Molecular dynamics and simulation analysis against superoxide dismutase (SOD) target of Micrococcus luteus with secondary metabolites from Bacillus licheniformis recognized by genome mining approach |
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Affiliation: | 1. Department of Biotechnology, KLE Technological University, Hubballi, Karnataka 580031, India;2. Department of Mechanical Engineering, College of Engineering, King Khalid University, Abha 61421, Saudi Arabia;3. Department of Biotechnology, M S Ramaiah Institute of Technology, Bangalore, Karnataka 560054, India;4. Department of Diagnostic dental science and Oral Biology, College of Dentistry, King Khalid University, Abha 61421, Saudi Arabia |
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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. |
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Keywords: | Genome mining Molecular dynamics Simulation SOD" },{" #name" :" keyword" ," $" :{" id" :" k0035" }," $$" :[{" #name" :" text" ," _" :" Superoxide Dismutase MDS" },{" #name" :" keyword" ," $" :{" id" :" k0045" }," $$" :[{" #name" :" text" ," _" :" Molecular Dynamics Simulations ROS" },{" #name" :" keyword" ," $" :{" id" :" k0055" }," $$" :[{" #name" :" text" ," _" :" Reactive Oxygen Species BGCs" },{" #name" :" keyword" ," $" :{" id" :" k0065" }," $$" :[{" #name" :" text" ," _" :" Biosynthetic Gene Clusters BAC" },{" #name" :" keyword" ," $" :{" id" :" k0095" }," $$" :[{" #name" :" text" ," _" :" Bacillibactin PAE" },{" #name" :" keyword" ," $" :{" id" :" k0105" }," $$" :[{" #name" :" text" ," _" :" Paenibactin FEN" },{" #name" :" keyword" ," $" :{" id" :" k0115" }," $$" :[{" #name" :" text" ," _" :" Fengycin SUR" },{" #name" :" keyword" ," $" :{" id" :" k0125" }," $$" :[{" #name" :" text" ," _" :" Surfactin LIC" },{" #name" :" keyword" ," $" :{" id" :" k0135" }," $$" :[{" #name" :" text" ," _" :" Lichenysin H2O2" },{" #name" :" keyword" ," $" :{" id" :" k0145" }," $$" :[{" #name" :" text" ," _" :" Hydrogen Peroxide Superoxide Radicals hydroxyl radicals KEGG" },{" #name" :" keyword" ," $" :{" id" :" k0175" }," $$" :[{" #name" :" text" ," _" :" Kyoto Encyclopedia of Genes and Genomes KO" },{" #name" :" keyword" ," $" :{" id" :" k0185" }," $$" :[{" #name" :" text" ," _" :" KEGG orthology SMs" },{" #name" :" keyword" ," $" :{" id" :" k0195" }," $$" :[{" #name" :" text" ," _" :" Specialized metabolites RMSD" },{" #name" :" keyword" ," $" :{" id" :" k0205" }," $$" :[{" #name" :" text" ," _" :" Root Mean Square Deviation RMSF" },{" #name" :" keyword" ," $" :{" id" :" k0215" }," $$" :[{" #name" :" text" ," _" :" Root Mean Square Fluctuations SASA" },{" #name" :" keyword" ," $" :{" id" :" k0225" }," $$" :[{" #name" :" text" ," _" :" Solvent Accessible Surface Area Rg" },{" #name" :" keyword" ," $" :{" id" :" k0235" }," $$" :[{" #name" :" text" ," _" :" Radius of Gyration MM-PBSA" },{" #name" :" keyword" ," $" :{" id" :" k0245" }," $$" :[{" #name" :" text" ," _" :" Molecular Mechanics Poisson-Boltzmann surface area CASTp" },{" #name" :" keyword" ," $" :{" id" :" k0255" }," $$" :[{" #name" :" text" ," _" :" Computed Atlas of Surface Topography of proteins |
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