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