Molecular dynamics and simulation analysis against superoxide dismutase (SOD) target of Micrococcus luteus with secondary metabolites from Bacillus licheniformis recognized by genome mining approach

Abstract

Micrococcus luteus, also known as M. luteus, is a bacterium that inhabits mucous membranes, human skin, and various environmental sources. It is commonly linked to infections, especially among individuals who have compromised immune systems. M. luteus is capable of synthesizing the enzyme superoxide dismutase (SOD) as a component of its protective response to reactive oxygen species (ROS). This enzyme serves as a promising target for drug development in various diseases. The current study utilized a subtractive genomics approach to identify potential therapeutic targets from M. luteus. Additionally, genome mining was employed to identify and characterize the biosynthetic gene clusters (BGCs) responsible for the production of secondary metabolites in Bacillus licheniformis (B. licheniformis), a bacterium known for its production of therapeutically relevant secondary metabolites. Subtractive genomics resulted in identification of important extracellular protein SOD as a drug target that plays a crucial role in shielding cells from damage caused by ROS. Genome mining resulted in identification of five potential ligands (secondary metabolites) from B. licheniformis such as, Bacillibactin (BAC), Paenibactin (PAE), Fengycin (FEN), Surfactin (SUR) and Lichenysin (LIC). Molecular docking was used to predict and analyze the binding interactions between these five ligands and target protein SOD. The resulting protein-ligand complexes were further analyzed for their motions and interactions of atoms and molecules over 250 ns using molecular dynamics (MD) simulation analysis. The analysis of MD simulations suggests, Bacillibactin as the probable candidate to arrest the activities of SOD. All the five compounds reported in this study were found to act by directly/indirectly interacting with ROS molecules, such as superoxide radicals (O₂-) and hydrogen peroxide (H₂O₂), and transforming them into less reactive species. This antioxidant activity contributes to its protective effects against oxidative stress-induced damage in cells making them likely candidate for various applications, including in the development of antioxidant-based therapies, nutraceuticals, and functional foods.

Keywords: Bacillus licheniformis; Genome mining; Micrococcus luteus; Molecular dynamics; Simulation.

Fig. 1

RMSD (Root Mean Square Deviation) is a measure of the average distance between the atoms of two superimposed molecular structures. It is commonly used in molecular dynamics simulations to assess the accuracy of the simulation and the stability of the protein structure over time. The comparative RMSD values were estimated with “gmx rms”. SOD-BACILLIBACTIN (SOD-BAC), SOD_LICHENYSIN (SOD-LIC) and SOD-PAENIBACTIN (SOD-PAE).

Fig. 2

RMSF (Root Mean Square Fluctuation) is a measure of the average deviation of atomic positions from their mean positions over time in molecular dynamic simulations. It provides information about the flexibility and dynamics of a protein structure. Regions with high RMSF values are typically more flexible, while regions with low RMSF values are typically more rigid. The figure indicates a comparative RMSF plot of the complexes with the active site residues encircled in red generated with “gmx rmsf”.

Fig. 3

RG (Radius of Gyration) is a measure of the compactness of a protein structure, calculated as the root mean square distance of each atom in the protein from the center of mass of the protein. It provides a quantitative measure of the overall size and shape of a protein structure. Protein structures with a smaller RG value are typically more compact and globular, while structures with a larger RG value are typically more extended and flexible. The “gmx gyrate” module was used to estimate the RG values for the comparative graphs plotted above.

Fig. 4

SASA (Solvent Accessible Surface Area) is a measure of the surface area of a protein structure that is accessible to solvent molecules. It can provide insights into conformational changes, protein–ligand interactions, and protein–protein interactions. The surface area values over the simulation were predicted with “gmx sasa” and the comparative graphs are plotted.

Fig. 5

Hydrogen bonds (H-bonds) are non-covalent interactions that play an essential role in the stabilization of protein–ligand interactions. The H-bond interactions were estimated with “hbond” function of gromacs. Figures indicate the H-bond interactions between the protein and its respective ligands over the simulation time.