and 20 kJ/mol respectively while the highest energy was shown by the apo-enzyme i.e. 20.7 kJ/mol. The PCA and the Gibbs free energy landscape data plots of the compounds showed lesser energy than the reference. FEL determines the motions, conformational and functional relationship of the simulated protein–ligand complex in terms of enthalpy and entropy [76, 93]. The MMPBSA calculation of both compounds showed higher binding energy than the reference. The relative binding energy (rBE) is calculated by the summation of numerous energy components, including van der Waals energy, electrostatic energy, internal energy derived from molecular mechanics, and polar contribution to solvation energy [52]. CNP0248071 showed higher binding energy (− 161.860 ± 3.044) followed by CNP0246073 (− 145.884 ± 3.499 kJ/mol) compared to the reference (− 133.459 ± 3.387 kJ/mol). This study suggests that the natural compounds screened could be useful in becoming potent phytochemical inhibitors and limiting the growth of harmful cyanobacterial biomass.
In this study, the chosen template target was carbonic anhydrase against a vast range of phytochemicals providing the initial foundation for inhibiting the growth of cyanobacteria. The other potential targets that can inhibit cyanobacterial biomass generation are photosystem I/II, the protein responsible for cell division, and the enzymes that are essential for cell wall synthesis [94]. The application of the natural compounds to inhibit the bloom-generating cyanobacteria can be assessed through the optimal dose and frequency that may control the biomass effectively [95]. They can be applied in the form of liquid, powder, and encapsulated nanomaterials such as polymeric nanoparticles, carbon nanotubes, graphene, and dendrimers [96]. The nanomaterials can be encapsulated using natural polymers such as chitosan, alginate, cyclodextrins, and phospholipids, thus, they are environment friendly [97, 98]. The small-scale trials and monitoring of the impact of molecules on cyanobacteria bloom formation, if found effective then scalability of the application methods for large-scale water bodies.
Conclusion
Carbonic anhydrase, a key enzyme in cyanobacterial photosynthesis, plays a crucial role in their rapid growth and proliferation, leading to harmful algal blooms. To mitigate the ecological and health risks posed by these blooms, it is imperative to identify effective inhibitors that can specifically target this enzyme. In this study, we utilized computational approaches to screen the COCONUT database for potential inhibitors. Two natural compounds, CNP0248071 and CNP0246073, were identified as promising candidates due to their superior binding affinity and stability at the zinc catalytic site of carbonic anhydrase, as evidenced by molecular docking and dynamics simulations. These compounds outperformed the FDA-approved drug ethoxzolamide, suggesting their potential as potent inhibitors. Given their natural origin and promising computational results, these compounds warrant further experimental validation to assess their efficacy in controlling cyanobacterial growth and their impact on non-target organisms. This study lays the groundwork for the development of novel, environmentally friendly strategies to combat the increasing threat of harmful algal blooms.
Acknowledgements
Archana Padhiary wishes to acknowledge Sambalpur University for providing the fellowship through the Sambalpur University Research Fellowship (SURF). Showkat Ahmad Mir wishes to acknowledge DBT-Builder Govt, of India to provide fellowship, and Aiswarya Pati wishes to acknowledge DST-inspired fellowship.
Author contributions
AP contributes to Conceptualization, Molecular docking simulation, Data Curation, and manuscript writing. SAM contributed to Molecular dynamics simulations, meta-conformational stability of sampled complexes, and Free energy calculations. AP contributed to formal analysis investigation and editing. BN and SAM contribute to Conceptualization, editing, and supervising. All authors read and accepted the final version of the manuscript for publication.
Funding
DBT Builder, BT/INF/22/SP45375/2022.
Data availability
Data will be available on special request to the corresponding author.
Declarations
Competing interest
All authors declare no conflict of interest.
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
References
1. Garcia-Pichel, F; Belnap, J; Neuer, S; Schanz, F. Estimates of global cyanobacterial biomass and its distribution. Algol Stud; 2003; 109,
2. Arrigo, KR. Marine microorganisms and global nutrient cycles. Nature; 2005; 437,
3. Price, GD; Badger, MR; Woodger, FJ; Long, BM. Advances in understanding the cyanobacterial CO2-concentrating-mechanism (CCM): functional components, Ci transporters, diversity, genetic regulation and prospects for engineering into plants. J of exp Bot; 2008; 59,
4. Mondal, M; Khanra, S; Tiwari, ON; Gayen, K; Halder, GN. Role of carbonic anhydrase on the way to biological carbon capture through microalgae—a mini review. Env Prog & Sust Energy; 2016; 35,
5. DiMario, RJ; Clayton, H; Mukherjee, A; Ludwig, M; Moroney, JV. Plant carbonic anhydrases: structures, locations, evolution, and physiological roles. Mol plant; 2017; 10,
6. Aspatwar, A; Haapanen, S; Parkkila, S. An update on the metabolic roles of carbonic anhydrases in the model alga Chlamydomonas reinhardtii. Metabolites; 2018; 8,
7. Rowlett, RS. Structure and catalytic mechanism of β-carbonic anhydrases. Carbon Anhydrase Mech Regul Links Dis Ind Appl; 2014; [DOI: https://dx.doi.org/10.1007/978-94-007-7359-2_4]
8. DiMario, RJ; Machingura, MC; Waldrop, GL; Moroney, JV. The many types of carbonic anhydrases in photosynthetic organisms. Plant sci; 2018; 268, pp. 11-17. [DOI: https://dx.doi.org/10.1016/j.plantsci.2017.12.002]
9. Kimber, MS; Pai, EF. The active site architecture of Pisum sativum β-carbonic anhydrase is a mirror image of that of α-carbonic anhydrases. EMBO J; 2000; [DOI: https://dx.doi.org/10.1093/emboj/19.7.1407]
10. Urbański, LJ; Angeli, A; Hytönen, VP; Di Fiore, A; Parkkila, S; De Simone, G; Supuran, CT. Inhibition of the newly discovered β-carbonic anhydrase from the protozoan pathogen Trichomonas vaginalis with inorganic anions and small molecules. J Inorg Biochem; 2020; 213, 111274. [DOI: https://dx.doi.org/10.1016/j.jinorgbio.2020.111274]
11. Rowlett, RS. Structure and catalytic mechanism of the β-carbonic anhydrases. Biochim Biophys Acta (BBA) Proteins Proteom; 2010; 1804,
12. Suhanovsky, MM; Sheppard, K; Rowlett, RS. β-Carbonic anhydrases: general features and medical implications. Carbonic anhydrases as biocatalysts; 2015; Elsevier: pp. 247-273. [DOI: https://dx.doi.org/10.1016/B978-0-444-63258-6.00014-7]
13. Supuran, CT. Scozzafava, A; Supuran, C; Conway, J. Carbonic anhydrases: catalytic and inhibition mechanisms, distribution and physiological roles. Carbonic anhydrase: its inhibitors and activators; 2004; CRC Press: pp. 1-23. [DOI: https://dx.doi.org/10.1201/9780203475300.ch1]
14. Sawaya, MR; Cannon, GC; Heinhorst, S; Tanaka, S; Williams, EB; Yeates, TO; Kerfeld, CA. The structure of β-carbonic anhydrase from the carboxysomal shell reveals a distinct subclass with one active site for the price of two. J of Biol Chem; 2006; 281,
15. Dittmann, E; Wiegand, C. Cyanobacterial toxins–occurrence, biosynthesis and impact on human affairs. Mol Nutr & food res; 2006; 50,
16. O’Neil, JM; Davis, TW; Burford, MA; Gobler, CJ. The rise of harmful cyanobacteria blooms: the potential roles of eutrophication and climate change. Harmful Algae; 2012; 14, pp. 313-334. [DOI: https://dx.doi.org/10.1002/mnfr.200500162]
17. Paerl, HW; Otten, TG. Harmful cyanobacterial blooms: causes, consequences, and controls. Micro Eco; 2013; 65, pp. 995-1010. [DOI: https://dx.doi.org/10.1007/s00248-012-0159-y]
18. Wurtsbaugh, WA; Paerl, HW; Dodds, WK. Nutrients, eutrophication and harmful algal blooms along the freshwater to marine continuum. Wil Inter Rev Wat; 2019; 6,
19. Glibert, PM; Burford, MA. Globally changing nutrient loads and harmful algal blooms: recent advances, new paradigms, and continuing challenges. Ocean; 2017; 30,
20. Gobler, CJ. Climate change and harmful algal blooms: insights and perspective. Harm Alg; 2020; 91, 101731. [DOI: https://dx.doi.org/10.1016/j.hal.2019.101731]
21. Cao, C; Zheng, B; Chen, Z; Huang, M; Zhang, J. Eutrophication and algal blooms in channel type reservoirs: a novel enclosure experiment by changing light intensity. J of Env Sci; 2011; 23,
22. Sin, Y; Lee, H. Changes in hydrology, water quality, and algal blooms in a freshwater system impounded with engineered structures in a temperate monsoon river estuary. J Hyd Reg Stu; 2020; 32, 100744. [DOI: https://dx.doi.org/10.1016/j.ejrh.2020.100744]
23. Grattan, LM; Holobaugh, S; Morris, JG, Jr. Harmful algal blooms and public health. Harmful Algae; 2016; 57, pp. 2-8. [DOI: https://dx.doi.org/10.1016/j.hal.2016.05.003]
24. Hallegraeff, GM. Harmful algal blooms: a global overview. Manual Harmful Mar Microalgae; 2003; 33, pp. 1-22.
25. Albertano, P. Whitton, BA. Cyanobacterial biofilms in monuments and caves. Ecology of cyanobacteria II: their diversity in space and time; 2012; Dordrecht, Springer Netherlands: pp. 317-343. [DOI: https://dx.doi.org/10.1007/978-94-007-3855-3_11]
26. Popović, S; Stupar, M; Unković, N; Subakov Simić, G; Ljaljević Grbić, M. Diversity of terrestrial cyanobacteria colonizing selected stone monuments in Serbia. Stud in Conser; 2018; 63,
27. Urzi CE, Krumbein WE, Warscheid T. On the question of biogenic colour changes of Mediterranean monuments (coating, crust, microstromatolite, patina, scialbatura, skin, rock varnish); 1992 (pp. 397–420)
28. Gaylarde, C. Influence of environment on microbial colonization of historic stone buildings with emphasis on cyanobacteria. Heritage; 2020; 3,
29. Agamah, FE; Mazandu, GK; Hassan, R; Bope, CD; Thomford, NE; Ghansah, A; Chimusa, ER. Computational/in silico methods in drug target and lead prediction. Brief Bioinform; 2020; 21,
30. Rim, KT. In silico prediction of toxicity and its applications for chemicals at work. Tox and env health sci; 2020; 12,
31. Hollingsworth, SA; Dror, RO. Molecular dynamics simulation for all. Neuron; 2018; 99,
32. Molecular Operating Environment (MOE) 2024.06; Chemical Computing ULC, 1010 Sherbrooke St. West, Suite #910, Montreal, QC, Cannada, H3A 2R7; 2024.
33. McGurn, LD; Moazami-Goudarzi, M; White, SA; Suwal, T; Brar, B; Tang, JQ; Kimber, MS. The structure, kinetics and interactions of the β-carboxysomal β-carbonic anhydrase CcaA. Biochem J; 2016; 473,
34. Sorokina, M; Steinbeck, C. Review on natural products databases: where to find data in 2020. J Cheminform; 2020; 12,
35. Pires, DE; Blundell, TL; Ascher, DB. pkCSM: predicting small-molecule pharmacokinetic and toxicity properties using graph-based signatures. J Med Chem; 2015; 58,
36. Kesharwani, RK; Vishwakarma, VK; Keservani, RK; Singh, P; Katiyar, N; Tripathi, S. Singh, DB. Role of ADMET tools in current scenario: application and limitations. Computer-aided drug design; 2020; Singapore, Springer: pp. 71-87. [DOI: https://dx.doi.org/10.1007/978-981-15-6815-2_4]
37. Neese, F. Software update: the ORCA program system, version 4.0. Wiley Interdiscip Rev: Comp Mol Sci; 2018; 8,
38. Neese, F. The ORCA program system. Wiley Interdiscip Rev: Comp Mol Sci; 2012; 2,
39. Parr, RG; Szentpály, LV; Liu, S. Electrophilicity index. J Amer Chem Soc; 1999; 121,
40. Abraham, MJ; Murtola, T; Schulz, R; Páll, S; Smith, JC; Hess, B; Lindahl, E. GROMACS: high performance molecular simulations through multi-level parallelism from laptops to supercomputers. Soft X; 2015; 1, pp. 19-25. [DOI: https://dx.doi.org/10.1016/j.softx.2015.06.001]
41. Gunsteren, WV; Billeter, SR; Eising, AA; Hünenberger, PH; Krüger, PKHC; Mark, AE; Tironi, IG. Biomolecular simulation: the GROMOS96 manual and user guide. Verl Fachver Hochschulverlag AG ETH Zurich; 1996; 86, pp. 1-1044.
42. Malde, AK; Zuo, L; Breeze, M; Stroet, M; Poger, D; Nair, PC; Mark, AE. An automated force field topology builder (ATB) and repository: version 1.0. J Chem Theory Comput; 2011; 7,
43. Jorgensen, WL; Jenson, C. Temperature dependence of TIP3P, SPC, and TIP4P water from NPT Monte Carlo simulations: Seeking temperatures of maximum density. J Comput Chem; 1998; 19,
44. Brooks, BR; Brooks, CL, III; Mackerell, AD, Jr; Nilsson, L; Petrella, RJ; Roux, B; Karplus, M. CHARMM: the biomolecular simulation program. J Comp Chem; 2009; 30,
45. Hess, B; Bekker, H; Berendsen, HJ; Fraaije, JG. LINCS: a linear constraint solver for molecular simulations. J Comput Chem; 1997; 18,
46. Darden, T; York, D; Pedersen, L. Particle mesh Ewald: An N⋅ log (N) method for Ewald sums in large systems. The J of chem Phy; 1993; 98,
47. Mir, SA; Muhammad, A; Padhiary, A; Ekka, NJ; Baitharu, I; Naik, PK; Nayak, B. Identification of potent EGFR-TKD inhibitors from NPACT database through combined computational approaches. J Biomol Str Dyn; 2023; 41,
48. Mir, SA; Nayak, B. Exploring binding stability of hydroxy-3-(4-hydroxyphenyl)-5-(4-nitrophenyl)-5, 5a, 7, 8, 9, 9a-hexahydrothiazolo [2, 3-b] quinazolin-6-one with T790M/L858R EGFR-TKD. J of Biomol Str and Dyn; 2023; 41,
49. Humphrey, W; Dalke, A; Schulten, K. VMD: visual molecular dynamics. J Mol Graph; 1996; 14,
50. Genheden, S; Ryde, U. The MM/PBSA and MM/GBSA methods to estimate ligand-binding affinities. Expert Opin Drug Discov; 2015; 10,
51. Kumari, R; Kumar, R; Lynn, A Open-Source Drug Discovery Consortium. g_mmpbsa—A gromacs tool for high-throughput MM-PBSA calculations. J Chem. information and modeling; 2014; 54,
52. Ahmad, S; Abbasi, HW; Shahid, S; Gul, S; Abbasi, SW. Molecular docking, simulation and MM-PBSA studies of nigella sativa compounds: a computational quest to identify potential natural antiviral for COVID-19 treatment. J of Biomol Str and Dyn; 2021; 39,
53. Carta, F; Supuran, CT; Scozzafava, A. Sulfonamides and their isosters as carbonic anhydrase inhibitors. Fut Med Chem; 2014; 6,
54. Masini, E; Carta, F; Scozzafava, A; Supuran, CT. Antiglaucoma carbonic anhydrase inhibitors: a patent review. Exp opinion on therapeutic patents; 2013; 23,
55. Covarrubias, AS; Bergfors, T; Jones, TA; Högbom, M. Structural mechanics of the pH-dependent activity of β-carbonic anhydrase from Mycobacterium tuberculosis. J Bio Chem; 2006; 281,
56. Supuran, CT; Scozzafava, A. Carbonic anhydrases as targets for medicinal chemistry. Bioorg & med Chem; 2007; 15,
57. Supuran, CT. Carbonic anhydrases-an overview. Curr Pharm Design; 2008; 14,
58. Alamri, MA. Pharmacoinformatics and molecular dynamic simulation studies to identify potential small-molecule inhibitors of WNK-SPAK/OSR1 signaling that mimic the RFQV motifs of WNK kinases. Arab J of Chem; 2020; 13,
59. Durán-Iturbide, NA; Díaz-Eufracio, BI; Medina-Franco, JL. In silico ADME/Tox profiling of natural products: A focus on BIOFACQUIM. ACS Omega; 2020; 5,
60. Kamber, M; Flückiger-Isler, S; Engelhardt, G; Jaeckh, R; Zeiger, E. Comparison of the Ames II and traditional Ames test responses with respect to mutagenicity, strain specificities, need for metabolism and correlation with rodent carcinogenicity. Mutagenesis; 2009; 24,
61. Singh, A; Bhat, TK; Sharma, OP. Clinical biochemistry of hepatotoxicity. J Clinic Toxicol S; 2011; 4, pp. 2161-495. [DOI: https://dx.doi.org/10.4172/2161-0495.S4-001]
62. Lohohola, PO; Mbala, BM; Bambi, SMN; Mawete, DT; Matondo, A; Mvondo, JGM. In silico ADME/T properties of quinine derivatives using SwissADME and pkCSM webservers. Int J Trop Disease Health; 2021; 42,
63. Khnifira, M; El Hamidi, S; Sadiq, M; Şimşek, S; Kaya, S; Barka, N; Abdennouri, M. Adsorption mechanisms investigation of methylene blue on the (0 0 1) zeolite 4A surface in aqueous medium by computational approach and molecular dynamics. Appl Surface Sci; 2022; 572, 151381. [DOI: https://dx.doi.org/10.1016/j.apsusc.2021.151381]
64. Ruiz-Morales, Y. HOMO−LUMO gap as an index of molecular size and structure for polycyclic aromatic hydrocarbons (PAHs) and asphaltenes: a theoretical study. I. J Phys Chem A; 2002; 106,
65. Miar, M; Shiroudi, A; Pourshamsian, K; Oliaey, AR; Hatamjafari, F. Theoretical investigations on the HOMO–LUMO gap and global reactivity descriptor studies, natural bond orbital, and nucleus-independent chemical shifts analyses of 3-phenylbenzo [d] thiazole-2 (3 H)-imine and its para-substituted derivatives: Solvent and substituent effects. J of Chem Res; 2021; 45,
66. Vektariene A, Vektaris G, Svoboda J. A theoretical approach to the nucleophilic behavior of benzofused thieno [3, 2-b] furans using DFT and HF based reactivity descriptors. Arkivoc: Online J Org Chem. 2009
67. Mir, SA; Mohanta, PP; Meher, RK; Raval, MK; Behera, AK; Nayak, B. Structural insights into conformational stability and binding of thiazolo-[2, 3-b] quinazolinone derivatives with EGFR-TKD and in-vitro study. Saudi J of Bio Sci; 2022; 29,
68. Aier, I; Varadwaj, PK; Raj, U. Structural insights into conformational stability of both wild-type and mutant EZH2 receptor. Sci Rep; 2016; 6,
69. Arnittali, M; Rissanou, AN; Harmandaris, V. Structure of biomolecules through molecular dynamics simulations. Proc Comp Sci; 2019; 156, pp. 69-78. [DOI: https://dx.doi.org/10.1016/j.procs.2019.08.181]
70. Bolhuis, PG. Sampling kinetic protein folding pathways using all-atom models. Computer simulations in condensed matter systems: from materials to chemical biology; 2006; Berlin, Heidelberg, Springer: pp. 393-433.
71. Abramyan, TM; Snyder, JA; Thyparambil, AA; Stuart, SJ; Latour, RA. Cluster analysis of molecular simulation trajectories for systems where both conformation and orientation of the sampled states are important. J of Comp Chem; 2016; 37,
72. Kushwaha, PP; Singh, AK; Bansal, T; Yadav, A; Prajapati, KS; Shuaib, M; Kumar, S. Identification of natural inhibitors against SARS-CoV-2 drugable targets using molecular docking, molecular dynamics simulation, and MM-PBSA approach. Front Cell Infect Microbiol; 2021; 11, 730288. [DOI: https://dx.doi.org/10.3389/fcimb.2021.730288]
73. Bagewadi, ZK; Khan, TY; Gangadharappa, B; Kamalapurkar, A; Shamsudeen, SM; Yaraguppi, DA. Molecular dynamics and simulation analysis against superoxide dismutase (SOD) target of Micrococcus luteus with secondary metabolites from Bacillus licheniformis recognized by genome mining approach. Saudi J of Bio Sci; 2023; 30,
74. Lobanov, MY; Bogatyreva, N; Galzitskaya, O. Radius of gyration as an indicator of protein structure compactness. Mol Bio; 2008; 42,
75. Yekeen, AA; Durojaye, OA; Idris, MO; Muritala, HF; Arise, RO. CHAPERONg: a tool for automated GROMACS-based molecular dynamics simulations and trajectory analyses. Comp and Str Biotech J; 2023; 21, pp. 4849-4858. [DOI: https://dx.doi.org/10.1016/j.csbj.2023.09.024]
76. Amadei, A; Linssen, AB; Berendsen, HJ. Essential dynamics of proteins. Proteins Str Func Bioinfor; 1993; 17,
77. Jolliffe, IT; Cadima, J. Principal component analysis: a review and recent developments. Philos Trans Royal Soc A: Math Phys Eng Sci; 2016; 374,
78. Gupta, S; Singh, AK; Kushwaha, PP; Prajapati, KS; Shuaib, M; Senapati, S; Kumar, S. Identification of potential natural inhibitors of SARS-CoV2 main protease by molecular docking and simulation studies. J of Biomol Str and Dyn; 2021; 39,
79. Bhardwaj, VK; Purohit, R. A lesson for the maestro of the replication fork: targeting the protein-binding interface of proliferating cell nuclear antigen for anticancer therapy. J of Cell Biochem; 2022; 123,
80. Badger, MR; Price, GD; Long, BM; Woodger, FJ. The environmental plasticity and ecological genomics of the cyanobacterial CO2 concentrating mechanism. J of exp Bot; 2006; 57,
81. Stepanova N, Nikitin O, Latypova V, Kondratyeva T. Cyanotoxins as a possible cause of fish and waterfowl death in the Kazanka River (Russia). In: Proceedings of the International Multidisciplinary Scientific GeoConference-SGEM, Albena, Bulgaria. 2018; 2–8.
82. Wood, R. Acute animal and human poisonings from cyanotoxin exposure—A review of the literature. Env international; 2016; 91, pp. 276-282. [DOI: https://dx.doi.org/10.1016/j.envint.2016.02.026]
83. Zimmerman, S; Innocenti, A; Casini, A; Ferry, JG; Scozzafava, A; Supuran, CT. Carbonic anhydrase inhibitors. Inhibition of the prokariotic beta and gamma-class enzymes from Archaea with sulfonamides. Bioorg Med Chem Lett; 2004; 14,
84. Nishimori, I; Minakuchi, T; Vullo, D; Scozzafava, A; Innocenti, A; Supuran, CT. Carbonic anhydrase inhibitors. Cloning, characterization, and inhibition studies of a new β-carbonic anhydrase from Mycobacterium tuberculosis. J Med Chem; 2009; 52,
85. Nishimori, I; Minakuchi, T; Kohsaki, T; Onishi, S; Takeuchi, H; Vullo, D; Supuran, CT. Carbonic anhydrase inhibitors: the β-carbonic anhydrase from Helicobacter pylori is a new target for sulfonamide and sulfamate inhibitors. Bioorg Med Chem Lett; 2007; 17,
86. Rahman, MM; Tikhomirova, A; Modak, JK; Hutton, ML; Supuran, CT; Roujeinikova, A. Antibacterial activity of ethoxzolamide against Helicobacter pylori strains SS1 and 26695. Gut patho; 2020; 12, pp. 1-7. [DOI: https://dx.doi.org/10.1186/s13099-020-00358-5]
87. Kufareva, I; Abagyan, R. Methods of protein structure comparison. Homol Model Methods Protoc; 2012; 857, pp. 231-257. [DOI: https://dx.doi.org/10.1007/978-1-61779-588-6_10]
88. Neveu, E; Popov, P; Hoffmann, A; Migliosi, A; Besseron, X; Danoy, G; Grudinin, S. RapidRMSD: rapid determination of RMSDs corresponding to motions of flexible molecules. Bioinfor; 2018; 34,
89. Castro-Alvarez, A; Costa, AM; Vilarrasa, J. The performance of several docking programs at reproducing protein–macrolide-like crystal structures. Mol; 2017; 22,
90. Skjaerven, L; Martinez, A; Reuter, N. Principal component and normal mode analysis of proteins; a quantitative comparison using the GroEL subunit. Proteins Struct Funct Bioinform; 2011; 79,
91. Fuglebakk, E; Echave, J; Reuter, N. Measuring and comparing structural fluctuation patterns in large protein datasets. Bioinformatics; 2012; 28,
92. Martínez, L. Automatic identification of mobile and rigid substructures in molecular dynamics simulations and fractional structural fluctuation analysis. PLoS ONE; 2015; 10,
93. Majewski, M; Ruiz-Carmona, S; Barril, X. An investigation of structural stability in protein-ligand complexes reveals the balance between order and disorder. Comm Chem; 2019; 2,
94. Badshah, SL; Stirbet, A; Siddiquee, M; Govindjee, G; Kang, DW; Bridgeman, T; Seo, Y. Inhibition of CO2 fixation as a potential target for the control of freshwater cyanobacterial harmful algal blooms. ACS ES&T Water; 2024; 4,
95. Li, H; Xing, R; Ji, X; Liu, Y; Chu, X; Gu, J; Wang, S; Wang, G; Zhao, S; Cao, X. Natural algicidal compounds: strategies for controlling harmful algae and application. Plant Phys Biochem; 2024; [DOI: https://dx.doi.org/10.1016/j.plaphy.2024.108981]
96. Paolino, D; Cosco, D; Cilurzo, F; Fresta, M. Innovative drug delivery systems for the administration of natural compounds. Curr Bioact Comp; 2007; 3,
97. Mondéjar-López, M; García-Simarro, MP; Navarro-Simarro, P; Gómez-Gómez, L; Ahrazem, O; Niza, E. A review on the encapsulation of “eco-friendly” compounds in natural polymer-based nanoparticles as next generation nano-agrochemicals for sustainable agriculture and crop management. Int J Biol Macromol; 2024; [DOI: https://dx.doi.org/10.1016/j.ijbiomac.2024.136030]
98. Idrees, H; Zaidi, SZJ; Sabir, A; Khan, RU; Zhang, X; Hassan, SU. A review of biodegradable natural polymer-based nanoparticles for drug delivery applications. Nanomat; 2020; 10,
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